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huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/tensorflow/translation/requirements.txt

datasets >= 1.4.0 tensorflow >= 2.3.0 evaluate >= 0.2.0

datasets >= 1.4.0 tensorflow >= 2.3.0 evaluate >= 0.2.0

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/pplm/requirements.txt

tensorboard scikit-learn seqeval psutil sacrebleu rouge-score tensorflow_datasets pytorch-lightning matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.1.3 fire pytest conllu sentencepiece != 0.1.92 protobuf transformers==3.5.1

tensorboard scikit-learn seqeval psutil sacrebleu rouge-score tensorflow_datasets pytorch-lightning matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.1.3 fire pytest conllu sentencepiece != 0.1.92 protobuf transformers==3.5.1

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/legacy/pytorch-lightning/requirements.txt

tensorboard scikit-learn seqeval psutil sacrebleu rouge-score tensorflow_datasets matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.1.3 fire pytest conllu sentencepiece != 0.1.92 protobuf ray

tensorboard scikit-learn seqeval psutil sacrebleu rouge-score tensorflow_datasets matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.1.3 fire pytest conllu sentencepiece != 0.1.92 protobuf ray

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/sagemaker/scripts/tensorflow/requirements.txt

git+https://github.com/huggingface/transformers.git@main # install main or adjust ist with vX.X.X for installing version specific transforms

git+https://github.com/huggingface/transformers.git@main # install main or adjust ist with vX.X.X for installing version specific transforms

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/self-training-text-classification/requirements.txt

accelerate datasets >= 1.8.0 protobuf scikit-learn scipy sentencepiece != 0.1.92 torch >= 1.3

accelerate datasets >= 1.8.0 protobuf scikit-learn scipy sentencepiece != 0.1.92 torch >= 1.3

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/information-gain-filtration/requirements.txt

matplotlib numpy>=1.17.2 joblib>=0.13.2 scipy torch>=1.10.1 transformers>=3.5

matplotlib numpy>=1.17.2 joblib>=0.13.2 scipy torch>=1.10.1 transformers>=3.5

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/image-pretraining/requirements.txt

torch>=1.5.0 torchvision>=0.6.0 datasets>=1.8.0

torch>=1.5.0 torchvision>=0.6.0 datasets>=1.8.0

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/fsner/requirements.txt

transformers>=4.9.2

transformers>=4.9.2

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/visual_bert/requirements.txt

appdirs==1.4.3 argon2-cffi==20.1.0 async-generator==1.10 attrs==20.2.0 backcall==0.2.0 CacheControl==0.12.6 certifi==2020.6.20 cffi==1.14.2 chardet==3.0.4 click==7.1.2 colorama==0.4.3 contextlib2==0.6.0 cycler==0.10.0 datasets==1.0.0 decorator==4.4.2 defusedxml==0.6.0 dill==0.3.2 distlib==0.3.0 distro==1.4.0 entrypoints==0.3 filelock==3.0.12 future==0.18.2 html5lib==1.0.1 idna==2.8 ipaddr==2.2.0 ipykernel==5.3.4 ipython ipython-genutils==0.2.0 ipywidgets==7.5.1 jedi==0.17.2 Jinja2>=2.11.3 joblib==1.2.0 jsonschema==3.2.0 jupyter==1.0.0 jupyter-client==6.1.7 jupyter-console==6.2.0 jupyter-core==4.6.3 jupyterlab-pygments==0.1.1 kiwisolver==1.2.0 lockfile==0.12.2 MarkupSafe==1.1.1 matplotlib==3.3.1 mistune==2.0.3 msgpack==0.6.2 nbclient==0.5.0 nbconvert==6.5.1 nbformat==5.0.7 nest-asyncio==1.4.0 notebook==6.4.12 numpy==1.22.0 opencv-python==4.4.0.42 packaging==20.3 pandas==1.1.2 pandocfilters==1.4.2 parso==0.7.1 pep517==0.8.2 pexpect==4.8.0 pickleshare==0.7.5 Pillow>=8.1.1 progress==1.5 prometheus-client==0.8.0 prompt-toolkit==3.0.7 ptyprocess==0.6.0 pyaml==20.4.0 pyarrow==1.0.1 pycparser==2.20 Pygments>=2.7.4 pyparsing==2.4.6 pyrsistent==0.16.0 python-dateutil==2.8.1 pytoml==0.1.21 pytz==2020.1 PyYAML>=5.4 pyzmq==19.0.2 qtconsole==4.7.7 QtPy==1.9.0 regex==2020.7.14 requests==2.22.0 retrying==1.3.3 sacremoses==0.0.43 Send2Trash==1.5.0 sentencepiece==0.1.91 six==1.14.0 terminado==0.8.3 testpath==0.4.4 tokenizers==0.8.1rc2 torch==1.6.0 torchvision==0.7.0 tornado==6.0.4 tqdm==4.48.2 traitlets git+https://github.com/huggingface/transformers.git urllib3==1.26.5 wcwidth==0.2.5 webencodings==0.5.1 wget==3.2 widgetsnbextension==3.5.1 xxhash==2.0.0

appdirs==1.4.3 argon2-cffi==20.1.0 async-generator==1.10 attrs==20.2.0 backcall==0.2.0 CacheControl==0.12.6 certifi==2020.6.20 cffi==1.14.2 chardet==3.0.4 click==7.1.2 colorama==0.4.3 contextlib2==0.6.0 cycler==0.10.0 datasets==1.0.0 decorator==4.4.2 defusedxml==0.6.0 dill==0.3.2 distlib==0.3.0 distro==1.4.0 entrypoints==0.3 filelock==3.0.12 future==0.18.2 html5lib==1.0.1 idna==2.8 ipaddr==2.2.0 ipykernel==5.3.4 ipython ipython-genutils==0.2.0 ipywidgets==7.5.1 jedi==0.17.2 Jinja2>=2.11.3 joblib==1.2.0 jsonschema==3.2.0 jupyter==1.0.0 jupyter-client==6.1.7 jupyter-console==6.2.0 jupyter-core==4.6.3 jupyterlab-pygments==0.1.1 kiwisolver==1.2.0 lockfile==0.12.2 MarkupSafe==1.1.1 matplotlib==3.3.1 mistune==2.0.3 msgpack==0.6.2 nbclient==0.5.0 nbconvert==6.5.1 nbformat==5.0.7 nest-asyncio==1.4.0 notebook==6.4.12 numpy==1.22.0 opencv-python==4.4.0.42 packaging==20.3 pandas==1.1.2 pandocfilters==1.4.2 parso==0.7.1 pep517==0.8.2 pexpect==4.8.0 pickleshare==0.7.5 Pillow>=8.1.1 progress==1.5 prometheus-client==0.8.0 prompt-toolkit==3.0.7 ptyprocess==0.6.0 pyaml==20.4.0 pyarrow==1.0.1 pycparser==2.20 Pygments>=2.7.4 pyparsing==2.4.6 pyrsistent==0.16.0 python-dateutil==2.8.1 pytoml==0.1.21 pytz==2020.1 PyYAML>=5.4 pyzmq==19.0.2 qtconsole==4.7.7 QtPy==1.9.0 regex==2020.7.14 requests==2.22.0 retrying==1.3.3 sacremoses==0.0.43 Send2Trash==1.5.0 sentencepiece==0.1.91 six==1.14.0 terminado==0.8.3 testpath==0.4.4 tokenizers==0.8.1rc2 torch==1.6.0 torchvision==0.7.0 tornado==6.0.4 tqdm==4.48.2 traitlets git+https://github.com/huggingface/transformers.git urllib3==1.26.5 wcwidth==0.2.5 webencodings==0.5.1 wget==3.2 widgetsnbextension==3.5.1 xxhash==2.0.0

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/legacy/seq2seq/requirements.txt

tensorboard scikit-learn seqeval psutil sacrebleu rouge-score tensorflow_datasets matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.1.3 fire pytest conllu sentencepiece != 0.1.92 protobuf

tensorboard scikit-learn seqeval psutil sacrebleu rouge-score tensorflow_datasets matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.1.3 fire pytest conllu sentencepiece != 0.1.92 protobuf

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/longform-qa/requirements.txt

datasets >= 1.1.3 faiss-cpu streamlit elasticsearch

datasets >= 1.1.3 faiss-cpu streamlit elasticsearch

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./utils/documentation_tests.txt

docs/source/en/quicktour.mdx docs/source/es/quicktour.mdx docs/source/en/pipeline_tutorial.mdx docs/source/en/autoclass_tutorial.mdx docs/source/en/task_summary.mdx docs/source/en/model_doc/markuplm.mdx docs/source/en/model_doc/speech_to_text.mdx docs/source/en/model_doc/switch_transformers.mdx docs/source/en/model_doc/t5.mdx docs/source/en/model_doc/t5v1.1.mdx docs/source/en/model_doc/byt5.mdx docs/source/en/model_doc/tapex.mdx docs/source/en/model_doc/donut.mdx docs/source/en/model_doc/encoder-decoder.mdx src/transformers/generation/utils.py src/transformers/generation/tf_utils.py src/transformers/models/albert/configuration_albert.py src/transformers/models/albert/modeling_albert.py src/transformers/models/albert/modeling_tf_albert.py src/transformers/models/bart/configuration_bart.py src/transformers/models/bart/modeling_bart.py src/transformers/models/beit/configuration_beit.py src/transformers/models/beit/modeling_beit.py src/transformers/models/bert/configuration_bert.py src/transformers/models/bert/modeling_bert.py src/transformers/models/bert/modeling_tf_bert.py src/transformers/models/bert_generation/configuration_bert_generation.py src/transformers/models/bigbird_pegasus/configuration_bigbird_pegasus.py src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py src/transformers/models/big_bird/configuration_big_bird.py src/transformers/models/big_bird/modeling_big_bird.py src/transformers/models/blenderbot/configuration_blenderbot.py src/transformers/models/blenderbot/modeling_blenderbot.py src/transformers/models/blenderbot_small/configuration_blenderbot_small.py src/transformers/models/blenderbot_small/modeling_blenderbot_small.py src/transformers/models/bloom/configuration_bloom.py src/transformers/models/camembert/configuration_camembert.py src/transformers/models/canine/configuration_canine.py src/transformers/models/clip/configuration_clip.py src/transformers/models/clipseg/modeling_clipseg.py src/transformers/models/codegen/configuration_codegen.py src/transformers/models/conditional_detr/configuration_conditional_detr.py src/transformers/models/conditional_detr/modeling_conditional_detr.py src/transformers/models/convbert/configuration_convbert.py src/transformers/models/convnext/configuration_convnext.py src/transformers/models/convnext/modeling_convnext.py src/transformers/models/ctrl/configuration_ctrl.py src/transformers/models/ctrl/modeling_ctrl.py src/transformers/models/cvt/configuration_cvt.py src/transformers/models/cvt/modeling_cvt.py src/transformers/models/data2vec/configuration_data2vec_audio.py src/transformers/models/data2vec/configuration_data2vec_text.py src/transformers/models/data2vec/configuration_data2vec_vision.py src/transformers/models/data2vec/modeling_data2vec_audio.py src/transformers/models/data2vec/modeling_data2vec_vision.py src/transformers/models/deberta/configuration_deberta.py src/transformers/models/deberta/modeling_deberta.py src/transformers/models/deberta_v2/configuration_deberta_v2.py src/transformers/models/deberta_v2/modeling_deberta_v2.py src/transformers/models/decision_transformer/configuration_decision_transformer.py src/transformers/models/deformable_detr/modeling_deformable_detr.py src/transformers/models/deit/configuration_deit.py src/transformers/models/deit/modeling_deit.py src/transformers/models/deit/modeling_tf_deit.py src/transformers/models/detr/configuration_detr.py src/transformers/models/detr/modeling_detr.py src/transformers/models/distilbert/configuration_distilbert.py src/transformers/models/dpr/configuration_dpr.py src/transformers/models/dpt/modeling_dpt.py src/transformers/models/electra/configuration_electra.py src/transformers/models/electra/modeling_electra.py src/transformers/models/electra/modeling_tf_electra.py src/transformers/models/ernie/configuration_ernie.py src/transformers/models/flava/configuration_flava.py src/transformers/models/fnet/configuration_fnet.py src/transformers/models/glpn/modeling_glpn.py src/transformers/models/gpt2/configuration_gpt2.py src/transformers/models/gpt2/modeling_gpt2.py src/transformers/models/gptj/modeling_gptj.py src/transformers/models/gpt_neo/configuration_gpt_neo.py src/transformers/models/gpt_neox/configuration_gpt_neox.py src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py src/transformers/models/groupvit/modeling_groupvit.py src/transformers/models/groupvit/modeling_tf_groupvit.py src/transformers/models/hubert/modeling_hubert.py src/transformers/models/imagegpt/configuration_imagegpt.py src/transformers/models/layoutlm/configuration_layoutlm.py src/transformers/models/layoutlm/modeling_layoutlm.py src/transformers/models/layoutlm/modeling_tf_layoutlm.py src/transformers/models/layoutlmv2/configuration_layoutlmv2.py src/transformers/models/layoutlmv2/modeling_layoutlmv2.py src/transformers/models/layoutlmv3/configuration_layoutlmv3.py src/transformers/models/layoutlmv3/modeling_layoutlmv3.py src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py src/transformers/models/levit/configuration_levit.py src/transformers/models/lilt/modeling_lilt.py src/transformers/models/longformer/modeling_longformer.py src/transformers/models/longformer/modeling_tf_longformer.py src/transformers/models/longt5/modeling_longt5.py src/transformers/models/marian/modeling_marian.py src/transformers/models/markuplm/modeling_markuplm.py src/transformers/models/maskformer/configuration_maskformer.py src/transformers/models/maskformer/modeling_maskformer.py src/transformers/models/mbart/configuration_mbart.py src/transformers/models/mbart/modeling_mbart.py src/transformers/models/mctct/configuration_mctct.py src/transformers/models/megatron_bert/configuration_megatron_bert.py src/transformers/models/mobilebert/configuration_mobilebert.py src/transformers/models/mobilebert/modeling_mobilebert.py src/transformers/models/mobilebert/modeling_tf_mobilebert.py src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py src/transformers/models/mobilevit/modeling_mobilevit.py src/transformers/models/mobilevit/modeling_tf_mobilevit.py src/transformers/models/nezha/configuration_nezha.py src/transformers/models/openai/configuration_openai.py src/transformers/models/opt/configuration_opt.py src/transformers/models/opt/modeling_opt.py src/transformers/models/opt/modeling_tf_opt.py src/transformers/models/owlvit/modeling_owlvit.py src/transformers/models/pegasus/configuration_pegasus.py src/transformers/models/pegasus/modeling_pegasus.py src/transformers/models/pegasus_x/configuration_pegasus_x.py src/transformers/models/perceiver/modeling_perceiver.py src/transformers/models/plbart/configuration_plbart.py src/transformers/models/plbart/modeling_plbart.py src/transformers/models/poolformer/configuration_poolformer.py src/transformers/models/poolformer/modeling_poolformer.py src/transformers/models/realm/configuration_realm.py src/transformers/models/reformer/configuration_reformer.py src/transformers/models/reformer/modeling_reformer.py src/transformers/models/regnet/modeling_regnet.py src/transformers/models/regnet/modeling_tf_regnet.py src/transformers/models/resnet/configuration_resnet.py src/transformers/models/resnet/modeling_resnet.py src/transformers/models/resnet/modeling_tf_resnet.py src/transformers/models/roberta/configuration_roberta.py src/transformers/models/roberta/modeling_roberta.py src/transformers/models/roberta/modeling_tf_roberta.py src/transformers/models/roc_bert/modeling_roc_bert.py src/transformers/models/roc_bert/tokenization_roc_bert.py src/transformers/models/segformer/modeling_segformer.py src/transformers/models/sew/configuration_sew.py src/transformers/models/sew/modeling_sew.py src/transformers/models/sew_d/configuration_sew_d.py src/transformers/models/sew_d/modeling_sew_d.py src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py src/transformers/models/speech_to_text/configuration_speech_to_text.py src/transformers/models/speech_to_text/modeling_speech_to_text.py src/transformers/models/speech_to_text_2/configuration_speech_to_text_2.py src/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py src/transformers/models/segformer/modeling_tf_segformer.py src/transformers/models/squeezebert/configuration_squeezebert.py src/transformers/models/swin/configuration_swin.py src/transformers/models/swin/modeling_swin.py src/transformers/models/swinv2/configuration_swinv2.py src/transformers/models/table_transformer/modeling_table_transformer.py src/transformers/models/time_series_transformer/configuration_time_series_transformer.py src/transformers/models/time_series_transformer/modeling_time_series_transformer.py src/transformers/models/trajectory_transformer/configuration_trajectory_transformer.py src/transformers/models/transfo_xl/configuration_transfo_xl.py src/transformers/models/trocr/configuration_trocr.py src/transformers/models/trocr/modeling_trocr.py src/transformers/models/unispeech/configuration_unispeech.py src/transformers/models/unispeech/modeling_unispeech.py src/transformers/models/unispeech_sat/modeling_unispeech_sat.py src/transformers/models/van/modeling_van.py src/transformers/models/videomae/modeling_videomae.py src/transformers/models/vilt/modeling_vilt.py src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py src/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py src/transformers/models/vit/configuration_vit.py src/transformers/models/vit/modeling_vit.py src/transformers/models/vit/modeling_tf_vit.py src/transformers/models/vit_mae/modeling_vit_mae.py src/transformers/models/vit_mae/configuration_vit_mae.py src/transformers/models/vit_msn/modeling_vit_msn.py src/transformers/models/visual_bert/configuration_visual_bert.py src/transformers/models/wav2vec2/configuration_wav2vec2.py src/transformers/models/wav2vec2/modeling_wav2vec2.py src/transformers/models/wav2vec2/tokenization_wav2vec2.py src/transformers/models/wav2vec2_conformer/configuration_wav2vec2_conformer.py src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py src/transformers/models/wavlm/configuration_wavlm.py src/transformers/models/wavlm/modeling_wavlm.py src/transformers/models/whisper/configuration_whisper.py src/transformers/models/whisper/modeling_whisper.py src/transformers/models/whisper/modeling_tf_whisper.py src/transformers/models/xlm/configuration_xlm.py src/transformers/models/xlm_roberta/configuration_xlm_roberta.py src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py src/transformers/models/xlnet/configuration_xlnet.py src/transformers/models/yolos/configuration_yolos.py src/transformers/models/yolos/modeling_yolos.py src/transformers/models/x_clip/modeling_x_clip.py src/transformers/models/yoso/configuration_yoso.py

docs/source/en/quicktour.mdx docs/source/es/quicktour.mdx docs/source/en/pipeline_tutorial.mdx docs/source/en/autoclass_tutorial.mdx docs/source/en/task_summary.mdx docs/source/en/model_doc/markuplm.mdx docs/source/en/model_doc/speech_to_text.mdx docs/source/en/model_doc/switch_transformers.mdx docs/source/en/model_doc/t5.mdx docs/source/en/model_doc/t5v1.1.mdx docs/source/en/model_doc/byt5.mdx docs/source/en/model_doc/tapex.mdx docs/source/en/model_doc/donut.mdx docs/source/en/model_doc/encoder-decoder.mdx src/transformers/generation/utils.py src/transformers/generation/tf_utils.py src/transformers/models/albert/configuration_albert.py src/transformers/models/albert/modeling_albert.py src/transformers/models/albert/modeling_tf_albert.py src/transformers/models/bart/configuration_bart.py src/transformers/models/bart/modeling_bart.py src/transformers/models/beit/configuration_beit.py src/transformers/models/beit/modeling_beit.py src/transformers/models/bert/configuration_bert.py src/transformers/models/bert/modeling_bert.py src/transformers/models/bert/modeling_tf_bert.py src/transformers/models/bert_generation/configuration_bert_generation.py src/transformers/models/bigbird_pegasus/configuration_bigbird_pegasus.py src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py src/transformers/models/big_bird/configuration_big_bird.py src/transformers/models/big_bird/modeling_big_bird.py src/transformers/models/blenderbot/configuration_blenderbot.py src/transformers/models/blenderbot/modeling_blenderbot.py src/transformers/models/blenderbot_small/configuration_blenderbot_small.py src/transformers/models/blenderbot_small/modeling_blenderbot_small.py src/transformers/models/bloom/configuration_bloom.py src/transformers/models/camembert/configuration_camembert.py src/transformers/models/canine/configuration_canine.py src/transformers/models/clip/configuration_clip.py src/transformers/models/clipseg/modeling_clipseg.py src/transformers/models/codegen/configuration_codegen.py src/transformers/models/conditional_detr/configuration_conditional_detr.py src/transformers/models/conditional_detr/modeling_conditional_detr.py src/transformers/models/convbert/configuration_convbert.py src/transformers/models/convnext/configuration_convnext.py src/transformers/models/convnext/modeling_convnext.py src/transformers/models/ctrl/configuration_ctrl.py src/transformers/models/ctrl/modeling_ctrl.py src/transformers/models/cvt/configuration_cvt.py src/transformers/models/cvt/modeling_cvt.py src/transformers/models/data2vec/configuration_data2vec_audio.py src/transformers/models/data2vec/configuration_data2vec_text.py src/transformers/models/data2vec/configuration_data2vec_vision.py src/transformers/models/data2vec/modeling_data2vec_audio.py src/transformers/models/data2vec/modeling_data2vec_vision.py src/transformers/models/deberta/configuration_deberta.py src/transformers/models/deberta/modeling_deberta.py src/transformers/models/deberta_v2/configuration_deberta_v2.py src/transformers/models/deberta_v2/modeling_deberta_v2.py src/transformers/models/decision_transformer/configuration_decision_transformer.py src/transformers/models/deformable_detr/modeling_deformable_detr.py src/transformers/models/deit/configuration_deit.py src/transformers/models/deit/modeling_deit.py src/transformers/models/deit/modeling_tf_deit.py src/transformers/models/detr/configuration_detr.py src/transformers/models/detr/modeling_detr.py src/transformers/models/distilbert/configuration_distilbert.py src/transformers/models/dpr/configuration_dpr.py src/transformers/models/dpt/modeling_dpt.py src/transformers/models/electra/configuration_electra.py src/transformers/models/electra/modeling_electra.py src/transformers/models/electra/modeling_tf_electra.py src/transformers/models/ernie/configuration_ernie.py src/transformers/models/flava/configuration_flava.py src/transformers/models/fnet/configuration_fnet.py src/transformers/models/glpn/modeling_glpn.py src/transformers/models/gpt2/configuration_gpt2.py src/transformers/models/gpt2/modeling_gpt2.py src/transformers/models/gptj/modeling_gptj.py src/transformers/models/gpt_neo/configuration_gpt_neo.py src/transformers/models/gpt_neox/configuration_gpt_neox.py src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py src/transformers/models/groupvit/modeling_groupvit.py src/transformers/models/groupvit/modeling_tf_groupvit.py src/transformers/models/hubert/modeling_hubert.py src/transformers/models/imagegpt/configuration_imagegpt.py src/transformers/models/layoutlm/configuration_layoutlm.py src/transformers/models/layoutlm/modeling_layoutlm.py src/transformers/models/layoutlm/modeling_tf_layoutlm.py src/transformers/models/layoutlmv2/configuration_layoutlmv2.py src/transformers/models/layoutlmv2/modeling_layoutlmv2.py src/transformers/models/layoutlmv3/configuration_layoutlmv3.py src/transformers/models/layoutlmv3/modeling_layoutlmv3.py src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py src/transformers/models/levit/configuration_levit.py src/transformers/models/lilt/modeling_lilt.py src/transformers/models/longformer/modeling_longformer.py src/transformers/models/longformer/modeling_tf_longformer.py src/transformers/models/longt5/modeling_longt5.py src/transformers/models/marian/modeling_marian.py src/transformers/models/markuplm/modeling_markuplm.py src/transformers/models/maskformer/configuration_maskformer.py src/transformers/models/maskformer/modeling_maskformer.py src/transformers/models/mbart/configuration_mbart.py src/transformers/models/mbart/modeling_mbart.py src/transformers/models/mctct/configuration_mctct.py src/transformers/models/megatron_bert/configuration_megatron_bert.py src/transformers/models/mobilebert/configuration_mobilebert.py src/transformers/models/mobilebert/modeling_mobilebert.py src/transformers/models/mobilebert/modeling_tf_mobilebert.py src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py src/transformers/models/mobilevit/modeling_mobilevit.py src/transformers/models/mobilevit/modeling_tf_mobilevit.py src/transformers/models/nezha/configuration_nezha.py src/transformers/models/openai/configuration_openai.py src/transformers/models/opt/configuration_opt.py src/transformers/models/opt/modeling_opt.py src/transformers/models/opt/modeling_tf_opt.py src/transformers/models/owlvit/modeling_owlvit.py src/transformers/models/pegasus/configuration_pegasus.py src/transformers/models/pegasus/modeling_pegasus.py src/transformers/models/pegasus_x/configuration_pegasus_x.py src/transformers/models/perceiver/modeling_perceiver.py src/transformers/models/plbart/configuration_plbart.py src/transformers/models/plbart/modeling_plbart.py src/transformers/models/poolformer/configuration_poolformer.py src/transformers/models/poolformer/modeling_poolformer.py src/transformers/models/realm/configuration_realm.py src/transformers/models/reformer/configuration_reformer.py src/transformers/models/reformer/modeling_reformer.py src/transformers/models/regnet/modeling_regnet.py src/transformers/models/regnet/modeling_tf_regnet.py src/transformers/models/resnet/configuration_resnet.py src/transformers/models/resnet/modeling_resnet.py src/transformers/models/resnet/modeling_tf_resnet.py src/transformers/models/roberta/configuration_roberta.py src/transformers/models/roberta/modeling_roberta.py src/transformers/models/roberta/modeling_tf_roberta.py src/transformers/models/roc_bert/modeling_roc_bert.py src/transformers/models/roc_bert/tokenization_roc_bert.py src/transformers/models/segformer/modeling_segformer.py src/transformers/models/sew/configuration_sew.py src/transformers/models/sew/modeling_sew.py src/transformers/models/sew_d/configuration_sew_d.py src/transformers/models/sew_d/modeling_sew_d.py src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py src/transformers/models/speech_to_text/configuration_speech_to_text.py src/transformers/models/speech_to_text/modeling_speech_to_text.py src/transformers/models/speech_to_text_2/configuration_speech_to_text_2.py src/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py src/transformers/models/segformer/modeling_tf_segformer.py src/transformers/models/squeezebert/configuration_squeezebert.py src/transformers/models/swin/configuration_swin.py src/transformers/models/swin/modeling_swin.py src/transformers/models/swinv2/configuration_swinv2.py src/transformers/models/table_transformer/modeling_table_transformer.py src/transformers/models/time_series_transformer/configuration_time_series_transformer.py src/transformers/models/time_series_transformer/modeling_time_series_transformer.py src/transformers/models/trajectory_transformer/configuration_trajectory_transformer.py src/transformers/models/transfo_xl/configuration_transfo_xl.py src/transformers/models/trocr/configuration_trocr.py src/transformers/models/trocr/modeling_trocr.py src/transformers/models/unispeech/configuration_unispeech.py src/transformers/models/unispeech/modeling_unispeech.py src/transformers/models/unispeech_sat/modeling_unispeech_sat.py src/transformers/models/van/modeling_van.py src/transformers/models/videomae/modeling_videomae.py src/transformers/models/vilt/modeling_vilt.py src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py src/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py src/transformers/models/vit/configuration_vit.py src/transformers/models/vit/modeling_vit.py src/transformers/models/vit/modeling_tf_vit.py src/transformers/models/vit_mae/modeling_vit_mae.py src/transformers/models/vit_mae/configuration_vit_mae.py src/transformers/models/vit_msn/modeling_vit_msn.py src/transformers/models/visual_bert/configuration_visual_bert.py src/transformers/models/wav2vec2/configuration_wav2vec2.py src/transformers/models/wav2vec2/modeling_wav2vec2.py src/transformers/models/wav2vec2/tokenization_wav2vec2.py src/transformers/models/wav2vec2_conformer/configuration_wav2vec2_conformer.py src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py src/transformers/models/wavlm/configuration_wavlm.py src/transformers/models/wavlm/modeling_wavlm.py src/transformers/models/whisper/configuration_whisper.py src/transformers/models/whisper/modeling_whisper.py src/transformers/models/whisper/modeling_tf_whisper.py src/transformers/models/xlm/configuration_xlm.py src/transformers/models/xlm_roberta/configuration_xlm_roberta.py src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py src/transformers/models/xlnet/configuration_xlnet.py src/transformers/models/yolos/configuration_yolos.py src/transformers/models/yolos/modeling_yolos.py src/transformers/models/x_clip/modeling_x_clip.py src/transformers/models/yoso/configuration_yoso.py

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/speech-recognition/requirements.txt

datasets >= 1.18.0 torch >= 1.5 torchaudio librosa jiwer evaluate

datasets >= 1.18.0 torch >= 1.5 torchaudio librosa jiwer evaluate

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/tensorflow/text-classification/requirements.txt

datasets >= 1.1.3 sentencepiece != 0.1.92 protobuf tensorflow >= 2.3 evaluate >= 0.2.0

datasets >= 1.1.3 sentencepiece != 0.1.92 protobuf tensorflow >= 2.3 evaluate >= 0.2.0

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/seq2seq-distillation/requirements.txt

tensorboard scikit-learn psutil sacrebleu rouge-score tensorflow_datasets pytorch-lightning matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.1.3 fire pytest conllu sentencepiece != 0.1.92 protobuf

tensorboard scikit-learn psutil sacrebleu rouge-score tensorflow_datasets pytorch-lightning matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.1.3 fire pytest conllu sentencepiece != 0.1.92 protobuf

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/fixtures/merges.txt

#version: 0.2 Ġ l Ġl o Ġlo w e r

#version: 0.2 Ġ l Ġl o Ġlo w e r

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt

[{"id": 8222595, "category_id": 17, "iscrowd": 0, "bbox": [18, 54, 301, 415], "area": 53306}, {"id": 8225432, "category_id": 17, "iscrowd": 0, "bbox": [349, 26, 291, 343], "area": 59627}, {"id": 8798150, "category_id": 63, "iscrowd": 0, "bbox": [1, 0, 639, 474], "area": 174579}, {"id": 14466198, "category_id": 75, "iscrowd": 0, "bbox": [42, 74, 133, 45], "area": 4068}, {"id": 12821912, "category_id": 75, "iscrowd": 0, "bbox": [333, 80, 38, 106], "area": 2118}, {"id": 10898909, "category_id": 93, "iscrowd": 0, "bbox": [0, 0, 640, 480], "area": 2750}]

[{"id": 8222595, "category_id": 17, "iscrowd": 0, "bbox": [18, 54, 301, 415], "area": 53306}, {"id": 8225432, "category_id": 17, "iscrowd": 0, "bbox": [349, 26, 291, 343], "area": 59627}, {"id": 8798150, "category_id": 63, "iscrowd": 0, "bbox": [1, 0, 639, 474], "area": 174579}, {"id": 14466198, "category_id": 75, "iscrowd": 0, "bbox": [42, 74, 133, 45], "area": 4068}, {"id": 12821912, "category_id": 75, "iscrowd": 0, "bbox": [333, 80, 38, 106], "area": 2118}, {"id": 10898909, "category_id": 93, "iscrowd": 0, "bbox": [0, 0, 640, 480], "area": 2750}]

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/flax/question-answering/requirements.txt

datasets >= 1.8.0 jax>=0.2.17 jaxlib>=0.1.68 flax>=0.3.5 optax>=0.0.8

datasets >= 1.8.0 jax>=0.2.17 jaxlib>=0.1.68 flax>=0.3.5 optax>=0.0.8

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/wav2vec2/requirements.txt

transformers datasets torch>=1.5.0 torchaudio jiwer==2.2.0 lang-trans==0.6.0 librosa==0.8.0

transformers datasets torch>=1.5.0 torchaudio jiwer==2.2.0 lang-trans==0.6.0 librosa==0.8.0

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/bertology/requirements.txt

transformers == 3.5.1

transformers == 3.5.1

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/_tests_requirements.txt

tensorboard scikit-learn seqeval psutil sacrebleu >= 1.4.12 git+https://github.com/huggingface/accelerate@main#egg=accelerate rouge-score tensorflow_datasets matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.13.3 fire pytest conllu sentencepiece != 0.1.92 protobuf torchvision jiwer librosa evaluate >= 0.2.0

tensorboard scikit-learn seqeval psutil sacrebleu >= 1.4.12 git+https://github.com/huggingface/accelerate@main#egg=accelerate rouge-score tensorflow_datasets matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.13.3 fire pytest conllu sentencepiece != 0.1.92 protobuf torchvision jiwer librosa evaluate >= 0.2.0

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/flax/text-classification/requirements.txt

datasets >= 1.1.3 jax>=0.2.8 jaxlib>=0.1.59 flax>=0.3.5 optax>=0.0.8

datasets >= 1.1.3 jax>=0.2.8 jaxlib>=0.1.59 flax>=0.3.5 optax>=0.0.8

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/codeparrot/requirements.txt

transformers==4.19.0 datasets==1.16.0 wandb==0.12.0 tensorboard==2.6.0 torch==1.11.0 huggingface-hub==0.1.0 git+https://github.com/huggingface/accelerate.git@3c45b6f760ad8745be9ebc9bbb26f5b04dea4abe datasketch==1.5.7 dpu_utils

transformers==4.19.0 datasets==1.16.0 wandb==0.12.0 tensorboard==2.6.0 torch==1.11.0 huggingface-hub==0.1.0 git+https://github.com/huggingface/accelerate.git@3c45b6f760ad8745be9ebc9bbb26f5b04dea4abe datasketch==1.5.7 dpu_utils

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/fixtures/input.txt

Who was Jim Henson ? ||| Jim Henson was a puppeteer

Who was Jim Henson ? ||| Jim Henson was a puppeteer

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/flax/language-modeling/requirements.txt

datasets >= 1.1.3 jax>=0.2.8 jaxlib>=0.1.59 flax>=0.3.5 optax>=0.0.9

datasets >= 1.1.3 jax>=0.2.8 jaxlib>=0.1.59 flax>=0.3.5 optax>=0.0.9

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/flax/vision/requirements.txt

jax>=0.2.8 jaxlib>=0.1.59 flax>=0.3.5 optax>=0.0.8 -f https://download.pytorch.org/whl/torch_stable.html torch==1.9.0+cpu -f https://download.pytorch.org/whl/torch_stable.html torchvision==0.10.0+cpu

jax>=0.2.8 jaxlib>=0.1.59 flax>=0.3.5 optax>=0.0.8 -f https://download.pytorch.org/whl/torch_stable.html torch==1.9.0+cpu -f https://download.pytorch.org/whl/torch_stable.html torchvision==0.10.0+cpu

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/mlm_wwm/requirements.txt

datasets >= 1.1.3 sentencepiece != 0.1.92 protobuf ltp

datasets >= 1.1.3 sentencepiece != 0.1.92 protobuf ltp

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/fixtures/tests_samples/GermEval/dev.txt

Gleich O darauf O entwirft O er O seine O Selbstdarstellung O " O Ecce B-OTH homo I-OTH " O in O enger O Auseinandersetzung O mit O diesem O Bild O Jesu B-PER . O 1980 O kam O der O Crown B-OTH als O Versuch O von O Toyota B-ORG , O sich O in O der O Oberen O Mittelklasse O zu O etablieren O , O auch O nach O Deutschland B-LOC . O – O 4:26 O # O Sometime B-OTH Ago/La I-OTH Fiesta I-OTH – O 23:18 O Alle O Stücke O wurden O von O Corea B-PER komponiert O mit O Ausnahme O der O einleitenden O Improvisation O zu O Sometime B-OTH Ago I-OTH . O Bis O 2013 O steigen O die O Mittel O aus O dem O EU-Budget B-ORGpart auf O rund O 120 O Millionen O Euro B-OTH . O Daraus O entwickelte O sich O im O Rokoko B-OTH die O Sitte O des O gemeinsamen O Weinens O im O Theater O , O das O die O Standesgrenzen O innerhalb O des O Publikums O überbrücken O sollte O . O Die O Spinne O hatte O sie O mit O Seidenfäden O an O ihrem O Schwanz O gefesselt O und O nach O oben O gezogen O . O In O Deutschland B-LOC ist O nach O StGB O eine O Anwerbung O für O die O Fremdenlegion O strafbar O . O Am O Donnerstag O wird O sich O zeigen O , O ob O die O Idee O der O DLR-Forscher B-ORGpart funktioniert O . O Der O sechste O Lauf O der O ADAC B-ORG GT I-ORG Mastersstand O ganz O klar O im O Mittelpunkt O des O Motorsport-Wochenendes O auf O dem O Eurospeedway B-ORG Lausitz I-ORG . O Nach O den O schwächeren O Vorgaben O der O Wall B-ORG Street I-ORG vom O Vortag O setzten O die O deutschen B-LOCderiv Standardwerte O ihren O Konsolidierungskurs O fort O . O Kolb B-PER war O seit O 1986 O im O Turnverein O als O Leiter O tätig O , O darunter O elf O Jahre O als O Hauptleiter O in O der O Männerriege O . O

Gleich O darauf O entwirft O er O seine O Selbstdarstellung O " O Ecce B-OTH homo I-OTH " O in O enger O Auseinandersetzung O mit O diesem O Bild O Jesu B-PER . O 1980 O kam O der O Crown B-OTH als O Versuch O von O Toyota B-ORG , O sich O in O der O Oberen O Mittelklasse O zu O etablieren O , O auch O nach O Deutschland B-LOC . O – O 4:26 O # O Sometime B-OTH Ago/La I-OTH Fiesta I-OTH – O 23:18 O Alle O Stücke O wurden O von O Corea B-PER komponiert O mit O Ausnahme O der O einleitenden O Improvisation O zu O Sometime B-OTH Ago I-OTH . O Bis O 2013 O steigen O die O Mittel O aus O dem O EU-Budget B-ORGpart auf O rund O 120 O Millionen O Euro B-OTH . O Daraus O entwickelte O sich O im O Rokoko B-OTH die O Sitte O des O gemeinsamen O Weinens O im O Theater O , O das O die O Standesgrenzen O innerhalb O des O Publikums O überbrücken O sollte O . O Die O Spinne O hatte O sie O mit O Seidenfäden O an O ihrem O Schwanz O gefesselt O und O nach O oben O gezogen O . O In O Deutschland B-LOC ist O nach O StGB O eine O Anwerbung O für O die O Fremdenlegion O strafbar O . O Am O Donnerstag O wird O sich O zeigen O , O ob O die O Idee O der O DLR-Forscher B-ORGpart funktioniert O . O Der O sechste O Lauf O der O ADAC B-ORG GT I-ORG Mastersstand O ganz O klar O im O Mittelpunkt O des O Motorsport-Wochenendes O auf O dem O Eurospeedway B-ORG Lausitz I-ORG . O Nach O den O schwächeren O Vorgaben O der O Wall B-ORG Street I-ORG vom O Vortag O setzten O die O deutschen B-LOCderiv Standardwerte O ihren O Konsolidierungskurs O fort O . O Kolb B-PER war O seit O 1986 O im O Turnverein O als O Leiter O tätig O , O darunter O elf O Jahre O als O Hauptleiter O in O der O Männerriege O . O

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/flax/token-classification/requirements.txt

datasets >= 1.8.0 jax>=0.2.8 jaxlib>=0.1.59 flax>=0.3.5 optax>=0.0.8 seqeval

datasets >= 1.8.0 jax>=0.2.8 jaxlib>=0.1.59 flax>=0.3.5 optax>=0.0.8 seqeval

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/text-generation/requirements.txt

sentencepiece != 0.1.92 protobuf torch >= 1.3

sentencepiece != 0.1.92 protobuf torch >= 1.3

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/xtreme-s/requirements.txt

datasets >= 1.18.0 torch >= 1.5 torchaudio librosa jiwer

datasets >= 1.18.0 torch >= 1.5 torchaudio librosa jiwer

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/tensorflow/_tests_requirements.txt

tensorflow tensorboard scikit-learn seqeval psutil sacrebleu >= 1.4.12 git+https://github.com/huggingface/accelerate@main#egg=accelerate rouge-score tensorflow_datasets matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.13.3 fire pytest conllu sentencepiece != 0.1.92 protobuf jiwer librosa evaluate >= 0.2.0

tensorflow tensorboard scikit-learn seqeval psutil sacrebleu >= 1.4.12 git+https://github.com/huggingface/accelerate@main#egg=accelerate rouge-score tensorflow_datasets matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.13.3 fire pytest conllu sentencepiece != 0.1.92 protobuf jiwer librosa evaluate >= 0.2.0

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/tensorflow/token-classification/requirements.txt

datasets >= 1.4.0 tensorflow >= 2.3.0 evaluate >= 0.2.0

datasets >= 1.4.0 tensorflow >= 2.3.0 evaluate >= 0.2.0

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/pytorch/audio-classification/requirements.txt

datasets>=1.14.0 evaluate librosa torchaudio torch>=1.6

datasets>=1.14.0 evaluate librosa torchaudio torch>=1.6

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/distillation/requirements.txt

transformers gitpython==3.0.2 tensorboard>=1.14.0 tensorboardX==1.8 psutil==5.6.6 scipy>=1.4.1

transformers gitpython==3.0.2 tensorboard>=1.14.0 tensorboardX==1.8 psutil==5.6.6 scipy>=1.4.1

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./src/transformers/models/videomae/modeling_videomae.py

# coding=utf-8 # Copyright 2022 Multimedia Computing Group, Nanjing University and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch VideoMAE (masked autoencoder) model.""" import collections.abc import math from copy import deepcopy from dataclasses import dataclass from typing import Optional, Set, Tuple, Union import numpy as np import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from ...utils.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .configuration_videomae import VideoMAEConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VideoMAEConfig" _CHECKPOINT_FOR_DOC = "MCG-NJU/videomae-base" VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "MCG-NJU/videomae-base", # See all VideoMAE models at https://huggingface.co/models?filter=videomae ] @dataclass class VideoMAEDecoderOutput(ModelOutput): """ Class for VideoMAEDecoder's outputs, with potential hidden states and attentions. Args: logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`): Pixel reconstruction logits. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class VideoMAEForPreTrainingOutput(ModelOutput): """ Class for VideoMAEForPreTraining's outputs, with potential hidden states and attentions. Args: loss (`torch.FloatTensor` of shape `(1,)`): Pixel reconstruction loss. logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`): Pixel reconstruction logits. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None # sin-cos position encoding # https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31 def get_sinusoid_encoding_table(n_position, d_hid): """Sinusoid position encoding table""" # TODO: make it with torch instead of numpy def get_position_angle_vec(position): return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 return torch.FloatTensor(sinusoid_table).unsqueeze(0) class VideoMAEEmbeddings(nn.Module): """ Construct the patch and position embeddings. """ def __init__(self, config): super().__init__() self.patch_embeddings = VideoMAEPatchEmbeddings(config) self.num_patches = self.patch_embeddings.num_patches # fixed sin-cos embedding self.position_embeddings = get_sinusoid_encoding_table(self.num_patches, config.hidden_size) self.config = config def forward(self, pixel_values, bool_masked_pos): # create patch embeddings embeddings = self.patch_embeddings(pixel_values) # add position embeddings embeddings = embeddings + self.position_embeddings.type_as(embeddings).to(embeddings.device).clone().detach() # only keep visible patches # ~bool_masked_pos means visible if bool_masked_pos is not None: batch_size, _, num_channels = embeddings.shape embeddings = embeddings[~bool_masked_pos] embeddings = embeddings.reshape(batch_size, -1, num_channels) return embeddings class VideoMAEPatchEmbeddings(nn.Module): """ Video to Patch Embedding. This module turns a batch of videos of shape (batch_size, num_frames, num_channels, height, width) into a tensor of shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder. The seq_len (the number of patches) equals (number of frames // tubelet_size) * (height // patch_size) * (width // patch_size). """ def __init__(self, config): super().__init__() image_size = config.image_size patch_size = config.patch_size num_channels = config.num_channels hidden_size = config.hidden_size num_frames = config.num_frames tubelet_size = config.tubelet_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) self.image_size = image_size self.patch_size = patch_size self.tubelet_size = int(tubelet_size) num_patches = ( (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) * (num_frames // self.tubelet_size) ) self.num_channels = num_channels self.num_patches = num_patches self.projection = nn.Conv3d( in_channels=num_channels, out_channels=hidden_size, kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]), stride=(self.tubelet_size, patch_size[0], patch_size[1]), ) def forward(self, pixel_values): batch_size, num_frames, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})." ) # permute to (batch_size, num_channels, num_frames, height, width) pixel_values = pixel_values.permute(0, 2, 1, 3, 4) embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) return embeddings class VideoMAESelfAttention(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=False) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=False) if config.qkv_bias: self.q_bias = nn.Parameter(torch.zeros(self.all_head_size)) self.v_bias = nn.Parameter(torch.zeros(self.all_head_size)) else: self.q_bias = None self.v_bias = None self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: k_bias = torch.zeros_like(self.v_bias, requires_grad=False) if self.q_bias is not None else None keys = nn.functional.linear(input=hidden_states, weight=self.key.weight, bias=k_bias) values = nn.functional.linear(input=hidden_states, weight=self.value.weight, bias=self.v_bias) queries = nn.functional.linear(input=hidden_states, weight=self.query.weight, bias=self.q_bias) key_layer = self.transpose_for_scores(keys) value_layer = self.transpose_for_scores(values) query_layer = self.transpose_for_scores(queries) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs # Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->VideoMAE class VideoMAESelfOutput(nn.Module): """ The residual connection is defined in VideoMAELayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->VideoMAE class VideoMAEAttention(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.attention = VideoMAESelfAttention(config) self.output = VideoMAESelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention(hidden_states, head_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.vit.modeling_vit.ViTIntermediate ViT->VideoMAE class VideoMAEIntermediate(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTOutput ViT->VideoMAE class VideoMAEOutput(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->VideoMAE class VideoMAELayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = VideoMAEAttention(config) self.intermediate = VideoMAEIntermediate(config) self.output = VideoMAEOutput(config) self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in VideoMAE, layernorm is applied before self-attention head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in VideoMAE, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs # Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->VideoMAE class VideoMAEEncoder(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([VideoMAELayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class VideoMAEPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = VideoMAEConfig base_model_prefix = "videomae" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv3d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, VideoMAEEncoder): module.gradient_checkpointing = value VIDEOMAE_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`VideoMAEConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ VIDEOMAE_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`VideoMAEFeatureExtractor`]. See [`VideoMAEFeatureExtractor.__call__`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare VideoMAE Model transformer outputting raw hidden-states without any specific head on top.", VIDEOMAE_START_DOCSTRING, ) class VideoMAEModel(VideoMAEPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.embeddings = VideoMAEEmbeddings(config) self.encoder = VideoMAEEncoder(config) if config.use_mean_pooling: self.layernorm = None else: self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values, bool_masked_pos=None, head_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Returns: Examples: ```python >>> from decord import VideoReader, cpu >>> import numpy as np >>> from transformers import VideoMAEFeatureExtractor, VideoMAEModel >>> from huggingface_hub import hf_hub_download >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): ... converted_len = int(clip_len * frame_sample_rate) ... end_idx = np.random.randint(converted_len, seg_len) ... start_idx = end_idx - converted_len ... indices = np.linspace(start_idx, end_idx, num=clip_len) ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) ... return indices >>> # video clip consists of 300 frames (10 seconds at 30 FPS) >>> file_path = hf_hub_download( ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ... ) >>> videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0)) >>> # sample 16 frames >>> videoreader.seek(0) >>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader)) >>> video = videoreader.get_batch(indices).asnumpy() >>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base") >>> model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base") >>> # prepare video for the model >>> inputs = feature_extractor(list(video), return_tensors="pt") >>> # forward pass >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 1568, 768] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings(pixel_values, bool_masked_pos) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] if self.layernorm is not None: sequence_output = self.layernorm(sequence_output) if not return_dict: return (sequence_output,) + encoder_outputs[1:] return BaseModelOutput( last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class VideoMAEDecoder(nn.Module): def __init__(self, config, num_patches): super().__init__() decoder_num_labels = config.num_channels * config.tubelet_size * config.patch_size**2 decoder_config = deepcopy(config) decoder_config.hidden_size = config.decoder_hidden_size decoder_config.num_hidden_layers = config.decoder_num_hidden_layers decoder_config.num_attention_heads = config.decoder_num_attention_heads decoder_config.intermediate_size = config.decoder_intermediate_size self.decoder_layers = nn.ModuleList( [VideoMAELayer(decoder_config) for _ in range(config.decoder_num_hidden_layers)] ) self.norm = nn.LayerNorm(config.decoder_hidden_size) self.head = ( nn.Linear(config.decoder_hidden_size, decoder_num_labels) if decoder_num_labels > 0 else nn.Identity() ) self.gradient_checkpointing = False self.config = config def forward( self, hidden_states, return_token_num, output_attentions=False, output_hidden_states=False, return_dict=True, ): # apply Transformer layers (blocks) all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.decoder_layers): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, None, ) else: layer_outputs = layer_module(hidden_states, head_mask=None, output_attentions=output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if return_token_num > 0: hidden_states = hidden_states[:, -return_token_num:] # predictor projection hidden_states = self.norm(hidden_states) logits = self.head(hidden_states) if not return_dict: return tuple(v for v in [logits, all_hidden_states, all_self_attentions] if v is not None) return VideoMAEDecoderOutput(logits=logits, hidden_states=all_hidden_states, attentions=all_self_attentions) @add_start_docstrings( "The VideoMAE Model transformer with the decoder on top for self-supervised pre-training.", VIDEOMAE_START_DOCSTRING, ) class VideoMAEForPreTraining(VideoMAEPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.videomae = VideoMAEModel(config) self.encoder_to_decoder = nn.Linear(config.hidden_size, config.decoder_hidden_size, bias=False) self.mask_token = nn.Parameter(torch.zeros(1, 1, config.decoder_hidden_size)) self.position_embeddings = get_sinusoid_encoding_table( self.videomae.embeddings.num_patches, config.decoder_hidden_size ) self.decoder = VideoMAEDecoder(config, num_patches=self.videomae.embeddings.num_patches) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=VideoMAEForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values, bool_masked_pos, head_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Returns: Examples: ```python >>> from transformers import VideoMAEFeatureExtractor, VideoMAEForPreTraining >>> import numpy as np >>> import torch >>> num_frames = 16 >>> video = list(np.random.randn(16, 3, 224, 224)) >>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base") >>> model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base") >>> pixel_values = feature_extractor(video, return_tensors="pt").pixel_values >>> num_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2 >>> seq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame >>> bool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool() >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) >>> loss = outputs.loss ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.videomae( pixel_values, bool_masked_pos=bool_masked_pos, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.encoder_to_decoder( sequence_output ) # [batch_size, num_visible_patches, decoder_hidden_size] batch_size, seq_len, num_channels = sequence_output.shape # we don't unshuffle the correct visible token order, but shuffle the position embeddings accordingly. if bool_masked_pos is None: raise ValueError("One must provided a boolean mask ") expanded_position_embeddings = self.position_embeddings.expand(batch_size, -1, -1).type_as(pixel_values) expanded_position_embeddings = expanded_position_embeddings.to(pixel_values.device).clone().detach() pos_emb_visible = expanded_position_embeddings[~bool_masked_pos].reshape(batch_size, -1, num_channels) pos_emb_mask = expanded_position_embeddings[bool_masked_pos].reshape(batch_size, -1, num_channels) # [batch_size, num_patches, decoder_hidden_size] x_full = torch.cat([sequence_output + pos_emb_visible, self.mask_token + pos_emb_mask], dim=1) # [batch_size, num_masked_patches, num_channels * patch_size * patch_size] decoder_outputs = self.decoder(x_full, pos_emb_mask.shape[1]) logits = decoder_outputs.logits loss = None with torch.no_grad(): # calculate the labels to be predicted # first, unnormalize the frames device = pixel_values.device mean = torch.as_tensor(IMAGENET_DEFAULT_MEAN).to(device)[None, None, :, None, None] std = torch.as_tensor(IMAGENET_DEFAULT_STD).to(device)[None, None, :, None, None] frames = pixel_values * std + mean # in [0, 1] batch_size, time, num_channels, height, width = frames.shape tubelet_size, patch_size = self.config.tubelet_size, self.config.patch_size if self.config.norm_pix_loss: # step 1: split up dimensions (time by tubelet_size, height by patch_size, width by patch_size) frames = frames.view( batch_size, time // tubelet_size, tubelet_size, num_channels, height // patch_size, patch_size, width // patch_size, patch_size, ) # step 2: move dimensions to concatenate: frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous() # step 3: concatenate: frames = frames.view( batch_size, time // tubelet_size * height // patch_size * width // patch_size, tubelet_size * patch_size * patch_size, num_channels, ) # step 4: normalize. The authors find that the mean is about 0.48 and standard deviation is about 0.08. frames_norm = (frames - frames.mean(dim=-2, keepdim=True)) / ( frames.var(dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6 ) # step 5: reshape to (batch_size, T//ts * H//ps * W//ps, ts * ps * ps * C) videos_patch = frames_norm.view( batch_size, time // tubelet_size * height // patch_size * width // patch_size, tubelet_size * patch_size * patch_size * num_channels, ) else: # step 1: split up dimensions (time by tubelet_size, height by patch_size, width by patch_size) frames = frames.view( batch_size, time // tubelet_size, tubelet_size, num_channels, height // patch_size, patch_size, width // patch_size, patch_size, ) # step 2: move dimensions to concatenate: (batch_size, T//ts, H//ps, W//ps, ts, ps, ps, C) frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous() # step 3: concatenate videos_patch = frames.view( batch_size, time // tubelet_size * height // patch_size * width // patch_size, tubelet_size * patch_size * patch_size * num_channels, ) batch_size, _, num_channels = videos_patch.shape labels = videos_patch[bool_masked_pos].reshape(batch_size, -1, num_channels) loss_fct = MSELoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return VideoMAEForPreTrainingOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """VideoMAE Model transformer with a video classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.""", VIDEOMAE_START_DOCSTRING, ) class VideoMAEForVideoClassification(VideoMAEPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.videomae = VideoMAEModel(config) # Classifier head self.fc_norm = nn.LayerNorm(config.hidden_size) if config.use_mean_pooling else None self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from decord import VideoReader, cpu >>> import torch >>> import numpy as np >>> from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification >>> from huggingface_hub import hf_hub_download >>> np.random.seed(0) >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): ... converted_len = int(clip_len * frame_sample_rate) ... end_idx = np.random.randint(converted_len, seg_len) ... start_idx = end_idx - converted_len ... indices = np.linspace(start_idx, end_idx, num=clip_len) ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) ... return indices >>> # video clip consists of 300 frames (10 seconds at 30 FPS) >>> file_path = hf_hub_download( ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ... ) >>> videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0)) >>> # sample 16 frames >>> videoreader.seek(0) >>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader)) >>> video = videoreader.get_batch(indices).asnumpy() >>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") >>> model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") >>> inputs = feature_extractor(list(video), return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) ... logits = outputs.logits >>> # model predicts one of the 400 Kinetics-400 classes >>> predicted_label = logits.argmax(-1).item() >>> print(model.config.id2label[predicted_label]) eating spaghetti ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.videomae( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] if self.fc_norm is not None: sequence_output = self.fc_norm(sequence_output.mean(1)) else: sequence_output = sequence_output[:, 0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

# coding=utf-8 # Copyright 2022 Multimedia Computing Group, Nanjing University and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch VideoMAE (masked autoencoder) model.""" import collections.abc import math from copy import deepcopy from dataclasses import dataclass from typing import Optional, Set, Tuple, Union import numpy as np import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from ...utils.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .configuration_videomae import VideoMAEConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VideoMAEConfig" _CHECKPOINT_FOR_DOC = "MCG-NJU/videomae-base" VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "MCG-NJU/videomae-base", # See all VideoMAE models at https://huggingface.co/models?filter=videomae ] @dataclass class VideoMAEDecoderOutput(ModelOutput): """ Class for VideoMAEDecoder's outputs, with potential hidden states and attentions. Args: logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`): Pixel reconstruction logits. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class VideoMAEForPreTrainingOutput(ModelOutput): """ Class for VideoMAEForPreTraining's outputs, with potential hidden states and attentions. Args: loss (`torch.FloatTensor` of shape `(1,)`): Pixel reconstruction loss. logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`): Pixel reconstruction logits. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None # sin-cos position encoding # https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31 def get_sinusoid_encoding_table(n_position, d_hid): """Sinusoid position encoding table""" # TODO: make it with torch instead of numpy def get_position_angle_vec(position): return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 return torch.FloatTensor(sinusoid_table).unsqueeze(0) class VideoMAEEmbeddings(nn.Module): """ Construct the patch and position embeddings. """ def __init__(self, config): super().__init__() self.patch_embeddings = VideoMAEPatchEmbeddings(config) self.num_patches = self.patch_embeddings.num_patches # fixed sin-cos embedding self.position_embeddings = get_sinusoid_encoding_table(self.num_patches, config.hidden_size) self.config = config def forward(self, pixel_values, bool_masked_pos): # create patch embeddings embeddings = self.patch_embeddings(pixel_values) # add position embeddings embeddings = embeddings + self.position_embeddings.type_as(embeddings).to(embeddings.device).clone().detach() # only keep visible patches # ~bool_masked_pos means visible if bool_masked_pos is not None: batch_size, _, num_channels = embeddings.shape embeddings = embeddings[~bool_masked_pos] embeddings = embeddings.reshape(batch_size, -1, num_channels) return embeddings class VideoMAEPatchEmbeddings(nn.Module): """ Video to Patch Embedding. This module turns a batch of videos of shape (batch_size, num_frames, num_channels, height, width) into a tensor of shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder. The seq_len (the number of patches) equals (number of frames // tubelet_size) * (height // patch_size) * (width // patch_size). """ def __init__(self, config): super().__init__() image_size = config.image_size patch_size = config.patch_size num_channels = config.num_channels hidden_size = config.hidden_size num_frames = config.num_frames tubelet_size = config.tubelet_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) self.image_size = image_size self.patch_size = patch_size self.tubelet_size = int(tubelet_size) num_patches = ( (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) * (num_frames // self.tubelet_size) ) self.num_channels = num_channels self.num_patches = num_patches self.projection = nn.Conv3d( in_channels=num_channels, out_channels=hidden_size, kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]), stride=(self.tubelet_size, patch_size[0], patch_size[1]), ) def forward(self, pixel_values): batch_size, num_frames, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})." ) # permute to (batch_size, num_channels, num_frames, height, width) pixel_values = pixel_values.permute(0, 2, 1, 3, 4) embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) return embeddings class VideoMAESelfAttention(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=False) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=False) if config.qkv_bias: self.q_bias = nn.Parameter(torch.zeros(self.all_head_size)) self.v_bias = nn.Parameter(torch.zeros(self.all_head_size)) else: self.q_bias = None self.v_bias = None self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: k_bias = torch.zeros_like(self.v_bias, requires_grad=False) if self.q_bias is not None else None keys = nn.functional.linear(input=hidden_states, weight=self.key.weight, bias=k_bias) values = nn.functional.linear(input=hidden_states, weight=self.value.weight, bias=self.v_bias) queries = nn.functional.linear(input=hidden_states, weight=self.query.weight, bias=self.q_bias) key_layer = self.transpose_for_scores(keys) value_layer = self.transpose_for_scores(values) query_layer = self.transpose_for_scores(queries) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs # Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->VideoMAE class VideoMAESelfOutput(nn.Module): """ The residual connection is defined in VideoMAELayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->VideoMAE class VideoMAEAttention(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.attention = VideoMAESelfAttention(config) self.output = VideoMAESelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention(hidden_states, head_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.vit.modeling_vit.ViTIntermediate ViT->VideoMAE class VideoMAEIntermediate(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTOutput ViT->VideoMAE class VideoMAEOutput(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->VideoMAE class VideoMAELayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = VideoMAEAttention(config) self.intermediate = VideoMAEIntermediate(config) self.output = VideoMAEOutput(config) self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in VideoMAE, layernorm is applied before self-attention head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in VideoMAE, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs # Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->VideoMAE class VideoMAEEncoder(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([VideoMAELayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class VideoMAEPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = VideoMAEConfig base_model_prefix = "videomae" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv3d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, VideoMAEEncoder): module.gradient_checkpointing = value VIDEOMAE_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`VideoMAEConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ VIDEOMAE_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`VideoMAEFeatureExtractor`]. See [`VideoMAEFeatureExtractor.__call__`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare VideoMAE Model transformer outputting raw hidden-states without any specific head on top.", VIDEOMAE_START_DOCSTRING, ) class VideoMAEModel(VideoMAEPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.embeddings = VideoMAEEmbeddings(config) self.encoder = VideoMAEEncoder(config) if config.use_mean_pooling: self.layernorm = None else: self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values, bool_masked_pos=None, head_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Returns: Examples: ```python >>> from decord import VideoReader, cpu >>> import numpy as np >>> from transformers import VideoMAEFeatureExtractor, VideoMAEModel >>> from huggingface_hub import hf_hub_download >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): ... converted_len = int(clip_len * frame_sample_rate) ... end_idx = np.random.randint(converted_len, seg_len) ... start_idx = end_idx - converted_len ... indices = np.linspace(start_idx, end_idx, num=clip_len) ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) ... return indices >>> # video clip consists of 300 frames (10 seconds at 30 FPS) >>> file_path = hf_hub_download( ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ... ) >>> videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0)) >>> # sample 16 frames >>> videoreader.seek(0) >>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader)) >>> video = videoreader.get_batch(indices).asnumpy() >>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base") >>> model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base") >>> # prepare video for the model >>> inputs = feature_extractor(list(video), return_tensors="pt") >>> # forward pass >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 1568, 768] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings(pixel_values, bool_masked_pos) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] if self.layernorm is not None: sequence_output = self.layernorm(sequence_output) if not return_dict: return (sequence_output,) + encoder_outputs[1:] return BaseModelOutput( last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class VideoMAEDecoder(nn.Module): def __init__(self, config, num_patches): super().__init__() decoder_num_labels = config.num_channels * config.tubelet_size * config.patch_size**2 decoder_config = deepcopy(config) decoder_config.hidden_size = config.decoder_hidden_size decoder_config.num_hidden_layers = config.decoder_num_hidden_layers decoder_config.num_attention_heads = config.decoder_num_attention_heads decoder_config.intermediate_size = config.decoder_intermediate_size self.decoder_layers = nn.ModuleList( [VideoMAELayer(decoder_config) for _ in range(config.decoder_num_hidden_layers)] ) self.norm = nn.LayerNorm(config.decoder_hidden_size) self.head = ( nn.Linear(config.decoder_hidden_size, decoder_num_labels) if decoder_num_labels > 0 else nn.Identity() ) self.gradient_checkpointing = False self.config = config def forward( self, hidden_states, return_token_num, output_attentions=False, output_hidden_states=False, return_dict=True, ): # apply Transformer layers (blocks) all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.decoder_layers): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, None, ) else: layer_outputs = layer_module(hidden_states, head_mask=None, output_attentions=output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if return_token_num > 0: hidden_states = hidden_states[:, -return_token_num:] # predictor projection hidden_states = self.norm(hidden_states) logits = self.head(hidden_states) if not return_dict: return tuple(v for v in [logits, all_hidden_states, all_self_attentions] if v is not None) return VideoMAEDecoderOutput(logits=logits, hidden_states=all_hidden_states, attentions=all_self_attentions) @add_start_docstrings( "The VideoMAE Model transformer with the decoder on top for self-supervised pre-training.", VIDEOMAE_START_DOCSTRING, ) class VideoMAEForPreTraining(VideoMAEPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.videomae = VideoMAEModel(config) self.encoder_to_decoder = nn.Linear(config.hidden_size, config.decoder_hidden_size, bias=False) self.mask_token = nn.Parameter(torch.zeros(1, 1, config.decoder_hidden_size)) self.position_embeddings = get_sinusoid_encoding_table( self.videomae.embeddings.num_patches, config.decoder_hidden_size ) self.decoder = VideoMAEDecoder(config, num_patches=self.videomae.embeddings.num_patches) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=VideoMAEForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values, bool_masked_pos, head_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" Returns: Examples: ```python >>> from transformers import VideoMAEFeatureExtractor, VideoMAEForPreTraining >>> import numpy as np >>> import torch >>> num_frames = 16 >>> video = list(np.random.randn(16, 3, 224, 224)) >>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base") >>> model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base") >>> pixel_values = feature_extractor(video, return_tensors="pt").pixel_values >>> num_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2 >>> seq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame >>> bool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool() >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) >>> loss = outputs.loss ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.videomae( pixel_values, bool_masked_pos=bool_masked_pos, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.encoder_to_decoder( sequence_output ) # [batch_size, num_visible_patches, decoder_hidden_size] batch_size, seq_len, num_channels = sequence_output.shape # we don't unshuffle the correct visible token order, but shuffle the position embeddings accordingly. if bool_masked_pos is None: raise ValueError("One must provided a boolean mask ") expanded_position_embeddings = self.position_embeddings.expand(batch_size, -1, -1).type_as(pixel_values) expanded_position_embeddings = expanded_position_embeddings.to(pixel_values.device).clone().detach() pos_emb_visible = expanded_position_embeddings[~bool_masked_pos].reshape(batch_size, -1, num_channels) pos_emb_mask = expanded_position_embeddings[bool_masked_pos].reshape(batch_size, -1, num_channels) # [batch_size, num_patches, decoder_hidden_size] x_full = torch.cat([sequence_output + pos_emb_visible, self.mask_token + pos_emb_mask], dim=1) # [batch_size, num_masked_patches, num_channels * patch_size * patch_size] decoder_outputs = self.decoder(x_full, pos_emb_mask.shape[1]) logits = decoder_outputs.logits loss = None with torch.no_grad(): # calculate the labels to be predicted # first, unnormalize the frames device = pixel_values.device mean = torch.as_tensor(IMAGENET_DEFAULT_MEAN).to(device)[None, None, :, None, None] std = torch.as_tensor(IMAGENET_DEFAULT_STD).to(device)[None, None, :, None, None] frames = pixel_values * std + mean # in [0, 1] batch_size, time, num_channels, height, width = frames.shape tubelet_size, patch_size = self.config.tubelet_size, self.config.patch_size if self.config.norm_pix_loss: # step 1: split up dimensions (time by tubelet_size, height by patch_size, width by patch_size) frames = frames.view( batch_size, time // tubelet_size, tubelet_size, num_channels, height // patch_size, patch_size, width // patch_size, patch_size, ) # step 2: move dimensions to concatenate: frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous() # step 3: concatenate: frames = frames.view( batch_size, time // tubelet_size * height // patch_size * width // patch_size, tubelet_size * patch_size * patch_size, num_channels, ) # step 4: normalize. The authors find that the mean is about 0.48 and standard deviation is about 0.08. frames_norm = (frames - frames.mean(dim=-2, keepdim=True)) / ( frames.var(dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6 ) # step 5: reshape to (batch_size, T//ts * H//ps * W//ps, ts * ps * ps * C) videos_patch = frames_norm.view( batch_size, time // tubelet_size * height // patch_size * width // patch_size, tubelet_size * patch_size * patch_size * num_channels, ) else: # step 1: split up dimensions (time by tubelet_size, height by patch_size, width by patch_size) frames = frames.view( batch_size, time // tubelet_size, tubelet_size, num_channels, height // patch_size, patch_size, width // patch_size, patch_size, ) # step 2: move dimensions to concatenate: (batch_size, T//ts, H//ps, W//ps, ts, ps, ps, C) frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous() # step 3: concatenate videos_patch = frames.view( batch_size, time // tubelet_size * height // patch_size * width // patch_size, tubelet_size * patch_size * patch_size * num_channels, ) batch_size, _, num_channels = videos_patch.shape labels = videos_patch[bool_masked_pos].reshape(batch_size, -1, num_channels) loss_fct = MSELoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return VideoMAEForPreTrainingOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """VideoMAE Model transformer with a video classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.""", VIDEOMAE_START_DOCSTRING, ) class VideoMAEForVideoClassification(VideoMAEPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.videomae = VideoMAEModel(config) # Classifier head self.fc_norm = nn.LayerNorm(config.hidden_size) if config.use_mean_pooling else None self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from decord import VideoReader, cpu >>> import torch >>> import numpy as np >>> from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification >>> from huggingface_hub import hf_hub_download >>> np.random.seed(0) >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): ... converted_len = int(clip_len * frame_sample_rate) ... end_idx = np.random.randint(converted_len, seg_len) ... start_idx = end_idx - converted_len ... indices = np.linspace(start_idx, end_idx, num=clip_len) ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) ... return indices >>> # video clip consists of 300 frames (10 seconds at 30 FPS) >>> file_path = hf_hub_download( ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ... ) >>> videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0)) >>> # sample 16 frames >>> videoreader.seek(0) >>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader)) >>> video = videoreader.get_batch(indices).asnumpy() >>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") >>> model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") >>> inputs = feature_extractor(list(video), return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) ... logits = outputs.logits >>> # model predicts one of the 400 Kinetics-400 classes >>> predicted_label = logits.argmax(-1).item() >>> print(model.config.id2label[predicted_label]) eating spaghetti ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.videomae( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] if self.fc_norm is not None: sequence_output = self.fc_norm(sequence_output.mean(1)) else: sequence_output = sequence_output[:, 0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/flax/language-modeling/run_bart_dlm_flax.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Pretraining the library models for denoising language modeling on a text file or a dataset. Here is the full list of checkpoints on the hub that can be pretrained by this script: https://huggingface.co/models?filter=bart """ # You can also adapt this script on your own denoising language modeling task. Pointers for this are left as comments. import json import logging import math import os import sys import time from dataclasses import asdict, dataclass, field from enum import Enum from itertools import chain from pathlib import Path from typing import Dict, List, Optional import nltk import numpy as np from datasets import load_dataset from tqdm import tqdm import flax import jax import jax.numpy as jnp import optax from flax import jax_utils, traverse_util from flax.jax_utils import pad_shard_unpad from flax.training import train_state from flax.training.common_utils import get_metrics, onehot, shard from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, AutoTokenizer, BartConfig, BatchEncoding, FlaxBartForConditionalGeneration, HfArgumentParser, PreTrainedTokenizerBase, is_tensorboard_available, set_seed, ) from transformers.models.bart.modeling_flax_bart import shift_tokens_right from transformers.utils import get_full_repo_name, send_example_telemetry MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class TrainingArguments: output_dir: str = field( metadata={"help": "The output directory where the model predictions and checkpoints will be written."}, ) overwrite_output_dir: bool = field( default=False, metadata={ "help": ( "Overwrite the content of the output directory. " "Use this to continue training if output_dir points to a checkpoint directory." ) }, ) do_train: bool = field(default=False, metadata={"help": "Whether to run training."}) do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."}) per_device_train_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."} ) per_device_eval_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."} ) learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."}) weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."}) adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"}) adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"}) adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}) adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."}) num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."}) warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."}) logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."}) save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."}) eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."}) seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."}) push_to_hub: bool = field( default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."} ) hub_model_id: str = field( default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."} ) hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."}) def __post_init__(self): if self.output_dir is not None: self.output_dir = os.path.expanduser(self.output_dir) def to_dict(self): """ Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates the token values by removing their value. """ d = asdict(self) for k, v in d.items(): if isinstance(v, Enum): d[k] = v.value if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum): d[k] = [x.value for x in v] if k.endswith("_token"): d[k] = f"<{k.upper()}>" return d @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) dtype: Optional[str] = field( default="float32", metadata={ "help": ( "Floating-point format in which the model weights should be initialized and trained. Choose one of" " `[float32, float16, bfloat16]`." ) }, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) train_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}, ) validation_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) max_seq_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total input sequence length after tokenization and masking. Sequences longer than this" " will be truncated. Default to the max input length of the model." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) mlm_probability: float = field( default=0.3, metadata={"help": "Ratio of tokens to mask for span masked language modeling loss"} ) permute_sentence_ratio: float = field( default=1.0, metadata={"help": "Ratio of sentences to be permuted in each document"} ) poisson_lambda: float = field( default=3.0, metadata={"help": "Mean of Poisson distribution used to generate span-lengths to be masked"} ) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." @flax.struct.dataclass class FlaxDataCollatorForBartDenoisingLM: """ Data collator used for BART denoising language modeling. The code is largely copied from `<https://github.com/morganmcg1/rotobart/blob/main/data_collator.py#L223>`__. For more information on how BART denoising language modeling works, one can take a look at the `official paper <https://arxiv.org/pdf/1910.13461.pdf>`__ or the `official code for preprocessing <https://github.com/facebookresearch/fairseq/blob/main/fairseq/data/denoising_dataset.py>`__ . Args: tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`): The tokenizer used for encoding the data mask_ratio (:obj:`float`): The probability with which to (randomly) mask tokens in the input poisson_lambda (:obj:`float`): Mean parameter of Poisson distribution used to generate span-lengths to be masked permute_sentence_ratio (:obj:`float`): Ratio of sentences to be permuted in each document decoder_start_token_id: (:obj:`int): The decoder start token id of the model """ tokenizer: PreTrainedTokenizerBase decoder_start_token_id: int mask_ratio: float = 0.3 poisson_lambda: float = 3.0 permute_sentence_ratio: float = 1.0 def __post_init__(self): if self.tokenizer.mask_token is None or self.tokenizer.eos_token is None: raise ValueError( "This tokenizer does not have a mask token or eos token token which is necessary for denoising" " language modeling. " ) def __call__(self, examples: List[Dict[str, List[int]]]) -> BatchEncoding: # convert list to dict and tensorize input batch = BatchEncoding( {k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()} ) batch["labels"] = batch["input_ids"].copy() batch["decoder_input_ids"] = shift_tokens_right( batch["labels"], self.tokenizer.pad_token_id, self.decoder_start_token_id ) # permuting sentences do_permute = False if self.permute_sentence_ratio > 0.0: batch["input_ids"] = self.permute_sentences(batch["input_ids"]) do_permute = True # masking span of tokens (text infilling in the paper) if self.mask_ratio: batch["input_ids"], batch["labels"] = self.span_mask_tokens( batch["input_ids"], batch["labels"], do_permute ) # ignore pad tokens batch["attention_mask"] = (batch["input_ids"] != self.tokenizer.pad_token_id).astype(int) batch["decoder_attention_mask"] = (batch["decoder_input_ids"] != self.tokenizer.pad_token_id).astype(int) return batch def permute_sentences(self, input_ids): """ Shuffle sentences in each document. """ results = input_ids.copy() # find end locations of sentences end_sentence_mask = input_ids == self.tokenizer.pad_token_id sentence_ends = np.argwhere(end_sentence_mask) sentence_ends[:, 1] += 1 example_has_multiple_sentences, num_sentences = np.unique(sentence_ends[:, 0], return_counts=True) num_sentences_map = {sent_idx: count for sent_idx, count in zip(example_has_multiple_sentences, num_sentences)} num_to_permute = np.ceil(num_sentences * self.permute_sentence_ratio).astype(int) num_to_permute_map = { sent_idx: count for sent_idx, count in zip(example_has_multiple_sentences, num_to_permute) } sentence_ends = np.split(sentence_ends[:, 1], np.unique(sentence_ends[:, 0], return_index=True)[1][1:]) sentence_ends_map = {sent_idx: count for sent_idx, count in zip(example_has_multiple_sentences, sentence_ends)} for i in range(input_ids.shape[0]): if i not in example_has_multiple_sentences: continue substitutions = np.random.permutation(num_sentences_map[i])[: num_to_permute_map[i]] ordering = np.arange(0, num_sentences_map[i]) ordering[substitutions] = substitutions[np.random.permutation(num_to_permute_map[i])] # write shuffled sentences into results index = 0 for j in ordering: sentence = input_ids[i, (sentence_ends_map[i][j - 1] if j > 0 else 0) : sentence_ends_map[i][j]] results[i, index : index + sentence.shape[0]] = sentence index += sentence.shape[0] return results def span_mask_tokens(self, input_ids, labels, do_permute): """ Sampling text spans with span lengths drawn from a Poisson distribution and masking them. """ special_tokens_mask_labels = [ self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist() ] special_tokens_mask_inputs = [ self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in input_ids.tolist() ] special_tokens_mask_labels = np.array(special_tokens_mask_labels, dtype=bool) special_tokens_mask_inputs = np.array(special_tokens_mask_inputs, dtype=bool) # determine how many tokens we need to mask in total is_token_mask = ~(input_ids == self.tokenizer.pad_token_id) & ~special_tokens_mask_inputs num_tokens_to_mask = int(math.ceil(is_token_mask.astype(float).sum() * self.mask_ratio)) if num_tokens_to_mask == 0: return input_ids, labels # generate a sufficient number of span lengths span_lengths = np.random.poisson(lam=self.poisson_lambda, size=(num_tokens_to_mask,)) while np.cumsum(span_lengths, 0)[-1] < num_tokens_to_mask: span_lengths = np.concatenate( [span_lengths, np.random.poisson(lam=self.poisson_lambda, size=(num_tokens_to_mask,))] ) # remove all spans of length 0 # note that BART inserts additional mask tokens where length == 0, # which we do not implement for now as it adds additional complexity span_lengths = span_lengths[span_lengths > 0] # trim to about num_tokens_to_mask tokens cutoff_idx = np.argmin(np.abs(np.cumsum(span_lengths, 0) - num_tokens_to_mask)) + 1 span_lengths = span_lengths[:cutoff_idx] # randomly choose starting positions for masking token_indices = np.argwhere(is_token_mask == 1) span_starts = np.random.permutation(token_indices.shape[0])[: span_lengths.shape[0]] # prepare mask masked_indices = np.array(token_indices[span_starts]) mask = np.full_like(input_ids, fill_value=False) # mask starting positions for mi in masked_indices: mask[tuple(mi)] = True span_lengths -= 1 # fill up spans max_index = input_ids.shape[1] - 1 remaining = (span_lengths > 0) & (masked_indices[:, 1] < max_index) while np.any(remaining): masked_indices[remaining, 1] += 1 for mi in masked_indices: mask[tuple(mi)] = True span_lengths -= 1 remaining = (span_lengths > 0) & (masked_indices[:, 1] < max_index) # place the mask tokens mask[np.where(special_tokens_mask_inputs)] = False input_ids[np.where(mask)] = self.tokenizer.mask_token_id if not do_permute: labels[np.where(mask == 0)] = -100 else: labels[np.where(special_tokens_mask_labels)] = -100 # remove mask tokens that are not starts of spans to_remove = (mask == 1) & np.roll((mask == 1), 1, 1) new_input_ids = np.full_like(input_ids, fill_value=self.tokenizer.pad_token_id) for i, example in enumerate(input_ids): new_example = example[~to_remove[i]] new_input_ids[i, : new_example.shape[0]] = new_example return new_input_ids, labels def generate_batch_splits(samples_idx: np.ndarray, batch_size: int, drop_last=True) -> np.ndarray: """Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned.""" num_samples = len(samples_idx) if drop_last: samples_to_remove = num_samples % batch_size if samples_to_remove != 0: samples_idx = samples_idx[:-samples_to_remove] sections_split = num_samples // batch_size samples_idx = samples_idx.reshape((sections_split, batch_size)) else: sections_split = math.ceil(num_samples / batch_size) samples_idx = np.array_split(samples_idx, sections_split) return samples_idx def write_train_metric(summary_writer, train_metrics, train_time, step): summary_writer.scalar("train_time", train_time, step) train_metrics = get_metrics(train_metrics) for key, vals in train_metrics.items(): tag = f"train_{key}" for i, val in enumerate(vals): summary_writer.scalar(tag, val, step - len(vals) + i + 1) def write_eval_metric(summary_writer, eval_metrics, step): for metric_name, value in eval_metrics.items(): summary_writer.scalar(f"eval_{metric_name}", value, step) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_bart_dlm", model_args, data_args, framework="flax") if ( os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty." "Use --overwrite_output_dir to overcome." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", level=logging.INFO, datefmt="[%X]", ) # Log on each process the small summary: logger = logging.getLogger(__name__) # Set the verbosity to info of the Transformers logger (on main process only): logger.info(f"Training/evaluation parameters {training_args}") # Set seed before initializing model. set_seed(training_args.seed) # Handle the repository creation if training_args.push_to_hub: if training_args.hub_model_id is None: repo_name = get_full_repo_name( Path(training_args.output_dir).absolute().name, token=training_args.hub_token ) else: repo_name = training_args.hub_model_id repo = Repository(training_args.output_dir, clone_from=repo_name) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. datasets = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) if "validation" not in datasets.keys(): datasets["validation"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[:{data_args.validation_split_percentage}%]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[{data_args.validation_split_percentage}%:]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.train_file.split(".")[-1] if extension == "txt": extension = "text" datasets = load_dataset( extension, data_files=data_files, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) if "validation" not in datasets.keys(): datasets["validation"] = load_dataset( extension, data_files=data_files, split=f"train[:{data_args.validation_split_percentage}%]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) datasets["train"] = load_dataset( extension, data_files=data_files, split=f"train[{data_args.validation_split_percentage}%:]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, use_auth_token=True if model_args.use_auth_token else None, ) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, use_auth_token=True if model_args.use_auth_token else None, ) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if model_args.config_name: config = BartConfig.from_pretrained( model_args.config_name, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer), use_auth_token=True if model_args.use_auth_token else None, ) elif model_args.model_name_or_path: config = BartConfig.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") # Preprocessing the datasets. # First we tokenize all the texts. if training_args.do_train: column_names = datasets["train"].column_names else: column_names = datasets["validation"].column_names text_column_name = "text" if "text" in column_names else column_names[0] max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) # Use Punkt Sentence Tokenizer to divide a document into a list of sentences nltk.download("punkt") sentence_tokenizer = nltk.data.load("tokenizers/punkt/english.pickle") def sentence_split_function(example): sents = sentence_tokenizer.tokenize(example["text"]) # use pad token as end of sentence indicator new_text = tokenizer.bos_token + f"{tokenizer.pad_token}".join(sents) + tokenizer.eos_token return {"text": new_text} split_datasets = datasets.map( sentence_split_function, batched=False, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, ) # Tokenize every text, then concatenate them together before splitting them in smaller parts. # Since we make sure that all sequences are of the same length, no attention_mask is needed. def tokenize_function(examples): return tokenizer(examples[text_column_name], add_special_tokens=False, return_attention_mask=False) tokenized_datasets = split_datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=text_column_name, load_from_cache_file=not data_args.overwrite_cache, ) # Main data processing function that will concatenate all texts from our dataset and generate chunks of # max_seq_length. def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. if total_length >= max_seq_length: total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value # might be slower to preprocess. # # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, ) # Enable tensorboard only on the master node has_tensorboard = is_tensorboard_available() if has_tensorboard and jax.process_index() == 0: try: from flax.metrics.tensorboard import SummaryWriter summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir)) except ImportError as ie: has_tensorboard = False logger.warning( f"Unable to display metrics through TensorBoard because some package are not installed: {ie}" ) else: logger.warning( "Unable to display metrics through TensorBoard because the package is not installed: " "Please run pip install tensorboard to enable." ) # Initialize our training rng = jax.random.PRNGKey(training_args.seed) dropout_rngs = jax.random.split(rng, jax.local_device_count()) if model_args.model_name_or_path: model = FlaxBartForConditionalGeneration.from_pretrained( model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype), use_auth_token=True if model_args.use_auth_token else None, ) else: config.vocab_size = len(tokenizer) model = FlaxBartForConditionalGeneration( config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype), ) # Data collator # This one will take care of randomly masking the tokens and permuting the sentences. data_collator = FlaxDataCollatorForBartDenoisingLM( tokenizer=tokenizer, decoder_start_token_id=model.config.decoder_start_token_id, mask_ratio=data_args.mlm_probability, poisson_lambda=data_args.poisson_lambda, permute_sentence_ratio=data_args.permute_sentence_ratio, ) # Store some constant num_epochs = int(training_args.num_train_epochs) train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() per_device_eval_batch_size = int(training_args.per_device_eval_batch_size) eval_batch_size = per_device_eval_batch_size * jax.device_count() num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs # Create learning rate schedule warmup_fn = optax.linear_schedule( init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps ) decay_fn = optax.linear_schedule( init_value=training_args.learning_rate, end_value=0, transition_steps=num_train_steps - training_args.warmup_steps, ) linear_decay_lr_schedule_fn = optax.join_schedules( schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps] ) # We use Optax's "masking" functionality to not apply weight decay # to bias and LayerNorm scale parameters. decay_mask_fn returns a # mask boolean with the same structure as the parameters. # The mask is True for parameters that should be decayed. def decay_mask_fn(params): flat_params = traverse_util.flatten_dict(params) # find out all LayerNorm parameters layer_norm_candidates = ["layernorm", "layer_norm", "ln"] layer_norm_named_params = set( [ layer[-2:] for layer_norm_name in layer_norm_candidates for layer in flat_params.keys() if layer_norm_name in "".join(layer).lower() ] ) flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params} return traverse_util.unflatten_dict(flat_mask) # create adam optimizer if training_args.adafactor: # We use the default parameters here to initialize adafactor, # For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74 optimizer = optax.adafactor( learning_rate=linear_decay_lr_schedule_fn, ) else: optimizer = optax.adamw( learning_rate=linear_decay_lr_schedule_fn, b1=training_args.adam_beta1, b2=training_args.adam_beta2, weight_decay=training_args.weight_decay, mask=decay_mask_fn, ) # Setup train state state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer) # Define gradient update step fn def train_step(state, batch, dropout_rng): dropout_rng, new_dropout_rng = jax.random.split(dropout_rng) def loss_fn(params): labels = batch.pop("labels") logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0] # compute loss, ignore padded input tokens and special tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # take average loss = loss.sum() num_labels = label_mask.sum() return loss, num_labels grad_fn = jax.value_and_grad(loss_fn, has_aux=True) (loss, num_labels), grad = grad_fn(state.params) num_labels = jax.lax.psum(num_labels, "batch") # true loss = total loss / total samples loss = jax.lax.psum(loss, "batch") loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss) # true grad = total grad / total samples grad = jax.lax.psum(grad, "batch") grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad) new_state = state.apply_gradients(grads=grad) metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)} return new_state, metrics, new_dropout_rng # Create parallel version of the train step p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,)) # Define eval fn def eval_step(params, batch): labels = batch.pop("labels") logits = model(**batch, params=params, train=False)[0] # compute loss, ignore padded input tokens and special tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # compute accuracy accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask # summarize metrics metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()} metrics = jax.lax.psum(metrics, axis_name="batch") return metrics p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,)) # Replicate the train state on each device state = jax_utils.replicate(state) train_time = 0 epochs = tqdm(range(num_epochs), desc="Epoch ... ", position=0) for epoch in epochs: # ======================== Training ================================ train_start = time.time() train_metrics = [] # Create sampling rng rng, input_rng = jax.random.split(rng) # Generate an epoch by shuffling sampling indices from the train dataset num_train_samples = len(tokenized_datasets["train"]) # Avoid using jax.numpy here in case of TPU training train_samples_idx = np.random.permutation(np.arange(num_train_samples)) train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size) # Gather the indexes for creating the batch and do a training step for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)): samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx] model_inputs = data_collator(samples) # Model forward model_inputs = shard(model_inputs.data) state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs) train_metrics.append(train_metric) cur_step = epoch * (num_train_samples // train_batch_size) + step if cur_step % training_args.logging_steps == 0 and cur_step > 0: # Save metrics train_metric = jax_utils.unreplicate(train_metric) train_time += time.time() - train_start if has_tensorboard and jax.process_index() == 0: write_train_metric(summary_writer, train_metrics, train_time, cur_step) epochs.write( f"Step... ({cur_step} | Loss: {train_metric['loss']}, Learning Rate:" f" {train_metric['learning_rate']})" ) train_metrics = [] if cur_step % training_args.eval_steps == 0 and cur_step > 0: # ======================== Evaluating ============================== num_eval_samples = len(tokenized_datasets["validation"]) # Avoid using jax.numpy here in case of TPU training eval_samples_idx = np.arange(num_eval_samples) eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) eval_metrics = [] for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)): samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx] model_inputs = data_collator(samples) # Model forward metrics = pad_shard_unpad(p_eval_step, static_return=True)( state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size ) eval_metrics.append(metrics) # normalize eval metrics eval_metrics = get_metrics(eval_metrics) eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics) eval_normalizer = eval_metrics.pop("normalizer") eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) # Update progress bar epochs.desc = f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})" # Save metrics if has_tensorboard and jax.process_index() == 0: write_eval_metric(summary_writer, eval_metrics, cur_step) if cur_step % training_args.save_steps == 0 and cur_step > 0: # save checkpoint after each epoch and push checkpoint to the hub if jax.process_index() == 0: params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params)) model.save_pretrained(training_args.output_dir, params=params) tokenizer.save_pretrained(training_args.output_dir) if training_args.push_to_hub: repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False) # Eval after training if training_args.do_eval: num_eval_samples = len(tokenized_datasets["validation"]) # Avoid using jax.numpy here in case of TPU training eval_samples_idx = np.arange(num_eval_samples) eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) eval_metrics = [] for _, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)): samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx] model_inputs = data_collator(samples) # Model forward metrics = pad_shard_unpad(p_eval_step, static_return=True)( state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size ) eval_metrics.append(metrics) # normalize eval metrics eval_metrics = get_metrics(eval_metrics) eval_metrics = jax.tree_util.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics) eval_normalizer = eval_metrics.pop("normalizer") eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) try: perplexity = math.exp(eval_metrics["loss"]) except OverflowError: perplexity = float("inf") eval_metrics["perplexity"] = perplexity if jax.process_index() == 0: eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()} path = os.path.join(training_args.output_dir, "eval_results.json") with open(path, "w") as f: json.dump(eval_metrics, f, indent=4, sort_keys=True) if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Pretraining the library models for denoising language modeling on a text file or a dataset. Here is the full list of checkpoints on the hub that can be pretrained by this script: https://huggingface.co/models?filter=bart """ # You can also adapt this script on your own denoising language modeling task. Pointers for this are left as comments. import json import logging import math import os import sys import time from dataclasses import asdict, dataclass, field from enum import Enum from itertools import chain from pathlib import Path from typing import Dict, List, Optional import nltk import numpy as np from datasets import load_dataset from tqdm import tqdm import flax import jax import jax.numpy as jnp import optax from flax import jax_utils, traverse_util from flax.jax_utils import pad_shard_unpad from flax.training import train_state from flax.training.common_utils import get_metrics, onehot, shard from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, AutoTokenizer, BartConfig, BatchEncoding, FlaxBartForConditionalGeneration, HfArgumentParser, PreTrainedTokenizerBase, is_tensorboard_available, set_seed, ) from transformers.models.bart.modeling_flax_bart import shift_tokens_right from transformers.utils import get_full_repo_name, send_example_telemetry MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class TrainingArguments: output_dir: str = field( metadata={"help": "The output directory where the model predictions and checkpoints will be written."}, ) overwrite_output_dir: bool = field( default=False, metadata={ "help": ( "Overwrite the content of the output directory. " "Use this to continue training if output_dir points to a checkpoint directory." ) }, ) do_train: bool = field(default=False, metadata={"help": "Whether to run training."}) do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."}) per_device_train_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."} ) per_device_eval_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."} ) learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."}) weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."}) adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"}) adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"}) adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}) adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."}) num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."}) warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."}) logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."}) save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."}) eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."}) seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."}) push_to_hub: bool = field( default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."} ) hub_model_id: str = field( default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."} ) hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."}) def __post_init__(self): if self.output_dir is not None: self.output_dir = os.path.expanduser(self.output_dir) def to_dict(self): """ Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates the token values by removing their value. """ d = asdict(self) for k, v in d.items(): if isinstance(v, Enum): d[k] = v.value if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum): d[k] = [x.value for x in v] if k.endswith("_token"): d[k] = f"<{k.upper()}>" return d @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) dtype: Optional[str] = field( default="float32", metadata={ "help": ( "Floating-point format in which the model weights should be initialized and trained. Choose one of" " `[float32, float16, bfloat16]`." ) }, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) train_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}, ) validation_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) max_seq_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total input sequence length after tokenization and masking. Sequences longer than this" " will be truncated. Default to the max input length of the model." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) mlm_probability: float = field( default=0.3, metadata={"help": "Ratio of tokens to mask for span masked language modeling loss"} ) permute_sentence_ratio: float = field( default=1.0, metadata={"help": "Ratio of sentences to be permuted in each document"} ) poisson_lambda: float = field( default=3.0, metadata={"help": "Mean of Poisson distribution used to generate span-lengths to be masked"} ) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." @flax.struct.dataclass class FlaxDataCollatorForBartDenoisingLM: """ Data collator used for BART denoising language modeling. The code is largely copied from `<https://github.com/morganmcg1/rotobart/blob/main/data_collator.py#L223>`__. For more information on how BART denoising language modeling works, one can take a look at the `official paper <https://arxiv.org/pdf/1910.13461.pdf>`__ or the `official code for preprocessing <https://github.com/facebookresearch/fairseq/blob/main/fairseq/data/denoising_dataset.py>`__ . Args: tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`): The tokenizer used for encoding the data mask_ratio (:obj:`float`): The probability with which to (randomly) mask tokens in the input poisson_lambda (:obj:`float`): Mean parameter of Poisson distribution used to generate span-lengths to be masked permute_sentence_ratio (:obj:`float`): Ratio of sentences to be permuted in each document decoder_start_token_id: (:obj:`int): The decoder start token id of the model """ tokenizer: PreTrainedTokenizerBase decoder_start_token_id: int mask_ratio: float = 0.3 poisson_lambda: float = 3.0 permute_sentence_ratio: float = 1.0 def __post_init__(self): if self.tokenizer.mask_token is None or self.tokenizer.eos_token is None: raise ValueError( "This tokenizer does not have a mask token or eos token token which is necessary for denoising" " language modeling. " ) def __call__(self, examples: List[Dict[str, List[int]]]) -> BatchEncoding: # convert list to dict and tensorize input batch = BatchEncoding( {k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()} ) batch["labels"] = batch["input_ids"].copy() batch["decoder_input_ids"] = shift_tokens_right( batch["labels"], self.tokenizer.pad_token_id, self.decoder_start_token_id ) # permuting sentences do_permute = False if self.permute_sentence_ratio > 0.0: batch["input_ids"] = self.permute_sentences(batch["input_ids"]) do_permute = True # masking span of tokens (text infilling in the paper) if self.mask_ratio: batch["input_ids"], batch["labels"] = self.span_mask_tokens( batch["input_ids"], batch["labels"], do_permute ) # ignore pad tokens batch["attention_mask"] = (batch["input_ids"] != self.tokenizer.pad_token_id).astype(int) batch["decoder_attention_mask"] = (batch["decoder_input_ids"] != self.tokenizer.pad_token_id).astype(int) return batch def permute_sentences(self, input_ids): """ Shuffle sentences in each document. """ results = input_ids.copy() # find end locations of sentences end_sentence_mask = input_ids == self.tokenizer.pad_token_id sentence_ends = np.argwhere(end_sentence_mask) sentence_ends[:, 1] += 1 example_has_multiple_sentences, num_sentences = np.unique(sentence_ends[:, 0], return_counts=True) num_sentences_map = {sent_idx: count for sent_idx, count in zip(example_has_multiple_sentences, num_sentences)} num_to_permute = np.ceil(num_sentences * self.permute_sentence_ratio).astype(int) num_to_permute_map = { sent_idx: count for sent_idx, count in zip(example_has_multiple_sentences, num_to_permute) } sentence_ends = np.split(sentence_ends[:, 1], np.unique(sentence_ends[:, 0], return_index=True)[1][1:]) sentence_ends_map = {sent_idx: count for sent_idx, count in zip(example_has_multiple_sentences, sentence_ends)} for i in range(input_ids.shape[0]): if i not in example_has_multiple_sentences: continue substitutions = np.random.permutation(num_sentences_map[i])[: num_to_permute_map[i]] ordering = np.arange(0, num_sentences_map[i]) ordering[substitutions] = substitutions[np.random.permutation(num_to_permute_map[i])] # write shuffled sentences into results index = 0 for j in ordering: sentence = input_ids[i, (sentence_ends_map[i][j - 1] if j > 0 else 0) : sentence_ends_map[i][j]] results[i, index : index + sentence.shape[0]] = sentence index += sentence.shape[0] return results def span_mask_tokens(self, input_ids, labels, do_permute): """ Sampling text spans with span lengths drawn from a Poisson distribution and masking them. """ special_tokens_mask_labels = [ self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist() ] special_tokens_mask_inputs = [ self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in input_ids.tolist() ] special_tokens_mask_labels = np.array(special_tokens_mask_labels, dtype=bool) special_tokens_mask_inputs = np.array(special_tokens_mask_inputs, dtype=bool) # determine how many tokens we need to mask in total is_token_mask = ~(input_ids == self.tokenizer.pad_token_id) & ~special_tokens_mask_inputs num_tokens_to_mask = int(math.ceil(is_token_mask.astype(float).sum() * self.mask_ratio)) if num_tokens_to_mask == 0: return input_ids, labels # generate a sufficient number of span lengths span_lengths = np.random.poisson(lam=self.poisson_lambda, size=(num_tokens_to_mask,)) while np.cumsum(span_lengths, 0)[-1] < num_tokens_to_mask: span_lengths = np.concatenate( [span_lengths, np.random.poisson(lam=self.poisson_lambda, size=(num_tokens_to_mask,))] ) # remove all spans of length 0 # note that BART inserts additional mask tokens where length == 0, # which we do not implement for now as it adds additional complexity span_lengths = span_lengths[span_lengths > 0] # trim to about num_tokens_to_mask tokens cutoff_idx = np.argmin(np.abs(np.cumsum(span_lengths, 0) - num_tokens_to_mask)) + 1 span_lengths = span_lengths[:cutoff_idx] # randomly choose starting positions for masking token_indices = np.argwhere(is_token_mask == 1) span_starts = np.random.permutation(token_indices.shape[0])[: span_lengths.shape[0]] # prepare mask masked_indices = np.array(token_indices[span_starts]) mask = np.full_like(input_ids, fill_value=False) # mask starting positions for mi in masked_indices: mask[tuple(mi)] = True span_lengths -= 1 # fill up spans max_index = input_ids.shape[1] - 1 remaining = (span_lengths > 0) & (masked_indices[:, 1] < max_index) while np.any(remaining): masked_indices[remaining, 1] += 1 for mi in masked_indices: mask[tuple(mi)] = True span_lengths -= 1 remaining = (span_lengths > 0) & (masked_indices[:, 1] < max_index) # place the mask tokens mask[np.where(special_tokens_mask_inputs)] = False input_ids[np.where(mask)] = self.tokenizer.mask_token_id if not do_permute: labels[np.where(mask == 0)] = -100 else: labels[np.where(special_tokens_mask_labels)] = -100 # remove mask tokens that are not starts of spans to_remove = (mask == 1) & np.roll((mask == 1), 1, 1) new_input_ids = np.full_like(input_ids, fill_value=self.tokenizer.pad_token_id) for i, example in enumerate(input_ids): new_example = example[~to_remove[i]] new_input_ids[i, : new_example.shape[0]] = new_example return new_input_ids, labels def generate_batch_splits(samples_idx: np.ndarray, batch_size: int, drop_last=True) -> np.ndarray: """Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned.""" num_samples = len(samples_idx) if drop_last: samples_to_remove = num_samples % batch_size if samples_to_remove != 0: samples_idx = samples_idx[:-samples_to_remove] sections_split = num_samples // batch_size samples_idx = samples_idx.reshape((sections_split, batch_size)) else: sections_split = math.ceil(num_samples / batch_size) samples_idx = np.array_split(samples_idx, sections_split) return samples_idx def write_train_metric(summary_writer, train_metrics, train_time, step): summary_writer.scalar("train_time", train_time, step) train_metrics = get_metrics(train_metrics) for key, vals in train_metrics.items(): tag = f"train_{key}" for i, val in enumerate(vals): summary_writer.scalar(tag, val, step - len(vals) + i + 1) def write_eval_metric(summary_writer, eval_metrics, step): for metric_name, value in eval_metrics.items(): summary_writer.scalar(f"eval_{metric_name}", value, step) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_bart_dlm", model_args, data_args, framework="flax") if ( os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty." "Use --overwrite_output_dir to overcome." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", level=logging.INFO, datefmt="[%X]", ) # Log on each process the small summary: logger = logging.getLogger(__name__) # Set the verbosity to info of the Transformers logger (on main process only): logger.info(f"Training/evaluation parameters {training_args}") # Set seed before initializing model. set_seed(training_args.seed) # Handle the repository creation if training_args.push_to_hub: if training_args.hub_model_id is None: repo_name = get_full_repo_name( Path(training_args.output_dir).absolute().name, token=training_args.hub_token ) else: repo_name = training_args.hub_model_id repo = Repository(training_args.output_dir, clone_from=repo_name) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. datasets = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) if "validation" not in datasets.keys(): datasets["validation"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[:{data_args.validation_split_percentage}%]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[{data_args.validation_split_percentage}%:]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.train_file.split(".")[-1] if extension == "txt": extension = "text" datasets = load_dataset( extension, data_files=data_files, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) if "validation" not in datasets.keys(): datasets["validation"] = load_dataset( extension, data_files=data_files, split=f"train[:{data_args.validation_split_percentage}%]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) datasets["train"] = load_dataset( extension, data_files=data_files, split=f"train[{data_args.validation_split_percentage}%:]", cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, use_auth_token=True if model_args.use_auth_token else None, ) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, use_auth_token=True if model_args.use_auth_token else None, ) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if model_args.config_name: config = BartConfig.from_pretrained( model_args.config_name, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer), use_auth_token=True if model_args.use_auth_token else None, ) elif model_args.model_name_or_path: config = BartConfig.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") # Preprocessing the datasets. # First we tokenize all the texts. if training_args.do_train: column_names = datasets["train"].column_names else: column_names = datasets["validation"].column_names text_column_name = "text" if "text" in column_names else column_names[0] max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) # Use Punkt Sentence Tokenizer to divide a document into a list of sentences nltk.download("punkt") sentence_tokenizer = nltk.data.load("tokenizers/punkt/english.pickle") def sentence_split_function(example): sents = sentence_tokenizer.tokenize(example["text"]) # use pad token as end of sentence indicator new_text = tokenizer.bos_token + f"{tokenizer.pad_token}".join(sents) + tokenizer.eos_token return {"text": new_text} split_datasets = datasets.map( sentence_split_function, batched=False, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, ) # Tokenize every text, then concatenate them together before splitting them in smaller parts. # Since we make sure that all sequences are of the same length, no attention_mask is needed. def tokenize_function(examples): return tokenizer(examples[text_column_name], add_special_tokens=False, return_attention_mask=False) tokenized_datasets = split_datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=text_column_name, load_from_cache_file=not data_args.overwrite_cache, ) # Main data processing function that will concatenate all texts from our dataset and generate chunks of # max_seq_length. def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. if total_length >= max_seq_length: total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value # might be slower to preprocess. # # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, ) # Enable tensorboard only on the master node has_tensorboard = is_tensorboard_available() if has_tensorboard and jax.process_index() == 0: try: from flax.metrics.tensorboard import SummaryWriter summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir)) except ImportError as ie: has_tensorboard = False logger.warning( f"Unable to display metrics through TensorBoard because some package are not installed: {ie}" ) else: logger.warning( "Unable to display metrics through TensorBoard because the package is not installed: " "Please run pip install tensorboard to enable." ) # Initialize our training rng = jax.random.PRNGKey(training_args.seed) dropout_rngs = jax.random.split(rng, jax.local_device_count()) if model_args.model_name_or_path: model = FlaxBartForConditionalGeneration.from_pretrained( model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype), use_auth_token=True if model_args.use_auth_token else None, ) else: config.vocab_size = len(tokenizer) model = FlaxBartForConditionalGeneration( config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype), ) # Data collator # This one will take care of randomly masking the tokens and permuting the sentences. data_collator = FlaxDataCollatorForBartDenoisingLM( tokenizer=tokenizer, decoder_start_token_id=model.config.decoder_start_token_id, mask_ratio=data_args.mlm_probability, poisson_lambda=data_args.poisson_lambda, permute_sentence_ratio=data_args.permute_sentence_ratio, ) # Store some constant num_epochs = int(training_args.num_train_epochs) train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() per_device_eval_batch_size = int(training_args.per_device_eval_batch_size) eval_batch_size = per_device_eval_batch_size * jax.device_count() num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs # Create learning rate schedule warmup_fn = optax.linear_schedule( init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps ) decay_fn = optax.linear_schedule( init_value=training_args.learning_rate, end_value=0, transition_steps=num_train_steps - training_args.warmup_steps, ) linear_decay_lr_schedule_fn = optax.join_schedules( schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps] ) # We use Optax's "masking" functionality to not apply weight decay # to bias and LayerNorm scale parameters. decay_mask_fn returns a # mask boolean with the same structure as the parameters. # The mask is True for parameters that should be decayed. def decay_mask_fn(params): flat_params = traverse_util.flatten_dict(params) # find out all LayerNorm parameters layer_norm_candidates = ["layernorm", "layer_norm", "ln"] layer_norm_named_params = set( [ layer[-2:] for layer_norm_name in layer_norm_candidates for layer in flat_params.keys() if layer_norm_name in "".join(layer).lower() ] ) flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params} return traverse_util.unflatten_dict(flat_mask) # create adam optimizer if training_args.adafactor: # We use the default parameters here to initialize adafactor, # For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74 optimizer = optax.adafactor( learning_rate=linear_decay_lr_schedule_fn, ) else: optimizer = optax.adamw( learning_rate=linear_decay_lr_schedule_fn, b1=training_args.adam_beta1, b2=training_args.adam_beta2, weight_decay=training_args.weight_decay, mask=decay_mask_fn, ) # Setup train state state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer) # Define gradient update step fn def train_step(state, batch, dropout_rng): dropout_rng, new_dropout_rng = jax.random.split(dropout_rng) def loss_fn(params): labels = batch.pop("labels") logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0] # compute loss, ignore padded input tokens and special tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # take average loss = loss.sum() num_labels = label_mask.sum() return loss, num_labels grad_fn = jax.value_and_grad(loss_fn, has_aux=True) (loss, num_labels), grad = grad_fn(state.params) num_labels = jax.lax.psum(num_labels, "batch") # true loss = total loss / total samples loss = jax.lax.psum(loss, "batch") loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss) # true grad = total grad / total samples grad = jax.lax.psum(grad, "batch") grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad) new_state = state.apply_gradients(grads=grad) metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)} return new_state, metrics, new_dropout_rng # Create parallel version of the train step p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,)) # Define eval fn def eval_step(params, batch): labels = batch.pop("labels") logits = model(**batch, params=params, train=False)[0] # compute loss, ignore padded input tokens and special tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # compute accuracy accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask # summarize metrics metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()} metrics = jax.lax.psum(metrics, axis_name="batch") return metrics p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,)) # Replicate the train state on each device state = jax_utils.replicate(state) train_time = 0 epochs = tqdm(range(num_epochs), desc="Epoch ... ", position=0) for epoch in epochs: # ======================== Training ================================ train_start = time.time() train_metrics = [] # Create sampling rng rng, input_rng = jax.random.split(rng) # Generate an epoch by shuffling sampling indices from the train dataset num_train_samples = len(tokenized_datasets["train"]) # Avoid using jax.numpy here in case of TPU training train_samples_idx = np.random.permutation(np.arange(num_train_samples)) train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size) # Gather the indexes for creating the batch and do a training step for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)): samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx] model_inputs = data_collator(samples) # Model forward model_inputs = shard(model_inputs.data) state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs) train_metrics.append(train_metric) cur_step = epoch * (num_train_samples // train_batch_size) + step if cur_step % training_args.logging_steps == 0 and cur_step > 0: # Save metrics train_metric = jax_utils.unreplicate(train_metric) train_time += time.time() - train_start if has_tensorboard and jax.process_index() == 0: write_train_metric(summary_writer, train_metrics, train_time, cur_step) epochs.write( f"Step... ({cur_step} | Loss: {train_metric['loss']}, Learning Rate:" f" {train_metric['learning_rate']})" ) train_metrics = [] if cur_step % training_args.eval_steps == 0 and cur_step > 0: # ======================== Evaluating ============================== num_eval_samples = len(tokenized_datasets["validation"]) # Avoid using jax.numpy here in case of TPU training eval_samples_idx = np.arange(num_eval_samples) eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) eval_metrics = [] for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)): samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx] model_inputs = data_collator(samples) # Model forward metrics = pad_shard_unpad(p_eval_step, static_return=True)( state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size ) eval_metrics.append(metrics) # normalize eval metrics eval_metrics = get_metrics(eval_metrics) eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics) eval_normalizer = eval_metrics.pop("normalizer") eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) # Update progress bar epochs.desc = f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})" # Save metrics if has_tensorboard and jax.process_index() == 0: write_eval_metric(summary_writer, eval_metrics, cur_step) if cur_step % training_args.save_steps == 0 and cur_step > 0: # save checkpoint after each epoch and push checkpoint to the hub if jax.process_index() == 0: params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params)) model.save_pretrained(training_args.output_dir, params=params) tokenizer.save_pretrained(training_args.output_dir) if training_args.push_to_hub: repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False) # Eval after training if training_args.do_eval: num_eval_samples = len(tokenized_datasets["validation"]) # Avoid using jax.numpy here in case of TPU training eval_samples_idx = np.arange(num_eval_samples) eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) eval_metrics = [] for _, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)): samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx] model_inputs = data_collator(samples) # Model forward metrics = pad_shard_unpad(p_eval_step, static_return=True)( state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size ) eval_metrics.append(metrics) # normalize eval metrics eval_metrics = get_metrics(eval_metrics) eval_metrics = jax.tree_util.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics) eval_normalizer = eval_metrics.pop("normalizer") eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) try: perplexity = math.exp(eval_metrics["loss"]) except OverflowError: perplexity = float("inf") eval_metrics["perplexity"] = perplexity if jax.process_index() == 0: eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()} path = os.path.join(training_args.output_dir, "eval_results.json") with open(path, "w") as f: json.dump(eval_metrics, f, indent=4, sort_keys=True) if __name__ == "__main__": main()

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./docs/source/en/model_doc/xlsr_wav2vec2.mdx

<!--Copyright 2021 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # XLSR-Wav2Vec2 ## Overview The XLSR-Wav2Vec2 model was proposed in [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli. The abstract from the paper is the following: *This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing XLSR-53, a large model pretrained in 53 languages.* Tips: - XLSR-Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. - XLSR-Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using [`Wav2Vec2CTCTokenizer`]. XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2's documentation page](wav2vec2). The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec).

<!--Copyright 2021 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # XLSR-Wav2Vec2 ## Overview The XLSR-Wav2Vec2 model was proposed in [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli. The abstract from the paper is the following: *This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing XLSR-53, a large model pretrained in 53 languages.* Tips: - XLSR-Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. - XLSR-Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using [`Wav2Vec2CTCTokenizer`]. XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2's documentation page](wav2vec2). The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec).

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/models/fsmt/__init__.py

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/models/owlvit/test_processor_owlvit.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import CLIPTokenizer, CLIPTokenizerFast from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available if is_vision_available(): from PIL import Image from transformers import OwlViTFeatureExtractor, OwlViTProcessor @require_vision class OwlViTProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() # fmt: off vocab = ["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"] # fmt: on vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""] self.special_tokens_map = {"unk_token": "<unk>"} self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) with open(self.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(self.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) feature_extractor_map = { "do_resize": True, "size": 20, "do_center_crop": True, "crop_size": 18, "do_normalize": True, "image_mean": [0.48145466, 0.4578275, 0.40821073], "image_std": [0.26862954, 0.26130258, 0.27577711], } self.feature_extractor_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME) with open(self.feature_extractor_file, "w", encoding="utf-8") as fp: json.dump(feature_extractor_map, fp) def get_tokenizer(self, **kwargs): return CLIPTokenizer.from_pretrained(self.tmpdirname, pad_token="!", **kwargs) def get_rust_tokenizer(self, **kwargs): return CLIPTokenizerFast.from_pretrained(self.tmpdirname, pad_token="!", **kwargs) def get_feature_extractor(self, **kwargs): return OwlViTFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs) def tearDown(self): shutil.rmtree(self.tmpdirname) def prepare_image_inputs(self): """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, or a list of PyTorch tensors if one specifies torchify=True. """ image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)] image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs] return image_inputs def test_save_load_pretrained_default(self): tokenizer_slow = self.get_tokenizer() tokenizer_fast = self.get_rust_tokenizer() feature_extractor = self.get_feature_extractor() processor_slow = OwlViTProcessor(tokenizer=tokenizer_slow, feature_extractor=feature_extractor) processor_slow.save_pretrained(self.tmpdirname) processor_slow = OwlViTProcessor.from_pretrained(self.tmpdirname, use_fast=False) processor_fast = OwlViTProcessor(tokenizer=tokenizer_fast, feature_extractor=feature_extractor) processor_fast.save_pretrained(self.tmpdirname) processor_fast = OwlViTProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab()) self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab()) self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab()) self.assertIsInstance(processor_slow.tokenizer, CLIPTokenizer) self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast) self.assertEqual(processor_slow.feature_extractor.to_json_string(), feature_extractor.to_json_string()) self.assertEqual(processor_fast.feature_extractor.to_json_string(), feature_extractor.to_json_string()) self.assertIsInstance(processor_slow.feature_extractor, OwlViTFeatureExtractor) self.assertIsInstance(processor_fast.feature_extractor, OwlViTFeatureExtractor) def test_save_load_pretrained_additional_features(self): processor = OwlViTProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()) processor.save_pretrained(self.tmpdirname) tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False) processor = OwlViTProcessor.from_pretrained( self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, CLIPTokenizerFast) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertIsInstance(processor.feature_extractor, OwlViTFeatureExtractor) def test_feature_extractor(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) image_input = self.prepare_image_inputs() input_feat_extract = feature_extractor(image_input, return_tensors="np") input_processor = processor(images=image_input, return_tensors="np") for key in input_feat_extract.keys(): self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2) def test_tokenizer(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) input_str = "lower newer" encoded_processor = processor(text=input_str, return_tensors="np") encoded_tok = tokenizer(input_str, return_tensors="np") for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key][0].tolist(), encoded_processor[key][0].tolist()) def test_processor(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) input_str = "lower newer" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"]) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_processor_with_text_list(self): model_name = "google/owlvit-base-patch32" processor = OwlViTProcessor.from_pretrained(model_name) input_text = ["cat", "nasa badge"] inputs = processor(text=input_text) seq_length = 16 self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"]) self.assertEqual(inputs["input_ids"].shape, (2, seq_length)) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_processor_with_nested_text_list(self): model_name = "google/owlvit-base-patch32" processor = OwlViTProcessor.from_pretrained(model_name) input_texts = [["cat", "nasa badge"], ["person"]] inputs = processor(text=input_texts) seq_length = 16 batch_size = len(input_texts) num_max_text_queries = max([len(texts) for texts in input_texts]) self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"]) self.assertEqual(inputs["input_ids"].shape, (batch_size * num_max_text_queries, seq_length)) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_processor_case(self): model_name = "google/owlvit-base-patch32" processor = OwlViTProcessor.from_pretrained(model_name) input_texts = ["cat", "nasa badge"] inputs = processor(text=input_texts) seq_length = 16 input_ids = inputs["input_ids"] predicted_ids = [ [49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [49406, 6841, 11301, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ] self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"]) self.assertEqual(inputs["input_ids"].shape, (2, seq_length)) self.assertListEqual(list(input_ids[0]), predicted_ids[0]) self.assertListEqual(list(input_ids[1]), predicted_ids[1]) def test_tokenizer_decode(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] decoded_processor = processor.batch_decode(predicted_ids) decoded_tok = tokenizer.batch_decode(predicted_ids) self.assertListEqual(decoded_tok, decoded_processor) def test_model_input_names(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) input_str = "lower newer" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), processor.model_input_names)

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import CLIPTokenizer, CLIPTokenizerFast from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available if is_vision_available(): from PIL import Image from transformers import OwlViTFeatureExtractor, OwlViTProcessor @require_vision class OwlViTProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() # fmt: off vocab = ["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"] # fmt: on vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""] self.special_tokens_map = {"unk_token": "<unk>"} self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) with open(self.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(self.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) feature_extractor_map = { "do_resize": True, "size": 20, "do_center_crop": True, "crop_size": 18, "do_normalize": True, "image_mean": [0.48145466, 0.4578275, 0.40821073], "image_std": [0.26862954, 0.26130258, 0.27577711], } self.feature_extractor_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME) with open(self.feature_extractor_file, "w", encoding="utf-8") as fp: json.dump(feature_extractor_map, fp) def get_tokenizer(self, **kwargs): return CLIPTokenizer.from_pretrained(self.tmpdirname, pad_token="!", **kwargs) def get_rust_tokenizer(self, **kwargs): return CLIPTokenizerFast.from_pretrained(self.tmpdirname, pad_token="!", **kwargs) def get_feature_extractor(self, **kwargs): return OwlViTFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs) def tearDown(self): shutil.rmtree(self.tmpdirname) def prepare_image_inputs(self): """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, or a list of PyTorch tensors if one specifies torchify=True. """ image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)] image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs] return image_inputs def test_save_load_pretrained_default(self): tokenizer_slow = self.get_tokenizer() tokenizer_fast = self.get_rust_tokenizer() feature_extractor = self.get_feature_extractor() processor_slow = OwlViTProcessor(tokenizer=tokenizer_slow, feature_extractor=feature_extractor) processor_slow.save_pretrained(self.tmpdirname) processor_slow = OwlViTProcessor.from_pretrained(self.tmpdirname, use_fast=False) processor_fast = OwlViTProcessor(tokenizer=tokenizer_fast, feature_extractor=feature_extractor) processor_fast.save_pretrained(self.tmpdirname) processor_fast = OwlViTProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab()) self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab()) self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab()) self.assertIsInstance(processor_slow.tokenizer, CLIPTokenizer) self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast) self.assertEqual(processor_slow.feature_extractor.to_json_string(), feature_extractor.to_json_string()) self.assertEqual(processor_fast.feature_extractor.to_json_string(), feature_extractor.to_json_string()) self.assertIsInstance(processor_slow.feature_extractor, OwlViTFeatureExtractor) self.assertIsInstance(processor_fast.feature_extractor, OwlViTFeatureExtractor) def test_save_load_pretrained_additional_features(self): processor = OwlViTProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()) processor.save_pretrained(self.tmpdirname) tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False) processor = OwlViTProcessor.from_pretrained( self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, CLIPTokenizerFast) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertIsInstance(processor.feature_extractor, OwlViTFeatureExtractor) def test_feature_extractor(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) image_input = self.prepare_image_inputs() input_feat_extract = feature_extractor(image_input, return_tensors="np") input_processor = processor(images=image_input, return_tensors="np") for key in input_feat_extract.keys(): self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2) def test_tokenizer(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) input_str = "lower newer" encoded_processor = processor(text=input_str, return_tensors="np") encoded_tok = tokenizer(input_str, return_tensors="np") for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key][0].tolist(), encoded_processor[key][0].tolist()) def test_processor(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) input_str = "lower newer" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"]) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_processor_with_text_list(self): model_name = "google/owlvit-base-patch32" processor = OwlViTProcessor.from_pretrained(model_name) input_text = ["cat", "nasa badge"] inputs = processor(text=input_text) seq_length = 16 self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"]) self.assertEqual(inputs["input_ids"].shape, (2, seq_length)) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_processor_with_nested_text_list(self): model_name = "google/owlvit-base-patch32" processor = OwlViTProcessor.from_pretrained(model_name) input_texts = [["cat", "nasa badge"], ["person"]] inputs = processor(text=input_texts) seq_length = 16 batch_size = len(input_texts) num_max_text_queries = max([len(texts) for texts in input_texts]) self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"]) self.assertEqual(inputs["input_ids"].shape, (batch_size * num_max_text_queries, seq_length)) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_processor_case(self): model_name = "google/owlvit-base-patch32" processor = OwlViTProcessor.from_pretrained(model_name) input_texts = ["cat", "nasa badge"] inputs = processor(text=input_texts) seq_length = 16 input_ids = inputs["input_ids"] predicted_ids = [ [49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [49406, 6841, 11301, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ] self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"]) self.assertEqual(inputs["input_ids"].shape, (2, seq_length)) self.assertListEqual(list(input_ids[0]), predicted_ids[0]) self.assertListEqual(list(input_ids[1]), predicted_ids[1]) def test_tokenizer_decode(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] decoded_processor = processor.batch_decode(predicted_ids) decoded_tok = tokenizer.batch_decode(predicted_ids) self.assertListEqual(decoded_tok, decoded_processor) def test_model_input_names(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) input_str = "lower newer" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), processor.model_input_names)

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./src/transformers/models/lxmert/modeling_tf_lxmert.py

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors, The HuggingFace Inc. team, and the # Lxmert Authors. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 LXMERT model.""" import warnings from dataclasses import dataclass from typing import Dict, Optional, Tuple, Union import numpy as np import tensorflow as tf from transformers.tf_utils import stable_softmax from ...activations_tf import get_tf_activation from ...modeling_tf_utils import ( TFModelInputType, TFPreTrainedModel, get_initializer, keras_serializable, shape_list, unpack_inputs, ) from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_lxmert import LxmertConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "unc-nlp/lxmert-base-uncased" _CONFIG_FOR_DOC = "LxmertConfig" _TOKENIZER_FOR_DOC = "LxmertTokenizer" TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "unc-nlp/lxmert-base-uncased", ] @dataclass class TFLxmertModelOutput(ModelOutput): """ Lxmert's outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language, visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the "relation-ship" encoder") Args: language_output (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the language encoder. vision_output (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the visual encoder. pooled_output (`tf.Tensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification, CLS, token) further processed by a Linear layer and a Tanh activation function. The Linear language_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape `(batch_size, sequence_length, hidden_size)`. vision_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape `(batch_size, sequence_length, hidden_size)`. language_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. vision_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ language_output: Optional[tf.Tensor] = None vision_output: Optional[tf.Tensor] = None pooled_output: Optional[tf.Tensor] = None language_hidden_states: Optional[Tuple[tf.Tensor]] = None vision_hidden_states: Optional[Tuple[tf.Tensor]] = None language_attentions: Optional[Tuple[tf.Tensor]] = None vision_attentions: Optional[Tuple[tf.Tensor]] = None cross_encoder_attentions: Optional[Tuple[tf.Tensor]] = None @dataclass class TFLxmertForPreTrainingOutput(ModelOutput): """ Output type of [`LxmertForPreTraining`]. Args: loss (*optional*, returned when `labels` is provided, `tf.Tensor` of shape `(1,)`): Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss. prediction_logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). cross_relationship_score: (`tf.Tensor` of shape `(batch_size, 2)`): Prediction scores of the textual matching objective (classification) head (scores of True/False continuation before SoftMax). question_answering_score: (`tf.Tensor` of shape `(batch_size, n_qa_answers)`): Prediction scores of question answering objective (classification). language_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape `(batch_size, sequence_length, hidden_size)`. vision_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape `(batch_size, sequence_length, hidden_size)`. language_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. vision_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[tf.Tensor] = None prediction_logits: Optional[tf.Tensor] = None cross_relationship_score: Optional[tf.Tensor] = None question_answering_score: Optional[tf.Tensor] = None language_hidden_states: Optional[Tuple[tf.Tensor]] = None vision_hidden_states: Optional[Tuple[tf.Tensor]] = None language_attentions: Optional[Tuple[tf.Tensor]] = None vision_attentions: Optional[Tuple[tf.Tensor]] = None cross_encoder_attentions: Optional[Tuple[tf.Tensor]] = None class TFLxmertVisualFeatureEncoder(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) # Object feature encoding self.visn_fc = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="visn_fc", ) self.visn_layer_norm = tf.keras.layers.LayerNormalization( epsilon=config.layer_norm_eps, name="visn_layer_norm" ) # Box position encoding self.box_fc = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="box_fc", ) self.box_layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="box_layer_norm") self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) def call(self, visn_input, training=False): feats, boxes = visn_input x = self.visn_fc(feats) x = self.visn_layer_norm(x) y = self.box_fc(boxes) y = self.box_layer_norm(y) output = (x + y) / 2 output = self.dropout(output, training=training) return output class TFLxmertEmbeddings(tf.keras.layers.Layer): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config, **kwargs): super().__init__(**kwargs) self.vocab_size = config.vocab_size self.type_vocab_size = config.type_vocab_size self.hidden_size = config.hidden_size self.max_position_embeddings = config.max_position_embeddings self.initializer_range = config.initializer_range self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) def build(self, input_shape): with tf.name_scope("word_embeddings"): self.weight = self.add_weight( name="weight", shape=[self.vocab_size, self.hidden_size], initializer=get_initializer(initializer_range=self.initializer_range), ) with tf.name_scope("token_type_embeddings"): self.token_type_embeddings = self.add_weight( name="embeddings", shape=[self.type_vocab_size, self.hidden_size], initializer=get_initializer(initializer_range=self.initializer_range), ) with tf.name_scope("position_embeddings"): self.position_embeddings = self.add_weight( name="embeddings", shape=[self.max_position_embeddings, self.hidden_size], initializer=get_initializer(initializer_range=self.initializer_range), ) super().build(input_shape) def call(self, input_ids=None, token_type_ids=None, inputs_embeds=None, training=False): """ Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. """ assert not (input_ids is None and inputs_embeds is None) if input_ids is not None: # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound # indices on GPU, returning zeros instead. This is a dangerous silent behavior. tf.debugging.assert_less( input_ids, tf.cast(self.vocab_size, dtype=input_ids.dtype), message=( "input_ids must be smaller than the embedding layer's input dimension (got" f" {tf.math.reduce_max(input_ids)} >= {self.vocab_size})" ), ) inputs_embeds = tf.gather(params=self.weight, indices=input_ids) input_shape = shape_list(inputs_embeds)[:-1] if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0) position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids) token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids) final_embeddings = inputs_embeds + position_embeds + token_type_embeds final_embeddings = self.LayerNorm(inputs=final_embeddings) final_embeddings = self.dropout(inputs=final_embeddings, training=training) return final_embeddings class TFLxmertAttention(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads}" ) self.num_attention_heads = config.num_attention_heads assert config.hidden_size % config.num_attention_heads == 0 self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = tf.keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query", ) self.key = tf.keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key", ) self.value = tf.keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value", ) self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x, batch_size): # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size)) return tf.transpose(x, perm=[0, 2, 1, 3]) def call(self, hidden_states, context, attention_mask, output_attentions, training=False): batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(context) mixed_value_layer = self.value(context) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) key_layer = self.transpose_for_scores(mixed_key_layer, batch_size) value_layer = self.transpose_for_scores(mixed_value_layer, batch_size) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = tf.matmul( query_layer, key_layer, transpose_b=True ) # (batch size, num_heads, seq_len_q, seq_len_k) dk = tf.cast(shape_list(key_layer)[-1], dtype=attention_scores.dtype) # scale attention_scores attention_scores = attention_scores / tf.math.sqrt(dk) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in TFLxmertModel call() function) attention_mask = tf.cast(attention_mask, dtype=attention_scores.dtype) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = stable_softmax(attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs, training=training) context_layer = tf.matmul(attention_probs, value_layer) context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3]) context_layer = tf.reshape( context_layer, (batch_size, -1, self.all_head_size) ) # (batch_size, seq_len_q, all_head_size) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class TFLxmertIntermediate(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense", ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class TFLxmertOutput(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense", ) self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) def call(self, hidden_states, input_tensor, training=False): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, training) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class TFLxmertAttentionOutput(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense", ) self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) def call(self, hidden_states, input_tensor, training=False): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class TFLxmertSelfAttentionLayer(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.self = TFLxmertAttention(config, name="self") self.attention_output = TFLxmertAttentionOutput(config, name="output") def call(self, input_tensor, attention_mask, output_attentions, training=False): # Self attention attends to itself, thus keys and queries are the same (input_tensor). self_output = self.self(input_tensor, input_tensor, attention_mask, output_attentions) if output_attentions: attention_probs = self_output[1] attention_output = self.attention_output(self_output[0], input_tensor) return (attention_output, attention_probs) if output_attentions else (attention_output,) class TFLxmertCrossAttentionLayer(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.att = TFLxmertAttention(config, name="att") self.attention_output = TFLxmertAttentionOutput(config, name="output") def call( self, input_tensor, ctx_tensor, ctx_att_mask, output_attentions=False, training=False, ): output = self.att(input_tensor, ctx_tensor, ctx_att_mask, output_attentions, training=training) if output_attentions: attention_probs = output[1] attention_output = self.attention_output(output[0], input_tensor, training=training) outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) return outputs class TFLxmertLayer(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.attention = TFLxmertSelfAttentionLayer(config, name="attention") self.intermediate = TFLxmertIntermediate(config, name="intermediate") self.transformer_output = TFLxmertOutput(config, name="output") def call(self, hidden_states, attention_mask, output_attentions, training=False): attention_outputs = self.attention(hidden_states, attention_mask, output_attentions, training=training) attention_output = attention_outputs[0] intermediate_output = self.intermediate(attention_output) layer_output = self.transformer_output(intermediate_output, attention_output, training=training) outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them return outputs class TFLxmertXLayer(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.visual_attention = TFLxmertCrossAttentionLayer(config, name="visual_attention") # Self-attention Layers self.lang_self_att = TFLxmertSelfAttentionLayer(config, name="lang_self_att") self.visn_self_att = TFLxmertSelfAttentionLayer(config, name="visn_self_att") # Intermediate and Output Layers (FFNs) self.lang_inter = TFLxmertIntermediate(config, name="lang_inter") self.lang_output = TFLxmertOutput(config, name="lang_output") self.visn_inter = TFLxmertIntermediate(config, name="visn_inter") self.visn_output = TFLxmertOutput(config, name="visn_output") def cross_att( self, lang_input, lang_attention_mask, visn_input, visn_attention_mask, output_attentions, training=False, ): # Cross Attention # Keras saving and loading model *does not work* with the same inputs for two layers. lang_attention_lang_input = tf.identity(lang_input) visn_attention_lang_input = tf.identity(lang_input) lang_attention_visn_input = tf.identity(visn_input) visn_attention_visn_input = tf.identity(visn_input) lang_att_output = self.visual_attention( lang_attention_lang_input, lang_attention_visn_input, visn_attention_mask, output_attentions=output_attentions, training=training, ) visn_att_output = self.visual_attention( visn_attention_visn_input, visn_attention_lang_input, lang_attention_mask, output_attentions=output_attentions, training=training, ) return lang_att_output, visn_att_output def self_att( self, lang_input, lang_attention_mask, visn_input, visn_attention_mask, training=False, ): # Self Attention output_attentions = False lang_att_output = self.lang_self_att(lang_input, lang_attention_mask, output_attentions, training=training) visn_att_output = self.visn_self_att(visn_input, visn_attention_mask, output_attentions, training=training) return lang_att_output[0], visn_att_output[0] def output_fc(self, lang_input, visn_input, training=False): # FC layers lang_inter_output = self.lang_inter(lang_input) visn_inter_output = self.visn_inter(visn_input) # Layer output lang_output = self.lang_output(lang_inter_output, lang_input, training) visn_output = self.visn_output(visn_inter_output, visn_input, training) return lang_output, visn_output def call( self, lang_feats, lang_attention_mask, visn_feats, visn_attention_mask, output_attentions, training=False, ): lang_att_output = lang_feats visn_att_output = visn_feats lang_att_output, visn_att_output = self.cross_att( lang_att_output, lang_attention_mask, visn_att_output, visn_attention_mask, output_attentions, training=training, ) attention_probs = lang_att_output[1:] lang_att_output, visn_att_output = self.self_att( lang_att_output[0], lang_attention_mask, visn_att_output[0], visn_attention_mask, training=training, ) lang_output, visn_output = self.output_fc(lang_att_output, visn_att_output, training=training) return (lang_output, visn_output, attention_probs[0]) if output_attentions else (lang_output, visn_output) class TFLxmertEncoder(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.visn_fc = TFLxmertVisualFeatureEncoder(config, name="visn_fc") # Number of layers self.num_l_layers = config.l_layers self.num_x_layers = config.x_layers self.num_r_layers = config.r_layers # Layers # Using self.layer instead of self.l_layer to support loading BERT weights. self.layer = [TFLxmertLayer(config, name=f"layer_._{i}") for i in range(self.num_l_layers)] self.x_layers = [TFLxmertXLayer(config, name=f"x_layers_._{i}") for i in range(self.num_x_layers)] self.r_layers = [TFLxmertLayer(config, name=f"r_layers_._{i}") for i in range(self.num_r_layers)] self.config = config def call( self, lang_feats=None, lang_attention_mask=None, visual_feats=None, visual_pos=None, visual_attention_mask=None, output_attentions=None, training=False, ): vision_hidden_states = () language_hidden_states = () vision_attentions = () if output_attentions or self.config.output_attentions else None language_attentions = () if output_attentions or self.config.output_attentions else None cross_encoder_attentions = () if output_attentions or self.config.output_attentions else None visual_feats = self.visn_fc([visual_feats, visual_pos], training=training) # Run language layers for layer_module in self.layer: l_outputs = layer_module(lang_feats, lang_attention_mask, output_attentions, training=training) lang_feats = l_outputs[0] language_hidden_states = language_hidden_states + (lang_feats,) if language_attentions is not None: language_attentions = language_attentions + (l_outputs[1],) # Run relational layers for layer_module in self.r_layers: v_outputs = layer_module( visual_feats, visual_attention_mask, output_attentions, training=training, ) visual_feats = v_outputs[0] vision_hidden_states = vision_hidden_states + (visual_feats,) if vision_attentions is not None: vision_attentions = vision_attentions + (v_outputs[1],) # Run cross-modality layers for layer_module in self.x_layers: x_outputs = layer_module( lang_feats, lang_attention_mask, visual_feats, visual_attention_mask, output_attentions, training=training, ) lang_feats, visual_feats = x_outputs[:2] vision_hidden_states = vision_hidden_states + (visual_feats,) language_hidden_states = language_hidden_states + (lang_feats,) if cross_encoder_attentions is not None: cross_encoder_attentions = cross_encoder_attentions + (x_outputs[2],) visual_encoder_outputs = ( vision_hidden_states, vision_attentions if output_attentions else None, ) lang_encoder_outputs = ( language_hidden_states, language_attentions if output_attentions else None, ) return ( visual_encoder_outputs, lang_encoder_outputs, cross_encoder_attentions if output_attentions else None, ) @keras_serializable class TFLxmertMainLayer(tf.keras.layers.Layer): config_class = LxmertConfig @property def dummy_inputs(self): """ Dummy inputs to build the network. Returns: tf.Tensor with dummy inputs """ batch_size = 2 num_visual_features = 10 input_ids = tf.constant([[3, 5, 6], [2, 3, 4]]) visual_feats = tf.random.uniform((batch_size, num_visual_features, self.config.visual_feat_dim)) visual_pos = tf.random.uniform((batch_size, num_visual_features, 4)) return { "input_ids": input_ids, "visual_feats": visual_feats, "visual_pos": visual_pos, } def __init__(self, config, **kwargs): super().__init__(**kwargs) self.config = config self.num_l_layers = config.l_layers self.num_x_layers = config.x_layers self.num_r_layers = config.r_layers self.initializer_range = config.initializer_range self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.return_dict = config.use_return_dict self.embeddings = TFLxmertEmbeddings(config, name="embeddings") self.encoder = TFLxmertEncoder(config, name="encoder") self.pooler = TFLxmertPooler(config, name="pooler") self.config = config def get_input_embeddings(self): return self.embeddings def set_input_embeddings(self, value): self.embeddings.weight = value self.embeddings.vocab_size = shape_list(value)[0] def _prune_heads(self, heads_to_prune): raise NotImplementedError @unpack_inputs def call( self, input_ids=None, visual_feats=None, visual_pos=None, attention_mask=None, visual_attention_mask=None, token_type_ids=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, ): if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if visual_pos is None or visual_feats is None: raise ValueError("visual_feats and visual_pos cannot be `None` in LXMERT's `call` method.") if attention_mask is None: attention_mask = tf.fill(input_shape, 1) if token_type_ids is None: token_type_ids = tf.fill(input_shape, 0) # Positional Word Embeddings embedding_output = self.embeddings(input_ids, token_type_ids, inputs_embeds, training) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1])) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype) one_cst = tf.constant(1.0, dtype=embedding_output.dtype) ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype) extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst) if visual_attention_mask is not None: extended_visual_attention_mask = tf.reshape(visual_attention_mask, (input_shape[0], 1, 1, input_shape[1])) extended_visual_attention_mask = tf.expand_dims(tf.expand_dims(visual_attention_mask, axis=1), axis=1) extended_visual_attention_mask = tf.cast(extended_visual_attention_mask, dtype=embedding_output.dtype) extended_visual_attention_mask = tf.multiply( tf.subtract(one_cst, extended_visual_attention_mask), ten_thousand_cst ) else: extended_visual_attention_mask = None # Run Lxmert encoder encoder_outputs = self.encoder( embedding_output, extended_attention_mask, visual_feats, visual_pos, extended_visual_attention_mask, output_attentions, training, ) visual_encoder_outputs, lang_encoder_outputs = encoder_outputs[:2] vision_hidden_states = visual_encoder_outputs[0] language_hidden_states = lang_encoder_outputs[0] all_attentions = () if output_attentions: language_attentions = lang_encoder_outputs[1] vision_attentions = visual_encoder_outputs[1] cross_encoder_attentions = encoder_outputs[2] all_attentions = ( language_attentions, vision_attentions, cross_encoder_attentions, ) hidden_states = (language_hidden_states, vision_hidden_states) if output_hidden_states else () visual_output = vision_hidden_states[-1] lang_output = language_hidden_states[-1] pooled_output = self.pooler(lang_output) if not return_dict: return (lang_output, visual_output, pooled_output) + hidden_states + all_attentions return TFLxmertModelOutput( pooled_output=pooled_output, language_output=lang_output, vision_output=visual_output, language_hidden_states=language_hidden_states if output_hidden_states else None, vision_hidden_states=vision_hidden_states if output_hidden_states else None, language_attentions=language_attentions if output_attentions else None, vision_attentions=vision_attentions if output_attentions else None, cross_encoder_attentions=cross_encoder_attentions if output_attentions else None, ) class TFLxmertPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = LxmertConfig base_model_prefix = "lxmert" @property def dummy_inputs(self) -> Dict[str, tf.Tensor]: return getattr(self, self.base_model_prefix).dummy_inputs @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None), tf.int64, name="input_ids"), "attention_mask": tf.TensorSpec((None, None), tf.int64, name="attention_mask"), "visual_feats": tf.TensorSpec((None, None, None), tf.float32, name="visual_feats"), "visual_pos": tf.TensorSpec((None, None, None), tf.float32, name="visual_pos"), "visual_attention_mask": tf.TensorSpec((None, None), tf.int64, name="visual_attention_mask"), "token_type_ids": tf.TensorSpec((None, None), tf.int64, name="token_type_ids"), } ] ) def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) LXMERT_START_DOCSTRING = r""" The LXMERT model was proposed in [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal. It's a vision and language transformer model, pre-trained on a variety of multi-modal datasets comprising of GQA, VQAv2.0, MCSCOCO captions, and Visual genome, using a combination of masked language modeling, region of interest feature regression, cross entropy loss for question answering attribute prediction, and object tag prediction. This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`LxmertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ LXMERT_INPUTS_DOCSTRING = r""" Args: input_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`LxmertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) visual_feats: (`tf.Tensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`): This input represents visual features. They ROI pooled object features from bounding boxes using a faster-RCNN model) These are currently not provided by the transformers library. visual_pos: (`tf.Tensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`): This input represents spacial features corresponding to their relative (via index) visual features. The pre-trained LXMERT model expects these spacial features to be normalized bounding boxes on a scale of 0 to 1. These are currently not provided by the transformers library. attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) visual_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): MMask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare Lxmert Model transformer outputting raw hidden-states without any specific head on top.", LXMERT_START_DOCSTRING, ) class TFLxmertModel(TFLxmertPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.lxmert = TFLxmertMainLayer(config, name="lxmert") @unpack_inputs @add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFLxmertModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, visual_feats: Optional[tf.Tensor] = None, visual_pos: Optional[tf.Tensor] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, visual_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[Tuple, TFLxmertModelOutput]: outputs = self.lxmert( input_ids, visual_feats, visual_pos, attention_mask, visual_attention_mask, token_type_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict, training, ) return outputs def serving_output(self, output): l_hs = tf.convert_to_tensor(output.language_hidden_states) if self.config.output_hidden_states else None v_hs = tf.convert_to_tensor(output.vision_hidden_states) if self.config.output_hidden_states else None l_attns = tf.convert_to_tensor(output.language_attentions) if self.config.output_attentions else None v_attns = tf.convert_to_tensor(output.vision_attentions) if self.config.output_attentions else None c_enc_attns = tf.convert_to_tensor(output.cross_encoder_attentions) if self.config.output_attentions else None return TFLxmertModelOutput( pooled_output=output.pooled_output, language_output=output.language_output, vision_output=output.vision_output, language_hidden_states=l_hs, vision_hidden_states=v_hs, language_attentions=l_attns, vision_attentions=v_attns, cross_encoder_attentions=c_enc_attns, ) class TFLxmertPooler(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) def call(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) return pooled_output # Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->Lxmert class TFLxmertPredictionHeadTransform(tf.keras.layers.Layer): def __init__(self, config: LxmertConfig, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense", ) if isinstance(config.hidden_act, str): self.transform_act_fn = get_tf_activation(config.hidden_act) else: self.transform_act_fn = config.hidden_act self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(inputs=hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->Lxmert class TFLxmertLMPredictionHead(tf.keras.layers.Layer): def __init__(self, config: LxmertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs): super().__init__(**kwargs) self.vocab_size = config.vocab_size self.hidden_size = config.hidden_size self.transform = TFLxmertPredictionHeadTransform(config, name="transform") # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.input_embeddings = input_embeddings def build(self, input_shape: tf.TensorShape): self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias") super().build(input_shape) def get_output_embeddings(self) -> tf.keras.layers.Layer: return self.input_embeddings def set_output_embeddings(self, value: tf.Variable): self.input_embeddings.weight = value self.input_embeddings.vocab_size = shape_list(value)[0] def get_bias(self) -> Dict[str, tf.Variable]: return {"bias": self.bias} def set_bias(self, value: tf.Variable): self.bias = value["bias"] self.vocab_size = shape_list(value["bias"])[0] def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.transform(hidden_states=hidden_states) seq_length = shape_list(hidden_states)[1] hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size]) hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True) hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size]) hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) return hidden_states # Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->Lxmert class TFLxmertMLMHead(tf.keras.layers.Layer): def __init__(self, config: LxmertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs): super().__init__(**kwargs) self.predictions = TFLxmertLMPredictionHead(config, input_embeddings, name="predictions") def call(self, sequence_output: tf.Tensor) -> tf.Tensor: prediction_scores = self.predictions(hidden_states=sequence_output) return prediction_scores class TFLxmertPreTrainingHeads(tf.keras.layers.Layer): def __init__(self, config, input_embeddings, **kwargs): super().__init__(**kwargs) self.predictions = TFLxmertLMPredictionHead(config, input_embeddings, name="predictions") self.seq_relationship = tf.keras.layers.Dense( 2, kernel_initializer=get_initializer(config.initializer_range), name="seq_relationship", ) def call(self, sequence_output, pooled_output): prediction_scores = self.predictions(sequence_output) seq_relationship_score = self.seq_relationship(pooled_output) return prediction_scores, seq_relationship_score class TFLxmertVisualAnswerHead(tf.keras.layers.Layer): def __init__(self, config, num_labels, **kwargs): super().__init__(**kwargs) hid_dim = config.hidden_size self.dense = tf.keras.layers.Dense( hid_dim * 2, kernel_initializer=get_initializer(config.initializer_range), name="logit_fc_._0", ) self.activation = get_tf_activation("gelu") self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="logit_fc_._2") self.dense_1 = tf.keras.layers.Dense( num_labels, kernel_initializer=get_initializer(config.initializer_range), name="logit_fc_._3", ) def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) hidden_states = self.layer_norm(hidden_states) hidden_states = self.dense_1(hidden_states) return hidden_states class TFLxmertVisualObjHead(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.transform = TFLxmertPredictionHeadTransform(config, name="transform") # Decide the use of visual losses visual_losses = {} if config.visual_obj_loss: visual_losses["obj"] = {"shape": (-1,), "num": config.num_object_labels} if config.visual_attr_loss: visual_losses["attr"] = {"shape": (-1,), "num": config.num_attr_labels} if config.visual_obj_loss: visual_losses["feat"] = {"shape": (-1, 2048), "num": config.visual_feat_dim} self.visual_losses = visual_losses # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder_dict = { key: tf.keras.layers.Dense( self.visual_losses[key]["num"], kernel_initializer=get_initializer(config.initializer_range), name=f"decoder_dict.{key}", ) for key in self.visual_losses } def call(self, hidden_states): hidden_states = self.transform(hidden_states) output = {} for key in self.visual_losses: output[key] = self.decoder_dict[key](hidden_states) return output @add_start_docstrings("""Lxmert Model with a `language modeling` head on top.""", LXMERT_START_DOCSTRING) class TFLxmertForPreTraining(TFLxmertPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.config = config self.num_qa_labels = config.num_qa_labels self.visual_loss_normalizer = config.visual_loss_normalizer # Use of pretraining tasks self.task_mask_lm = config.task_mask_lm self.task_obj_predict = config.task_obj_predict self.task_matched = config.task_matched self.task_qa = config.task_qa # Lxmert backbone self.lxmert = TFLxmertMainLayer(config, name="lxmert") # Pre-training heads self.cls = TFLxmertPreTrainingHeads(config, self.lxmert.embeddings, name="cls") if self.task_obj_predict: self.obj_predict_head = TFLxmertVisualObjHead(config, name="obj_predict_head") if self.task_qa: self.answer_head = TFLxmertVisualAnswerHead(config, self.num_qa_labels, name="answer_head") # Loss functions self.loss_fcts = { "l2": tf.keras.losses.Huber(delta=1.0, name="huber_loss"), "visn_ce": tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), "ce": tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), } visual_losses = {} if config.visual_obj_loss: visual_losses["obj"] = { "shape": (-1,), "num": config.num_object_labels, "loss": "visn_ce", } if config.visual_attr_loss: visual_losses["attr"] = { "shape": (-1,), "num": config.num_attr_labels, "loss": "visn_ce", } if config.visual_obj_loss: visual_losses["feat"] = { "shape": (-1, config.visual_feat_dim), "num": config.visual_feat_dim, "loss": "l2", } self.visual_losses = visual_losses @property def dummy_inputs(self): """ Dummy inputs to build the network. Returns: tf.Tensor with dummy inputs """ batch_size = 2 num_visual_features = 10 input_ids = tf.constant([[3, 5, 6], [2, 3, 4]]) visual_feats = tf.random.uniform((batch_size, num_visual_features, self.config.visual_feat_dim)) visual_pos = tf.random.uniform((batch_size, num_visual_features, 4)) if self.config.task_obj_predict: obj_labels = {} if self.config.visual_attr_loss and self.config.task_obj_predict: obj_labels["attr"] = ( tf.ones([batch_size, num_visual_features]), tf.ones([batch_size, num_visual_features]), ) if self.config.visual_feat_loss and self.config.task_obj_predict: obj_labels["feat"] = ( tf.ones([batch_size, num_visual_features, self.config.visual_feat_dim]), tf.ones([batch_size, num_visual_features]), ) if self.config.visual_obj_loss and self.config.task_obj_predict: obj_labels["obj"] = ( tf.ones([batch_size, num_visual_features]), tf.ones([batch_size, num_visual_features]), ) return { **{ "input_ids": input_ids, "visual_feats": visual_feats, "visual_pos": visual_pos, }, **({"obj_labels": obj_labels} if self.config.task_obj_predict else {}), } def get_lm_head(self): return self.cls.predictions def get_prefix_bias_name(self): warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.cls.name + "/" + self.cls.predictions.name @unpack_inputs @add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFLxmertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids=None, visual_feats=None, visual_pos=None, attention_mask=None, visual_attention_mask=None, token_type_ids=None, inputs_embeds=None, masked_lm_labels=None, obj_labels=None, matched_label=None, ans=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, ): r""" masked_lm_labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` obj_labels: (`Dict[Str: Tuple[tf.Tensor, tf.Tensor]]`, *optional*, defaults to `None`): each key is named after each one of the visual losses and each element of the tuple is of the shape `(batch_size, num_features)` and `(batch_size, num_features, visual_feature_dim)` for each the label id and the label score respectively matched_label (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the whether or not the text input matches the image (classification) loss. Input should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`: - 0 indicates that the sentence does not match the image, - 1 indicates that the sentence does match the image. ans (`Torch.Tensor` of shape `(batch_size)`, *optional*, defaults to `None`): a one hot representation hof the correct answer *optional* Returns: """ lxmert_output = self.lxmert( input_ids, visual_feats, visual_pos, attention_mask, visual_attention_mask, token_type_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict, training, ) lang_output, visual_output, pooled_output = ( lxmert_output[0], lxmert_output[1], lxmert_output[2], ) lang_prediction_scores, cross_relationship_score = self.cls(lang_output, pooled_output) if self.task_qa: answer_score = self.answer_head(pooled_output) else: answer_score = pooled_output[0][0] total_loss = ( None if (masked_lm_labels is None and matched_label is None and obj_labels is None and ans is None) else tf.constant(0.0) ) losses = () if masked_lm_labels is not None and self.task_mask_lm: masked_lm_loss = self.loss_fcts["ce"]( tf.reshape(masked_lm_labels, [-1]), tf.reshape(lang_prediction_scores, [-1, self.config.vocab_size]), ) total_loss += masked_lm_loss losses += (masked_lm_loss,) if matched_label is not None and self.task_matched: matched_loss = self.loss_fcts["ce"]( tf.reshape(matched_label, [-1]), tf.reshape(cross_relationship_score, [-1, 2]), ) total_loss += matched_loss losses += (matched_loss,) if obj_labels is not None and self.task_obj_predict: total_visn_loss = 0.0 visn_prediction_scores_dict = self.obj_predict_head(visual_output) for key, key_info in self.visual_losses.items(): label, mask_conf = obj_labels[key] output_dim = key_info["num"] loss_fct_name = key_info["loss"] label_shape = key_info["shape"] weight = self.visual_loss_normalizer visn_loss_fct = self.loss_fcts[loss_fct_name] visn_prediction_scores = visn_prediction_scores_dict[key] visn_loss = visn_loss_fct( tf.reshape(label, label_shape), tf.reshape(visn_prediction_scores, [-1, output_dim]), ) if visn_loss.ndim > 1: # Regression Losses visn_loss = tf.reduce_mean(visn_loss) visn_loss = tf.reduce_mean(visn_loss * tf.cast(tf.reshape(mask_conf, [-1]), visn_loss.dtype)) * weight total_visn_loss += visn_loss losses += (visn_loss,) total_loss += total_visn_loss if ans is not None and self.task_qa: answer_loss = self.loss_fcts["ce"]( tf.reshape(ans, [-1]), tf.reshape(answer_score, [-1, self.num_qa_labels]) ) # exclude "*2" here to match the effect of QA losses. # Previous: (loss *0) for 6 epochs, (loss *2) for 6 epochs. (Used 10 instead of 6 in EMNLP paper) # Now : (loss *1) for 12 epochs # # * 2 # Multiply by 2 because > half of the data will not have label total_loss += answer_loss losses += (answer_loss,) # return total_loss, tf.stack(losses)[tf.new_axis, ...], answer_score.detach() if not return_dict: output = ( lang_prediction_scores, cross_relationship_score, answer_score, ) + lxmert_output[3:] return ((total_loss,) + output) if total_loss is not None else output return TFLxmertForPreTrainingOutput( loss=total_loss, prediction_logits=lang_prediction_scores, cross_relationship_score=cross_relationship_score, question_answering_score=answer_score, language_hidden_states=lxmert_output.language_hidden_states, vision_hidden_states=lxmert_output.vision_hidden_states, language_attentions=lxmert_output.language_attentions, vision_attentions=lxmert_output.vision_attentions, cross_encoder_attentions=lxmert_output.cross_encoder_attentions, ) def serving_output(self, output): l_hs = tf.convert_to_tensor(output.language_hidden_states) if self.config.output_hidden_states else None v_hs = tf.convert_to_tensor(output.vision_hidden_states) if self.config.output_hidden_states else None l_attns = tf.convert_to_tensor(output.language_attentions) if self.config.output_attentions else None v_attns = tf.convert_to_tensor(output.vision_attentions) if self.config.output_attentions else None c_enc_attns = tf.convert_to_tensor(output.cross_encoder_attentions) if self.config.output_attentions else None return TFLxmertForPreTrainingOutput( prediction_logits=output.prediction_logits, cross_relationship_score=output.cross_relationship_score, question_answering_score=output.question_answering_score, language_hidden_states=l_hs, vision_hidden_states=v_hs, language_attentions=l_attns, vision_attentions=v_attns, cross_encoder_attentions=c_enc_attns, )

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors, The HuggingFace Inc. team, and the # Lxmert Authors. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 LXMERT model.""" import warnings from dataclasses import dataclass from typing import Dict, Optional, Tuple, Union import numpy as np import tensorflow as tf from transformers.tf_utils import stable_softmax from ...activations_tf import get_tf_activation from ...modeling_tf_utils import ( TFModelInputType, TFPreTrainedModel, get_initializer, keras_serializable, shape_list, unpack_inputs, ) from ...utils import ( ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_lxmert import LxmertConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "unc-nlp/lxmert-base-uncased" _CONFIG_FOR_DOC = "LxmertConfig" _TOKENIZER_FOR_DOC = "LxmertTokenizer" TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "unc-nlp/lxmert-base-uncased", ] @dataclass class TFLxmertModelOutput(ModelOutput): """ Lxmert's outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language, visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the "relation-ship" encoder") Args: language_output (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the language encoder. vision_output (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the visual encoder. pooled_output (`tf.Tensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification, CLS, token) further processed by a Linear layer and a Tanh activation function. The Linear language_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape `(batch_size, sequence_length, hidden_size)`. vision_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape `(batch_size, sequence_length, hidden_size)`. language_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. vision_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ language_output: Optional[tf.Tensor] = None vision_output: Optional[tf.Tensor] = None pooled_output: Optional[tf.Tensor] = None language_hidden_states: Optional[Tuple[tf.Tensor]] = None vision_hidden_states: Optional[Tuple[tf.Tensor]] = None language_attentions: Optional[Tuple[tf.Tensor]] = None vision_attentions: Optional[Tuple[tf.Tensor]] = None cross_encoder_attentions: Optional[Tuple[tf.Tensor]] = None @dataclass class TFLxmertForPreTrainingOutput(ModelOutput): """ Output type of [`LxmertForPreTraining`]. Args: loss (*optional*, returned when `labels` is provided, `tf.Tensor` of shape `(1,)`): Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss. prediction_logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). cross_relationship_score: (`tf.Tensor` of shape `(batch_size, 2)`): Prediction scores of the textual matching objective (classification) head (scores of True/False continuation before SoftMax). question_answering_score: (`tf.Tensor` of shape `(batch_size, n_qa_answers)`): Prediction scores of question answering objective (classification). language_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape `(batch_size, sequence_length, hidden_size)`. vision_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape `(batch_size, sequence_length, hidden_size)`. language_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. vision_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[tf.Tensor] = None prediction_logits: Optional[tf.Tensor] = None cross_relationship_score: Optional[tf.Tensor] = None question_answering_score: Optional[tf.Tensor] = None language_hidden_states: Optional[Tuple[tf.Tensor]] = None vision_hidden_states: Optional[Tuple[tf.Tensor]] = None language_attentions: Optional[Tuple[tf.Tensor]] = None vision_attentions: Optional[Tuple[tf.Tensor]] = None cross_encoder_attentions: Optional[Tuple[tf.Tensor]] = None class TFLxmertVisualFeatureEncoder(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) # Object feature encoding self.visn_fc = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="visn_fc", ) self.visn_layer_norm = tf.keras.layers.LayerNormalization( epsilon=config.layer_norm_eps, name="visn_layer_norm" ) # Box position encoding self.box_fc = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="box_fc", ) self.box_layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="box_layer_norm") self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) def call(self, visn_input, training=False): feats, boxes = visn_input x = self.visn_fc(feats) x = self.visn_layer_norm(x) y = self.box_fc(boxes) y = self.box_layer_norm(y) output = (x + y) / 2 output = self.dropout(output, training=training) return output class TFLxmertEmbeddings(tf.keras.layers.Layer): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config, **kwargs): super().__init__(**kwargs) self.vocab_size = config.vocab_size self.type_vocab_size = config.type_vocab_size self.hidden_size = config.hidden_size self.max_position_embeddings = config.max_position_embeddings self.initializer_range = config.initializer_range self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) def build(self, input_shape): with tf.name_scope("word_embeddings"): self.weight = self.add_weight( name="weight", shape=[self.vocab_size, self.hidden_size], initializer=get_initializer(initializer_range=self.initializer_range), ) with tf.name_scope("token_type_embeddings"): self.token_type_embeddings = self.add_weight( name="embeddings", shape=[self.type_vocab_size, self.hidden_size], initializer=get_initializer(initializer_range=self.initializer_range), ) with tf.name_scope("position_embeddings"): self.position_embeddings = self.add_weight( name="embeddings", shape=[self.max_position_embeddings, self.hidden_size], initializer=get_initializer(initializer_range=self.initializer_range), ) super().build(input_shape) def call(self, input_ids=None, token_type_ids=None, inputs_embeds=None, training=False): """ Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. """ assert not (input_ids is None and inputs_embeds is None) if input_ids is not None: # Note: tf.gather, on which the embedding layer is based, won't check positive out of bound # indices on GPU, returning zeros instead. This is a dangerous silent behavior. tf.debugging.assert_less( input_ids, tf.cast(self.vocab_size, dtype=input_ids.dtype), message=( "input_ids must be smaller than the embedding layer's input dimension (got" f" {tf.math.reduce_max(input_ids)} >= {self.vocab_size})" ), ) inputs_embeds = tf.gather(params=self.weight, indices=input_ids) input_shape = shape_list(inputs_embeds)[:-1] if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0) position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids) token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids) final_embeddings = inputs_embeds + position_embeds + token_type_embeds final_embeddings = self.LayerNorm(inputs=final_embeddings) final_embeddings = self.dropout(inputs=final_embeddings, training=training) return final_embeddings class TFLxmertAttention(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads}" ) self.num_attention_heads = config.num_attention_heads assert config.hidden_size % config.num_attention_heads == 0 self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = tf.keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query", ) self.key = tf.keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key", ) self.value = tf.keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value", ) self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x, batch_size): # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size)) return tf.transpose(x, perm=[0, 2, 1, 3]) def call(self, hidden_states, context, attention_mask, output_attentions, training=False): batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(context) mixed_value_layer = self.value(context) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) key_layer = self.transpose_for_scores(mixed_key_layer, batch_size) value_layer = self.transpose_for_scores(mixed_value_layer, batch_size) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = tf.matmul( query_layer, key_layer, transpose_b=True ) # (batch size, num_heads, seq_len_q, seq_len_k) dk = tf.cast(shape_list(key_layer)[-1], dtype=attention_scores.dtype) # scale attention_scores attention_scores = attention_scores / tf.math.sqrt(dk) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in TFLxmertModel call() function) attention_mask = tf.cast(attention_mask, dtype=attention_scores.dtype) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = stable_softmax(attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs, training=training) context_layer = tf.matmul(attention_probs, value_layer) context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3]) context_layer = tf.reshape( context_layer, (batch_size, -1, self.all_head_size) ) # (batch_size, seq_len_q, all_head_size) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class TFLxmertIntermediate(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense", ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class TFLxmertOutput(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense", ) self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) def call(self, hidden_states, input_tensor, training=False): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, training) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class TFLxmertAttentionOutput(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense", ) self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) def call(self, hidden_states, input_tensor, training=False): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class TFLxmertSelfAttentionLayer(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.self = TFLxmertAttention(config, name="self") self.attention_output = TFLxmertAttentionOutput(config, name="output") def call(self, input_tensor, attention_mask, output_attentions, training=False): # Self attention attends to itself, thus keys and queries are the same (input_tensor). self_output = self.self(input_tensor, input_tensor, attention_mask, output_attentions) if output_attentions: attention_probs = self_output[1] attention_output = self.attention_output(self_output[0], input_tensor) return (attention_output, attention_probs) if output_attentions else (attention_output,) class TFLxmertCrossAttentionLayer(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.att = TFLxmertAttention(config, name="att") self.attention_output = TFLxmertAttentionOutput(config, name="output") def call( self, input_tensor, ctx_tensor, ctx_att_mask, output_attentions=False, training=False, ): output = self.att(input_tensor, ctx_tensor, ctx_att_mask, output_attentions, training=training) if output_attentions: attention_probs = output[1] attention_output = self.attention_output(output[0], input_tensor, training=training) outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) return outputs class TFLxmertLayer(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.attention = TFLxmertSelfAttentionLayer(config, name="attention") self.intermediate = TFLxmertIntermediate(config, name="intermediate") self.transformer_output = TFLxmertOutput(config, name="output") def call(self, hidden_states, attention_mask, output_attentions, training=False): attention_outputs = self.attention(hidden_states, attention_mask, output_attentions, training=training) attention_output = attention_outputs[0] intermediate_output = self.intermediate(attention_output) layer_output = self.transformer_output(intermediate_output, attention_output, training=training) outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them return outputs class TFLxmertXLayer(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.visual_attention = TFLxmertCrossAttentionLayer(config, name="visual_attention") # Self-attention Layers self.lang_self_att = TFLxmertSelfAttentionLayer(config, name="lang_self_att") self.visn_self_att = TFLxmertSelfAttentionLayer(config, name="visn_self_att") # Intermediate and Output Layers (FFNs) self.lang_inter = TFLxmertIntermediate(config, name="lang_inter") self.lang_output = TFLxmertOutput(config, name="lang_output") self.visn_inter = TFLxmertIntermediate(config, name="visn_inter") self.visn_output = TFLxmertOutput(config, name="visn_output") def cross_att( self, lang_input, lang_attention_mask, visn_input, visn_attention_mask, output_attentions, training=False, ): # Cross Attention # Keras saving and loading model *does not work* with the same inputs for two layers. lang_attention_lang_input = tf.identity(lang_input) visn_attention_lang_input = tf.identity(lang_input) lang_attention_visn_input = tf.identity(visn_input) visn_attention_visn_input = tf.identity(visn_input) lang_att_output = self.visual_attention( lang_attention_lang_input, lang_attention_visn_input, visn_attention_mask, output_attentions=output_attentions, training=training, ) visn_att_output = self.visual_attention( visn_attention_visn_input, visn_attention_lang_input, lang_attention_mask, output_attentions=output_attentions, training=training, ) return lang_att_output, visn_att_output def self_att( self, lang_input, lang_attention_mask, visn_input, visn_attention_mask, training=False, ): # Self Attention output_attentions = False lang_att_output = self.lang_self_att(lang_input, lang_attention_mask, output_attentions, training=training) visn_att_output = self.visn_self_att(visn_input, visn_attention_mask, output_attentions, training=training) return lang_att_output[0], visn_att_output[0] def output_fc(self, lang_input, visn_input, training=False): # FC layers lang_inter_output = self.lang_inter(lang_input) visn_inter_output = self.visn_inter(visn_input) # Layer output lang_output = self.lang_output(lang_inter_output, lang_input, training) visn_output = self.visn_output(visn_inter_output, visn_input, training) return lang_output, visn_output def call( self, lang_feats, lang_attention_mask, visn_feats, visn_attention_mask, output_attentions, training=False, ): lang_att_output = lang_feats visn_att_output = visn_feats lang_att_output, visn_att_output = self.cross_att( lang_att_output, lang_attention_mask, visn_att_output, visn_attention_mask, output_attentions, training=training, ) attention_probs = lang_att_output[1:] lang_att_output, visn_att_output = self.self_att( lang_att_output[0], lang_attention_mask, visn_att_output[0], visn_attention_mask, training=training, ) lang_output, visn_output = self.output_fc(lang_att_output, visn_att_output, training=training) return (lang_output, visn_output, attention_probs[0]) if output_attentions else (lang_output, visn_output) class TFLxmertEncoder(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.visn_fc = TFLxmertVisualFeatureEncoder(config, name="visn_fc") # Number of layers self.num_l_layers = config.l_layers self.num_x_layers = config.x_layers self.num_r_layers = config.r_layers # Layers # Using self.layer instead of self.l_layer to support loading BERT weights. self.layer = [TFLxmertLayer(config, name=f"layer_._{i}") for i in range(self.num_l_layers)] self.x_layers = [TFLxmertXLayer(config, name=f"x_layers_._{i}") for i in range(self.num_x_layers)] self.r_layers = [TFLxmertLayer(config, name=f"r_layers_._{i}") for i in range(self.num_r_layers)] self.config = config def call( self, lang_feats=None, lang_attention_mask=None, visual_feats=None, visual_pos=None, visual_attention_mask=None, output_attentions=None, training=False, ): vision_hidden_states = () language_hidden_states = () vision_attentions = () if output_attentions or self.config.output_attentions else None language_attentions = () if output_attentions or self.config.output_attentions else None cross_encoder_attentions = () if output_attentions or self.config.output_attentions else None visual_feats = self.visn_fc([visual_feats, visual_pos], training=training) # Run language layers for layer_module in self.layer: l_outputs = layer_module(lang_feats, lang_attention_mask, output_attentions, training=training) lang_feats = l_outputs[0] language_hidden_states = language_hidden_states + (lang_feats,) if language_attentions is not None: language_attentions = language_attentions + (l_outputs[1],) # Run relational layers for layer_module in self.r_layers: v_outputs = layer_module( visual_feats, visual_attention_mask, output_attentions, training=training, ) visual_feats = v_outputs[0] vision_hidden_states = vision_hidden_states + (visual_feats,) if vision_attentions is not None: vision_attentions = vision_attentions + (v_outputs[1],) # Run cross-modality layers for layer_module in self.x_layers: x_outputs = layer_module( lang_feats, lang_attention_mask, visual_feats, visual_attention_mask, output_attentions, training=training, ) lang_feats, visual_feats = x_outputs[:2] vision_hidden_states = vision_hidden_states + (visual_feats,) language_hidden_states = language_hidden_states + (lang_feats,) if cross_encoder_attentions is not None: cross_encoder_attentions = cross_encoder_attentions + (x_outputs[2],) visual_encoder_outputs = ( vision_hidden_states, vision_attentions if output_attentions else None, ) lang_encoder_outputs = ( language_hidden_states, language_attentions if output_attentions else None, ) return ( visual_encoder_outputs, lang_encoder_outputs, cross_encoder_attentions if output_attentions else None, ) @keras_serializable class TFLxmertMainLayer(tf.keras.layers.Layer): config_class = LxmertConfig @property def dummy_inputs(self): """ Dummy inputs to build the network. Returns: tf.Tensor with dummy inputs """ batch_size = 2 num_visual_features = 10 input_ids = tf.constant([[3, 5, 6], [2, 3, 4]]) visual_feats = tf.random.uniform((batch_size, num_visual_features, self.config.visual_feat_dim)) visual_pos = tf.random.uniform((batch_size, num_visual_features, 4)) return { "input_ids": input_ids, "visual_feats": visual_feats, "visual_pos": visual_pos, } def __init__(self, config, **kwargs): super().__init__(**kwargs) self.config = config self.num_l_layers = config.l_layers self.num_x_layers = config.x_layers self.num_r_layers = config.r_layers self.initializer_range = config.initializer_range self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.return_dict = config.use_return_dict self.embeddings = TFLxmertEmbeddings(config, name="embeddings") self.encoder = TFLxmertEncoder(config, name="encoder") self.pooler = TFLxmertPooler(config, name="pooler") self.config = config def get_input_embeddings(self): return self.embeddings def set_input_embeddings(self, value): self.embeddings.weight = value self.embeddings.vocab_size = shape_list(value)[0] def _prune_heads(self, heads_to_prune): raise NotImplementedError @unpack_inputs def call( self, input_ids=None, visual_feats=None, visual_pos=None, attention_mask=None, visual_attention_mask=None, token_type_ids=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, ): if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if visual_pos is None or visual_feats is None: raise ValueError("visual_feats and visual_pos cannot be `None` in LXMERT's `call` method.") if attention_mask is None: attention_mask = tf.fill(input_shape, 1) if token_type_ids is None: token_type_ids = tf.fill(input_shape, 0) # Positional Word Embeddings embedding_output = self.embeddings(input_ids, token_type_ids, inputs_embeds, training) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1])) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype) one_cst = tf.constant(1.0, dtype=embedding_output.dtype) ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype) extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst) if visual_attention_mask is not None: extended_visual_attention_mask = tf.reshape(visual_attention_mask, (input_shape[0], 1, 1, input_shape[1])) extended_visual_attention_mask = tf.expand_dims(tf.expand_dims(visual_attention_mask, axis=1), axis=1) extended_visual_attention_mask = tf.cast(extended_visual_attention_mask, dtype=embedding_output.dtype) extended_visual_attention_mask = tf.multiply( tf.subtract(one_cst, extended_visual_attention_mask), ten_thousand_cst ) else: extended_visual_attention_mask = None # Run Lxmert encoder encoder_outputs = self.encoder( embedding_output, extended_attention_mask, visual_feats, visual_pos, extended_visual_attention_mask, output_attentions, training, ) visual_encoder_outputs, lang_encoder_outputs = encoder_outputs[:2] vision_hidden_states = visual_encoder_outputs[0] language_hidden_states = lang_encoder_outputs[0] all_attentions = () if output_attentions: language_attentions = lang_encoder_outputs[1] vision_attentions = visual_encoder_outputs[1] cross_encoder_attentions = encoder_outputs[2] all_attentions = ( language_attentions, vision_attentions, cross_encoder_attentions, ) hidden_states = (language_hidden_states, vision_hidden_states) if output_hidden_states else () visual_output = vision_hidden_states[-1] lang_output = language_hidden_states[-1] pooled_output = self.pooler(lang_output) if not return_dict: return (lang_output, visual_output, pooled_output) + hidden_states + all_attentions return TFLxmertModelOutput( pooled_output=pooled_output, language_output=lang_output, vision_output=visual_output, language_hidden_states=language_hidden_states if output_hidden_states else None, vision_hidden_states=vision_hidden_states if output_hidden_states else None, language_attentions=language_attentions if output_attentions else None, vision_attentions=vision_attentions if output_attentions else None, cross_encoder_attentions=cross_encoder_attentions if output_attentions else None, ) class TFLxmertPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = LxmertConfig base_model_prefix = "lxmert" @property def dummy_inputs(self) -> Dict[str, tf.Tensor]: return getattr(self, self.base_model_prefix).dummy_inputs @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None), tf.int64, name="input_ids"), "attention_mask": tf.TensorSpec((None, None), tf.int64, name="attention_mask"), "visual_feats": tf.TensorSpec((None, None, None), tf.float32, name="visual_feats"), "visual_pos": tf.TensorSpec((None, None, None), tf.float32, name="visual_pos"), "visual_attention_mask": tf.TensorSpec((None, None), tf.int64, name="visual_attention_mask"), "token_type_ids": tf.TensorSpec((None, None), tf.int64, name="token_type_ids"), } ] ) def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) LXMERT_START_DOCSTRING = r""" The LXMERT model was proposed in [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal. It's a vision and language transformer model, pre-trained on a variety of multi-modal datasets comprising of GQA, VQAv2.0, MCSCOCO captions, and Visual genome, using a combination of masked language modeling, region of interest feature regression, cross entropy loss for question answering attribute prediction, and object tag prediction. This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`LxmertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ LXMERT_INPUTS_DOCSTRING = r""" Args: input_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`LxmertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) visual_feats: (`tf.Tensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`): This input represents visual features. They ROI pooled object features from bounding boxes using a faster-RCNN model) These are currently not provided by the transformers library. visual_pos: (`tf.Tensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`): This input represents spacial features corresponding to their relative (via index) visual features. The pre-trained LXMERT model expects these spacial features to be normalized bounding boxes on a scale of 0 to 1. These are currently not provided by the transformers library. attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) visual_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): MMask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare Lxmert Model transformer outputting raw hidden-states without any specific head on top.", LXMERT_START_DOCSTRING, ) class TFLxmertModel(TFLxmertPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.lxmert = TFLxmertMainLayer(config, name="lxmert") @unpack_inputs @add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFLxmertModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: Optional[TFModelInputType] = None, visual_feats: Optional[tf.Tensor] = None, visual_pos: Optional[tf.Tensor] = None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, visual_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[Tuple, TFLxmertModelOutput]: outputs = self.lxmert( input_ids, visual_feats, visual_pos, attention_mask, visual_attention_mask, token_type_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict, training, ) return outputs def serving_output(self, output): l_hs = tf.convert_to_tensor(output.language_hidden_states) if self.config.output_hidden_states else None v_hs = tf.convert_to_tensor(output.vision_hidden_states) if self.config.output_hidden_states else None l_attns = tf.convert_to_tensor(output.language_attentions) if self.config.output_attentions else None v_attns = tf.convert_to_tensor(output.vision_attentions) if self.config.output_attentions else None c_enc_attns = tf.convert_to_tensor(output.cross_encoder_attentions) if self.config.output_attentions else None return TFLxmertModelOutput( pooled_output=output.pooled_output, language_output=output.language_output, vision_output=output.vision_output, language_hidden_states=l_hs, vision_hidden_states=v_hs, language_attentions=l_attns, vision_attentions=v_attns, cross_encoder_attentions=c_enc_attns, ) class TFLxmertPooler(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) def call(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) return pooled_output # Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->Lxmert class TFLxmertPredictionHeadTransform(tf.keras.layers.Layer): def __init__(self, config: LxmertConfig, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense", ) if isinstance(config.hidden_act, str): self.transform_act_fn = get_tf_activation(config.hidden_act) else: self.transform_act_fn = config.hidden_act self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(inputs=hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->Lxmert class TFLxmertLMPredictionHead(tf.keras.layers.Layer): def __init__(self, config: LxmertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs): super().__init__(**kwargs) self.vocab_size = config.vocab_size self.hidden_size = config.hidden_size self.transform = TFLxmertPredictionHeadTransform(config, name="transform") # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.input_embeddings = input_embeddings def build(self, input_shape: tf.TensorShape): self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias") super().build(input_shape) def get_output_embeddings(self) -> tf.keras.layers.Layer: return self.input_embeddings def set_output_embeddings(self, value: tf.Variable): self.input_embeddings.weight = value self.input_embeddings.vocab_size = shape_list(value)[0] def get_bias(self) -> Dict[str, tf.Variable]: return {"bias": self.bias} def set_bias(self, value: tf.Variable): self.bias = value["bias"] self.vocab_size = shape_list(value["bias"])[0] def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.transform(hidden_states=hidden_states) seq_length = shape_list(hidden_states)[1] hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size]) hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True) hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size]) hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) return hidden_states # Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->Lxmert class TFLxmertMLMHead(tf.keras.layers.Layer): def __init__(self, config: LxmertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs): super().__init__(**kwargs) self.predictions = TFLxmertLMPredictionHead(config, input_embeddings, name="predictions") def call(self, sequence_output: tf.Tensor) -> tf.Tensor: prediction_scores = self.predictions(hidden_states=sequence_output) return prediction_scores class TFLxmertPreTrainingHeads(tf.keras.layers.Layer): def __init__(self, config, input_embeddings, **kwargs): super().__init__(**kwargs) self.predictions = TFLxmertLMPredictionHead(config, input_embeddings, name="predictions") self.seq_relationship = tf.keras.layers.Dense( 2, kernel_initializer=get_initializer(config.initializer_range), name="seq_relationship", ) def call(self, sequence_output, pooled_output): prediction_scores = self.predictions(sequence_output) seq_relationship_score = self.seq_relationship(pooled_output) return prediction_scores, seq_relationship_score class TFLxmertVisualAnswerHead(tf.keras.layers.Layer): def __init__(self, config, num_labels, **kwargs): super().__init__(**kwargs) hid_dim = config.hidden_size self.dense = tf.keras.layers.Dense( hid_dim * 2, kernel_initializer=get_initializer(config.initializer_range), name="logit_fc_._0", ) self.activation = get_tf_activation("gelu") self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="logit_fc_._2") self.dense_1 = tf.keras.layers.Dense( num_labels, kernel_initializer=get_initializer(config.initializer_range), name="logit_fc_._3", ) def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) hidden_states = self.layer_norm(hidden_states) hidden_states = self.dense_1(hidden_states) return hidden_states class TFLxmertVisualObjHead(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.transform = TFLxmertPredictionHeadTransform(config, name="transform") # Decide the use of visual losses visual_losses = {} if config.visual_obj_loss: visual_losses["obj"] = {"shape": (-1,), "num": config.num_object_labels} if config.visual_attr_loss: visual_losses["attr"] = {"shape": (-1,), "num": config.num_attr_labels} if config.visual_obj_loss: visual_losses["feat"] = {"shape": (-1, 2048), "num": config.visual_feat_dim} self.visual_losses = visual_losses # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder_dict = { key: tf.keras.layers.Dense( self.visual_losses[key]["num"], kernel_initializer=get_initializer(config.initializer_range), name=f"decoder_dict.{key}", ) for key in self.visual_losses } def call(self, hidden_states): hidden_states = self.transform(hidden_states) output = {} for key in self.visual_losses: output[key] = self.decoder_dict[key](hidden_states) return output @add_start_docstrings("""Lxmert Model with a `language modeling` head on top.""", LXMERT_START_DOCSTRING) class TFLxmertForPreTraining(TFLxmertPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.config = config self.num_qa_labels = config.num_qa_labels self.visual_loss_normalizer = config.visual_loss_normalizer # Use of pretraining tasks self.task_mask_lm = config.task_mask_lm self.task_obj_predict = config.task_obj_predict self.task_matched = config.task_matched self.task_qa = config.task_qa # Lxmert backbone self.lxmert = TFLxmertMainLayer(config, name="lxmert") # Pre-training heads self.cls = TFLxmertPreTrainingHeads(config, self.lxmert.embeddings, name="cls") if self.task_obj_predict: self.obj_predict_head = TFLxmertVisualObjHead(config, name="obj_predict_head") if self.task_qa: self.answer_head = TFLxmertVisualAnswerHead(config, self.num_qa_labels, name="answer_head") # Loss functions self.loss_fcts = { "l2": tf.keras.losses.Huber(delta=1.0, name="huber_loss"), "visn_ce": tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), "ce": tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), } visual_losses = {} if config.visual_obj_loss: visual_losses["obj"] = { "shape": (-1,), "num": config.num_object_labels, "loss": "visn_ce", } if config.visual_attr_loss: visual_losses["attr"] = { "shape": (-1,), "num": config.num_attr_labels, "loss": "visn_ce", } if config.visual_obj_loss: visual_losses["feat"] = { "shape": (-1, config.visual_feat_dim), "num": config.visual_feat_dim, "loss": "l2", } self.visual_losses = visual_losses @property def dummy_inputs(self): """ Dummy inputs to build the network. Returns: tf.Tensor with dummy inputs """ batch_size = 2 num_visual_features = 10 input_ids = tf.constant([[3, 5, 6], [2, 3, 4]]) visual_feats = tf.random.uniform((batch_size, num_visual_features, self.config.visual_feat_dim)) visual_pos = tf.random.uniform((batch_size, num_visual_features, 4)) if self.config.task_obj_predict: obj_labels = {} if self.config.visual_attr_loss and self.config.task_obj_predict: obj_labels["attr"] = ( tf.ones([batch_size, num_visual_features]), tf.ones([batch_size, num_visual_features]), ) if self.config.visual_feat_loss and self.config.task_obj_predict: obj_labels["feat"] = ( tf.ones([batch_size, num_visual_features, self.config.visual_feat_dim]), tf.ones([batch_size, num_visual_features]), ) if self.config.visual_obj_loss and self.config.task_obj_predict: obj_labels["obj"] = ( tf.ones([batch_size, num_visual_features]), tf.ones([batch_size, num_visual_features]), ) return { **{ "input_ids": input_ids, "visual_feats": visual_feats, "visual_pos": visual_pos, }, **({"obj_labels": obj_labels} if self.config.task_obj_predict else {}), } def get_lm_head(self): return self.cls.predictions def get_prefix_bias_name(self): warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.cls.name + "/" + self.cls.predictions.name @unpack_inputs @add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFLxmertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids=None, visual_feats=None, visual_pos=None, attention_mask=None, visual_attention_mask=None, token_type_ids=None, inputs_embeds=None, masked_lm_labels=None, obj_labels=None, matched_label=None, ans=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, ): r""" masked_lm_labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` obj_labels: (`Dict[Str: Tuple[tf.Tensor, tf.Tensor]]`, *optional*, defaults to `None`): each key is named after each one of the visual losses and each element of the tuple is of the shape `(batch_size, num_features)` and `(batch_size, num_features, visual_feature_dim)` for each the label id and the label score respectively matched_label (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the whether or not the text input matches the image (classification) loss. Input should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`: - 0 indicates that the sentence does not match the image, - 1 indicates that the sentence does match the image. ans (`Torch.Tensor` of shape `(batch_size)`, *optional*, defaults to `None`): a one hot representation hof the correct answer *optional* Returns: """ lxmert_output = self.lxmert( input_ids, visual_feats, visual_pos, attention_mask, visual_attention_mask, token_type_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict, training, ) lang_output, visual_output, pooled_output = ( lxmert_output[0], lxmert_output[1], lxmert_output[2], ) lang_prediction_scores, cross_relationship_score = self.cls(lang_output, pooled_output) if self.task_qa: answer_score = self.answer_head(pooled_output) else: answer_score = pooled_output[0][0] total_loss = ( None if (masked_lm_labels is None and matched_label is None and obj_labels is None and ans is None) else tf.constant(0.0) ) losses = () if masked_lm_labels is not None and self.task_mask_lm: masked_lm_loss = self.loss_fcts["ce"]( tf.reshape(masked_lm_labels, [-1]), tf.reshape(lang_prediction_scores, [-1, self.config.vocab_size]), ) total_loss += masked_lm_loss losses += (masked_lm_loss,) if matched_label is not None and self.task_matched: matched_loss = self.loss_fcts["ce"]( tf.reshape(matched_label, [-1]), tf.reshape(cross_relationship_score, [-1, 2]), ) total_loss += matched_loss losses += (matched_loss,) if obj_labels is not None and self.task_obj_predict: total_visn_loss = 0.0 visn_prediction_scores_dict = self.obj_predict_head(visual_output) for key, key_info in self.visual_losses.items(): label, mask_conf = obj_labels[key] output_dim = key_info["num"] loss_fct_name = key_info["loss"] label_shape = key_info["shape"] weight = self.visual_loss_normalizer visn_loss_fct = self.loss_fcts[loss_fct_name] visn_prediction_scores = visn_prediction_scores_dict[key] visn_loss = visn_loss_fct( tf.reshape(label, label_shape), tf.reshape(visn_prediction_scores, [-1, output_dim]), ) if visn_loss.ndim > 1: # Regression Losses visn_loss = tf.reduce_mean(visn_loss) visn_loss = tf.reduce_mean(visn_loss * tf.cast(tf.reshape(mask_conf, [-1]), visn_loss.dtype)) * weight total_visn_loss += visn_loss losses += (visn_loss,) total_loss += total_visn_loss if ans is not None and self.task_qa: answer_loss = self.loss_fcts["ce"]( tf.reshape(ans, [-1]), tf.reshape(answer_score, [-1, self.num_qa_labels]) ) # exclude "*2" here to match the effect of QA losses. # Previous: (loss *0) for 6 epochs, (loss *2) for 6 epochs. (Used 10 instead of 6 in EMNLP paper) # Now : (loss *1) for 12 epochs # # * 2 # Multiply by 2 because > half of the data will not have label total_loss += answer_loss losses += (answer_loss,) # return total_loss, tf.stack(losses)[tf.new_axis, ...], answer_score.detach() if not return_dict: output = ( lang_prediction_scores, cross_relationship_score, answer_score, ) + lxmert_output[3:] return ((total_loss,) + output) if total_loss is not None else output return TFLxmertForPreTrainingOutput( loss=total_loss, prediction_logits=lang_prediction_scores, cross_relationship_score=cross_relationship_score, question_answering_score=answer_score, language_hidden_states=lxmert_output.language_hidden_states, vision_hidden_states=lxmert_output.vision_hidden_states, language_attentions=lxmert_output.language_attentions, vision_attentions=lxmert_output.vision_attentions, cross_encoder_attentions=lxmert_output.cross_encoder_attentions, ) def serving_output(self, output): l_hs = tf.convert_to_tensor(output.language_hidden_states) if self.config.output_hidden_states else None v_hs = tf.convert_to_tensor(output.vision_hidden_states) if self.config.output_hidden_states else None l_attns = tf.convert_to_tensor(output.language_attentions) if self.config.output_attentions else None v_attns = tf.convert_to_tensor(output.vision_attentions) if self.config.output_attentions else None c_enc_attns = tf.convert_to_tensor(output.cross_encoder_attentions) if self.config.output_attentions else None return TFLxmertForPreTrainingOutput( prediction_logits=output.prediction_logits, cross_relationship_score=output.cross_relationship_score, question_answering_score=output.question_answering_score, language_hidden_states=l_hs, vision_hidden_states=v_hs, language_attentions=l_attns, vision_attentions=v_attns, cross_encoder_attentions=c_enc_attns, )

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./.git/objects/79/acf681c101fd99d7aa2df820daed9a241e2335

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huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/models/deberta_v2/test_tokenization_deberta_v2.py

# coding=utf-8 # Copyright 2019 Hugging Face inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import DebertaV2Tokenizer, DebertaV2TokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/spiece.model") @require_sentencepiece @require_tokenizers class DebertaV2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = DebertaV2Tokenizer rust_tokenizer_class = DebertaV2TokenizerFast test_sentencepiece = True test_sentencepiece_ignore_case = True def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, unk_token="<unk>") tokenizer.save_pretrained(self.tmpdirname) def get_input_output_texts(self, tokenizer): input_text = "this is a test" output_text = "this is a test" return input_text, output_text def test_convert_token_and_id(self): """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" token = "<pad>" token_id = 0 self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id) self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token) def test_get_vocab(self): vocab_keys = list(self.get_tokenizer().get_vocab().keys()) self.assertEqual(vocab_keys[0], "<pad>") self.assertEqual(vocab_keys[1], "<unk>") self.assertEqual(vocab_keys[-1], "[PAD]") self.assertEqual(len(vocab_keys), 30_001) def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 30_000) def test_do_lower_case(self): # fmt: off sequence = " \tHeLLo!how \n Are yoU? " tokens_target = ["▁hello", "!", "how", "▁are", "▁you", "?"] # fmt: on tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, do_lower_case=True) tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, tokens_target) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, do_lower_case=True) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(rust_tokens, tokens_target) @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.") def test_sentencepiece_tokenize_and_convert_tokens_to_string(self): pass @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.") def test_sentencepiece_tokenize_and_decode(self): pass def test_split_by_punct(self): # fmt: off sequence = "I was born in 92000, and this is falsé." tokens_target = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ] # fmt: on tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, split_by_punct=True) tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, tokens_target) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, split_by_punct=True) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(rust_tokens, tokens_target) def test_do_lower_case_split_by_punct(self): # fmt: off sequence = "I was born in 92000, and this is falsé." tokens_target = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ] # fmt: on tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, do_lower_case=True, split_by_punct=True) tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, tokens_target) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, do_lower_case=True, split_by_punct=True) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(rust_tokens, tokens_target) def test_do_lower_case_split_by_punct_false(self): # fmt: off sequence = "I was born in 92000, and this is falsé." tokens_target = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ] # fmt: on tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, do_lower_case=True, split_by_punct=False) tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, tokens_target) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, do_lower_case=True, split_by_punct=False) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(rust_tokens, tokens_target) def test_do_lower_case_false_split_by_punct(self): # fmt: off sequence = "I was born in 92000, and this is falsé." tokens_target = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ] # fmt: on tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, do_lower_case=False, split_by_punct=True) tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, tokens_target) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, do_lower_case=False, split_by_punct=True) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(rust_tokens, tokens_target) def test_do_lower_case_false_split_by_punct_false(self): # fmt: off sequence = " \tHeLLo!how \n Are yoU? " tokens_target = ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"] # fmt: on tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, do_lower_case=False, split_by_punct=False) tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, tokens_target) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, do_lower_case=False, split_by_punct=False) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(rust_tokens, tokens_target) def test_rust_and_python_full_tokenizers(self): tokenizer = self.get_tokenizer() rust_tokenizer = self.get_rust_tokenizer() sequence = "I was born in 92000, and this is falsé." tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, rust_tokens) ids = tokenizer.encode(sequence, add_special_tokens=False) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, rust_ids) rust_tokenizer = self.get_rust_tokenizer() ids = tokenizer.encode(sequence) rust_ids = rust_tokenizer.encode(sequence) self.assertListEqual(ids, rust_ids) def test_full_tokenizer(self): sequence = "This is a test" ids_target = [13, 1, 4398, 25, 21, 1289] tokens_target = ["▁", "T", "his", "▁is", "▁a", "▁test"] back_tokens_target = ["▁", "<unk>", "his", "▁is", "▁a", "▁test"] tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, keep_accents=True) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, keep_accents=True) ids = tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, ids_target) tokens = tokenizer.tokenize(sequence) self.assertListEqual(tokens, tokens_target) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual(back_tokens, back_tokens_target) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(rust_ids, ids_target) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(rust_tokens, tokens_target) rust_back_tokens = rust_tokenizer.convert_ids_to_tokens(rust_ids) self.assertListEqual(rust_back_tokens, back_tokens_target) # fmt: off sequence = "I was born in 92000, and this is falsé." ids_target = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9] tokens_target = ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ] back_tokens_target = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ] # fmt: on ids = tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, ids_target) tokens = tokenizer.tokenize(sequence) self.assertListEqual(tokens, tokens_target) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual(back_tokens, back_tokens_target) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(rust_ids, ids_target) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(rust_tokens, tokens_target) rust_back_tokens = rust_tokenizer.convert_ids_to_tokens(rust_ids) self.assertListEqual(rust_back_tokens, back_tokens_target) def test_sequence_builders(self): tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB) text = tokenizer.encode("sequence builders") text_2 = tokenizer.encode("multi-sequence build") encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id], encoded_sentence) self.assertEqual( [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [tokenizer.sep_token_id], encoded_pair, ) @slow def test_tokenizer_integration(self): # fmt: off expected_encoding = {'input_ids': [[1, 39867, 36, 19390, 486, 27, 35052, 81436, 18, 60685, 1225, 7, 35052, 81436, 18, 9367, 16899, 18, 15937, 53, 594, 773, 18, 16287, 30465, 36, 15937, 6, 41139, 38, 36979, 60763, 191, 6, 34132, 99, 6, 50538, 390, 43230, 6, 34132, 2779, 20850, 14, 699, 1072, 1194, 36, 382, 10901, 53, 7, 699, 1072, 2084, 36, 20422, 630, 53, 19, 105, 3049, 1896, 1053, 16899, 1506, 11, 37978, 4243, 7, 1237, 31869, 200, 16566, 654, 6, 35052, 81436, 7, 55630, 13593, 4, 2], [1, 26, 15011, 13, 667, 8, 1053, 18, 23611, 1237, 72356, 12820, 34, 104134, 1209, 35, 13313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 15785, 14951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="microsoft/deberta-v2-xlarge", revision="ad6e42c1532ddf3a15c39246b63f5559d558b670", )

# coding=utf-8 # Copyright 2019 Hugging Face inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import DebertaV2Tokenizer, DebertaV2TokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/spiece.model") @require_sentencepiece @require_tokenizers class DebertaV2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = DebertaV2Tokenizer rust_tokenizer_class = DebertaV2TokenizerFast test_sentencepiece = True test_sentencepiece_ignore_case = True def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, unk_token="<unk>") tokenizer.save_pretrained(self.tmpdirname) def get_input_output_texts(self, tokenizer): input_text = "this is a test" output_text = "this is a test" return input_text, output_text def test_convert_token_and_id(self): """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" token = "<pad>" token_id = 0 self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id) self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token) def test_get_vocab(self): vocab_keys = list(self.get_tokenizer().get_vocab().keys()) self.assertEqual(vocab_keys[0], "<pad>") self.assertEqual(vocab_keys[1], "<unk>") self.assertEqual(vocab_keys[-1], "[PAD]") self.assertEqual(len(vocab_keys), 30_001) def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 30_000) def test_do_lower_case(self): # fmt: off sequence = " \tHeLLo!how \n Are yoU? " tokens_target = ["▁hello", "!", "how", "▁are", "▁you", "?"] # fmt: on tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, do_lower_case=True) tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, tokens_target) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, do_lower_case=True) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(rust_tokens, tokens_target) @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.") def test_sentencepiece_tokenize_and_convert_tokens_to_string(self): pass @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.") def test_sentencepiece_tokenize_and_decode(self): pass def test_split_by_punct(self): # fmt: off sequence = "I was born in 92000, and this is falsé." tokens_target = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ] # fmt: on tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, split_by_punct=True) tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, tokens_target) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, split_by_punct=True) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(rust_tokens, tokens_target) def test_do_lower_case_split_by_punct(self): # fmt: off sequence = "I was born in 92000, and this is falsé." tokens_target = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ] # fmt: on tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, do_lower_case=True, split_by_punct=True) tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, tokens_target) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, do_lower_case=True, split_by_punct=True) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(rust_tokens, tokens_target) def test_do_lower_case_split_by_punct_false(self): # fmt: off sequence = "I was born in 92000, and this is falsé." tokens_target = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ] # fmt: on tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, do_lower_case=True, split_by_punct=False) tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, tokens_target) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, do_lower_case=True, split_by_punct=False) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(rust_tokens, tokens_target) def test_do_lower_case_false_split_by_punct(self): # fmt: off sequence = "I was born in 92000, and this is falsé." tokens_target = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ] # fmt: on tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, do_lower_case=False, split_by_punct=True) tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, tokens_target) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, do_lower_case=False, split_by_punct=True) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(rust_tokens, tokens_target) def test_do_lower_case_false_split_by_punct_false(self): # fmt: off sequence = " \tHeLLo!how \n Are yoU? " tokens_target = ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"] # fmt: on tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, do_lower_case=False, split_by_punct=False) tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, tokens_target) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, do_lower_case=False, split_by_punct=False) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(rust_tokens, tokens_target) def test_rust_and_python_full_tokenizers(self): tokenizer = self.get_tokenizer() rust_tokenizer = self.get_rust_tokenizer() sequence = "I was born in 92000, and this is falsé." tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False)) rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False)) self.assertListEqual(tokens, rust_tokens) ids = tokenizer.encode(sequence, add_special_tokens=False) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, rust_ids) rust_tokenizer = self.get_rust_tokenizer() ids = tokenizer.encode(sequence) rust_ids = rust_tokenizer.encode(sequence) self.assertListEqual(ids, rust_ids) def test_full_tokenizer(self): sequence = "This is a test" ids_target = [13, 1, 4398, 25, 21, 1289] tokens_target = ["▁", "T", "his", "▁is", "▁a", "▁test"] back_tokens_target = ["▁", "<unk>", "his", "▁is", "▁a", "▁test"] tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, keep_accents=True) rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, keep_accents=True) ids = tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, ids_target) tokens = tokenizer.tokenize(sequence) self.assertListEqual(tokens, tokens_target) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual(back_tokens, back_tokens_target) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(rust_ids, ids_target) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(rust_tokens, tokens_target) rust_back_tokens = rust_tokenizer.convert_ids_to_tokens(rust_ids) self.assertListEqual(rust_back_tokens, back_tokens_target) # fmt: off sequence = "I was born in 92000, and this is falsé." ids_target = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9] tokens_target = ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ] back_tokens_target = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ] # fmt: on ids = tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, ids_target) tokens = tokenizer.tokenize(sequence) self.assertListEqual(tokens, tokens_target) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual(back_tokens, back_tokens_target) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(rust_ids, ids_target) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(rust_tokens, tokens_target) rust_back_tokens = rust_tokenizer.convert_ids_to_tokens(rust_ids) self.assertListEqual(rust_back_tokens, back_tokens_target) def test_sequence_builders(self): tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB) text = tokenizer.encode("sequence builders") text_2 = tokenizer.encode("multi-sequence build") encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id], encoded_sentence) self.assertEqual( [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [tokenizer.sep_token_id], encoded_pair, ) @slow def test_tokenizer_integration(self): # fmt: off expected_encoding = {'input_ids': [[1, 39867, 36, 19390, 486, 27, 35052, 81436, 18, 60685, 1225, 7, 35052, 81436, 18, 9367, 16899, 18, 15937, 53, 594, 773, 18, 16287, 30465, 36, 15937, 6, 41139, 38, 36979, 60763, 191, 6, 34132, 99, 6, 50538, 390, 43230, 6, 34132, 2779, 20850, 14, 699, 1072, 1194, 36, 382, 10901, 53, 7, 699, 1072, 2084, 36, 20422, 630, 53, 19, 105, 3049, 1896, 1053, 16899, 1506, 11, 37978, 4243, 7, 1237, 31869, 200, 16566, 654, 6, 35052, 81436, 7, 55630, 13593, 4, 2], [1, 26, 15011, 13, 667, 8, 1053, 18, 23611, 1237, 72356, 12820, 34, 104134, 1209, 35, 13313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 15785, 14951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="microsoft/deberta-v2-xlarge", revision="ad6e42c1532ddf3a15c39246b63f5559d558b670", )

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./src/transformers/models/squeezebert/tokenization_squeezebert_fast.py

# coding=utf-8 # Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for SqueezeBERT.""" import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_squeezebert import SqueezeBertTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "squeezebert/squeezebert-uncased": ( "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt" ), "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt", "squeezebert/squeezebert-mnli-headless": ( "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt" ), }, "tokenizer_file": { "squeezebert/squeezebert-uncased": ( "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json" ), "squeezebert/squeezebert-mnli": ( "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json" ), "squeezebert/squeezebert-mnli-headless": ( "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json" ), }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "squeezebert/squeezebert-uncased": 512, "squeezebert/squeezebert-mnli": 512, "squeezebert/squeezebert-mnli-headless": 512, } PRETRAINED_INIT_CONFIGURATION = { "squeezebert/squeezebert-uncased": {"do_lower_case": True}, "squeezebert/squeezebert-mnli": {"do_lower_case": True}, "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True}, } # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast with Bert->SqueezeBert,BERT->SqueezeBERT class SqueezeBertTokenizerFast(PreTrainedTokenizerFast): r""" Construct a "fast" SqueezeBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece. This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): File containing the vocabulary. do_lower_case (`bool`, *optional*, defaults to `True`): Whether or not to lowercase the input when tokenizing. unk_token (`str`, *optional*, defaults to `"[UNK]"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. sep_token (`str`, *optional*, defaults to `"[SEP]"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. pad_token (`str`, *optional*, defaults to `"[PAD]"`): The token used for padding, for example when batching sequences of different lengths. cls_token (`str`, *optional*, defaults to `"[CLS]"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. mask_token (`str`, *optional*, defaults to `"[MASK]"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. clean_text (`bool`, *optional*, defaults to `True`): Whether or not to clean the text before tokenization by removing any control characters and replacing all whitespaces by the classic one. tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this issue](https://github.com/huggingface/transformers/issues/328)). strip_accents (`bool`, *optional*): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original SqueezeBERT). wordpieces_prefix (`str`, *optional*, defaults to `"##"`): The prefix for subwords. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES slow_tokenizer_class = SqueezeBertTokenizer def __init__( self, vocab_file=None, tokenizer_file=None, do_lower_case=True, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", tokenize_chinese_chars=True, strip_accents=None, **kwargs ): super().__init__( vocab_file, tokenizer_file=tokenizer_file, do_lower_case=do_lower_case, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, **kwargs, ) normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__()) if ( normalizer_state.get("lowercase", do_lower_case) != do_lower_case or normalizer_state.get("strip_accents", strip_accents) != strip_accents or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars ): normalizer_class = getattr(normalizers, normalizer_state.pop("type")) normalizer_state["lowercase"] = do_lower_case normalizer_state["strip_accents"] = strip_accents normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state) self.do_lower_case = do_lower_case def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A SqueezeBERT sequence has the following format: - single sequence: `[CLS] X [SEP]` - pair of sequences: `[CLS] A [SEP] B [SEP]` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id] if token_ids_1: output += token_ids_1 + [self.sep_token_id] return output def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A SqueezeBERT sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files)

# coding=utf-8 # Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for SqueezeBERT.""" import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_squeezebert import SqueezeBertTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "squeezebert/squeezebert-uncased": ( "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt" ), "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt", "squeezebert/squeezebert-mnli-headless": ( "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt" ), }, "tokenizer_file": { "squeezebert/squeezebert-uncased": ( "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json" ), "squeezebert/squeezebert-mnli": ( "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json" ), "squeezebert/squeezebert-mnli-headless": ( "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json" ), }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "squeezebert/squeezebert-uncased": 512, "squeezebert/squeezebert-mnli": 512, "squeezebert/squeezebert-mnli-headless": 512, } PRETRAINED_INIT_CONFIGURATION = { "squeezebert/squeezebert-uncased": {"do_lower_case": True}, "squeezebert/squeezebert-mnli": {"do_lower_case": True}, "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True}, } # Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast with Bert->SqueezeBert,BERT->SqueezeBERT class SqueezeBertTokenizerFast(PreTrainedTokenizerFast): r""" Construct a "fast" SqueezeBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece. This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): File containing the vocabulary. do_lower_case (`bool`, *optional*, defaults to `True`): Whether or not to lowercase the input when tokenizing. unk_token (`str`, *optional*, defaults to `"[UNK]"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. sep_token (`str`, *optional*, defaults to `"[SEP]"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. pad_token (`str`, *optional*, defaults to `"[PAD]"`): The token used for padding, for example when batching sequences of different lengths. cls_token (`str`, *optional*, defaults to `"[CLS]"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. mask_token (`str`, *optional*, defaults to `"[MASK]"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. clean_text (`bool`, *optional*, defaults to `True`): Whether or not to clean the text before tokenization by removing any control characters and replacing all whitespaces by the classic one. tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this issue](https://github.com/huggingface/transformers/issues/328)). strip_accents (`bool`, *optional*): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original SqueezeBERT). wordpieces_prefix (`str`, *optional*, defaults to `"##"`): The prefix for subwords. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES slow_tokenizer_class = SqueezeBertTokenizer def __init__( self, vocab_file=None, tokenizer_file=None, do_lower_case=True, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", tokenize_chinese_chars=True, strip_accents=None, **kwargs ): super().__init__( vocab_file, tokenizer_file=tokenizer_file, do_lower_case=do_lower_case, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, **kwargs, ) normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__()) if ( normalizer_state.get("lowercase", do_lower_case) != do_lower_case or normalizer_state.get("strip_accents", strip_accents) != strip_accents or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars ): normalizer_class = getattr(normalizers, normalizer_state.pop("type")) normalizer_state["lowercase"] = do_lower_case normalizer_state["strip_accents"] = strip_accents normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state) self.do_lower_case = do_lower_case def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A SqueezeBERT sequence has the following format: - single sequence: `[CLS] X [SEP]` - pair of sequences: `[CLS] A [SEP] B [SEP]` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id] if token_ids_1: output += token_ids_1 + [self.sep_token_id] return output def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A SqueezeBERT sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files)

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./docs/source/de/_toctree.yml

- sections: - local: index title: 🤗 Transformers - local: quicktour title: Schnellstart - local: installation title: Installation title: Erste Schritte - sections: - local: pipeline_tutorial title: Pipelines für Inferenzen - local: autoclass_tutorial title: Laden von vortrainierten Instanzen mit einer AutoClass - local: preprocessing title: Vorverarbeiten - local: training title: Optimierung eines vortrainierten Modells - local: accelerate title: Verteiltes Training mit 🤗 Accelerate - local: model_sharing title: Ein Modell teilen title: Tutorials

- sections: - local: index title: 🤗 Transformers - local: quicktour title: Schnellstart - local: installation title: Installation title: Erste Schritte - sections: - local: pipeline_tutorial title: Pipelines für Inferenzen - local: autoclass_tutorial title: Laden von vortrainierten Instanzen mit einer AutoClass - local: preprocessing title: Vorverarbeiten - local: training title: Optimierung eines vortrainierten Modells - local: accelerate title: Verteiltes Training mit 🤗 Accelerate - local: model_sharing title: Ein Modell teilen title: Tutorials

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./src/transformers/models/glpn/feature_extraction_glpn.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Feature extractor class for GLPN.""" from ...utils import logging from .image_processing_glpn import GLPNImageProcessor logger = logging.get_logger(__name__) # Feature extractor for GLPN is being replaced by image processor GLPNFeatureExtractor = GLPNImageProcessor

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Feature extractor class for GLPN.""" from ...utils import logging from .image_processing_glpn import GLPNImageProcessor logger = logging.get_logger(__name__) # Feature extractor for GLPN is being replaced by image processor GLPNFeatureExtractor = GLPNImageProcessor

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./.git/objects/42/4caa4447aac94fb44979a1fa0748a2d67a9106

x
+)JMU057e040031Q,+dXD[,VWj{V2,)517>9?(13|SUd+X.TU/u@V^X
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x
+)JMU057e040031Q,+dXD[,VWj{V2,)517>9?(13|SUd+X.TU/u@V^X
RWܦ'f{wGD\Terb^JfJbIj|zj^jQbI~HASewveϸӑ^ T_Zvĩ?NztZJK`K۲Pi99%Eɩ`1;C,~B OF䑥:F,RSɴox4>BLSGl{o6u{؁U;yGpAd'ee&|*Quw懏1+gvnk#\}0S֜VfݾT]I>sjˏ~}^gd,́~3٥Оjd݋s&ngl5:w[.gvIsꔜX

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./.github/workflows/self-scheduled.yml

name: Self-hosted runner (scheduled) # Note that each job's dependencies go into a corresponding docker file. # # For example for `run_all_tests_torch_cuda_extensions_gpu` the docker image is # `huggingface/transformers-pytorch-deepspeed-latest-gpu`, which can be found at # `docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile` on: repository_dispatch: schedule: - cron: "0 2 * * *" env: HF_HOME: /mnt/cache TRANSFORMERS_IS_CI: yes OMP_NUM_THREADS: 8 MKL_NUM_THREADS: 8 RUN_SLOW: yes SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} TF_FORCE_GPU_ALLOW_GROWTH: true RUN_PT_TF_CROSS_TESTS: 1 jobs: check_runner_status: name: Check Runner Status runs-on: ubuntu-latest steps: - name: Checkout transformers uses: actions/checkout@v2 with: fetch-depth: 2 - name: Check Runner Status run: python utils/check_self_hosted_runner.py --target_runners single-gpu-scheduled-ci-runner-docker,multi-gpu-scheduled-ci-runner-docker --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }} check_runners: name: Check Runners needs: check_runner_status strategy: matrix: machine_type: [single-gpu, multi-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} container: image: huggingface/transformers-all-latest-gpu options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ steps: - name: NVIDIA-SMI run: | nvidia-smi setup: name: Setup needs: check_runners strategy: matrix: machine_type: [single-gpu, multi-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} container: image: huggingface/transformers-all-latest-gpu options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ outputs: matrix: ${{ steps.set-matrix.outputs.matrix }} steps: - name: Update clone working-directory: /transformers run: | git fetch && git checkout ${{ github.sha }} - name: Cleanup working-directory: /transformers run: | rm -rf tests/__pycache__ rm -rf tests/models/__pycache__ rm -rf reports - name: Show installed libraries and their versions working-directory: /transformers run: pip freeze - id: set-matrix name: Identify models to test working-directory: /transformers/tests run: | echo "::set-output name=matrix::$(python3 -c 'import os; tests = os.getcwd(); model_tests = os.listdir(os.path.join(tests, "models")); d1 = sorted(list(filter(os.path.isdir, os.listdir(tests)))); d2 = sorted(list(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))); d1.remove("models"); d = d2 + d1; print(d)')" - name: NVIDIA-SMI run: | nvidia-smi run_tests_single_gpu: name: Model tests strategy: fail-fast: false matrix: folders: ${{ fromJson(needs.setup.outputs.matrix) }} machine_type: [single-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} container: image: huggingface/transformers-all-latest-gpu options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ needs: setup steps: - name: Echo folder ${{ matrix.folders }} shell: bash # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to # set the artifact folder names (because the character `/` is not allowed). run: | echo "${{ matrix.folders }}" matrix_folders=${{ matrix.folders }} matrix_folders=${matrix_folders/'models/'/'models_'} echo "$matrix_folders" echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV - name: Update clone working-directory: /transformers run: git fetch && git checkout ${{ github.sha }} - name: NVIDIA-SMI run: | nvidia-smi - name: Environment working-directory: /transformers run: | python3 utils/print_env.py - name: Show installed libraries and their versions working-directory: /transformers run: pip freeze - name: Run all tests on GPU working-directory: /transformers run: python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} tests/${{ matrix.folders }} - name: Failure short reports if: ${{ failure() }} continue-on-error: true run: cat /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt - name: Test suite reports artifacts if: ${{ always() }} uses: actions/upload-artifact@v2 with: name: ${{ matrix.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports path: /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} run_tests_multi_gpu: name: Model tests strategy: fail-fast: false matrix: folders: ${{ fromJson(needs.setup.outputs.matrix) }} machine_type: [multi-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} container: image: huggingface/transformers-all-latest-gpu options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ needs: setup steps: - name: Echo folder ${{ matrix.folders }} shell: bash # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to # set the artifact folder names (because the character `/` is not allowed). run: | echo "${{ matrix.folders }}" matrix_folders=${{ matrix.folders }} matrix_folders=${matrix_folders/'models/'/'models_'} echo "$matrix_folders" echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV - name: Update clone working-directory: /transformers run: git fetch && git checkout ${{ github.sha }} - name: NVIDIA-SMI run: | nvidia-smi - name: Environment working-directory: /transformers run: | python3 utils/print_env.py - name: Show installed libraries and their versions working-directory: /transformers run: pip freeze - name: Run all tests on GPU working-directory: /transformers run: python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} tests/${{ matrix.folders }} - name: Failure short reports if: ${{ failure() }} continue-on-error: true run: cat /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt - name: Test suite reports artifacts if: ${{ always() }} uses: actions/upload-artifact@v2 with: name: ${{ matrix.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports path: /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} run_examples_gpu: name: Examples directory runs-on: [self-hosted, single-gpu-docker] container: image: huggingface/transformers-all-latest-gpu options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ needs: setup steps: - name: Update clone working-directory: /transformers run: git fetch && git checkout ${{ github.sha }} - name: NVIDIA-SMI run: | nvidia-smi - name: Environment working-directory: /transformers run: | python3 utils/print_env.py - name: Show installed libraries and their versions working-directory: /transformers run: pip freeze - name: Run examples tests on GPU working-directory: /transformers run: | pip install -r examples/pytorch/_tests_requirements.txt python3 -m pytest -v --make-reports=single-gpu_examples_gpu examples/pytorch - name: Failure short reports if: ${{ failure() }} continue-on-error: true run: cat /transformers/reports/single-gpu_examples_gpu/failures_short.txt - name: Test suite reports artifacts if: ${{ always() }} uses: actions/upload-artifact@v2 with: name: single-gpu_run_examples_gpu path: /transformers/reports/single-gpu_examples_gpu run_pipelines_torch_gpu: name: PyTorch pipelines strategy: fail-fast: false matrix: machine_type: [single-gpu, multi-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} container: image: huggingface/transformers-pytorch-gpu options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ needs: setup steps: - name: Update clone working-directory: /transformers run: git fetch && git checkout ${{ github.sha }} - name: NVIDIA-SMI run: | nvidia-smi - name: Environment working-directory: /transformers run: | python3 utils/print_env.py - name: Show installed libraries and their versions working-directory: /transformers run: pip freeze - name: Run all pipeline tests on GPU working-directory: /transformers run: | python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ matrix.machine_type }}_tests_torch_pipeline_gpu tests/pipelines - name: Failure short reports if: ${{ failure() }} continue-on-error: true run: cat /transformers/reports/${{ matrix.machine_type }}_tests_torch_pipeline_gpu/failures_short.txt - name: Test suite reports artifacts if: ${{ always() }} uses: actions/upload-artifact@v2 with: name: ${{ matrix.machine_type }}_run_tests_torch_pipeline_gpu path: /transformers/reports/${{ matrix.machine_type }}_tests_torch_pipeline_gpu run_pipelines_tf_gpu: name: TensorFlow pipelines strategy: fail-fast: false matrix: machine_type: [single-gpu, multi-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} container: image: huggingface/transformers-tensorflow-gpu options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ needs: setup steps: - name: Update clone working-directory: /transformers run: | git fetch && git checkout ${{ github.sha }} - name: NVIDIA-SMI run: | nvidia-smi - name: Environment working-directory: /transformers run: | python3 utils/print_env.py - name: Show installed libraries and their versions working-directory: /transformers run: pip freeze - name: Run all pipeline tests on GPU working-directory: /transformers run: | python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ matrix.machine_type }}_tests_tf_pipeline_gpu tests/pipelines - name: Failure short reports if: ${{ always() }} run: | cat /transformers/reports/${{ matrix.machine_type }}_tests_tf_pipeline_gpu/failures_short.txt - name: Test suite reports artifacts if: ${{ always() }} uses: actions/upload-artifact@v2 with: name: ${{ matrix.machine_type }}_run_tests_tf_pipeline_gpu path: /transformers/reports/${{ matrix.machine_type }}_tests_tf_pipeline_gpu run_all_tests_torch_cuda_extensions_gpu: name: Torch CUDA extension tests strategy: fail-fast: false matrix: machine_type: [single-gpu, multi-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} needs: setup container: image: huggingface/transformers-pytorch-deepspeed-latest-gpu options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ steps: - name: Update clone working-directory: /workspace/transformers run: git fetch && git checkout ${{ github.sha }} - name: Remove cached torch extensions run: rm -rf /github/home/.cache/torch_extensions/ # To avoid unknown test failures - name: Pre build DeepSpeed *again* working-directory: /workspace run: | python3 -m pip uninstall -y deepspeed DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_AIO=1 DS_BUILD_UTILS=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check - name: NVIDIA-SMI run: | nvidia-smi - name: Environment working-directory: /workspace/transformers run: | python utils/print_env.py - name: Show installed libraries and their versions working-directory: /workspace/transformers run: pip freeze - name: Run all tests on GPU working-directory: /workspace/transformers run: | python -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu tests/deepspeed tests/extended - name: Failure short reports if: ${{ failure() }} continue-on-error: true run: cat /workspace/transformers/reports/${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu/failures_short.txt - name: Test suite reports artifacts if: ${{ always() }} uses: actions/upload-artifact@v2 with: name: ${{ matrix.machine_type }}_run_tests_torch_cuda_extensions_gpu_test_reports path: /workspace/transformers/reports/${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu send_results: name: Send results to webhook runs-on: ubuntu-latest if: always() needs: [ check_runner_status, check_runners, setup, run_tests_single_gpu, run_tests_multi_gpu, run_examples_gpu, run_pipelines_tf_gpu, run_pipelines_torch_gpu, run_all_tests_torch_cuda_extensions_gpu ] steps: - name: Preliminary job status shell: bash # For the meaning of these environment variables, see the job `Setup` run: | echo "Runner availability: ${{ needs.check_runner_status.result }}" echo "Runner status: ${{ needs.check_runners.result }}" echo "Setup status: ${{ needs.setup.result }}" - uses: actions/checkout@v2 - uses: actions/download-artifact@v2 - name: Send message to Slack env: CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }} CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }} CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }} CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }} CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }} CI_EVENT: scheduled RUNNER_STATUS: ${{ needs.check_runner_status.result }} RUNNER_ENV_STATUS: ${{ needs.check_runners.result }} SETUP_STATUS: ${{ needs.setup.result }} # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`. run: | pip install slack_sdk pip show slack_sdk python utils/notification_service.py "${{ needs.setup.outputs.matrix }}"

name: Self-hosted runner (scheduled) # Note that each job's dependencies go into a corresponding docker file. # # For example for `run_all_tests_torch_cuda_extensions_gpu` the docker image is # `huggingface/transformers-pytorch-deepspeed-latest-gpu`, which can be found at # `docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile` on: repository_dispatch: schedule: - cron: "0 2 * * *" env: HF_HOME: /mnt/cache TRANSFORMERS_IS_CI: yes OMP_NUM_THREADS: 8 MKL_NUM_THREADS: 8 RUN_SLOW: yes SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} TF_FORCE_GPU_ALLOW_GROWTH: true RUN_PT_TF_CROSS_TESTS: 1 jobs: check_runner_status: name: Check Runner Status runs-on: ubuntu-latest steps: - name: Checkout transformers uses: actions/checkout@v2 with: fetch-depth: 2 - name: Check Runner Status run: python utils/check_self_hosted_runner.py --target_runners single-gpu-scheduled-ci-runner-docker,multi-gpu-scheduled-ci-runner-docker --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }} check_runners: name: Check Runners needs: check_runner_status strategy: matrix: machine_type: [single-gpu, multi-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} container: image: huggingface/transformers-all-latest-gpu options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ steps: - name: NVIDIA-SMI run: | nvidia-smi setup: name: Setup needs: check_runners strategy: matrix: machine_type: [single-gpu, multi-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} container: image: huggingface/transformers-all-latest-gpu options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ outputs: matrix: ${{ steps.set-matrix.outputs.matrix }} steps: - name: Update clone working-directory: /transformers run: | git fetch && git checkout ${{ github.sha }} - name: Cleanup working-directory: /transformers run: | rm -rf tests/__pycache__ rm -rf tests/models/__pycache__ rm -rf reports - name: Show installed libraries and their versions working-directory: /transformers run: pip freeze - id: set-matrix name: Identify models to test working-directory: /transformers/tests run: | echo "::set-output name=matrix::$(python3 -c 'import os; tests = os.getcwd(); model_tests = os.listdir(os.path.join(tests, "models")); d1 = sorted(list(filter(os.path.isdir, os.listdir(tests)))); d2 = sorted(list(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))); d1.remove("models"); d = d2 + d1; print(d)')" - name: NVIDIA-SMI run: | nvidia-smi run_tests_single_gpu: name: Model tests strategy: fail-fast: false matrix: folders: ${{ fromJson(needs.setup.outputs.matrix) }} machine_type: [single-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} container: image: huggingface/transformers-all-latest-gpu options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ needs: setup steps: - name: Echo folder ${{ matrix.folders }} shell: bash # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to # set the artifact folder names (because the character `/` is not allowed). run: | echo "${{ matrix.folders }}" matrix_folders=${{ matrix.folders }} matrix_folders=${matrix_folders/'models/'/'models_'} echo "$matrix_folders" echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV - name: Update clone working-directory: /transformers run: git fetch && git checkout ${{ github.sha }} - name: NVIDIA-SMI run: | nvidia-smi - name: Environment working-directory: /transformers run: | python3 utils/print_env.py - name: Show installed libraries and their versions working-directory: /transformers run: pip freeze - name: Run all tests on GPU working-directory: /transformers run: python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} tests/${{ matrix.folders }} - name: Failure short reports if: ${{ failure() }} continue-on-error: true run: cat /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt - name: Test suite reports artifacts if: ${{ always() }} uses: actions/upload-artifact@v2 with: name: ${{ matrix.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports path: /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} run_tests_multi_gpu: name: Model tests strategy: fail-fast: false matrix: folders: ${{ fromJson(needs.setup.outputs.matrix) }} machine_type: [multi-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} container: image: huggingface/transformers-all-latest-gpu options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ needs: setup steps: - name: Echo folder ${{ matrix.folders }} shell: bash # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to # set the artifact folder names (because the character `/` is not allowed). run: | echo "${{ matrix.folders }}" matrix_folders=${{ matrix.folders }} matrix_folders=${matrix_folders/'models/'/'models_'} echo "$matrix_folders" echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV - name: Update clone working-directory: /transformers run: git fetch && git checkout ${{ github.sha }} - name: NVIDIA-SMI run: | nvidia-smi - name: Environment working-directory: /transformers run: | python3 utils/print_env.py - name: Show installed libraries and their versions working-directory: /transformers run: pip freeze - name: Run all tests on GPU working-directory: /transformers run: python3 -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} tests/${{ matrix.folders }} - name: Failure short reports if: ${{ failure() }} continue-on-error: true run: cat /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt - name: Test suite reports artifacts if: ${{ always() }} uses: actions/upload-artifact@v2 with: name: ${{ matrix.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports path: /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }} run_examples_gpu: name: Examples directory runs-on: [self-hosted, single-gpu-docker] container: image: huggingface/transformers-all-latest-gpu options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ needs: setup steps: - name: Update clone working-directory: /transformers run: git fetch && git checkout ${{ github.sha }} - name: NVIDIA-SMI run: | nvidia-smi - name: Environment working-directory: /transformers run: | python3 utils/print_env.py - name: Show installed libraries and their versions working-directory: /transformers run: pip freeze - name: Run examples tests on GPU working-directory: /transformers run: | pip install -r examples/pytorch/_tests_requirements.txt python3 -m pytest -v --make-reports=single-gpu_examples_gpu examples/pytorch - name: Failure short reports if: ${{ failure() }} continue-on-error: true run: cat /transformers/reports/single-gpu_examples_gpu/failures_short.txt - name: Test suite reports artifacts if: ${{ always() }} uses: actions/upload-artifact@v2 with: name: single-gpu_run_examples_gpu path: /transformers/reports/single-gpu_examples_gpu run_pipelines_torch_gpu: name: PyTorch pipelines strategy: fail-fast: false matrix: machine_type: [single-gpu, multi-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} container: image: huggingface/transformers-pytorch-gpu options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ needs: setup steps: - name: Update clone working-directory: /transformers run: git fetch && git checkout ${{ github.sha }} - name: NVIDIA-SMI run: | nvidia-smi - name: Environment working-directory: /transformers run: | python3 utils/print_env.py - name: Show installed libraries and their versions working-directory: /transformers run: pip freeze - name: Run all pipeline tests on GPU working-directory: /transformers run: | python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ matrix.machine_type }}_tests_torch_pipeline_gpu tests/pipelines - name: Failure short reports if: ${{ failure() }} continue-on-error: true run: cat /transformers/reports/${{ matrix.machine_type }}_tests_torch_pipeline_gpu/failures_short.txt - name: Test suite reports artifacts if: ${{ always() }} uses: actions/upload-artifact@v2 with: name: ${{ matrix.machine_type }}_run_tests_torch_pipeline_gpu path: /transformers/reports/${{ matrix.machine_type }}_tests_torch_pipeline_gpu run_pipelines_tf_gpu: name: TensorFlow pipelines strategy: fail-fast: false matrix: machine_type: [single-gpu, multi-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} container: image: huggingface/transformers-tensorflow-gpu options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ needs: setup steps: - name: Update clone working-directory: /transformers run: | git fetch && git checkout ${{ github.sha }} - name: NVIDIA-SMI run: | nvidia-smi - name: Environment working-directory: /transformers run: | python3 utils/print_env.py - name: Show installed libraries and their versions working-directory: /transformers run: pip freeze - name: Run all pipeline tests on GPU working-directory: /transformers run: | python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ matrix.machine_type }}_tests_tf_pipeline_gpu tests/pipelines - name: Failure short reports if: ${{ always() }} run: | cat /transformers/reports/${{ matrix.machine_type }}_tests_tf_pipeline_gpu/failures_short.txt - name: Test suite reports artifacts if: ${{ always() }} uses: actions/upload-artifact@v2 with: name: ${{ matrix.machine_type }}_run_tests_tf_pipeline_gpu path: /transformers/reports/${{ matrix.machine_type }}_tests_tf_pipeline_gpu run_all_tests_torch_cuda_extensions_gpu: name: Torch CUDA extension tests strategy: fail-fast: false matrix: machine_type: [single-gpu, multi-gpu] runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }} needs: setup container: image: huggingface/transformers-pytorch-deepspeed-latest-gpu options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ steps: - name: Update clone working-directory: /workspace/transformers run: git fetch && git checkout ${{ github.sha }} - name: Remove cached torch extensions run: rm -rf /github/home/.cache/torch_extensions/ # To avoid unknown test failures - name: Pre build DeepSpeed *again* working-directory: /workspace run: | python3 -m pip uninstall -y deepspeed DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_AIO=1 DS_BUILD_UTILS=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check - name: NVIDIA-SMI run: | nvidia-smi - name: Environment working-directory: /workspace/transformers run: | python utils/print_env.py - name: Show installed libraries and their versions working-directory: /workspace/transformers run: pip freeze - name: Run all tests on GPU working-directory: /workspace/transformers run: | python -m pytest -v --make-reports=${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu tests/deepspeed tests/extended - name: Failure short reports if: ${{ failure() }} continue-on-error: true run: cat /workspace/transformers/reports/${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu/failures_short.txt - name: Test suite reports artifacts if: ${{ always() }} uses: actions/upload-artifact@v2 with: name: ${{ matrix.machine_type }}_run_tests_torch_cuda_extensions_gpu_test_reports path: /workspace/transformers/reports/${{ matrix.machine_type }}_tests_torch_cuda_extensions_gpu send_results: name: Send results to webhook runs-on: ubuntu-latest if: always() needs: [ check_runner_status, check_runners, setup, run_tests_single_gpu, run_tests_multi_gpu, run_examples_gpu, run_pipelines_tf_gpu, run_pipelines_torch_gpu, run_all_tests_torch_cuda_extensions_gpu ] steps: - name: Preliminary job status shell: bash # For the meaning of these environment variables, see the job `Setup` run: | echo "Runner availability: ${{ needs.check_runner_status.result }}" echo "Runner status: ${{ needs.check_runners.result }}" echo "Setup status: ${{ needs.setup.result }}" - uses: actions/checkout@v2 - uses: actions/download-artifact@v2 - name: Send message to Slack env: CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }} CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }} CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }} CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }} CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }} CI_EVENT: scheduled RUNNER_STATUS: ${{ needs.check_runner_status.result }} RUNNER_ENV_STATUS: ${{ needs.check_runners.result }} SETUP_STATUS: ${{ needs.setup.result }} # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`. run: | pip install slack_sdk pip show slack_sdk python utils/notification_service.py "${{ needs.setup.outputs.matrix }}"

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./src/transformers/models/convnext/modeling_tf_convnext.py

# coding=utf-8 # Copyright 2022 Meta Platforms Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 ConvNext model.""" from typing import Dict, Optional, Tuple, Union import numpy as np import tensorflow as tf from transformers import shape_list from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling, TFSequenceClassifierOutput from ...modeling_tf_utils import ( TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras_serializable, unpack_inputs, ) from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from .configuration_convnext import ConvNextConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "ConvNextConfig" _CHECKPOINT_FOR_DOC = "facebook/convnext-tiny-224" class TFConvNextDropPath(tf.keras.layers.Layer): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). References: (1) github.com:rwightman/pytorch-image-models """ def __init__(self, drop_path, **kwargs): super().__init__(**kwargs) self.drop_path = drop_path def call(self, x, training=None): if training: keep_prob = 1 - self.drop_path shape = (tf.shape(x)[0],) + (1,) * (len(tf.shape(x)) - 1) random_tensor = keep_prob + tf.random.uniform(shape, 0, 1) random_tensor = tf.floor(random_tensor) return (x / keep_prob) * random_tensor return x class TFConvNextEmbeddings(tf.keras.layers.Layer): """This class is comparable to (and inspired by) the SwinEmbeddings class found in src/transformers/models/swin/modeling_swin.py. """ def __init__(self, config, **kwargs): super().__init__(**kwargs) self.patch_embeddings = tf.keras.layers.Conv2D( filters=config.hidden_sizes[0], kernel_size=config.patch_size, strides=config.patch_size, name="patch_embeddings", kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", ) self.layernorm = tf.keras.layers.LayerNormalization(epsilon=1e-6, name="layernorm") self.num_channels = config.num_channels def call(self, pixel_values): if isinstance(pixel_values, dict): pixel_values = pixel_values["pixel_values"] num_channels = shape_list(pixel_values)[1] if tf.executing_eagerly() and num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format. # So change the input format from `NCHW` to `NHWC`. # shape = (batch_size, in_height, in_width, in_channels=num_channels) pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1)) embeddings = self.patch_embeddings(pixel_values) embeddings = self.layernorm(embeddings) return embeddings class TFConvNextLayer(tf.keras.layers.Layer): """This corresponds to the `Block` class in the original implementation. There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C, H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Linear]; Permute back The authors used (2) as they find it slightly faster in PyTorch. Since we already permuted the inputs to follow NHWC ordering, we can just apply the operations straight-away without the permutation. Args: config ([`ConvNextConfig`]): Model configuration class. dim (`int`): Number of input channels. drop_path (`float`): Stochastic depth rate. Default: 0.0. """ def __init__(self, config, dim, drop_path=0.0, **kwargs): super().__init__(**kwargs) self.dim = dim self.config = config self.dwconv = tf.keras.layers.Conv2D( filters=dim, kernel_size=7, padding="same", groups=dim, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="dwconv", ) # depthwise conv self.layernorm = tf.keras.layers.LayerNormalization( epsilon=1e-6, name="layernorm", ) self.pwconv1 = tf.keras.layers.Dense( units=4 * dim, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="pwconv1", ) # pointwise/1x1 convs, implemented with linear layers self.act = get_tf_activation(config.hidden_act) self.pwconv2 = tf.keras.layers.Dense( units=dim, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="pwconv2", ) # Using `layers.Activation` instead of `tf.identity` to better control `training` # behaviour. self.drop_path = ( TFConvNextDropPath(drop_path, name="drop_path") if drop_path > 0.0 else tf.keras.layers.Activation("linear", name="drop_path") ) def build(self, input_shape: tf.TensorShape): # PT's `nn.Parameters` must be mapped to a TF layer weight to inherit the same name hierarchy (and vice-versa) self.layer_scale_parameter = ( self.add_weight( shape=(self.dim,), initializer=tf.keras.initializers.Constant(value=self.config.layer_scale_init_value), trainable=True, name="layer_scale_parameter", ) if self.config.layer_scale_init_value > 0 else None ) super().build(input_shape) def call(self, hidden_states, training=False): input = hidden_states x = self.dwconv(hidden_states) x = self.layernorm(x) x = self.pwconv1(x) x = self.act(x) x = self.pwconv2(x) if self.layer_scale_parameter is not None: x = self.layer_scale_parameter * x x = input + self.drop_path(x, training=training) return x class TFConvNextStage(tf.keras.layers.Layer): """ConvNext stage, consisting of an optional downsampling layer + multiple residual blocks. Args: config ([`ConvNextConfig`]): Model configuration class. in_channels (`int`): Number of input channels. out_channels (`int`): Number of output channels. depth (`int`): Number of residual blocks. drop_path_rates(`List[float]`): Stochastic depth rates for each layer. """ def __init__( self, config, in_channels, out_channels, kernel_size=2, stride=2, depth=2, drop_path_rates=None, **kwargs ): super().__init__(**kwargs) if in_channels != out_channels or stride > 1: self.downsampling_layer = [ tf.keras.layers.LayerNormalization( epsilon=1e-6, name="downsampling_layer.0", ), # Inputs to this layer will follow NHWC format since we # transposed the inputs from NCHW to NHWC in the `TFConvNextEmbeddings` # layer. All the outputs throughout the model will be in NHWC # from this point on until the output where we again change to # NCHW. tf.keras.layers.Conv2D( filters=out_channels, kernel_size=kernel_size, strides=stride, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="downsampling_layer.1", ), ] else: self.downsampling_layer = [tf.identity] drop_path_rates = drop_path_rates or [0.0] * depth self.layers = [ TFConvNextLayer( config, dim=out_channels, drop_path=drop_path_rates[j], name=f"layers.{j}", ) for j in range(depth) ] def call(self, hidden_states): for layer in self.downsampling_layer: hidden_states = layer(hidden_states) for layer in self.layers: hidden_states = layer(hidden_states) return hidden_states class TFConvNextEncoder(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.stages = [] drop_path_rates = tf.linspace(0.0, config.drop_path_rate, sum(config.depths)) drop_path_rates = tf.split(drop_path_rates, config.depths) drop_path_rates = [x.numpy().tolist() for x in drop_path_rates] prev_chs = config.hidden_sizes[0] for i in range(config.num_stages): out_chs = config.hidden_sizes[i] stage = TFConvNextStage( config, in_channels=prev_chs, out_channels=out_chs, stride=2 if i > 0 else 1, depth=config.depths[i], drop_path_rates=drop_path_rates[i], name=f"stages.{i}", ) self.stages.append(stage) prev_chs = out_chs def call(self, hidden_states, output_hidden_states=False, return_dict=True): all_hidden_states = () if output_hidden_states else None for i, layer_module in enumerate(self.stages): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) hidden_states = layer_module(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states] if v is not None) return TFBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states) @keras_serializable class TFConvNextMainLayer(tf.keras.layers.Layer): config_class = ConvNextConfig def __init__(self, config: ConvNextConfig, add_pooling_layer: bool = True, **kwargs): super().__init__(**kwargs) self.config = config self.embeddings = TFConvNextEmbeddings(config, name="embeddings") self.encoder = TFConvNextEncoder(config, name="encoder") self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm") # We are setting the `data_format` like so because from here on we will revert to the # NCHW output format self.pooler = tf.keras.layers.GlobalAvgPool2D(data_format="channels_first") if add_pooling_layer else None @unpack_inputs def call( self, pixel_values: Optional[TFModelInputType] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") embedding_output = self.embeddings(pixel_values, training=training) encoder_outputs = self.encoder( embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) last_hidden_state = encoder_outputs[0] # Change to NCHW output format have uniformity in the modules last_hidden_state = tf.transpose(last_hidden_state, perm=(0, 3, 1, 2)) pooled_output = self.layernorm(self.pooler(last_hidden_state)) # Change the other hidden state outputs to NCHW as well if output_hidden_states: hidden_states = tuple([tf.transpose(h, perm=(0, 3, 1, 2)) for h in encoder_outputs[1]]) if not return_dict: hidden_states = hidden_states if output_hidden_states else () return (last_hidden_state, pooled_output) + hidden_states return TFBaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states, ) class TFConvNextPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ConvNextConfig base_model_prefix = "convnext" main_input_name = "pixel_values" @property def dummy_inputs(self) -> Dict[str, tf.Tensor]: """ Dummy inputs to build the network. Returns: `Dict[str, tf.Tensor]`: The dummy inputs. """ VISION_DUMMY_INPUTS = tf.random.uniform( shape=( 3, self.config.num_channels, self.config.image_size, self.config.image_size, ), dtype=tf.float32, ) return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)} @tf.function( input_signature=[ { "pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"), } ] ) def serving(self, inputs): """ Method used for serving the model. Args: inputs (`Dict[str, tf.Tensor]`): The input of the saved model as a dictionary of tensors. """ output = self.call(inputs) return self.serving_output(output) CONVNEXT_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `pixel_values` only and nothing else: `model(pixel_values)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`ConvNextConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ CONVNEXT_INPUTS_DOCSTRING = r""" Args: pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`ConvNextFeatureExtractor`]. See [`ConvNextFeatureExtractor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. """ @add_start_docstrings( "The bare ConvNext model outputting raw features without any specific head on top.", CONVNEXT_START_DOCSTRING, ) class TFConvNextModel(TFConvNextPreTrainedModel): def __init__(self, config, *inputs, add_pooling_layer=True, **kwargs): super().__init__(config, *inputs, **kwargs) self.convnext = TFConvNextMainLayer(config, add_pooling_layer=add_pooling_layer, name="convnext") @unpack_inputs @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values: Optional[TFModelInputType] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: r""" Returns: Examples: ```python >>> from transformers import ConvNextFeatureExtractor, TFConvNextModel >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224") >>> model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224") >>> inputs = feature_extractor(images=image, return_tensors="tf") >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state ```""" output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") outputs = self.convnext( pixel_values=pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) if not return_dict: return (outputs[0],) + outputs[1:] return TFBaseModelOutputWithPooling( last_hidden_state=outputs.last_hidden_state, pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, ) def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling: # hidden_states not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions return TFBaseModelOutputWithPooling( last_hidden_state=output.last_hidden_state, pooler_output=output.pooler_output, hidden_states=output.hidden_states, ) @add_start_docstrings( """ ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. """, CONVNEXT_START_DOCSTRING, ) class TFConvNextForImageClassification(TFConvNextPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config: ConvNextConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.convnext = TFConvNextMainLayer(config, name="convnext") # Classifier head self.classifier = tf.keras.layers.Dense( units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="classifier", ) @unpack_inputs @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values: Optional[TFModelInputType] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import ConvNextFeatureExtractor, TFConvNextForImageClassification >>> import tensorflow as tf >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224") >>> model = TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224") >>> inputs = feature_extractor(images=image, return_tensors="tf") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> # model predicts one of the 1000 ImageNet classes >>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0] >>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)]) ```""" output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") outputs = self.convnext( pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) pooled_output = outputs.pooler_output if return_dict else outputs[1] logits = self.classifier(pooled_output) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, ) def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput: # hidden_states not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions return TFSequenceClassifierOutput(logits=output.logits, hidden_states=output.hidden_states)

# coding=utf-8 # Copyright 2022 Meta Platforms Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 ConvNext model.""" from typing import Dict, Optional, Tuple, Union import numpy as np import tensorflow as tf from transformers import shape_list from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling, TFSequenceClassifierOutput from ...modeling_tf_utils import ( TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras_serializable, unpack_inputs, ) from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from .configuration_convnext import ConvNextConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "ConvNextConfig" _CHECKPOINT_FOR_DOC = "facebook/convnext-tiny-224" class TFConvNextDropPath(tf.keras.layers.Layer): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). References: (1) github.com:rwightman/pytorch-image-models """ def __init__(self, drop_path, **kwargs): super().__init__(**kwargs) self.drop_path = drop_path def call(self, x, training=None): if training: keep_prob = 1 - self.drop_path shape = (tf.shape(x)[0],) + (1,) * (len(tf.shape(x)) - 1) random_tensor = keep_prob + tf.random.uniform(shape, 0, 1) random_tensor = tf.floor(random_tensor) return (x / keep_prob) * random_tensor return x class TFConvNextEmbeddings(tf.keras.layers.Layer): """This class is comparable to (and inspired by) the SwinEmbeddings class found in src/transformers/models/swin/modeling_swin.py. """ def __init__(self, config, **kwargs): super().__init__(**kwargs) self.patch_embeddings = tf.keras.layers.Conv2D( filters=config.hidden_sizes[0], kernel_size=config.patch_size, strides=config.patch_size, name="patch_embeddings", kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", ) self.layernorm = tf.keras.layers.LayerNormalization(epsilon=1e-6, name="layernorm") self.num_channels = config.num_channels def call(self, pixel_values): if isinstance(pixel_values, dict): pixel_values = pixel_values["pixel_values"] num_channels = shape_list(pixel_values)[1] if tf.executing_eagerly() and num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format. # So change the input format from `NCHW` to `NHWC`. # shape = (batch_size, in_height, in_width, in_channels=num_channels) pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1)) embeddings = self.patch_embeddings(pixel_values) embeddings = self.layernorm(embeddings) return embeddings class TFConvNextLayer(tf.keras.layers.Layer): """This corresponds to the `Block` class in the original implementation. There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C, H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Linear]; Permute back The authors used (2) as they find it slightly faster in PyTorch. Since we already permuted the inputs to follow NHWC ordering, we can just apply the operations straight-away without the permutation. Args: config ([`ConvNextConfig`]): Model configuration class. dim (`int`): Number of input channels. drop_path (`float`): Stochastic depth rate. Default: 0.0. """ def __init__(self, config, dim, drop_path=0.0, **kwargs): super().__init__(**kwargs) self.dim = dim self.config = config self.dwconv = tf.keras.layers.Conv2D( filters=dim, kernel_size=7, padding="same", groups=dim, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="dwconv", ) # depthwise conv self.layernorm = tf.keras.layers.LayerNormalization( epsilon=1e-6, name="layernorm", ) self.pwconv1 = tf.keras.layers.Dense( units=4 * dim, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="pwconv1", ) # pointwise/1x1 convs, implemented with linear layers self.act = get_tf_activation(config.hidden_act) self.pwconv2 = tf.keras.layers.Dense( units=dim, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="pwconv2", ) # Using `layers.Activation` instead of `tf.identity` to better control `training` # behaviour. self.drop_path = ( TFConvNextDropPath(drop_path, name="drop_path") if drop_path > 0.0 else tf.keras.layers.Activation("linear", name="drop_path") ) def build(self, input_shape: tf.TensorShape): # PT's `nn.Parameters` must be mapped to a TF layer weight to inherit the same name hierarchy (and vice-versa) self.layer_scale_parameter = ( self.add_weight( shape=(self.dim,), initializer=tf.keras.initializers.Constant(value=self.config.layer_scale_init_value), trainable=True, name="layer_scale_parameter", ) if self.config.layer_scale_init_value > 0 else None ) super().build(input_shape) def call(self, hidden_states, training=False): input = hidden_states x = self.dwconv(hidden_states) x = self.layernorm(x) x = self.pwconv1(x) x = self.act(x) x = self.pwconv2(x) if self.layer_scale_parameter is not None: x = self.layer_scale_parameter * x x = input + self.drop_path(x, training=training) return x class TFConvNextStage(tf.keras.layers.Layer): """ConvNext stage, consisting of an optional downsampling layer + multiple residual blocks. Args: config ([`ConvNextConfig`]): Model configuration class. in_channels (`int`): Number of input channels. out_channels (`int`): Number of output channels. depth (`int`): Number of residual blocks. drop_path_rates(`List[float]`): Stochastic depth rates for each layer. """ def __init__( self, config, in_channels, out_channels, kernel_size=2, stride=2, depth=2, drop_path_rates=None, **kwargs ): super().__init__(**kwargs) if in_channels != out_channels or stride > 1: self.downsampling_layer = [ tf.keras.layers.LayerNormalization( epsilon=1e-6, name="downsampling_layer.0", ), # Inputs to this layer will follow NHWC format since we # transposed the inputs from NCHW to NHWC in the `TFConvNextEmbeddings` # layer. All the outputs throughout the model will be in NHWC # from this point on until the output where we again change to # NCHW. tf.keras.layers.Conv2D( filters=out_channels, kernel_size=kernel_size, strides=stride, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="downsampling_layer.1", ), ] else: self.downsampling_layer = [tf.identity] drop_path_rates = drop_path_rates or [0.0] * depth self.layers = [ TFConvNextLayer( config, dim=out_channels, drop_path=drop_path_rates[j], name=f"layers.{j}", ) for j in range(depth) ] def call(self, hidden_states): for layer in self.downsampling_layer: hidden_states = layer(hidden_states) for layer in self.layers: hidden_states = layer(hidden_states) return hidden_states class TFConvNextEncoder(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.stages = [] drop_path_rates = tf.linspace(0.0, config.drop_path_rate, sum(config.depths)) drop_path_rates = tf.split(drop_path_rates, config.depths) drop_path_rates = [x.numpy().tolist() for x in drop_path_rates] prev_chs = config.hidden_sizes[0] for i in range(config.num_stages): out_chs = config.hidden_sizes[i] stage = TFConvNextStage( config, in_channels=prev_chs, out_channels=out_chs, stride=2 if i > 0 else 1, depth=config.depths[i], drop_path_rates=drop_path_rates[i], name=f"stages.{i}", ) self.stages.append(stage) prev_chs = out_chs def call(self, hidden_states, output_hidden_states=False, return_dict=True): all_hidden_states = () if output_hidden_states else None for i, layer_module in enumerate(self.stages): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) hidden_states = layer_module(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states] if v is not None) return TFBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states) @keras_serializable class TFConvNextMainLayer(tf.keras.layers.Layer): config_class = ConvNextConfig def __init__(self, config: ConvNextConfig, add_pooling_layer: bool = True, **kwargs): super().__init__(**kwargs) self.config = config self.embeddings = TFConvNextEmbeddings(config, name="embeddings") self.encoder = TFConvNextEncoder(config, name="encoder") self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm") # We are setting the `data_format` like so because from here on we will revert to the # NCHW output format self.pooler = tf.keras.layers.GlobalAvgPool2D(data_format="channels_first") if add_pooling_layer else None @unpack_inputs def call( self, pixel_values: Optional[TFModelInputType] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") embedding_output = self.embeddings(pixel_values, training=training) encoder_outputs = self.encoder( embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) last_hidden_state = encoder_outputs[0] # Change to NCHW output format have uniformity in the modules last_hidden_state = tf.transpose(last_hidden_state, perm=(0, 3, 1, 2)) pooled_output = self.layernorm(self.pooler(last_hidden_state)) # Change the other hidden state outputs to NCHW as well if output_hidden_states: hidden_states = tuple([tf.transpose(h, perm=(0, 3, 1, 2)) for h in encoder_outputs[1]]) if not return_dict: hidden_states = hidden_states if output_hidden_states else () return (last_hidden_state, pooled_output) + hidden_states return TFBaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states, ) class TFConvNextPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ConvNextConfig base_model_prefix = "convnext" main_input_name = "pixel_values" @property def dummy_inputs(self) -> Dict[str, tf.Tensor]: """ Dummy inputs to build the network. Returns: `Dict[str, tf.Tensor]`: The dummy inputs. """ VISION_DUMMY_INPUTS = tf.random.uniform( shape=( 3, self.config.num_channels, self.config.image_size, self.config.image_size, ), dtype=tf.float32, ) return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)} @tf.function( input_signature=[ { "pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"), } ] ) def serving(self, inputs): """ Method used for serving the model. Args: inputs (`Dict[str, tf.Tensor]`): The input of the saved model as a dictionary of tensors. """ output = self.call(inputs) return self.serving_output(output) CONVNEXT_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `pixel_values` only and nothing else: `model(pixel_values)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`ConvNextConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ CONVNEXT_INPUTS_DOCSTRING = r""" Args: pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`ConvNextFeatureExtractor`]. See [`ConvNextFeatureExtractor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. """ @add_start_docstrings( "The bare ConvNext model outputting raw features without any specific head on top.", CONVNEXT_START_DOCSTRING, ) class TFConvNextModel(TFConvNextPreTrainedModel): def __init__(self, config, *inputs, add_pooling_layer=True, **kwargs): super().__init__(config, *inputs, **kwargs) self.convnext = TFConvNextMainLayer(config, add_pooling_layer=add_pooling_layer, name="convnext") @unpack_inputs @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values: Optional[TFModelInputType] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: r""" Returns: Examples: ```python >>> from transformers import ConvNextFeatureExtractor, TFConvNextModel >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224") >>> model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224") >>> inputs = feature_extractor(images=image, return_tensors="tf") >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state ```""" output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") outputs = self.convnext( pixel_values=pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) if not return_dict: return (outputs[0],) + outputs[1:] return TFBaseModelOutputWithPooling( last_hidden_state=outputs.last_hidden_state, pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, ) def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling: # hidden_states not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions return TFBaseModelOutputWithPooling( last_hidden_state=output.last_hidden_state, pooler_output=output.pooler_output, hidden_states=output.hidden_states, ) @add_start_docstrings( """ ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. """, CONVNEXT_START_DOCSTRING, ) class TFConvNextForImageClassification(TFConvNextPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config: ConvNextConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.convnext = TFConvNextMainLayer(config, name="convnext") # Classifier head self.classifier = tf.keras.layers.Dense( units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="classifier", ) @unpack_inputs @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values: Optional[TFModelInputType] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[Union[np.ndarray, tf.Tensor]] = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import ConvNextFeatureExtractor, TFConvNextForImageClassification >>> import tensorflow as tf >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224") >>> model = TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224") >>> inputs = feature_extractor(images=image, return_tensors="tf") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> # model predicts one of the 1000 ImageNet classes >>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0] >>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)]) ```""" output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") outputs = self.convnext( pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) pooled_output = outputs.pooler_output if return_dict else outputs[1] logits = self.classifier(pooled_output) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, ) def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput: # hidden_states not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions return TFSequenceClassifierOutput(logits=output.logits, hidden_states=output.hidden_states)

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./docs/source/en/model_doc/mctct.mdx

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # M-CTC-T ## Overview The M-CTC-T model was proposed in [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on Common Voice and VoxPopuli, the model is trained on Common Voice only. The labels are unnormalized character-level transcripts (punctuation and capitalization are not removed). The model takes as input Mel filterbank features from a 16Khz audio signal. The abstract from the paper is the following: *Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual speech recognition systems. In this work, we extend pseudo-labeling to massively multilingual speech recognition with 60 languages. We propose a simple pseudo-labeling recipe that works well even with low-resource languages: train a supervised multilingual model, fine-tune it with semi-supervised learning on a target language, generate pseudo-labels for that language, and train a final model using pseudo-labels for all languages, either from scratch or by fine-tuning. Experiments on the labeled Common Voice and unlabeled VoxPopuli datasets show that our recipe can yield a model with better performance for many languages that also transfers well to LibriSpeech.* This model was contributed by [cwkeam](https://huggingface.co/cwkeam). The original code can be found [here](https://github.com/flashlight/wav2letter/tree/main/recipes/mling_pl). ## MCTCTConfig [[autodoc]] MCTCTConfig ## MCTCTFeatureExtractor [[autodoc]] MCTCTFeatureExtractor - __call__ ## MCTCTProcessor [[autodoc]] MCTCTProcessor - __call__ - from_pretrained - save_pretrained - batch_decode - decode ## MCTCTModel [[autodoc]] MCTCTModel - forward ## MCTCTForCTC [[autodoc]] MCTCTForCTC - forward

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # M-CTC-T ## Overview The M-CTC-T model was proposed in [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on Common Voice and VoxPopuli, the model is trained on Common Voice only. The labels are unnormalized character-level transcripts (punctuation and capitalization are not removed). The model takes as input Mel filterbank features from a 16Khz audio signal. The abstract from the paper is the following: *Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual speech recognition systems. In this work, we extend pseudo-labeling to massively multilingual speech recognition with 60 languages. We propose a simple pseudo-labeling recipe that works well even with low-resource languages: train a supervised multilingual model, fine-tune it with semi-supervised learning on a target language, generate pseudo-labels for that language, and train a final model using pseudo-labels for all languages, either from scratch or by fine-tuning. Experiments on the labeled Common Voice and unlabeled VoxPopuli datasets show that our recipe can yield a model with better performance for many languages that also transfers well to LibriSpeech.* This model was contributed by [cwkeam](https://huggingface.co/cwkeam). The original code can be found [here](https://github.com/flashlight/wav2letter/tree/main/recipes/mling_pl). ## MCTCTConfig [[autodoc]] MCTCTConfig ## MCTCTFeatureExtractor [[autodoc]] MCTCTFeatureExtractor - __call__ ## MCTCTProcessor [[autodoc]] MCTCTProcessor - __call__ - from_pretrained - save_pretrained - batch_decode - decode ## MCTCTModel [[autodoc]] MCTCTModel - forward ## MCTCTForCTC [[autodoc]] MCTCTForCTC - forward

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./src/transformers/models/bigbird_pegasus/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _import_structure = { "configuration_bigbird_pegasus": [ "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdPegasusConfig", "BigBirdPegasusOnnxConfig", ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_bigbird_pegasus"] = [ "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST", "BigBirdPegasusForCausalLM", "BigBirdPegasusForConditionalGeneration", "BigBirdPegasusForQuestionAnswering", "BigBirdPegasusForSequenceClassification", "BigBirdPegasusModel", "BigBirdPegasusPreTrainedModel", ] if TYPE_CHECKING: from .configuration_bigbird_pegasus import ( BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdPegasusConfig, BigBirdPegasusOnnxConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_bigbird_pegasus import ( BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST, BigBirdPegasusForCausalLM, BigBirdPegasusForConditionalGeneration, BigBirdPegasusForQuestionAnswering, BigBirdPegasusForSequenceClassification, BigBirdPegasusModel, BigBirdPegasusPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _import_structure = { "configuration_bigbird_pegasus": [ "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdPegasusConfig", "BigBirdPegasusOnnxConfig", ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_bigbird_pegasus"] = [ "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST", "BigBirdPegasusForCausalLM", "BigBirdPegasusForConditionalGeneration", "BigBirdPegasusForQuestionAnswering", "BigBirdPegasusForSequenceClassification", "BigBirdPegasusModel", "BigBirdPegasusPreTrainedModel", ] if TYPE_CHECKING: from .configuration_bigbird_pegasus import ( BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdPegasusConfig, BigBirdPegasusOnnxConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_bigbird_pegasus import ( BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST, BigBirdPegasusForCausalLM, BigBirdPegasusForConditionalGeneration, BigBirdPegasusForQuestionAnswering, BigBirdPegasusForSequenceClassification, BigBirdPegasusModel, BigBirdPegasusPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./utils/check_repo.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib import inspect import os import re import warnings from collections import OrderedDict from difflib import get_close_matches from pathlib import Path from transformers import is_flax_available, is_tf_available, is_torch_available from transformers.models.auto import get_values from transformers.utils import ENV_VARS_TRUE_VALUES # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_repo.py PATH_TO_TRANSFORMERS = "src/transformers" PATH_TO_TESTS = "tests" PATH_TO_DOC = "docs/source/en" # Update this list with models that are supposed to be private. PRIVATE_MODELS = [ "DPRSpanPredictor", "LongT5Stack", "RealmBertModel", "T5Stack", "SwitchTransformersStack", "TFDPRSpanPredictor", ] # Update this list for models that are not tested with a comment explaining the reason it should not be. # Being in this list is an exception and should **not** be the rule. IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [ # models to ignore for not tested "CLIPSegDecoder", # Building part of bigger (tested) model. "TableTransformerEncoder", # Building part of bigger (tested) model. "TableTransformerDecoder", # Building part of bigger (tested) model. "TimeSeriesTransformerEncoder", # Building part of bigger (tested) model. "TimeSeriesTransformerDecoder", # Building part of bigger (tested) model. "JukeboxVQVAE", # Building part of bigger (tested) model. "JukeboxPrior", # Building part of bigger (tested) model. "DeformableDetrEncoder", # Building part of bigger (tested) model. "DeformableDetrDecoder", # Building part of bigger (tested) model. "OPTDecoder", # Building part of bigger (tested) model. "WhisperDecoder", # Building part of bigger (tested) model. "WhisperEncoder", # Building part of bigger (tested) model. "DecisionTransformerGPT2Model", # Building part of bigger (tested) model. "SegformerDecodeHead", # Building part of bigger (tested) model. "PLBartEncoder", # Building part of bigger (tested) model. "PLBartDecoder", # Building part of bigger (tested) model. "PLBartDecoderWrapper", # Building part of bigger (tested) model. "BigBirdPegasusEncoder", # Building part of bigger (tested) model. "BigBirdPegasusDecoder", # Building part of bigger (tested) model. "BigBirdPegasusDecoderWrapper", # Building part of bigger (tested) model. "DetrEncoder", # Building part of bigger (tested) model. "DetrDecoder", # Building part of bigger (tested) model. "DetrDecoderWrapper", # Building part of bigger (tested) model. "ConditionalDetrEncoder", # Building part of bigger (tested) model. "ConditionalDetrDecoder", # Building part of bigger (tested) model. "M2M100Encoder", # Building part of bigger (tested) model. "M2M100Decoder", # Building part of bigger (tested) model. "MCTCTEncoder", # Building part of bigger (tested) model. "Speech2TextEncoder", # Building part of bigger (tested) model. "Speech2TextDecoder", # Building part of bigger (tested) model. "LEDEncoder", # Building part of bigger (tested) model. "LEDDecoder", # Building part of bigger (tested) model. "BartDecoderWrapper", # Building part of bigger (tested) model. "BartEncoder", # Building part of bigger (tested) model. "BertLMHeadModel", # Needs to be setup as decoder. "BlenderbotSmallEncoder", # Building part of bigger (tested) model. "BlenderbotSmallDecoderWrapper", # Building part of bigger (tested) model. "BlenderbotEncoder", # Building part of bigger (tested) model. "BlenderbotDecoderWrapper", # Building part of bigger (tested) model. "MBartEncoder", # Building part of bigger (tested) model. "MBartDecoderWrapper", # Building part of bigger (tested) model. "MegatronBertLMHeadModel", # Building part of bigger (tested) model. "MegatronBertEncoder", # Building part of bigger (tested) model. "MegatronBertDecoder", # Building part of bigger (tested) model. "MegatronBertDecoderWrapper", # Building part of bigger (tested) model. "MvpDecoderWrapper", # Building part of bigger (tested) model. "MvpEncoder", # Building part of bigger (tested) model. "PegasusEncoder", # Building part of bigger (tested) model. "PegasusDecoderWrapper", # Building part of bigger (tested) model. "PegasusXEncoder", # Building part of bigger (tested) model. "PegasusXDecoder", # Building part of bigger (tested) model. "PegasusXDecoderWrapper", # Building part of bigger (tested) model. "DPREncoder", # Building part of bigger (tested) model. "ProphetNetDecoderWrapper", # Building part of bigger (tested) model. "RealmBertModel", # Building part of bigger (tested) model. "RealmReader", # Not regular model. "RealmScorer", # Not regular model. "RealmForOpenQA", # Not regular model. "ReformerForMaskedLM", # Needs to be setup as decoder. "Speech2Text2DecoderWrapper", # Building part of bigger (tested) model. "TFDPREncoder", # Building part of bigger (tested) model. "TFElectraMainLayer", # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?) "TFRobertaForMultipleChoice", # TODO: fix "TrOCRDecoderWrapper", # Building part of bigger (tested) model. "TFWhisperEncoder", # Building part of bigger (tested) model. "TFWhisperDecoder", # Building part of bigger (tested) model. "SeparableConv1D", # Building part of bigger (tested) model. "FlaxBartForCausalLM", # Building part of bigger (tested) model. "FlaxBertForCausalLM", # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM. "OPTDecoderWrapper", "TFSegformerDecodeHead", # Not a regular model. ] # Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't # trigger the common tests. TEST_FILES_WITH_NO_COMMON_TESTS = [ "models/decision_transformer/test_modeling_decision_transformer.py", "models/camembert/test_modeling_camembert.py", "models/mt5/test_modeling_flax_mt5.py", "models/mbart/test_modeling_mbart.py", "models/mt5/test_modeling_mt5.py", "models/pegasus/test_modeling_pegasus.py", "models/camembert/test_modeling_tf_camembert.py", "models/mt5/test_modeling_tf_mt5.py", "models/xlm_roberta/test_modeling_tf_xlm_roberta.py", "models/xlm_roberta/test_modeling_flax_xlm_roberta.py", "models/xlm_prophetnet/test_modeling_xlm_prophetnet.py", "models/xlm_roberta/test_modeling_xlm_roberta.py", "models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py", "models/vision_text_dual_encoder/test_modeling_flax_vision_text_dual_encoder.py", "models/decision_transformer/test_modeling_decision_transformer.py", ] # Update this list for models that are not in any of the auto MODEL_XXX_MAPPING. Being in this list is an exception and # should **not** be the rule. IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [ # models to ignore for model xxx mapping "CLIPSegForImageSegmentation", "CLIPSegVisionModel", "CLIPSegTextModel", "EsmForProteinFolding", "TimeSeriesTransformerForPrediction", "JukeboxVQVAE", "JukeboxPrior", "PegasusXEncoder", "PegasusXDecoder", "PegasusXDecoderWrapper", "PegasusXEncoder", "PegasusXDecoder", "PegasusXDecoderWrapper", "DPTForDepthEstimation", "DecisionTransformerGPT2Model", "GLPNForDepthEstimation", "ViltForImagesAndTextClassification", "ViltForImageAndTextRetrieval", "ViltForTokenClassification", "ViltForMaskedLM", "XGLMEncoder", "XGLMDecoder", "XGLMDecoderWrapper", "PerceiverForMultimodalAutoencoding", "PerceiverForOpticalFlow", "SegformerDecodeHead", "TFSegformerDecodeHead", "FlaxBeitForMaskedImageModeling", "PLBartEncoder", "PLBartDecoder", "PLBartDecoderWrapper", "BeitForMaskedImageModeling", "CLIPTextModel", "CLIPTextModelWithProjection", "CLIPVisionModel", "CLIPVisionModelWithProjection", "GroupViTTextModel", "GroupViTVisionModel", "TFCLIPTextModel", "TFCLIPVisionModel", "TFGroupViTTextModel", "TFGroupViTVisionModel", "FlaxCLIPTextModel", "FlaxCLIPVisionModel", "FlaxWav2Vec2ForCTC", "DetrForSegmentation", "ConditionalDetrForSegmentation", "DPRReader", "FlaubertForQuestionAnswering", "FlavaImageCodebook", "FlavaTextModel", "FlavaImageModel", "FlavaMultimodalModel", "GPT2DoubleHeadsModel", "LayoutLMForQuestionAnswering", "LukeForMaskedLM", "LukeForEntityClassification", "LukeForEntityPairClassification", "LukeForEntitySpanClassification", "OpenAIGPTDoubleHeadsModel", "OwlViTTextModel", "OwlViTVisionModel", "OwlViTForObjectDetection", "RagModel", "RagSequenceForGeneration", "RagTokenForGeneration", "RealmEmbedder", "RealmForOpenQA", "RealmScorer", "RealmReader", "TFDPRReader", "TFGPT2DoubleHeadsModel", "TFLayoutLMForQuestionAnswering", "TFOpenAIGPTDoubleHeadsModel", "TFRagModel", "TFRagSequenceForGeneration", "TFRagTokenForGeneration", "Wav2Vec2ForCTC", "HubertForCTC", "SEWForCTC", "SEWDForCTC", "XLMForQuestionAnswering", "XLNetForQuestionAnswering", "SeparableConv1D", "VisualBertForRegionToPhraseAlignment", "VisualBertForVisualReasoning", "VisualBertForQuestionAnswering", "VisualBertForMultipleChoice", "TFWav2Vec2ForCTC", "TFHubertForCTC", "MaskFormerForInstanceSegmentation", "XCLIPVisionModel", "XCLIPTextModel", ] # Update this list for models that have multiple model types for the same # model doc MODEL_TYPE_TO_DOC_MAPPING = OrderedDict( [ ("data2vec-text", "data2vec"), ("data2vec-audio", "data2vec"), ("data2vec-vision", "data2vec"), ("donut-swin", "donut"), ] ) # This is to make sure the transformers module imported is the one in the repo. spec = importlib.util.spec_from_file_location( "transformers", os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), submodule_search_locations=[PATH_TO_TRANSFORMERS], ) transformers = spec.loader.load_module() def check_model_list(): """Check the model list inside the transformers library.""" # Get the models from the directory structure of `src/transformers/models/` models_dir = os.path.join(PATH_TO_TRANSFORMERS, "models") _models = [] for model in os.listdir(models_dir): model_dir = os.path.join(models_dir, model) if os.path.isdir(model_dir) and "__init__.py" in os.listdir(model_dir): _models.append(model) # Get the models from the directory structure of `src/transformers/models/` models = [model for model in dir(transformers.models) if not model.startswith("__")] missing_models = sorted(list(set(_models).difference(models))) if missing_models: raise Exception( f"The following models should be included in {models_dir}/__init__.py: {','.join(missing_models)}." ) # If some modeling modules should be ignored for all checks, they should be added in the nested list # _ignore_modules of this function. def get_model_modules(): """Get the model modules inside the transformers library.""" _ignore_modules = [ "modeling_auto", "modeling_encoder_decoder", "modeling_marian", "modeling_mmbt", "modeling_outputs", "modeling_retribert", "modeling_utils", "modeling_flax_auto", "modeling_flax_encoder_decoder", "modeling_flax_utils", "modeling_speech_encoder_decoder", "modeling_flax_speech_encoder_decoder", "modeling_flax_vision_encoder_decoder", "modeling_transfo_xl_utilities", "modeling_tf_auto", "modeling_tf_encoder_decoder", "modeling_tf_outputs", "modeling_tf_pytorch_utils", "modeling_tf_utils", "modeling_tf_transfo_xl_utilities", "modeling_tf_vision_encoder_decoder", "modeling_vision_encoder_decoder", ] modules = [] for model in dir(transformers.models): # There are some magic dunder attributes in the dir, we ignore them if not model.startswith("__"): model_module = getattr(transformers.models, model) for submodule in dir(model_module): if submodule.startswith("modeling") and submodule not in _ignore_modules: modeling_module = getattr(model_module, submodule) if inspect.ismodule(modeling_module): modules.append(modeling_module) return modules def get_models(module, include_pretrained=False): """Get the objects in module that are models.""" models = [] model_classes = (transformers.PreTrainedModel, transformers.TFPreTrainedModel, transformers.FlaxPreTrainedModel) for attr_name in dir(module): if not include_pretrained and ("Pretrained" in attr_name or "PreTrained" in attr_name): continue attr = getattr(module, attr_name) if isinstance(attr, type) and issubclass(attr, model_classes) and attr.__module__ == module.__name__: models.append((attr_name, attr)) return models def is_a_private_model(model): """Returns True if the model should not be in the main init.""" if model in PRIVATE_MODELS: return True # Wrapper, Encoder and Decoder are all privates if model.endswith("Wrapper"): return True if model.endswith("Encoder"): return True if model.endswith("Decoder"): return True return False def check_models_are_in_init(): """Checks all models defined in the library are in the main init.""" models_not_in_init = [] dir_transformers = dir(transformers) for module in get_model_modules(): models_not_in_init += [ model[0] for model in get_models(module, include_pretrained=True) if model[0] not in dir_transformers ] # Remove private models models_not_in_init = [model for model in models_not_in_init if not is_a_private_model(model)] if len(models_not_in_init) > 0: raise Exception(f"The following models should be in the main init: {','.join(models_not_in_init)}.") # If some test_modeling files should be ignored when checking models are all tested, they should be added in the # nested list _ignore_files of this function. def get_model_test_files(): """Get the model test files. The returned files should NOT contain the `tests` (i.e. `PATH_TO_TESTS` defined in this script). They will be considered as paths relative to `tests`. A caller has to use `os.path.join(PATH_TO_TESTS, ...)` to access the files. """ _ignore_files = [ "test_modeling_common", "test_modeling_encoder_decoder", "test_modeling_flax_encoder_decoder", "test_modeling_flax_speech_encoder_decoder", "test_modeling_marian", "test_modeling_tf_common", "test_modeling_tf_encoder_decoder", ] test_files = [] # Check both `PATH_TO_TESTS` and `PATH_TO_TESTS/models` model_test_root = os.path.join(PATH_TO_TESTS, "models") model_test_dirs = [] for x in os.listdir(model_test_root): x = os.path.join(model_test_root, x) if os.path.isdir(x): model_test_dirs.append(x) for target_dir in [PATH_TO_TESTS] + model_test_dirs: for file_or_dir in os.listdir(target_dir): path = os.path.join(target_dir, file_or_dir) if os.path.isfile(path): filename = os.path.split(path)[-1] if "test_modeling" in filename and not os.path.splitext(filename)[0] in _ignore_files: file = os.path.join(*path.split(os.sep)[1:]) test_files.append(file) return test_files # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the tester class # for the all_model_classes variable. def find_tested_models(test_file): """Parse the content of test_file to detect what's in all_model_classes""" # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the class with open(os.path.join(PATH_TO_TESTS, test_file), "r", encoding="utf-8", newline="\n") as f: content = f.read() all_models = re.findall(r"all_model_classes\s+=\s+\(\s*\(([^\)]*)\)", content) # Check with one less parenthesis as well all_models += re.findall(r"all_model_classes\s+=\s+\(([^\)]*)\)", content) if len(all_models) > 0: model_tested = [] for entry in all_models: for line in entry.split(","): name = line.strip() if len(name) > 0: model_tested.append(name) return model_tested def check_models_are_tested(module, test_file): """Check models defined in module are tested in test_file.""" # XxxPreTrainedModel are not tested defined_models = get_models(module) tested_models = find_tested_models(test_file) if tested_models is None: if test_file.replace(os.path.sep, "/") in TEST_FILES_WITH_NO_COMMON_TESTS: return return [ f"{test_file} should define `all_model_classes` to apply common tests to the models it tests. " + "If this intentional, add the test filename to `TEST_FILES_WITH_NO_COMMON_TESTS` in the file " + "`utils/check_repo.py`." ] failures = [] for model_name, _ in defined_models: if model_name not in tested_models and model_name not in IGNORE_NON_TESTED: failures.append( f"{model_name} is defined in {module.__name__} but is not tested in " + f"{os.path.join(PATH_TO_TESTS, test_file)}. Add it to the all_model_classes in that file." + "If common tests should not applied to that model, add its name to `IGNORE_NON_TESTED`" + "in the file `utils/check_repo.py`." ) return failures def check_all_models_are_tested(): """Check all models are properly tested.""" modules = get_model_modules() test_files = get_model_test_files() failures = [] for module in modules: test_file = [file for file in test_files if f"test_{module.__name__.split('.')[-1]}.py" in file] if len(test_file) == 0: failures.append(f"{module.__name__} does not have its corresponding test file {test_file}.") elif len(test_file) > 1: failures.append(f"{module.__name__} has several test files: {test_file}.") else: test_file = test_file[0] new_failures = check_models_are_tested(module, test_file) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) def get_all_auto_configured_models(): """Return the list of all models in at least one auto class.""" result = set() # To avoid duplicates we concatenate all model classes in a set. if is_torch_available(): for attr_name in dir(transformers.models.auto.modeling_auto): if attr_name.startswith("MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_auto, attr_name))) if is_tf_available(): for attr_name in dir(transformers.models.auto.modeling_tf_auto): if attr_name.startswith("TF_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_tf_auto, attr_name))) if is_flax_available(): for attr_name in dir(transformers.models.auto.modeling_flax_auto): if attr_name.startswith("FLAX_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_flax_auto, attr_name))) return [cls for cls in result] def ignore_unautoclassed(model_name): """Rules to determine if `name` should be in an auto class.""" # Special white list if model_name in IGNORE_NON_AUTO_CONFIGURED: return True # Encoder and Decoder should be ignored if "Encoder" in model_name or "Decoder" in model_name: return True return False def check_models_are_auto_configured(module, all_auto_models): """Check models defined in module are each in an auto class.""" defined_models = get_models(module) failures = [] for model_name, _ in defined_models: if model_name not in all_auto_models and not ignore_unautoclassed(model_name): failures.append( f"{model_name} is defined in {module.__name__} but is not present in any of the auto mapping. " "If that is intended behavior, add its name to `IGNORE_NON_AUTO_CONFIGURED` in the file " "`utils/check_repo.py`." ) return failures def check_all_models_are_auto_configured(): """Check all models are each in an auto class.""" missing_backends = [] if not is_torch_available(): missing_backends.append("PyTorch") if not is_tf_available(): missing_backends.append("TensorFlow") if not is_flax_available(): missing_backends.append("Flax") if len(missing_backends) > 0: missing = ", ".join(missing_backends) if os.getenv("TRANSFORMERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES: raise Exception( "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " f"Transformers repo, the following are missing: {missing}." ) else: warnings.warn( "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " f"Transformers repo, the following are missing: {missing}. While it's probably fine as long as you " "didn't make any change in one of those backends modeling files, you should probably execute the " "command above to be on the safe side." ) modules = get_model_modules() all_auto_models = get_all_auto_configured_models() failures = [] for module in modules: new_failures = check_models_are_auto_configured(module, all_auto_models) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) _re_decorator = re.compile(r"^\s*@(\S+)\s+$") def check_decorator_order(filename): """Check that in the test file `filename` the slow decorator is always last.""" with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() decorator_before = None errors = [] for i, line in enumerate(lines): search = _re_decorator.search(line) if search is not None: decorator_name = search.groups()[0] if decorator_before is not None and decorator_name.startswith("parameterized"): errors.append(i) decorator_before = decorator_name elif decorator_before is not None: decorator_before = None return errors def check_all_decorator_order(): """Check that in all test files, the slow decorator is always last.""" errors = [] for fname in os.listdir(PATH_TO_TESTS): if fname.endswith(".py"): filename = os.path.join(PATH_TO_TESTS, fname) new_errors = check_decorator_order(filename) errors += [f"- {filename}, line {i}" for i in new_errors] if len(errors) > 0: msg = "\n".join(errors) raise ValueError( "The parameterized decorator (and its variants) should always be first, but this is not the case in the" f" following files:\n{msg}" ) def find_all_documented_objects(): """Parse the content of all doc files to detect which classes and functions it documents""" documented_obj = [] for doc_file in Path(PATH_TO_DOC).glob("**/*.rst"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall(r"(?:autoclass|autofunction):: transformers.(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] for doc_file in Path(PATH_TO_DOC).glob("**/*.mdx"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall("\[\[autodoc\]\]\s+(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] return documented_obj # One good reason for not being documented is to be deprecated. Put in this list deprecated objects. DEPRECATED_OBJECTS = [ "AutoModelWithLMHead", "BartPretrainedModel", "DataCollator", "DataCollatorForSOP", "GlueDataset", "GlueDataTrainingArguments", "LineByLineTextDataset", "LineByLineWithRefDataset", "LineByLineWithSOPTextDataset", "PretrainedBartModel", "PretrainedFSMTModel", "SingleSentenceClassificationProcessor", "SquadDataTrainingArguments", "SquadDataset", "SquadExample", "SquadFeatures", "SquadV1Processor", "SquadV2Processor", "TFAutoModelWithLMHead", "TFBartPretrainedModel", "TextDataset", "TextDatasetForNextSentencePrediction", "Wav2Vec2ForMaskedLM", "Wav2Vec2Tokenizer", "glue_compute_metrics", "glue_convert_examples_to_features", "glue_output_modes", "glue_processors", "glue_tasks_num_labels", "squad_convert_examples_to_features", "xnli_compute_metrics", "xnli_output_modes", "xnli_processors", "xnli_tasks_num_labels", "TFTrainer", "TFTrainingArguments", ] # Exceptionally, some objects should not be documented after all rules passed. # ONLY PUT SOMETHING IN THIS LIST AS A LAST RESORT! UNDOCUMENTED_OBJECTS = [ "AddedToken", # This is a tokenizers class. "BasicTokenizer", # Internal, should never have been in the main init. "CharacterTokenizer", # Internal, should never have been in the main init. "DPRPretrainedReader", # Like an Encoder. "DummyObject", # Just picked by mistake sometimes. "MecabTokenizer", # Internal, should never have been in the main init. "ModelCard", # Internal type. "SqueezeBertModule", # Internal building block (should have been called SqueezeBertLayer) "TFDPRPretrainedReader", # Like an Encoder. "TransfoXLCorpus", # Internal type. "WordpieceTokenizer", # Internal, should never have been in the main init. "absl", # External module "add_end_docstrings", # Internal, should never have been in the main init. "add_start_docstrings", # Internal, should never have been in the main init. "convert_tf_weight_name_to_pt_weight_name", # Internal used to convert model weights "logger", # Internal logger "logging", # External module "requires_backends", # Internal function ] # This list should be empty. Objects in it should get their own doc page. SHOULD_HAVE_THEIR_OWN_PAGE = [ # Benchmarks "PyTorchBenchmark", "PyTorchBenchmarkArguments", "TensorFlowBenchmark", "TensorFlowBenchmarkArguments", ] def ignore_undocumented(name): """Rules to determine if `name` should be undocumented.""" # NOT DOCUMENTED ON PURPOSE. # Constants uppercase are not documented. if name.isupper(): return True # PreTrainedModels / Encoders / Decoders / Layers / Embeddings / Attention are not documented. if ( name.endswith("PreTrainedModel") or name.endswith("Decoder") or name.endswith("Encoder") or name.endswith("Layer") or name.endswith("Embeddings") or name.endswith("Attention") ): return True # Submodules are not documented. if os.path.isdir(os.path.join(PATH_TO_TRANSFORMERS, name)) or os.path.isfile( os.path.join(PATH_TO_TRANSFORMERS, f"{name}.py") ): return True # All load functions are not documented. if name.startswith("load_tf") or name.startswith("load_pytorch"): return True # is_xxx_available functions are not documented. if name.startswith("is_") and name.endswith("_available"): return True # Deprecated objects are not documented. if name in DEPRECATED_OBJECTS or name in UNDOCUMENTED_OBJECTS: return True # MMBT model does not really work. if name.startswith("MMBT"): return True if name in SHOULD_HAVE_THEIR_OWN_PAGE: return True return False def check_all_objects_are_documented(): """Check all models are properly documented.""" documented_objs = find_all_documented_objects() modules = transformers._modules objects = [c for c in dir(transformers) if c not in modules and not c.startswith("_")] undocumented_objs = [c for c in objects if c not in documented_objs and not ignore_undocumented(c)] if len(undocumented_objs) > 0: raise Exception( "The following objects are in the public init so should be documented:\n - " + "\n - ".join(undocumented_objs) ) check_docstrings_are_in_md() check_model_type_doc_match() def check_model_type_doc_match(): """Check all doc pages have a corresponding model type.""" model_doc_folder = Path(PATH_TO_DOC) / "model_doc" model_docs = [m.stem for m in model_doc_folder.glob("*.mdx")] model_types = list(transformers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys()) model_types = [MODEL_TYPE_TO_DOC_MAPPING[m] if m in MODEL_TYPE_TO_DOC_MAPPING else m for m in model_types] errors = [] for m in model_docs: if m not in model_types and m != "auto": close_matches = get_close_matches(m, model_types) error_message = f"{m} is not a proper model identifier." if len(close_matches) > 0: close_matches = "/".join(close_matches) error_message += f" Did you mean {close_matches}?" errors.append(error_message) if len(errors) > 0: raise ValueError( "Some model doc pages do not match any existing model type:\n" + "\n".join(errors) + "\nYou can add any missing model type to the `MODEL_NAMES_MAPPING` constant in " "models/auto/configuration_auto.py." ) # Re pattern to catch :obj:`xx`, :class:`xx`, :func:`xx` or :meth:`xx`. _re_rst_special_words = re.compile(r":(?:obj|func|class|meth):`([^`]+)`") # Re pattern to catch things between double backquotes. _re_double_backquotes = re.compile(r"(^|[^`])``([^`]+)``([^`]|$)") # Re pattern to catch example introduction. _re_rst_example = re.compile(r"^\s*Example.*::\s*$", flags=re.MULTILINE) def is_rst_docstring(docstring): """ Returns `True` if `docstring` is written in rst. """ if _re_rst_special_words.search(docstring) is not None: return True if _re_double_backquotes.search(docstring) is not None: return True if _re_rst_example.search(docstring) is not None: return True return False def check_docstrings_are_in_md(): """Check all docstrings are in md""" files_with_rst = [] for file in Path(PATH_TO_TRANSFORMERS).glob("**/*.py"): with open(file, encoding="utf-8") as f: code = f.read() docstrings = code.split('"""') for idx, docstring in enumerate(docstrings): if idx % 2 == 0 or not is_rst_docstring(docstring): continue files_with_rst.append(file) break if len(files_with_rst) > 0: raise ValueError( "The following files have docstrings written in rst:\n" + "\n".join([f"- {f}" for f in files_with_rst]) + "\nTo fix this run `doc-builder convert path_to_py_file` after installing `doc-builder`\n" "(`pip install git+https://github.com/huggingface/doc-builder`)" ) def check_repo_quality(): """Check all models are properly tested and documented.""" print("Checking all models are included.") check_model_list() print("Checking all models are public.") check_models_are_in_init() print("Checking all models are properly tested.") check_all_decorator_order() check_all_models_are_tested() print("Checking all objects are properly documented.") check_all_objects_are_documented() print("Checking all models are in at least one auto class.") check_all_models_are_auto_configured() if __name__ == "__main__": check_repo_quality()

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib import inspect import os import re import warnings from collections import OrderedDict from difflib import get_close_matches from pathlib import Path from transformers import is_flax_available, is_tf_available, is_torch_available from transformers.models.auto import get_values from transformers.utils import ENV_VARS_TRUE_VALUES # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_repo.py PATH_TO_TRANSFORMERS = "src/transformers" PATH_TO_TESTS = "tests" PATH_TO_DOC = "docs/source/en" # Update this list with models that are supposed to be private. PRIVATE_MODELS = [ "DPRSpanPredictor", "LongT5Stack", "RealmBertModel", "T5Stack", "SwitchTransformersStack", "TFDPRSpanPredictor", ] # Update this list for models that are not tested with a comment explaining the reason it should not be. # Being in this list is an exception and should **not** be the rule. IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [ # models to ignore for not tested "CLIPSegDecoder", # Building part of bigger (tested) model. "TableTransformerEncoder", # Building part of bigger (tested) model. "TableTransformerDecoder", # Building part of bigger (tested) model. "TimeSeriesTransformerEncoder", # Building part of bigger (tested) model. "TimeSeriesTransformerDecoder", # Building part of bigger (tested) model. "JukeboxVQVAE", # Building part of bigger (tested) model. "JukeboxPrior", # Building part of bigger (tested) model. "DeformableDetrEncoder", # Building part of bigger (tested) model. "DeformableDetrDecoder", # Building part of bigger (tested) model. "OPTDecoder", # Building part of bigger (tested) model. "WhisperDecoder", # Building part of bigger (tested) model. "WhisperEncoder", # Building part of bigger (tested) model. "DecisionTransformerGPT2Model", # Building part of bigger (tested) model. "SegformerDecodeHead", # Building part of bigger (tested) model. "PLBartEncoder", # Building part of bigger (tested) model. "PLBartDecoder", # Building part of bigger (tested) model. "PLBartDecoderWrapper", # Building part of bigger (tested) model. "BigBirdPegasusEncoder", # Building part of bigger (tested) model. "BigBirdPegasusDecoder", # Building part of bigger (tested) model. "BigBirdPegasusDecoderWrapper", # Building part of bigger (tested) model. "DetrEncoder", # Building part of bigger (tested) model. "DetrDecoder", # Building part of bigger (tested) model. "DetrDecoderWrapper", # Building part of bigger (tested) model. "ConditionalDetrEncoder", # Building part of bigger (tested) model. "ConditionalDetrDecoder", # Building part of bigger (tested) model. "M2M100Encoder", # Building part of bigger (tested) model. "M2M100Decoder", # Building part of bigger (tested) model. "MCTCTEncoder", # Building part of bigger (tested) model. "Speech2TextEncoder", # Building part of bigger (tested) model. "Speech2TextDecoder", # Building part of bigger (tested) model. "LEDEncoder", # Building part of bigger (tested) model. "LEDDecoder", # Building part of bigger (tested) model. "BartDecoderWrapper", # Building part of bigger (tested) model. "BartEncoder", # Building part of bigger (tested) model. "BertLMHeadModel", # Needs to be setup as decoder. "BlenderbotSmallEncoder", # Building part of bigger (tested) model. "BlenderbotSmallDecoderWrapper", # Building part of bigger (tested) model. "BlenderbotEncoder", # Building part of bigger (tested) model. "BlenderbotDecoderWrapper", # Building part of bigger (tested) model. "MBartEncoder", # Building part of bigger (tested) model. "MBartDecoderWrapper", # Building part of bigger (tested) model. "MegatronBertLMHeadModel", # Building part of bigger (tested) model. "MegatronBertEncoder", # Building part of bigger (tested) model. "MegatronBertDecoder", # Building part of bigger (tested) model. "MegatronBertDecoderWrapper", # Building part of bigger (tested) model. "MvpDecoderWrapper", # Building part of bigger (tested) model. "MvpEncoder", # Building part of bigger (tested) model. "PegasusEncoder", # Building part of bigger (tested) model. "PegasusDecoderWrapper", # Building part of bigger (tested) model. "PegasusXEncoder", # Building part of bigger (tested) model. "PegasusXDecoder", # Building part of bigger (tested) model. "PegasusXDecoderWrapper", # Building part of bigger (tested) model. "DPREncoder", # Building part of bigger (tested) model. "ProphetNetDecoderWrapper", # Building part of bigger (tested) model. "RealmBertModel", # Building part of bigger (tested) model. "RealmReader", # Not regular model. "RealmScorer", # Not regular model. "RealmForOpenQA", # Not regular model. "ReformerForMaskedLM", # Needs to be setup as decoder. "Speech2Text2DecoderWrapper", # Building part of bigger (tested) model. "TFDPREncoder", # Building part of bigger (tested) model. "TFElectraMainLayer", # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?) "TFRobertaForMultipleChoice", # TODO: fix "TrOCRDecoderWrapper", # Building part of bigger (tested) model. "TFWhisperEncoder", # Building part of bigger (tested) model. "TFWhisperDecoder", # Building part of bigger (tested) model. "SeparableConv1D", # Building part of bigger (tested) model. "FlaxBartForCausalLM", # Building part of bigger (tested) model. "FlaxBertForCausalLM", # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM. "OPTDecoderWrapper", "TFSegformerDecodeHead", # Not a regular model. ] # Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't # trigger the common tests. TEST_FILES_WITH_NO_COMMON_TESTS = [ "models/decision_transformer/test_modeling_decision_transformer.py", "models/camembert/test_modeling_camembert.py", "models/mt5/test_modeling_flax_mt5.py", "models/mbart/test_modeling_mbart.py", "models/mt5/test_modeling_mt5.py", "models/pegasus/test_modeling_pegasus.py", "models/camembert/test_modeling_tf_camembert.py", "models/mt5/test_modeling_tf_mt5.py", "models/xlm_roberta/test_modeling_tf_xlm_roberta.py", "models/xlm_roberta/test_modeling_flax_xlm_roberta.py", "models/xlm_prophetnet/test_modeling_xlm_prophetnet.py", "models/xlm_roberta/test_modeling_xlm_roberta.py", "models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py", "models/vision_text_dual_encoder/test_modeling_flax_vision_text_dual_encoder.py", "models/decision_transformer/test_modeling_decision_transformer.py", ] # Update this list for models that are not in any of the auto MODEL_XXX_MAPPING. Being in this list is an exception and # should **not** be the rule. IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [ # models to ignore for model xxx mapping "CLIPSegForImageSegmentation", "CLIPSegVisionModel", "CLIPSegTextModel", "EsmForProteinFolding", "TimeSeriesTransformerForPrediction", "JukeboxVQVAE", "JukeboxPrior", "PegasusXEncoder", "PegasusXDecoder", "PegasusXDecoderWrapper", "PegasusXEncoder", "PegasusXDecoder", "PegasusXDecoderWrapper", "DPTForDepthEstimation", "DecisionTransformerGPT2Model", "GLPNForDepthEstimation", "ViltForImagesAndTextClassification", "ViltForImageAndTextRetrieval", "ViltForTokenClassification", "ViltForMaskedLM", "XGLMEncoder", "XGLMDecoder", "XGLMDecoderWrapper", "PerceiverForMultimodalAutoencoding", "PerceiverForOpticalFlow", "SegformerDecodeHead", "TFSegformerDecodeHead", "FlaxBeitForMaskedImageModeling", "PLBartEncoder", "PLBartDecoder", "PLBartDecoderWrapper", "BeitForMaskedImageModeling", "CLIPTextModel", "CLIPTextModelWithProjection", "CLIPVisionModel", "CLIPVisionModelWithProjection", "GroupViTTextModel", "GroupViTVisionModel", "TFCLIPTextModel", "TFCLIPVisionModel", "TFGroupViTTextModel", "TFGroupViTVisionModel", "FlaxCLIPTextModel", "FlaxCLIPVisionModel", "FlaxWav2Vec2ForCTC", "DetrForSegmentation", "ConditionalDetrForSegmentation", "DPRReader", "FlaubertForQuestionAnswering", "FlavaImageCodebook", "FlavaTextModel", "FlavaImageModel", "FlavaMultimodalModel", "GPT2DoubleHeadsModel", "LayoutLMForQuestionAnswering", "LukeForMaskedLM", "LukeForEntityClassification", "LukeForEntityPairClassification", "LukeForEntitySpanClassification", "OpenAIGPTDoubleHeadsModel", "OwlViTTextModel", "OwlViTVisionModel", "OwlViTForObjectDetection", "RagModel", "RagSequenceForGeneration", "RagTokenForGeneration", "RealmEmbedder", "RealmForOpenQA", "RealmScorer", "RealmReader", "TFDPRReader", "TFGPT2DoubleHeadsModel", "TFLayoutLMForQuestionAnswering", "TFOpenAIGPTDoubleHeadsModel", "TFRagModel", "TFRagSequenceForGeneration", "TFRagTokenForGeneration", "Wav2Vec2ForCTC", "HubertForCTC", "SEWForCTC", "SEWDForCTC", "XLMForQuestionAnswering", "XLNetForQuestionAnswering", "SeparableConv1D", "VisualBertForRegionToPhraseAlignment", "VisualBertForVisualReasoning", "VisualBertForQuestionAnswering", "VisualBertForMultipleChoice", "TFWav2Vec2ForCTC", "TFHubertForCTC", "MaskFormerForInstanceSegmentation", "XCLIPVisionModel", "XCLIPTextModel", ] # Update this list for models that have multiple model types for the same # model doc MODEL_TYPE_TO_DOC_MAPPING = OrderedDict( [ ("data2vec-text", "data2vec"), ("data2vec-audio", "data2vec"), ("data2vec-vision", "data2vec"), ("donut-swin", "donut"), ] ) # This is to make sure the transformers module imported is the one in the repo. spec = importlib.util.spec_from_file_location( "transformers", os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), submodule_search_locations=[PATH_TO_TRANSFORMERS], ) transformers = spec.loader.load_module() def check_model_list(): """Check the model list inside the transformers library.""" # Get the models from the directory structure of `src/transformers/models/` models_dir = os.path.join(PATH_TO_TRANSFORMERS, "models") _models = [] for model in os.listdir(models_dir): model_dir = os.path.join(models_dir, model) if os.path.isdir(model_dir) and "__init__.py" in os.listdir(model_dir): _models.append(model) # Get the models from the directory structure of `src/transformers/models/` models = [model for model in dir(transformers.models) if not model.startswith("__")] missing_models = sorted(list(set(_models).difference(models))) if missing_models: raise Exception( f"The following models should be included in {models_dir}/__init__.py: {','.join(missing_models)}." ) # If some modeling modules should be ignored for all checks, they should be added in the nested list # _ignore_modules of this function. def get_model_modules(): """Get the model modules inside the transformers library.""" _ignore_modules = [ "modeling_auto", "modeling_encoder_decoder", "modeling_marian", "modeling_mmbt", "modeling_outputs", "modeling_retribert", "modeling_utils", "modeling_flax_auto", "modeling_flax_encoder_decoder", "modeling_flax_utils", "modeling_speech_encoder_decoder", "modeling_flax_speech_encoder_decoder", "modeling_flax_vision_encoder_decoder", "modeling_transfo_xl_utilities", "modeling_tf_auto", "modeling_tf_encoder_decoder", "modeling_tf_outputs", "modeling_tf_pytorch_utils", "modeling_tf_utils", "modeling_tf_transfo_xl_utilities", "modeling_tf_vision_encoder_decoder", "modeling_vision_encoder_decoder", ] modules = [] for model in dir(transformers.models): # There are some magic dunder attributes in the dir, we ignore them if not model.startswith("__"): model_module = getattr(transformers.models, model) for submodule in dir(model_module): if submodule.startswith("modeling") and submodule not in _ignore_modules: modeling_module = getattr(model_module, submodule) if inspect.ismodule(modeling_module): modules.append(modeling_module) return modules def get_models(module, include_pretrained=False): """Get the objects in module that are models.""" models = [] model_classes = (transformers.PreTrainedModel, transformers.TFPreTrainedModel, transformers.FlaxPreTrainedModel) for attr_name in dir(module): if not include_pretrained and ("Pretrained" in attr_name or "PreTrained" in attr_name): continue attr = getattr(module, attr_name) if isinstance(attr, type) and issubclass(attr, model_classes) and attr.__module__ == module.__name__: models.append((attr_name, attr)) return models def is_a_private_model(model): """Returns True if the model should not be in the main init.""" if model in PRIVATE_MODELS: return True # Wrapper, Encoder and Decoder are all privates if model.endswith("Wrapper"): return True if model.endswith("Encoder"): return True if model.endswith("Decoder"): return True return False def check_models_are_in_init(): """Checks all models defined in the library are in the main init.""" models_not_in_init = [] dir_transformers = dir(transformers) for module in get_model_modules(): models_not_in_init += [ model[0] for model in get_models(module, include_pretrained=True) if model[0] not in dir_transformers ] # Remove private models models_not_in_init = [model for model in models_not_in_init if not is_a_private_model(model)] if len(models_not_in_init) > 0: raise Exception(f"The following models should be in the main init: {','.join(models_not_in_init)}.") # If some test_modeling files should be ignored when checking models are all tested, they should be added in the # nested list _ignore_files of this function. def get_model_test_files(): """Get the model test files. The returned files should NOT contain the `tests` (i.e. `PATH_TO_TESTS` defined in this script). They will be considered as paths relative to `tests`. A caller has to use `os.path.join(PATH_TO_TESTS, ...)` to access the files. """ _ignore_files = [ "test_modeling_common", "test_modeling_encoder_decoder", "test_modeling_flax_encoder_decoder", "test_modeling_flax_speech_encoder_decoder", "test_modeling_marian", "test_modeling_tf_common", "test_modeling_tf_encoder_decoder", ] test_files = [] # Check both `PATH_TO_TESTS` and `PATH_TO_TESTS/models` model_test_root = os.path.join(PATH_TO_TESTS, "models") model_test_dirs = [] for x in os.listdir(model_test_root): x = os.path.join(model_test_root, x) if os.path.isdir(x): model_test_dirs.append(x) for target_dir in [PATH_TO_TESTS] + model_test_dirs: for file_or_dir in os.listdir(target_dir): path = os.path.join(target_dir, file_or_dir) if os.path.isfile(path): filename = os.path.split(path)[-1] if "test_modeling" in filename and not os.path.splitext(filename)[0] in _ignore_files: file = os.path.join(*path.split(os.sep)[1:]) test_files.append(file) return test_files # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the tester class # for the all_model_classes variable. def find_tested_models(test_file): """Parse the content of test_file to detect what's in all_model_classes""" # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the class with open(os.path.join(PATH_TO_TESTS, test_file), "r", encoding="utf-8", newline="\n") as f: content = f.read() all_models = re.findall(r"all_model_classes\s+=\s+\(\s*\(([^\)]*)\)", content) # Check with one less parenthesis as well all_models += re.findall(r"all_model_classes\s+=\s+\(([^\)]*)\)", content) if len(all_models) > 0: model_tested = [] for entry in all_models: for line in entry.split(","): name = line.strip() if len(name) > 0: model_tested.append(name) return model_tested def check_models_are_tested(module, test_file): """Check models defined in module are tested in test_file.""" # XxxPreTrainedModel are not tested defined_models = get_models(module) tested_models = find_tested_models(test_file) if tested_models is None: if test_file.replace(os.path.sep, "/") in TEST_FILES_WITH_NO_COMMON_TESTS: return return [ f"{test_file} should define `all_model_classes` to apply common tests to the models it tests. " + "If this intentional, add the test filename to `TEST_FILES_WITH_NO_COMMON_TESTS` in the file " + "`utils/check_repo.py`." ] failures = [] for model_name, _ in defined_models: if model_name not in tested_models and model_name not in IGNORE_NON_TESTED: failures.append( f"{model_name} is defined in {module.__name__} but is not tested in " + f"{os.path.join(PATH_TO_TESTS, test_file)}. Add it to the all_model_classes in that file." + "If common tests should not applied to that model, add its name to `IGNORE_NON_TESTED`" + "in the file `utils/check_repo.py`." ) return failures def check_all_models_are_tested(): """Check all models are properly tested.""" modules = get_model_modules() test_files = get_model_test_files() failures = [] for module in modules: test_file = [file for file in test_files if f"test_{module.__name__.split('.')[-1]}.py" in file] if len(test_file) == 0: failures.append(f"{module.__name__} does not have its corresponding test file {test_file}.") elif len(test_file) > 1: failures.append(f"{module.__name__} has several test files: {test_file}.") else: test_file = test_file[0] new_failures = check_models_are_tested(module, test_file) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) def get_all_auto_configured_models(): """Return the list of all models in at least one auto class.""" result = set() # To avoid duplicates we concatenate all model classes in a set. if is_torch_available(): for attr_name in dir(transformers.models.auto.modeling_auto): if attr_name.startswith("MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_auto, attr_name))) if is_tf_available(): for attr_name in dir(transformers.models.auto.modeling_tf_auto): if attr_name.startswith("TF_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_tf_auto, attr_name))) if is_flax_available(): for attr_name in dir(transformers.models.auto.modeling_flax_auto): if attr_name.startswith("FLAX_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_flax_auto, attr_name))) return [cls for cls in result] def ignore_unautoclassed(model_name): """Rules to determine if `name` should be in an auto class.""" # Special white list if model_name in IGNORE_NON_AUTO_CONFIGURED: return True # Encoder and Decoder should be ignored if "Encoder" in model_name or "Decoder" in model_name: return True return False def check_models_are_auto_configured(module, all_auto_models): """Check models defined in module are each in an auto class.""" defined_models = get_models(module) failures = [] for model_name, _ in defined_models: if model_name not in all_auto_models and not ignore_unautoclassed(model_name): failures.append( f"{model_name} is defined in {module.__name__} but is not present in any of the auto mapping. " "If that is intended behavior, add its name to `IGNORE_NON_AUTO_CONFIGURED` in the file " "`utils/check_repo.py`." ) return failures def check_all_models_are_auto_configured(): """Check all models are each in an auto class.""" missing_backends = [] if not is_torch_available(): missing_backends.append("PyTorch") if not is_tf_available(): missing_backends.append("TensorFlow") if not is_flax_available(): missing_backends.append("Flax") if len(missing_backends) > 0: missing = ", ".join(missing_backends) if os.getenv("TRANSFORMERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES: raise Exception( "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " f"Transformers repo, the following are missing: {missing}." ) else: warnings.warn( "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " f"Transformers repo, the following are missing: {missing}. While it's probably fine as long as you " "didn't make any change in one of those backends modeling files, you should probably execute the " "command above to be on the safe side." ) modules = get_model_modules() all_auto_models = get_all_auto_configured_models() failures = [] for module in modules: new_failures = check_models_are_auto_configured(module, all_auto_models) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) _re_decorator = re.compile(r"^\s*@(\S+)\s+$") def check_decorator_order(filename): """Check that in the test file `filename` the slow decorator is always last.""" with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() decorator_before = None errors = [] for i, line in enumerate(lines): search = _re_decorator.search(line) if search is not None: decorator_name = search.groups()[0] if decorator_before is not None and decorator_name.startswith("parameterized"): errors.append(i) decorator_before = decorator_name elif decorator_before is not None: decorator_before = None return errors def check_all_decorator_order(): """Check that in all test files, the slow decorator is always last.""" errors = [] for fname in os.listdir(PATH_TO_TESTS): if fname.endswith(".py"): filename = os.path.join(PATH_TO_TESTS, fname) new_errors = check_decorator_order(filename) errors += [f"- {filename}, line {i}" for i in new_errors] if len(errors) > 0: msg = "\n".join(errors) raise ValueError( "The parameterized decorator (and its variants) should always be first, but this is not the case in the" f" following files:\n{msg}" ) def find_all_documented_objects(): """Parse the content of all doc files to detect which classes and functions it documents""" documented_obj = [] for doc_file in Path(PATH_TO_DOC).glob("**/*.rst"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall(r"(?:autoclass|autofunction):: transformers.(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] for doc_file in Path(PATH_TO_DOC).glob("**/*.mdx"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall("\[\[autodoc\]\]\s+(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] return documented_obj # One good reason for not being documented is to be deprecated. Put in this list deprecated objects. DEPRECATED_OBJECTS = [ "AutoModelWithLMHead", "BartPretrainedModel", "DataCollator", "DataCollatorForSOP", "GlueDataset", "GlueDataTrainingArguments", "LineByLineTextDataset", "LineByLineWithRefDataset", "LineByLineWithSOPTextDataset", "PretrainedBartModel", "PretrainedFSMTModel", "SingleSentenceClassificationProcessor", "SquadDataTrainingArguments", "SquadDataset", "SquadExample", "SquadFeatures", "SquadV1Processor", "SquadV2Processor", "TFAutoModelWithLMHead", "TFBartPretrainedModel", "TextDataset", "TextDatasetForNextSentencePrediction", "Wav2Vec2ForMaskedLM", "Wav2Vec2Tokenizer", "glue_compute_metrics", "glue_convert_examples_to_features", "glue_output_modes", "glue_processors", "glue_tasks_num_labels", "squad_convert_examples_to_features", "xnli_compute_metrics", "xnli_output_modes", "xnli_processors", "xnli_tasks_num_labels", "TFTrainer", "TFTrainingArguments", ] # Exceptionally, some objects should not be documented after all rules passed. # ONLY PUT SOMETHING IN THIS LIST AS A LAST RESORT! UNDOCUMENTED_OBJECTS = [ "AddedToken", # This is a tokenizers class. "BasicTokenizer", # Internal, should never have been in the main init. "CharacterTokenizer", # Internal, should never have been in the main init. "DPRPretrainedReader", # Like an Encoder. "DummyObject", # Just picked by mistake sometimes. "MecabTokenizer", # Internal, should never have been in the main init. "ModelCard", # Internal type. "SqueezeBertModule", # Internal building block (should have been called SqueezeBertLayer) "TFDPRPretrainedReader", # Like an Encoder. "TransfoXLCorpus", # Internal type. "WordpieceTokenizer", # Internal, should never have been in the main init. "absl", # External module "add_end_docstrings", # Internal, should never have been in the main init. "add_start_docstrings", # Internal, should never have been in the main init. "convert_tf_weight_name_to_pt_weight_name", # Internal used to convert model weights "logger", # Internal logger "logging", # External module "requires_backends", # Internal function ] # This list should be empty. Objects in it should get their own doc page. SHOULD_HAVE_THEIR_OWN_PAGE = [ # Benchmarks "PyTorchBenchmark", "PyTorchBenchmarkArguments", "TensorFlowBenchmark", "TensorFlowBenchmarkArguments", ] def ignore_undocumented(name): """Rules to determine if `name` should be undocumented.""" # NOT DOCUMENTED ON PURPOSE. # Constants uppercase are not documented. if name.isupper(): return True # PreTrainedModels / Encoders / Decoders / Layers / Embeddings / Attention are not documented. if ( name.endswith("PreTrainedModel") or name.endswith("Decoder") or name.endswith("Encoder") or name.endswith("Layer") or name.endswith("Embeddings") or name.endswith("Attention") ): return True # Submodules are not documented. if os.path.isdir(os.path.join(PATH_TO_TRANSFORMERS, name)) or os.path.isfile( os.path.join(PATH_TO_TRANSFORMERS, f"{name}.py") ): return True # All load functions are not documented. if name.startswith("load_tf") or name.startswith("load_pytorch"): return True # is_xxx_available functions are not documented. if name.startswith("is_") and name.endswith("_available"): return True # Deprecated objects are not documented. if name in DEPRECATED_OBJECTS or name in UNDOCUMENTED_OBJECTS: return True # MMBT model does not really work. if name.startswith("MMBT"): return True if name in SHOULD_HAVE_THEIR_OWN_PAGE: return True return False def check_all_objects_are_documented(): """Check all models are properly documented.""" documented_objs = find_all_documented_objects() modules = transformers._modules objects = [c for c in dir(transformers) if c not in modules and not c.startswith("_")] undocumented_objs = [c for c in objects if c not in documented_objs and not ignore_undocumented(c)] if len(undocumented_objs) > 0: raise Exception( "The following objects are in the public init so should be documented:\n - " + "\n - ".join(undocumented_objs) ) check_docstrings_are_in_md() check_model_type_doc_match() def check_model_type_doc_match(): """Check all doc pages have a corresponding model type.""" model_doc_folder = Path(PATH_TO_DOC) / "model_doc" model_docs = [m.stem for m in model_doc_folder.glob("*.mdx")] model_types = list(transformers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys()) model_types = [MODEL_TYPE_TO_DOC_MAPPING[m] if m in MODEL_TYPE_TO_DOC_MAPPING else m for m in model_types] errors = [] for m in model_docs: if m not in model_types and m != "auto": close_matches = get_close_matches(m, model_types) error_message = f"{m} is not a proper model identifier." if len(close_matches) > 0: close_matches = "/".join(close_matches) error_message += f" Did you mean {close_matches}?" errors.append(error_message) if len(errors) > 0: raise ValueError( "Some model doc pages do not match any existing model type:\n" + "\n".join(errors) + "\nYou can add any missing model type to the `MODEL_NAMES_MAPPING` constant in " "models/auto/configuration_auto.py." ) # Re pattern to catch :obj:`xx`, :class:`xx`, :func:`xx` or :meth:`xx`. _re_rst_special_words = re.compile(r":(?:obj|func|class|meth):`([^`]+)`") # Re pattern to catch things between double backquotes. _re_double_backquotes = re.compile(r"(^|[^`])``([^`]+)``([^`]|$)") # Re pattern to catch example introduction. _re_rst_example = re.compile(r"^\s*Example.*::\s*$", flags=re.MULTILINE) def is_rst_docstring(docstring): """ Returns `True` if `docstring` is written in rst. """ if _re_rst_special_words.search(docstring) is not None: return True if _re_double_backquotes.search(docstring) is not None: return True if _re_rst_example.search(docstring) is not None: return True return False def check_docstrings_are_in_md(): """Check all docstrings are in md""" files_with_rst = [] for file in Path(PATH_TO_TRANSFORMERS).glob("**/*.py"): with open(file, encoding="utf-8") as f: code = f.read() docstrings = code.split('"""') for idx, docstring in enumerate(docstrings): if idx % 2 == 0 or not is_rst_docstring(docstring): continue files_with_rst.append(file) break if len(files_with_rst) > 0: raise ValueError( "The following files have docstrings written in rst:\n" + "\n".join([f"- {f}" for f in files_with_rst]) + "\nTo fix this run `doc-builder convert path_to_py_file` after installing `doc-builder`\n" "(`pip install git+https://github.com/huggingface/doc-builder`)" ) def check_repo_quality(): """Check all models are properly tested and documented.""" print("Checking all models are included.") check_model_list() print("Checking all models are public.") check_models_are_in_init() print("Checking all models are properly tested.") check_all_decorator_order() check_all_models_are_tested() print("Checking all objects are properly documented.") check_all_objects_are_documented() print("Checking all models are in at least one auto class.") check_all_models_are_auto_configured() if __name__ == "__main__": check_repo_quality()

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./.github/workflows/build-docker-images.yml

name: Build docker images (scheduled) on: push: branches: - docker-image* repository_dispatch: workflow_call: inputs: image_postfix: required: true type: string schedule: - cron: "0 1 * * *" concurrency: group: docker-images-builds cancel-in-progress: false jobs: latest-docker: name: "Latest PyTorch + TensorFlow [dev]" runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v3 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-all-latest-gpu build-args: | REF=main push: true tags: huggingface/transformers-all-latest-gpu${{ inputs.image_postfix }} # Push CI images still need to be re-built daily - name: Build and push (for Push CI) in a daily basis # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`. # The later case is useful for manual image building for debugging purpose. Use another tag in this case! if: inputs.image_postfix != '-push-ci' uses: docker/build-push-action@v2 with: context: ./docker/transformers-all-latest-gpu build-args: | REF=main push: true tags: huggingface/transformers-all-latest-gpu-push-ci latest-with-torch-nightly-docker: name: "Nightly PyTorch + Stable TensorFlow" # Push CI doesn't need this image if: inputs.image_postfix != '-push-ci' runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-all-latest-gpu build-args: | REF=main PYTORCH=pre push: true tags: huggingface/transformers-all-latest-torch-nightly-gpu latest-torch-deepspeed-docker: name: "Latest PyTorch + DeepSpeed" runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-pytorch-deepspeed-latest-gpu build-args: | REF=main push: true tags: huggingface/transformers-pytorch-deepspeed-latest-gpu${{ inputs.image_postfix }} # Can't build 2 images in a single job `latest-torch-deepspeed-docker` (for `nvcr.io/nvidia`) latest-torch-deepspeed-docker-for-push-ci-daily-build: name: "Latest PyTorch + DeepSpeed (Push CI - Daily Build)" # Can't run in parallel, otherwise get an error: # `Error response from daemon: Get "https://registry-1.docker.io/v2/": received unexpected HTTP status: 503 Service Unavailable` needs: latest-torch-deepspeed-docker runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} # Push CI images still need to be re-built daily - name: Build and push (for Push CI) in a daily basis # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`. # The later case is useful for manual image building for debugging purpose. Use another tag in this case! if: inputs.image_postfix != '-push-ci' uses: docker/build-push-action@v2 with: context: ./docker/transformers-pytorch-deepspeed-latest-gpu build-args: | REF=main push: true tags: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci nightly-torch-deepspeed-docker: name: "Nightly PyTorch + DeepSpeed" # Push CI doesn't need this image if: inputs.image_postfix != '-push-ci' runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-pytorch-deepspeed-nightly-gpu build-args: | REF=main push: true tags: huggingface/transformers-pytorch-deepspeed-nightly-gpu doc-builder: name: "Doc builder" # Push CI doesn't need this image if: inputs.image_postfix != '-push-ci' runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-doc-builder push: true tags: huggingface/transformers-doc-builder latest-pytorch: name: "Latest PyTorch [dev]" # Push CI doesn't need this image if: inputs.image_postfix != '-push-ci' runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-pytorch-gpu build-args: | REF=main push: true tags: huggingface/transformers-pytorch-gpu latest-tensorflow: name: "Latest TensorFlow [dev]" # Push CI doesn't need this image if: inputs.image_postfix != '-push-ci' runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-tensorflow-gpu build-args: | REF=main push: true tags: huggingface/transformers-tensorflow-gpu

name: Build docker images (scheduled) on: push: branches: - docker-image* repository_dispatch: workflow_call: inputs: image_postfix: required: true type: string schedule: - cron: "0 1 * * *" concurrency: group: docker-images-builds cancel-in-progress: false jobs: latest-docker: name: "Latest PyTorch + TensorFlow [dev]" runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v3 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-all-latest-gpu build-args: | REF=main push: true tags: huggingface/transformers-all-latest-gpu${{ inputs.image_postfix }} # Push CI images still need to be re-built daily - name: Build and push (for Push CI) in a daily basis # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`. # The later case is useful for manual image building for debugging purpose. Use another tag in this case! if: inputs.image_postfix != '-push-ci' uses: docker/build-push-action@v2 with: context: ./docker/transformers-all-latest-gpu build-args: | REF=main push: true tags: huggingface/transformers-all-latest-gpu-push-ci latest-with-torch-nightly-docker: name: "Nightly PyTorch + Stable TensorFlow" # Push CI doesn't need this image if: inputs.image_postfix != '-push-ci' runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-all-latest-gpu build-args: | REF=main PYTORCH=pre push: true tags: huggingface/transformers-all-latest-torch-nightly-gpu latest-torch-deepspeed-docker: name: "Latest PyTorch + DeepSpeed" runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-pytorch-deepspeed-latest-gpu build-args: | REF=main push: true tags: huggingface/transformers-pytorch-deepspeed-latest-gpu${{ inputs.image_postfix }} # Can't build 2 images in a single job `latest-torch-deepspeed-docker` (for `nvcr.io/nvidia`) latest-torch-deepspeed-docker-for-push-ci-daily-build: name: "Latest PyTorch + DeepSpeed (Push CI - Daily Build)" # Can't run in parallel, otherwise get an error: # `Error response from daemon: Get "https://registry-1.docker.io/v2/": received unexpected HTTP status: 503 Service Unavailable` needs: latest-torch-deepspeed-docker runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} # Push CI images still need to be re-built daily - name: Build and push (for Push CI) in a daily basis # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`. # The later case is useful for manual image building for debugging purpose. Use another tag in this case! if: inputs.image_postfix != '-push-ci' uses: docker/build-push-action@v2 with: context: ./docker/transformers-pytorch-deepspeed-latest-gpu build-args: | REF=main push: true tags: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci nightly-torch-deepspeed-docker: name: "Nightly PyTorch + DeepSpeed" # Push CI doesn't need this image if: inputs.image_postfix != '-push-ci' runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-pytorch-deepspeed-nightly-gpu build-args: | REF=main push: true tags: huggingface/transformers-pytorch-deepspeed-nightly-gpu doc-builder: name: "Doc builder" # Push CI doesn't need this image if: inputs.image_postfix != '-push-ci' runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-doc-builder push: true tags: huggingface/transformers-doc-builder latest-pytorch: name: "Latest PyTorch [dev]" # Push CI doesn't need this image if: inputs.image_postfix != '-push-ci' runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-pytorch-gpu build-args: | REF=main push: true tags: huggingface/transformers-pytorch-gpu latest-tensorflow: name: "Latest TensorFlow [dev]" # Push CI doesn't need this image if: inputs.image_postfix != '-push-ci' runs-on: ubuntu-latest steps: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v1 - name: Check out code uses: actions/checkout@v2 - name: Login to DockerHub uses: docker/login-action@v1 with: username: ${{ secrets.DOCKERHUB_USERNAME }} password: ${{ secrets.DOCKERHUB_PASSWORD }} - name: Build and push uses: docker/build-push-action@v2 with: context: ./docker/transformers-tensorflow-gpu build-args: | REF=main push: true tags: huggingface/transformers-tensorflow-gpu

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_tf_{{cookiecutter.lowercase_modelname}}.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. {% if cookiecutter.is_encoder_decoder_model == "False" %} import unittest from transformers import is_tf_available, {{cookiecutter.camelcase_modelname}}Config from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_tf_available(): import tensorflow as tf from transformers import ( TF{{cookiecutter.camelcase_modelname}}ForCausalLM, TF{{cookiecutter.camelcase_modelname}}ForMaskedLM, TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice, TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification, TF{{cookiecutter.camelcase_modelname}}ForTokenClassification, TF{{cookiecutter.camelcase_modelname}}Model, ) class TF{{cookiecutter.camelcase_modelname}}ModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_input_mask = True self.use_token_type_ids = True self.use_labels = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 5 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = {{cookiecutter.camelcase_modelname}}Config( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, return_dict=True, ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TF{{cookiecutter.camelcase_modelname}}Model(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_causal_lm_base_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.is_decoder = True model = TF{{cookiecutter.camelcase_modelname}}Model(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TF{{cookiecutter.camelcase_modelname}}Model(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, } result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states) # Also check the case where encoder outputs are not passed result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_causal_lm_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.is_decoder = True model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } prediction_scores = model(inputs)["logits"] self.parent.assertListEqual( list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size] ) def create_and_check_causal_lm_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, } result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states) prediction_scores = result["logits"] self.parent.assertListEqual( list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size] ) def create_and_check_causal_lm_model_past( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and attn_mask next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0] output_from_past = model( next_tokens, past_key_values=past_key_values, output_hidden_states=True ).hidden_states[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6) def create_and_check_causal_lm_model_past_with_attn_mask( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) # create attention mask half_seq_length = self.seq_length // 2 attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32) attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32) attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1) # first forward pass outputs = model(input_ids, attention_mask=attn_mask, use_cache=True) # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) past_key_values = outputs.past_key_values # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1 random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size) vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change) condition = tf.transpose( tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size)) ) input_ids = tf.where(condition, random_other_next_tokens, input_ids) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) attn_mask = tf.concat( [attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)], axis=1, ) output_from_no_past = model( next_input_ids, attention_mask=attn_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True ).hidden_states[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6) def create_and_check_causal_lm_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=input_mask, use_cache=True) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, output_hidden_states=True, ).hidden_states[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] encoder_hidden_states = encoder_hidden_states[:1, :, :] encoder_attention_mask = encoder_attention_mask[:1, :] self.batch_size = 1 # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, ).hidden_states[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = ( ( TF{{cookiecutter.camelcase_modelname}}Model, TF{{cookiecutter.camelcase_modelname}}ForCausalLM, TF{{cookiecutter.camelcase_modelname}}ForMaskedLM, TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification, TF{{cookiecutter.camelcase_modelname}}ForTokenClassification, TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice, ) if is_tf_available() else () ) test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TF{{cookiecutter.camelcase_modelname}}ModelTester(self) self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): """Test the base model""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="Template classes interact badly with this test.") def test_keras_fit(self): pass def test_causal_lm_base_model(self): """Test the base model of the causal LM model is_deocder=True, no cross_attention, no encoder outputs """ config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs) def test_model_as_decoder(self): """Test the base model as a decoder (of an encoder-decoder architecture) is_deocder=True + cross_attention + pass encoder outputs """ config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_causal_lm(self): """Test the causal LM model""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model(*config_and_inputs) def test_causal_lm_model_as_decoder(self): """Test the causal LM model as a decoder""" config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_causal_lm_model_as_decoder(*config_and_inputs) def test_causal_lm_model_past(self): """Test causal LM model with `past_key_values`""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model_past(*config_and_inputs) def test_causal_lm_model_past_with_attn_mask(self): """Test the causal LM model with `past_key_values` and `attention_mask`""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs) def test_causal_lm_model_past_with_large_inputs(self): """Test the causal LM model with `past_key_values` and a longer decoder sequence length""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model_past_large_inputs(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): """Similar to `test_causal_lm_model_past_with_large_inputs` but with cross-attention""" config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): model = TF{{cookiecutter.camelcase_modelname}}Model.from_pretrained("{{cookiecutter.checkpoint_identifier}}") self.assertIsNotNone(model) @require_tf class TF{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = TF{{cookiecutter.camelcase_modelname}}ForMaskedLM.from_pretrained("{{cookiecutter.checkpoint_identifier}}") input_ids = tf.constant([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] # TODO Replace vocab size vocab_size = 32000 expected_shape = [1, 6, vocab_size] self.assertEqual(output.shape, expected_shape) print(output[:, :3, :3]) # TODO Replace values below with what was printed above. expected_slice = tf.constant( [ [ [-0.05243197, -0.04498899, 0.05512108], [-0.07444685, -0.01064632, 0.04352357], [-0.05020351, 0.05530146, 0.00700043], ] ] ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4) {% else %} import unittest from transformers import ( is_tf_available, {{cookiecutter.camelcase_modelname}}Config, {{cookiecutter.camelcase_modelname}}Tokenizer, ) from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import ( TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, TF{{cookiecutter.camelcase_modelname}}Model, ) @require_tf class TF{{cookiecutter.camelcase_modelname}}ModelTester: config_cls = {{cookiecutter.camelcase_modelname}}Config config_updates = {} hidden_act = "gelu" def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id def prepare_config_and_inputs_for_common(self): input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size) eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1) input_ids = tf.concat([input_ids, eos_tensor], axis=1) decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.config_cls( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_ids=[2], bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.pad_token_id, **self.config_updates, ) inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TF{{cookiecutter.camelcase_modelname}}Model(config=config).get_decoder() input_ids = inputs_dict["input_ids"] input_ids = input_ids[:1, :] attention_mask = inputs_dict["attention_mask"][:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, ): if attention_mask is None: attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int32) if decoder_attention_mask is None: decoder_attention_mask = tf.concat([tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int32), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int32)], axis=-1) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, } @require_tf class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, TF{{cookiecutter.camelcase_modelname}}Model) if is_tf_available() else () all_generative_model_classes = (TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TF{{cookiecutter.camelcase_modelname}}ModelTester(self) self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in self.all_generative_model_classes: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert isinstance(name, dict) for k, v in name.items(): assert isinstance(v, tf.Variable) else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None @unittest.skip(reason="Template classes interact badly with this test.") def test_keras_fit(self): pass def _assert_tensors_equal(a, b, atol=1e-12, prefix=""): """If tensors not close, or a and b arent both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if tf.debugging.assert_near(a, b, atol=atol): return True raise except Exception: if len(prefix) > 0: prefix = f"{prefix}: " raise AssertionError(f"{prefix}{a} != {b}") def _long_tensor(tok_lst): return tf.constant(tok_lst, dtype=tf.int32) TOLERANCE = 1e-4 @slow @require_sentencepiece @require_tokenizers @require_tf class TF{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase): def test_inference_no_head(self): model = TF{{cookiecutter.camelcase_modelname}}Model.from_pretrained('{{cookiecutter.checkpoint_identifier}}') # change to intended input here input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids) output = model(**inputs_dict)[0] expected_shape = (1, 11, 1024) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = tf.Tensor( [[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE) def test_inference_with_head(self): model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') # change to intended input here input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids) output = model(**inputs_dict)[0] expected_shape = (1, 11, 1024) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = tf.Tensor( [[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE) def test_seq_to_seq_generation(self): hf = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') tok = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') batch_input = [ # string 1, # string 2, # string 3, # string 4, ] # The below article tests that we don't add any hypotheses outside of the top n_beams dct = tok.batch_encode_plus( batch_input, max_length=512, padding="max_length", truncation_strategy="only_first", truncation=True, return_tensors="tf", ) hypotheses_batch = hf.generate( input_ids=dct["input_ids"], attention_mask=dct["attention_mask"], num_beams=2, ) EXPECTED = [ # here expected 1, # here expected 2, # here expected 3, # here expected 4, ] generated = tok.batch_decode( hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True ) assert generated == EXPECTED {%- endif %}

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. {% if cookiecutter.is_encoder_decoder_model == "False" %} import unittest from transformers import is_tf_available, {{cookiecutter.camelcase_modelname}}Config from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_tf_available(): import tensorflow as tf from transformers import ( TF{{cookiecutter.camelcase_modelname}}ForCausalLM, TF{{cookiecutter.camelcase_modelname}}ForMaskedLM, TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice, TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification, TF{{cookiecutter.camelcase_modelname}}ForTokenClassification, TF{{cookiecutter.camelcase_modelname}}Model, ) class TF{{cookiecutter.camelcase_modelname}}ModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_input_mask = True self.use_token_type_ids = True self.use_labels = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 5 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = {{cookiecutter.camelcase_modelname}}Config( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, return_dict=True, ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TF{{cookiecutter.camelcase_modelname}}Model(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_causal_lm_base_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.is_decoder = True model = TF{{cookiecutter.camelcase_modelname}}Model(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TF{{cookiecutter.camelcase_modelname}}Model(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, } result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states) # Also check the case where encoder outputs are not passed result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_causal_lm_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.is_decoder = True model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } prediction_scores = model(inputs)["logits"] self.parent.assertListEqual( list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size] ) def create_and_check_causal_lm_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, } result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states) prediction_scores = result["logits"] self.parent.assertListEqual( list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size] ) def create_and_check_causal_lm_model_past( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and attn_mask next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0] output_from_past = model( next_tokens, past_key_values=past_key_values, output_hidden_states=True ).hidden_states[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6) def create_and_check_causal_lm_model_past_with_attn_mask( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) # create attention mask half_seq_length = self.seq_length // 2 attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32) attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32) attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1) # first forward pass outputs = model(input_ids, attention_mask=attn_mask, use_cache=True) # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) past_key_values = outputs.past_key_values # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1 random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size) vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change) condition = tf.transpose( tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size)) ) input_ids = tf.where(condition, random_other_next_tokens, input_ids) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) attn_mask = tf.concat( [attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)], axis=1, ) output_from_no_past = model( next_input_ids, attention_mask=attn_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True ).hidden_states[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6) def create_and_check_causal_lm_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=input_mask, use_cache=True) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, output_hidden_states=True, ).hidden_states[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] encoder_hidden_states = encoder_hidden_states[:1, :, :] encoder_attention_mask = encoder_attention_mask[:1, :] self.batch_size = 1 # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, ).hidden_states[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = ( ( TF{{cookiecutter.camelcase_modelname}}Model, TF{{cookiecutter.camelcase_modelname}}ForCausalLM, TF{{cookiecutter.camelcase_modelname}}ForMaskedLM, TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering, TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification, TF{{cookiecutter.camelcase_modelname}}ForTokenClassification, TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice, ) if is_tf_available() else () ) test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TF{{cookiecutter.camelcase_modelname}}ModelTester(self) self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): """Test the base model""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="Template classes interact badly with this test.") def test_keras_fit(self): pass def test_causal_lm_base_model(self): """Test the base model of the causal LM model is_deocder=True, no cross_attention, no encoder outputs """ config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs) def test_model_as_decoder(self): """Test the base model as a decoder (of an encoder-decoder architecture) is_deocder=True + cross_attention + pass encoder outputs """ config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_causal_lm(self): """Test the causal LM model""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model(*config_and_inputs) def test_causal_lm_model_as_decoder(self): """Test the causal LM model as a decoder""" config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_causal_lm_model_as_decoder(*config_and_inputs) def test_causal_lm_model_past(self): """Test causal LM model with `past_key_values`""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model_past(*config_and_inputs) def test_causal_lm_model_past_with_attn_mask(self): """Test the causal LM model with `past_key_values` and `attention_mask`""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs) def test_causal_lm_model_past_with_large_inputs(self): """Test the causal LM model with `past_key_values` and a longer decoder sequence length""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model_past_large_inputs(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): """Similar to `test_causal_lm_model_past_with_large_inputs` but with cross-attention""" config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): model = TF{{cookiecutter.camelcase_modelname}}Model.from_pretrained("{{cookiecutter.checkpoint_identifier}}") self.assertIsNotNone(model) @require_tf class TF{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = TF{{cookiecutter.camelcase_modelname}}ForMaskedLM.from_pretrained("{{cookiecutter.checkpoint_identifier}}") input_ids = tf.constant([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] # TODO Replace vocab size vocab_size = 32000 expected_shape = [1, 6, vocab_size] self.assertEqual(output.shape, expected_shape) print(output[:, :3, :3]) # TODO Replace values below with what was printed above. expected_slice = tf.constant( [ [ [-0.05243197, -0.04498899, 0.05512108], [-0.07444685, -0.01064632, 0.04352357], [-0.05020351, 0.05530146, 0.00700043], ] ] ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4) {% else %} import unittest from transformers import ( is_tf_available, {{cookiecutter.camelcase_modelname}}Config, {{cookiecutter.camelcase_modelname}}Tokenizer, ) from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import ( TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, TF{{cookiecutter.camelcase_modelname}}Model, ) @require_tf class TF{{cookiecutter.camelcase_modelname}}ModelTester: config_cls = {{cookiecutter.camelcase_modelname}}Config config_updates = {} hidden_act = "gelu" def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id def prepare_config_and_inputs_for_common(self): input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size) eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1) input_ids = tf.concat([input_ids, eos_tensor], axis=1) decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.config_cls( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_ids=[2], bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.pad_token_id, **self.config_updates, ) inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TF{{cookiecutter.camelcase_modelname}}Model(config=config).get_decoder() input_ids = inputs_dict["input_ids"] input_ids = input_ids[:1, :] attention_mask = inputs_dict["attention_mask"][:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, ): if attention_mask is None: attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int32) if decoder_attention_mask is None: decoder_attention_mask = tf.concat([tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int32), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int32)], axis=-1) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, } @require_tf class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, TF{{cookiecutter.camelcase_modelname}}Model) if is_tf_available() else () all_generative_model_classes = (TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TF{{cookiecutter.camelcase_modelname}}ModelTester(self) self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in self.all_generative_model_classes: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert isinstance(name, dict) for k, v in name.items(): assert isinstance(v, tf.Variable) else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None @unittest.skip(reason="Template classes interact badly with this test.") def test_keras_fit(self): pass def _assert_tensors_equal(a, b, atol=1e-12, prefix=""): """If tensors not close, or a and b arent both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if tf.debugging.assert_near(a, b, atol=atol): return True raise except Exception: if len(prefix) > 0: prefix = f"{prefix}: " raise AssertionError(f"{prefix}{a} != {b}") def _long_tensor(tok_lst): return tf.constant(tok_lst, dtype=tf.int32) TOLERANCE = 1e-4 @slow @require_sentencepiece @require_tokenizers @require_tf class TF{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase): def test_inference_no_head(self): model = TF{{cookiecutter.camelcase_modelname}}Model.from_pretrained('{{cookiecutter.checkpoint_identifier}}') # change to intended input here input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids) output = model(**inputs_dict)[0] expected_shape = (1, 11, 1024) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = tf.Tensor( [[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE) def test_inference_with_head(self): model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') # change to intended input here input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids) output = model(**inputs_dict)[0] expected_shape = (1, 11, 1024) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = tf.Tensor( [[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]], ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE) def test_seq_to_seq_generation(self): hf = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}') tok = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}') batch_input = [ # string 1, # string 2, # string 3, # string 4, ] # The below article tests that we don't add any hypotheses outside of the top n_beams dct = tok.batch_encode_plus( batch_input, max_length=512, padding="max_length", truncation_strategy="only_first", truncation=True, return_tensors="tf", ) hypotheses_batch = hf.generate( input_ids=dct["input_ids"], attention_mask=dct["attention_mask"], num_beams=2, ) EXPECTED = [ # here expected 1, # here expected 2, # here expected 3, # here expected 4, ] generated = tok.batch_decode( hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True ) assert generated == EXPECTED {%- endif %}

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/generation/test_flax_utils.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random import unittest import numpy as np import transformers from transformers import is_flax_available, is_torch_available from transformers.testing_utils import is_pt_flax_cross_test, require_flax if is_flax_available(): import os import jax.numpy as jnp from jax import jit from transformers import AutoTokenizer, FlaxAutoModelForCausalLM from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12" # assumed parallelism: 8 if is_torch_available(): import torch def ids_tensor(shape, vocab_size, rng=None): """Creates a random int32 tensor of the shape within the vocab size.""" if rng is None: rng = random.Random() total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.randint(0, vocab_size - 1)) output = np.array(values, dtype=jnp.int32).reshape(shape) return output def random_attention_mask(shape, rng=None): attn_mask = ids_tensor(shape, vocab_size=2, rng=rng) # make sure that at least one token is attended to for each batch attn_mask[:, -1] = 1 return attn_mask @require_flax class FlaxGenerationTesterMixin: model_tester = None all_generative_model_classes = () def _get_input_ids_and_config(self): config, inputs = self.model_tester.prepare_config_and_inputs_for_common() # cut to half length & take max batch_size 3 max_batch_size = 2 sequence_length = inputs["input_ids"].shape[-1] // 2 input_ids = inputs["input_ids"][:max_batch_size, :sequence_length] attention_mask = jnp.ones_like(input_ids) attention_mask = attention_mask[:max_batch_size, :sequence_length] # generate max 5 tokens max_length = input_ids.shape[-1] + 5 if config.eos_token_id is not None and config.pad_token_id is None: # hack to allow generate for models such as GPT2 as is done in `generate()` config.pad_token_id = config.eos_token_id return config, input_ids, attention_mask, max_length @is_pt_flax_cross_test def test_greedy_generate_pt_fx(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.do_sample = False config.max_length = max_length config.decoder_start_token_id = 0 for model_class in self.all_generative_model_classes: flax_model = model_class(config) pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning pt_model_class = getattr(transformers, pt_model_class_name) pt_model = pt_model_class(config).eval() pt_model = load_flax_weights_in_pytorch_model(pt_model, flax_model.params) flax_generation_outputs = flax_model.generate(input_ids).sequences pt_generation_outputs = pt_model.generate(torch.tensor(input_ids, dtype=torch.long)) if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]: flax_generation_outputs = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]] self.assertListEqual(pt_generation_outputs.numpy().tolist(), flax_generation_outputs.tolist()) def test_greedy_generate(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.do_sample = False config.max_length = max_length for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_sample_generate(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.do_sample = True config.max_length = max_length for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_beam_search_generate(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.do_sample = False config.max_length = max_length config.num_beams = 2 for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_sample_generate_logits_warper(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.do_sample = True config.max_length = max_length config.temperature = 0.8 config.top_k = 10 config.top_p = 0.3 config.min_length = 1 config.forced_bos_token_id = 8 config.forced_eos_token_id = 9 for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_greedy_generate_logits_warper(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.max_length = max_length config.min_length = 1 config.forced_bos_token_id = 8 config.forced_eos_token_id = 9 for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_beam_search_generate_logits_warper(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.max_length = max_length config.num_beams = 2 config.min_length = 1 config.forced_bos_token_id = 8 config.forced_eos_token_id = 9 for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_greedy_generate_attn_mask(self): config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # pad attention mask on the left attention_mask = attention_mask.at[(0, 0)].set(0) config.do_sample = False config.max_length = max_length for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids, attention_mask=attention_mask).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids, attention_mask=attention_mask).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_sample_generate_attn_mask(self): config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # pad attention mask on the left attention_mask = attention_mask.at[(0, 0)].set(0) config.do_sample = True config.max_length = max_length for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids, attention_mask=attention_mask).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids, attention_mask=attention_mask).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_beam_search_generate_attn_mask(self): config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # pad attention mask on the left attention_mask = attention_mask.at[(0, 0)].set(0) config.num_beams = 2 config.max_length = max_length for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids, attention_mask=attention_mask).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids, attention_mask=attention_mask).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) @require_flax class FlaxGenerationIntegrationTests(unittest.TestCase): def test_validate_generation_inputs(self): tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert") model = FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only") encoder_input_str = "Hello world" input_ids = tokenizer(encoder_input_str, return_tensors="np").input_ids # typos are quickly detected (the correct argument is `do_sample`) with self.assertRaisesRegex(ValueError, "do_samples"): model.generate(input_ids, do_samples=True) # arbitrary arguments that will not be used anywhere are also not accepted with self.assertRaisesRegex(ValueError, "foo"): fake_model_kwargs = {"foo": "bar"} model.generate(input_ids, **fake_model_kwargs)

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random import unittest import numpy as np import transformers from transformers import is_flax_available, is_torch_available from transformers.testing_utils import is_pt_flax_cross_test, require_flax if is_flax_available(): import os import jax.numpy as jnp from jax import jit from transformers import AutoTokenizer, FlaxAutoModelForCausalLM from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12" # assumed parallelism: 8 if is_torch_available(): import torch def ids_tensor(shape, vocab_size, rng=None): """Creates a random int32 tensor of the shape within the vocab size.""" if rng is None: rng = random.Random() total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.randint(0, vocab_size - 1)) output = np.array(values, dtype=jnp.int32).reshape(shape) return output def random_attention_mask(shape, rng=None): attn_mask = ids_tensor(shape, vocab_size=2, rng=rng) # make sure that at least one token is attended to for each batch attn_mask[:, -1] = 1 return attn_mask @require_flax class FlaxGenerationTesterMixin: model_tester = None all_generative_model_classes = () def _get_input_ids_and_config(self): config, inputs = self.model_tester.prepare_config_and_inputs_for_common() # cut to half length & take max batch_size 3 max_batch_size = 2 sequence_length = inputs["input_ids"].shape[-1] // 2 input_ids = inputs["input_ids"][:max_batch_size, :sequence_length] attention_mask = jnp.ones_like(input_ids) attention_mask = attention_mask[:max_batch_size, :sequence_length] # generate max 5 tokens max_length = input_ids.shape[-1] + 5 if config.eos_token_id is not None and config.pad_token_id is None: # hack to allow generate for models such as GPT2 as is done in `generate()` config.pad_token_id = config.eos_token_id return config, input_ids, attention_mask, max_length @is_pt_flax_cross_test def test_greedy_generate_pt_fx(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.do_sample = False config.max_length = max_length config.decoder_start_token_id = 0 for model_class in self.all_generative_model_classes: flax_model = model_class(config) pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning pt_model_class = getattr(transformers, pt_model_class_name) pt_model = pt_model_class(config).eval() pt_model = load_flax_weights_in_pytorch_model(pt_model, flax_model.params) flax_generation_outputs = flax_model.generate(input_ids).sequences pt_generation_outputs = pt_model.generate(torch.tensor(input_ids, dtype=torch.long)) if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]: flax_generation_outputs = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]] self.assertListEqual(pt_generation_outputs.numpy().tolist(), flax_generation_outputs.tolist()) def test_greedy_generate(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.do_sample = False config.max_length = max_length for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_sample_generate(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.do_sample = True config.max_length = max_length for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_beam_search_generate(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.do_sample = False config.max_length = max_length config.num_beams = 2 for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_sample_generate_logits_warper(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.do_sample = True config.max_length = max_length config.temperature = 0.8 config.top_k = 10 config.top_p = 0.3 config.min_length = 1 config.forced_bos_token_id = 8 config.forced_eos_token_id = 9 for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_greedy_generate_logits_warper(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.max_length = max_length config.min_length = 1 config.forced_bos_token_id = 8 config.forced_eos_token_id = 9 for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_beam_search_generate_logits_warper(self): config, input_ids, _, max_length = self._get_input_ids_and_config() config.max_length = max_length config.num_beams = 2 config.min_length = 1 config.forced_bos_token_id = 8 config.forced_eos_token_id = 9 for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_greedy_generate_attn_mask(self): config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # pad attention mask on the left attention_mask = attention_mask.at[(0, 0)].set(0) config.do_sample = False config.max_length = max_length for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids, attention_mask=attention_mask).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids, attention_mask=attention_mask).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_sample_generate_attn_mask(self): config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # pad attention mask on the left attention_mask = attention_mask.at[(0, 0)].set(0) config.do_sample = True config.max_length = max_length for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids, attention_mask=attention_mask).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids, attention_mask=attention_mask).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) def test_beam_search_generate_attn_mask(self): config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # pad attention mask on the left attention_mask = attention_mask.at[(0, 0)].set(0) config.num_beams = 2 config.max_length = max_length for model_class in self.all_generative_model_classes: model = model_class(config) generation_outputs = model.generate(input_ids, attention_mask=attention_mask).sequences self.assertEqual(generation_outputs.shape[-1], max_length) jit_generate = jit(model.generate) jit_generation_outputs = jit_generate(input_ids, attention_mask=attention_mask).sequences self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist()) @require_flax class FlaxGenerationIntegrationTests(unittest.TestCase): def test_validate_generation_inputs(self): tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert") model = FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only") encoder_input_str = "Hello world" input_ids = tokenizer(encoder_input_str, return_tensors="np").input_ids # typos are quickly detected (the correct argument is `do_sample`) with self.assertRaisesRegex(ValueError, "do_samples"): model.generate(input_ids, do_samples=True) # arbitrary arguments that will not be used anywhere are also not accepted with self.assertRaisesRegex(ValueError, "foo"): fake_model_kwargs = {"foo": "bar"} model.generate(input_ids, **fake_model_kwargs)

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./.git/objects/3d/ece8e4239ae951a70edb6c3fa48d30878d0da5

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}V{<Tyn.m(V0oҢ"%IY 33gfNfshƪMwTlXJxCauEF%Jn=gn$3?<yBF8:88;,q'P->iHJdJU0Q #DZ9nzb-e:[$Ip) Ev3ƭwmj#y ِ靨ՐW;.nqv >HA4U\]rLKujOJ 2Р?uΎԈ88eMjL,Ғٍ)/"^
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y""!|MɎ;(k=,hq^e<91BTM':LZ%izm$,=@HE+,ͪLQ,Jmyx9!XyKUMc%0C̕&=9512}[ a?Ȭt\R3GrH
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huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/models/electra/test_modeling_tf_electra.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import ElectraConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_tf_available(): import tensorflow as tf from transformers.models.electra.modeling_tf_electra import ( TFElectraForMaskedLM, TFElectraForMultipleChoice, TFElectraForPreTraining, TFElectraForQuestionAnswering, TFElectraForSequenceClassification, TFElectraForTokenClassification, TFElectraModel, ) class TFElectraModelTester: def __init__( self, parent, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_input_mask = True self.use_token_type_ids = True self.use_labels = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 5 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None self.embedding_size = 128 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = ElectraConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFElectraModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_causal_lm_base_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.is_decoder = True model = TFElectraModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TFElectraModel(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, } result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states) # Also check the case where encoder outputs are not passed result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_causal_lm_base_model_past( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TFElectraModel(config=config) # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and attn_mask next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0] output_from_past = model( next_tokens, past_key_values=past_key_values, output_hidden_states=True ).hidden_states[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6) def create_and_check_causal_lm_base_model_past_with_attn_mask( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TFElectraModel(config=config) # create attention mask half_seq_length = self.seq_length // 2 attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32) attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32) attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1) # first forward pass outputs = model(input_ids, attention_mask=attn_mask, use_cache=True) # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) past_key_values = outputs.past_key_values # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1 random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size) vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change) condition = tf.transpose( tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size)) ) input_ids = tf.where(condition, random_other_next_tokens, input_ids) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) attn_mask = tf.concat( [attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)], axis=1, ) output_from_no_past = model( next_input_ids, attention_mask=attn_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True ).hidden_states[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6) def create_and_check_causal_lm_base_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TFElectraModel(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=input_mask, use_cache=True) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, output_hidden_states=True, ).hidden_states[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TFElectraModel(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] encoder_hidden_states = encoder_hidden_states[:1, :, :] encoder_attention_mask = encoder_attention_mask[:1, :] self.batch_size = 1 # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, ).hidden_states[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFElectraForMaskedLM(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_pretraining( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFElectraForPreTraining(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length)) def create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFElectraForSequenceClassification(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = TFElectraForMultipleChoice(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFElectraForQuestionAnswering(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFElectraForTokenClassification(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = ( ( TFElectraModel, TFElectraForMaskedLM, TFElectraForPreTraining, TFElectraForTokenClassification, TFElectraForMultipleChoice, TFElectraForSequenceClassification, TFElectraForQuestionAnswering, ) if is_tf_available() else () ) test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFElectraModelTester(self) self.config_tester = ConfigTester(self, config_class=ElectraConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): """Test the base model""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_causal_lm_base_model(self): """Test the base model of the causal LM model is_deocder=True, no cross_attention, no encoder outputs """ config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs) def test_model_as_decoder(self): """Test the base model as a decoder (of an encoder-decoder architecture) is_deocder=True + cross_attention + pass encoder outputs """ config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*config_and_inputs) def test_causal_lm_base_model_past(self): """Test causal LM base model with `past_key_values`""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_base_model_past(*config_and_inputs) def test_causal_lm_base_model_past_with_attn_mask(self): """Test the causal LM base model with `past_key_values` and `attention_mask`""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_base_model_past_with_attn_mask(*config_and_inputs) def test_causal_lm_base_model_past_with_large_inputs(self): """Test the causal LM base model with `past_key_values` and a longer decoder sequence length""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_base_model_past_large_inputs(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): """Similar to `test_causal_lm_base_model_past_with_large_inputs` but with cross-attention""" config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): # for model_name in TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["google/electra-small-discriminator"]: model = TFElectraModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf class TFElectraModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = TFElectraForPreTraining.from_pretrained("lysandre/tiny-electra-random") input_ids = tf.constant([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] expected_shape = [1, 6] self.assertEqual(output.shape, expected_shape) print(output[:, :3]) expected_slice = tf.constant([[-0.24651965, 0.8835437, 1.823782]]) tf.debugging.assert_near(output[:, :3], expected_slice, atol=1e-4)

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import ElectraConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_tf_available(): import tensorflow as tf from transformers.models.electra.modeling_tf_electra import ( TFElectraForMaskedLM, TFElectraForMultipleChoice, TFElectraForPreTraining, TFElectraForQuestionAnswering, TFElectraForSequenceClassification, TFElectraForTokenClassification, TFElectraModel, ) class TFElectraModelTester: def __init__( self, parent, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_input_mask = True self.use_token_type_ids = True self.use_labels = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 5 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None self.embedding_size = 128 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = ElectraConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFElectraModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_causal_lm_base_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.is_decoder = True model = TFElectraModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TFElectraModel(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, } result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states) # Also check the case where encoder outputs are not passed result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_causal_lm_base_model_past( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TFElectraModel(config=config) # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and attn_mask next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0] output_from_past = model( next_tokens, past_key_values=past_key_values, output_hidden_states=True ).hidden_states[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6) def create_and_check_causal_lm_base_model_past_with_attn_mask( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TFElectraModel(config=config) # create attention mask half_seq_length = self.seq_length // 2 attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32) attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32) attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1) # first forward pass outputs = model(input_ids, attention_mask=attn_mask, use_cache=True) # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) past_key_values = outputs.past_key_values # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1 random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size) vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change) condition = tf.transpose( tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size)) ) input_ids = tf.where(condition, random_other_next_tokens, input_ids) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) attn_mask = tf.concat( [attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)], axis=1, ) output_from_no_past = model( next_input_ids, attention_mask=attn_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True ).hidden_states[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6) def create_and_check_causal_lm_base_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TFElectraModel(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=input_mask, use_cache=True) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, output_hidden_states=True, ).hidden_states[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TFElectraModel(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] encoder_hidden_states = encoder_hidden_states[:1, :, :] encoder_attention_mask = encoder_attention_mask[:1, :] self.batch_size = 1 # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, ).hidden_states[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFElectraForMaskedLM(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_pretraining( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFElectraForPreTraining(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length)) def create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFElectraForSequenceClassification(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = TFElectraForMultipleChoice(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFElectraForQuestionAnswering(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFElectraForTokenClassification(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class TFElectraModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = ( ( TFElectraModel, TFElectraForMaskedLM, TFElectraForPreTraining, TFElectraForTokenClassification, TFElectraForMultipleChoice, TFElectraForSequenceClassification, TFElectraForQuestionAnswering, ) if is_tf_available() else () ) test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFElectraModelTester(self) self.config_tester = ConfigTester(self, config_class=ElectraConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): """Test the base model""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_causal_lm_base_model(self): """Test the base model of the causal LM model is_deocder=True, no cross_attention, no encoder outputs """ config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs) def test_model_as_decoder(self): """Test the base model as a decoder (of an encoder-decoder architecture) is_deocder=True + cross_attention + pass encoder outputs """ config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*config_and_inputs) def test_causal_lm_base_model_past(self): """Test causal LM base model with `past_key_values`""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_base_model_past(*config_and_inputs) def test_causal_lm_base_model_past_with_attn_mask(self): """Test the causal LM base model with `past_key_values` and `attention_mask`""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_base_model_past_with_attn_mask(*config_and_inputs) def test_causal_lm_base_model_past_with_large_inputs(self): """Test the causal LM base model with `past_key_values` and a longer decoder sequence length""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_base_model_past_large_inputs(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): """Similar to `test_causal_lm_base_model_past_with_large_inputs` but with cross-attention""" config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): # for model_name in TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["google/electra-small-discriminator"]: model = TFElectraModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf class TFElectraModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = TFElectraForPreTraining.from_pretrained("lysandre/tiny-electra-random") input_ids = tf.constant([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] expected_shape = [1, 6] self.assertEqual(output.shape, expected_shape) print(output[:, :3]) expected_slice = tf.constant([[-0.24651965, 0.8835437, 1.823782]]) tf.debugging.assert_near(output[:, :3], expected_slice, atol=1e-4)

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./src/transformers/models/hubert/convert_hubert_original_pytorch_checkpoint_to_pytorch.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Hubert checkpoint.""" import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( HubertConfig, HubertForCTC, HubertModel, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, logging, ) logging.set_verbosity_info() logger = logging.get_logger(__name__) MAPPING = { "post_extract_proj": "feature_projection.projection", "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", "self_attn.k_proj": "encoder.layers.*.attention.k_proj", "self_attn.v_proj": "encoder.layers.*.attention.v_proj", "self_attn.q_proj": "encoder.layers.*.attention.q_proj", "self_attn.out_proj": "encoder.layers.*.attention.out_proj", "self_attn_layer_norm": "encoder.layers.*.layer_norm", "fc1": "encoder.layers.*.feed_forward.intermediate_dense", "fc2": "encoder.layers.*.feed_forward.output_dense", "final_layer_norm": "encoder.layers.*.final_layer_norm", "encoder.layer_norm": "encoder.layer_norm", "w2v_model.layer_norm": "feature_projection.layer_norm", "w2v_encoder.proj": "lm_head", "mask_emb": "masked_spec_embed", } def set_recursively(hf_pointer, key, value, full_name, weight_type): for attribute in key.split("."): hf_pointer = getattr(hf_pointer, attribute) if weight_type is not None: hf_shape = getattr(hf_pointer, weight_type).shape else: hf_shape = hf_pointer.shape assert hf_shape == value.shape, ( f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be" f" {value.shape} for {full_name}" ) if weight_type == "weight": hf_pointer.weight.data = value elif weight_type == "weight_g": hf_pointer.weight_g.data = value elif weight_type == "weight_v": hf_pointer.weight_v.data = value elif weight_type == "bias": hf_pointer.bias.data = value else: hf_pointer.data = value logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.") def recursively_load_weights(fairseq_model, hf_model, is_finetuned): unused_weights = [] fairseq_dict = fairseq_model.state_dict() feature_extractor = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor for name, value in fairseq_dict.items(): is_used = False if "conv_layers" in name: load_conv_layer( name, value, feature_extractor, unused_weights, hf_model.config.feat_extract_norm == "group", ) is_used = True else: for key, mapped_key in MAPPING.items(): mapped_key = "hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key if key in name or (key.split("w2v_model.")[-1] == name.split(".")[0] and not is_finetuned): is_used = True if "*" in mapped_key: layer_index = name.split(key)[0].split(".")[-2] mapped_key = mapped_key.replace("*", layer_index) if "weight_g" in name: weight_type = "weight_g" elif "weight_v" in name: weight_type = "weight_v" elif "weight" in name: weight_type = "weight" elif "bias" in name: weight_type = "bias" else: weight_type = None set_recursively(hf_model, mapped_key, value, name, weight_type) continue if not is_used: unused_weights.append(name) logger.warning(f"Unused weights: {unused_weights}") def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm): name = full_name.split("conv_layers.")[-1] items = name.split(".") layer_id = int(items[0]) type_id = int(items[1]) if type_id == 0: if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, ( f"{full_name} has size {value.shape}, but" f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." ) feature_extractor.conv_layers[layer_id].conv.bias.data = value logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, ( f"{full_name} has size {value.shape}, but" f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." ) feature_extractor.conv_layers[layer_id].conv.weight.data = value logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, ( f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was" " found." ) feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, ( f"{full_name} has size {value.shape}, but" f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found." ) feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") else: unused_weights.append(full_name) @torch.no_grad() def convert_hubert_checkpoint( checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True ): """ Copy/paste/tweak model's weights to transformers design. """ if config_path is not None: config = HubertConfig.from_pretrained(config_path) else: config = HubertConfig() if is_finetuned: if dict_path: target_dict = Dictionary.load(dict_path) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq config.bos_token_id = target_dict.pad_index config.pad_token_id = target_dict.bos_index config.eos_token_id = target_dict.eos_index config.vocab_size = len(target_dict.symbols) vocab_path = os.path.join(pytorch_dump_folder_path, "vocab.json") if not os.path.isdir(pytorch_dump_folder_path): logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(pytorch_dump_folder_path)) return os.makedirs(pytorch_dump_folder_path, exist_ok=True) with open(vocab_path, "w", encoding="utf-8") as vocab_handle: json.dump(target_dict.indices, vocab_handle) tokenizer = Wav2Vec2CTCTokenizer( vocab_path, unk_token=target_dict.unk_word, pad_token=target_dict.pad_word, bos_token=target_dict.bos_word, eos_token=target_dict.eos_word, word_delimiter_token="|", do_lower_case=False, ) return_attention_mask = True if config.feat_extract_norm == "layer" else False feature_extractor = Wav2Vec2FeatureExtractor( feature_size=1, sampling_rate=16000, padding_value=0, do_normalize=True, return_attention_mask=return_attention_mask, ) processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) processor.save_pretrained(pytorch_dump_folder_path) hf_wav2vec = HubertForCTC(config) else: hf_wav2vec = HubertModel(config) if is_finetuned: model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])} ) else: model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]) model = model[0].eval() recursively_load_weights(model, hf_wav2vec, is_finetuned) hf_wav2vec.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") parser.add_argument( "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not" ) args = parser.parse_args() convert_hubert_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned )

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Hubert checkpoint.""" import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( HubertConfig, HubertForCTC, HubertModel, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, logging, ) logging.set_verbosity_info() logger = logging.get_logger(__name__) MAPPING = { "post_extract_proj": "feature_projection.projection", "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", "self_attn.k_proj": "encoder.layers.*.attention.k_proj", "self_attn.v_proj": "encoder.layers.*.attention.v_proj", "self_attn.q_proj": "encoder.layers.*.attention.q_proj", "self_attn.out_proj": "encoder.layers.*.attention.out_proj", "self_attn_layer_norm": "encoder.layers.*.layer_norm", "fc1": "encoder.layers.*.feed_forward.intermediate_dense", "fc2": "encoder.layers.*.feed_forward.output_dense", "final_layer_norm": "encoder.layers.*.final_layer_norm", "encoder.layer_norm": "encoder.layer_norm", "w2v_model.layer_norm": "feature_projection.layer_norm", "w2v_encoder.proj": "lm_head", "mask_emb": "masked_spec_embed", } def set_recursively(hf_pointer, key, value, full_name, weight_type): for attribute in key.split("."): hf_pointer = getattr(hf_pointer, attribute) if weight_type is not None: hf_shape = getattr(hf_pointer, weight_type).shape else: hf_shape = hf_pointer.shape assert hf_shape == value.shape, ( f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be" f" {value.shape} for {full_name}" ) if weight_type == "weight": hf_pointer.weight.data = value elif weight_type == "weight_g": hf_pointer.weight_g.data = value elif weight_type == "weight_v": hf_pointer.weight_v.data = value elif weight_type == "bias": hf_pointer.bias.data = value else: hf_pointer.data = value logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.") def recursively_load_weights(fairseq_model, hf_model, is_finetuned): unused_weights = [] fairseq_dict = fairseq_model.state_dict() feature_extractor = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor for name, value in fairseq_dict.items(): is_used = False if "conv_layers" in name: load_conv_layer( name, value, feature_extractor, unused_weights, hf_model.config.feat_extract_norm == "group", ) is_used = True else: for key, mapped_key in MAPPING.items(): mapped_key = "hubert." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key if key in name or (key.split("w2v_model.")[-1] == name.split(".")[0] and not is_finetuned): is_used = True if "*" in mapped_key: layer_index = name.split(key)[0].split(".")[-2] mapped_key = mapped_key.replace("*", layer_index) if "weight_g" in name: weight_type = "weight_g" elif "weight_v" in name: weight_type = "weight_v" elif "weight" in name: weight_type = "weight" elif "bias" in name: weight_type = "bias" else: weight_type = None set_recursively(hf_model, mapped_key, value, name, weight_type) continue if not is_used: unused_weights.append(name) logger.warning(f"Unused weights: {unused_weights}") def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm): name = full_name.split("conv_layers.")[-1] items = name.split(".") layer_id = int(items[0]) type_id = int(items[1]) if type_id == 0: if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, ( f"{full_name} has size {value.shape}, but" f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." ) feature_extractor.conv_layers[layer_id].conv.bias.data = value logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, ( f"{full_name} has size {value.shape}, but" f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." ) feature_extractor.conv_layers[layer_id].conv.weight.data = value logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.") elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, ( f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was" " found." ) feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, ( f"{full_name} has size {value.shape}, but" f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found." ) feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.") else: unused_weights.append(full_name) @torch.no_grad() def convert_hubert_checkpoint( checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True ): """ Copy/paste/tweak model's weights to transformers design. """ if config_path is not None: config = HubertConfig.from_pretrained(config_path) else: config = HubertConfig() if is_finetuned: if dict_path: target_dict = Dictionary.load(dict_path) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq config.bos_token_id = target_dict.pad_index config.pad_token_id = target_dict.bos_index config.eos_token_id = target_dict.eos_index config.vocab_size = len(target_dict.symbols) vocab_path = os.path.join(pytorch_dump_folder_path, "vocab.json") if not os.path.isdir(pytorch_dump_folder_path): logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(pytorch_dump_folder_path)) return os.makedirs(pytorch_dump_folder_path, exist_ok=True) with open(vocab_path, "w", encoding="utf-8") as vocab_handle: json.dump(target_dict.indices, vocab_handle) tokenizer = Wav2Vec2CTCTokenizer( vocab_path, unk_token=target_dict.unk_word, pad_token=target_dict.pad_word, bos_token=target_dict.bos_word, eos_token=target_dict.eos_word, word_delimiter_token="|", do_lower_case=False, ) return_attention_mask = True if config.feat_extract_norm == "layer" else False feature_extractor = Wav2Vec2FeatureExtractor( feature_size=1, sampling_rate=16000, padding_value=0, do_normalize=True, return_attention_mask=return_attention_mask, ) processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) processor.save_pretrained(pytorch_dump_folder_path) hf_wav2vec = HubertForCTC(config) else: hf_wav2vec = HubertModel(config) if is_finetuned: model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])} ) else: model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]) model = model[0].eval() recursively_load_weights(model, hf_wav2vec, is_finetuned) hf_wav2vec.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") parser.add_argument( "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not" ) args = parser.parse_args() convert_hubert_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned )

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax VisionTextDualEncoder model.""" from typing import Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.traverse_util import flatten_dict, unflatten_dict from ...modeling_flax_utils import FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring from ...utils import add_start_docstrings, logging from ..auto.configuration_auto import AutoConfig from ..auto.modeling_flax_auto import FLAX_MODEL_MAPPING, FlaxAutoModel from ..clip.modeling_flax_clip import FlaxCLIPOutput, FlaxCLIPVisionModel from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VisionTextDualEncoderConfig" VISION_TEXT_DUAL_ENCODER_START_DOCSTRING = r""" This class can be used to initialize a vision-text dual encoder model with any pretrained vision autoencoding model as the vision encoder and any pretrained text model as the text encoder. The vision and text encoders are loaded via the [`~FlaxAutoModel.from_pretrained`] method. The projection layers are automatically added to the model and should be fine-tuned on a downstream task, like contrastive image-text modeling. In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991) it is shown how leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment on new zero-shot vision tasks such as image classification or retrieval. After such a Vision-Text-Dual-Encoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information). This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`VisionTextDualEncoderConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using a feature extractor (e.g. if you use ViT as the encoder, you should use [`ViTFeatureExtractor`]). See [`ViTFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class FlaxVisionTextDualEncoderModule(nn.Module): config: VisionTextDualEncoderConfig dtype: jnp.dtype = jnp.float32 def setup(self): vision_config = self.config.vision_config text_config = self.config.text_config self.vision_embed_dim = vision_config.hidden_size self.text_embed_dim = text_config.hidden_size self.projection_dim = self.config.projection_dim vision_module = FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__, FlaxCLIPVisionModel).module_class text_module = FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class self.vision_model = vision_module(vision_config, dtype=self.dtype) self.text_model = text_module(text_config, dtype=self.dtype) self.visual_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.text_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.logit_scale = self.param( "logit_scale", lambda _, shape: jnp.ones(shape) * self.config.logit_scale_init_value, [] ) def __call__( self, input_ids=None, pixel_values=None, attention_mask=None, position_ids=None, token_type_ids=None, deterministic: bool = True, output_attentions=None, output_hidden_states=None, return_dict=None, ): return_dict = return_dict if return_dict is not None else self.config.return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) # normalized features image_embeds = image_embeds / jnp.linalg.norm(image_embeds, axis=-1, keepdims=True) text_embeds = text_embeds / jnp.linalg.norm(text_embeds, axis=-1, keepdims=True) # cosine similarity as logits logit_scale = jnp.exp(self.logit_scale) logits_per_text = jnp.matmul(text_embeds, image_embeds.T) * logit_scale logits_per_image = logits_per_text.T if not return_dict: return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return FlaxCLIPOutput( logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) @add_start_docstrings(VISION_TEXT_DUAL_ENCODER_START_DOCSTRING) class FlaxVisionTextDualEncoderModel(FlaxPreTrainedModel): config_class = VisionTextDualEncoderConfig module_class = FlaxVisionTextDualEncoderModule def __init__( self, config: VisionTextDualEncoderConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): if not _do_init: raise ValueError( "`FlaxVisionTextDualEncoderModel` cannot be created without initializing, `_do_init` must be `True`." ) if input_shape is None: input_shape = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3)) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensor input_ids = jnp.zeros(input_shape[0], dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape[0]) token_type_ids = jnp.ones_like(input_ids) attention_mask = jnp.ones_like(input_ids) pixel_values = jax.random.normal(rng, input_shape[1]) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_ids, pixel_values, attention_mask, position_ids, token_type_ids)[ "params" ] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def __call__( self, input_ids, pixel_values, attention_mask=None, position_ids=None, token_type_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(pixel_values, dtype=jnp.float32), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), jnp.array(token_type_ids, dtype="i4"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) def get_text_features( self, input_ids, attention_mask=None, position_ids=None, token_type_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False, ): r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) Returns: text_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of text model. """ if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, input_ids, attention_mask, position_ids, token_type_ids, deterministic): text_outputs = module.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, token_type_ids=token_type_ids, deterministic=deterministic, ) pooled_output = text_outputs[1] text_features = module.text_projection(pooled_output) return text_features return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), jnp.array(token_type_ids, dtype="i4"), not train, method=_get_features, rngs=rngs, ) def get_image_features( self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False ): r""" Args: pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`ImageFeatureExtractionMixin`]. See [`ImageFeatureExtractionMixin.__call__`] for details. Returns: image_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of vision model. """ # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, pixel_values, deterministic): vision_outputs = module.vision_model(pixel_values=pixel_values, deterministic=deterministic) pooled_output = vision_outputs[1] # pooled_output image_features = module.visual_projection(pooled_output) return image_features return self.module.apply( {"params": params or self.params}, jnp.array(pixel_values, dtype=jnp.float32), not train, method=_get_features, rngs=rngs, ) @classmethod def from_vision_text_pretrained( cls, vision_model_name_or_path: str = None, text_model_name_or_path: str = None, *model_args, **kwargs, ) -> FlaxPreTrainedModel: """ Params: vision_model_name_or_path (`str`, *optional*, defaults to `None`): Information necessary to initiate the vision model. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided conversion scripts and loading the Flax model afterwards. text_model_name_or_path (`str`, *optional*): Information necessary to initiate the text model. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided conversion scripts and loading the Flax model afterwards. model_args (remaining positional arguments, *optional*): All remaning positional arguments will be passed to the underlying model's `__init__` method. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). - To update the text configuration, use the prefix *text_* for each configuration parameter. - To update the vision configuration, use the prefix *vision_* for each configuration parameter. - To update the parent model configuration, do not use a prefix for each configuration parameter. Behaves differently depending on whether a `config` is provided or automatically loaded. Example: ```python >>> from transformers import FlaxVisionTextDualEncoderModel >>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized. >>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( ... "google/vit-base-patch16-224", "bert-base-uncased" ... ) >>> # saving model after fine-tuning >>> model.save_pretrained("./vit-bert") >>> # load fine-tuned model >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("./vit-bert") ```""" kwargs_vision = { argument[len("vision_") :]: value for argument, value in kwargs.items() if argument.startswith("vision_") } kwargs_text = { argument[len("text_") :]: value for argument, value in kwargs.items() if argument.startswith("text_") } # remove text, vision kwargs from kwargs for key in kwargs_vision.keys(): del kwargs["vision_" + key] for key in kwargs_text.keys(): del kwargs["text_" + key] # Load and initialize the text and vision model vision_model = kwargs_vision.pop("model", None) if vision_model is None: if vision_model_name_or_path is None: raise ValueError( "If `vision_model` is not defined as an argument, a `vision_model_name_or_path` has to be defined" ) if "config" not in kwargs_vision: vision_config = AutoConfig.from_pretrained(vision_model_name_or_path) if vision_config.model_type == "clip": kwargs_vision["config"] = vision_config.vision_config vision_model = FlaxCLIPVisionModel.from_pretrained( vision_model_name_or_path, *model_args, **kwargs_vision ) else: kwargs_vision["config"] = vision_config vision_model = FlaxAutoModel.from_pretrained(vision_model_name_or_path, *model_args, **kwargs_vision) text_model = kwargs_text.pop("model", None) if text_model is None: if text_model_name_or_path is None: raise ValueError( "If `text_model` is not defined as an argument, a `text_model_name_or_path` has to be defined" ) if "config" not in kwargs_text: text_config = AutoConfig.from_pretrained(text_model_name_or_path) kwargs_text["config"] = text_config text_model = FlaxAutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text) # instantiate config with corresponding kwargs dtype = kwargs.pop("dtype", jnp.float32) config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config, **kwargs) # init model model = cls(config, *model_args, dtype=dtype, **kwargs) model.params["vision_model"] = vision_model.params model.params["text_model"] = text_model.params # the projection layers are always newly initialized when loading the model # using pre-trained vision and text model. logger.warning( "The projection layer and logit scale weights `[('visual_projection', 'kernel'), ('text_projection'," " 'kernel'), ('logit_scale',)]` are newly initialized. You should probably TRAIN this model on a" " down-stream task to be able to use it for predictions and inference." ) return model VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING = r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> import jax >>> from transformers import ( ... FlaxVisionTextDualEncoderModel, ... VisionTextDualEncoderProcessor, ... ViTFeatureExtractor, ... BertTokenizer, ... ) >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224") >>> processor = VisionTextDualEncoderProcessor(feature_extractor, tokenizer) >>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( ... "google/vit-base-patch16-224", "bert-base-uncased" ... ) >>> # contrastive training >>> urls = [ ... "http://images.cocodataset.org/val2017/000000039769.jpg", ... "https://farm3.staticflickr.com/2674/5850229113_4fe05d5265_z.jpg", ... ] >>> images = [Image.open(requests.get(url, stream=True).raw) for url in urls] >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=images, return_tensors="np", padding=True ... ) >>> outputs = model( ... input_ids=inputs.input_ids, ... attention_mask=inputs.attention_mask, ... pixel_values=inputs.pixel_values, ... ) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> # save and load from pretrained >>> model.save_pretrained("vit-bert") >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("vit-bert") >>> # inference >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = jax.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities ``` """ overwrite_call_docstring( FlaxVisionTextDualEncoderModel, VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING + VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING, ) append_replace_return_docstrings( FlaxVisionTextDualEncoderModel, output_type=FlaxCLIPOutput, config_class=_CONFIG_FOR_DOC )

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax VisionTextDualEncoder model.""" from typing import Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.traverse_util import flatten_dict, unflatten_dict from ...modeling_flax_utils import FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring from ...utils import add_start_docstrings, logging from ..auto.configuration_auto import AutoConfig from ..auto.modeling_flax_auto import FLAX_MODEL_MAPPING, FlaxAutoModel from ..clip.modeling_flax_clip import FlaxCLIPOutput, FlaxCLIPVisionModel from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VisionTextDualEncoderConfig" VISION_TEXT_DUAL_ENCODER_START_DOCSTRING = r""" This class can be used to initialize a vision-text dual encoder model with any pretrained vision autoencoding model as the vision encoder and any pretrained text model as the text encoder. The vision and text encoders are loaded via the [`~FlaxAutoModel.from_pretrained`] method. The projection layers are automatically added to the model and should be fine-tuned on a downstream task, like contrastive image-text modeling. In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991) it is shown how leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment on new zero-shot vision tasks such as image classification or retrieval. After such a Vision-Text-Dual-Encoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information). This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`VisionTextDualEncoderConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using a feature extractor (e.g. if you use ViT as the encoder, you should use [`ViTFeatureExtractor`]). See [`ViTFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class FlaxVisionTextDualEncoderModule(nn.Module): config: VisionTextDualEncoderConfig dtype: jnp.dtype = jnp.float32 def setup(self): vision_config = self.config.vision_config text_config = self.config.text_config self.vision_embed_dim = vision_config.hidden_size self.text_embed_dim = text_config.hidden_size self.projection_dim = self.config.projection_dim vision_module = FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__, FlaxCLIPVisionModel).module_class text_module = FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class self.vision_model = vision_module(vision_config, dtype=self.dtype) self.text_model = text_module(text_config, dtype=self.dtype) self.visual_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.text_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.logit_scale = self.param( "logit_scale", lambda _, shape: jnp.ones(shape) * self.config.logit_scale_init_value, [] ) def __call__( self, input_ids=None, pixel_values=None, attention_mask=None, position_ids=None, token_type_ids=None, deterministic: bool = True, output_attentions=None, output_hidden_states=None, return_dict=None, ): return_dict = return_dict if return_dict is not None else self.config.return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) # normalized features image_embeds = image_embeds / jnp.linalg.norm(image_embeds, axis=-1, keepdims=True) text_embeds = text_embeds / jnp.linalg.norm(text_embeds, axis=-1, keepdims=True) # cosine similarity as logits logit_scale = jnp.exp(self.logit_scale) logits_per_text = jnp.matmul(text_embeds, image_embeds.T) * logit_scale logits_per_image = logits_per_text.T if not return_dict: return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return FlaxCLIPOutput( logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) @add_start_docstrings(VISION_TEXT_DUAL_ENCODER_START_DOCSTRING) class FlaxVisionTextDualEncoderModel(FlaxPreTrainedModel): config_class = VisionTextDualEncoderConfig module_class = FlaxVisionTextDualEncoderModule def __init__( self, config: VisionTextDualEncoderConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): if not _do_init: raise ValueError( "`FlaxVisionTextDualEncoderModel` cannot be created without initializing, `_do_init` must be `True`." ) if input_shape is None: input_shape = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3)) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensor input_ids = jnp.zeros(input_shape[0], dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape[0]) token_type_ids = jnp.ones_like(input_ids) attention_mask = jnp.ones_like(input_ids) pixel_values = jax.random.normal(rng, input_shape[1]) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_ids, pixel_values, attention_mask, position_ids, token_type_ids)[ "params" ] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def __call__( self, input_ids, pixel_values, attention_mask=None, position_ids=None, token_type_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(pixel_values, dtype=jnp.float32), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), jnp.array(token_type_ids, dtype="i4"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) def get_text_features( self, input_ids, attention_mask=None, position_ids=None, token_type_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False, ): r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) Returns: text_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of text model. """ if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if token_type_ids is None: token_type_ids = jnp.zeros_like(input_ids) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, input_ids, attention_mask, position_ids, token_type_ids, deterministic): text_outputs = module.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, token_type_ids=token_type_ids, deterministic=deterministic, ) pooled_output = text_outputs[1] text_features = module.text_projection(pooled_output) return text_features return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), jnp.array(token_type_ids, dtype="i4"), not train, method=_get_features, rngs=rngs, ) def get_image_features( self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False ): r""" Args: pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`ImageFeatureExtractionMixin`]. See [`ImageFeatureExtractionMixin.__call__`] for details. Returns: image_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of vision model. """ # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, pixel_values, deterministic): vision_outputs = module.vision_model(pixel_values=pixel_values, deterministic=deterministic) pooled_output = vision_outputs[1] # pooled_output image_features = module.visual_projection(pooled_output) return image_features return self.module.apply( {"params": params or self.params}, jnp.array(pixel_values, dtype=jnp.float32), not train, method=_get_features, rngs=rngs, ) @classmethod def from_vision_text_pretrained( cls, vision_model_name_or_path: str = None, text_model_name_or_path: str = None, *model_args, **kwargs, ) -> FlaxPreTrainedModel: """ Params: vision_model_name_or_path (`str`, *optional*, defaults to `None`): Information necessary to initiate the vision model. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided conversion scripts and loading the Flax model afterwards. text_model_name_or_path (`str`, *optional*): Information necessary to initiate the text model. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - A path or url to a *PyTorch checkpoint folder* (e.g, `./pt_model`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch checkpoint in a Flax model using the provided conversion scripts and loading the Flax model afterwards. model_args (remaining positional arguments, *optional*): All remaning positional arguments will be passed to the underlying model's `__init__` method. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). - To update the text configuration, use the prefix *text_* for each configuration parameter. - To update the vision configuration, use the prefix *vision_* for each configuration parameter. - To update the parent model configuration, do not use a prefix for each configuration parameter. Behaves differently depending on whether a `config` is provided or automatically loaded. Example: ```python >>> from transformers import FlaxVisionTextDualEncoderModel >>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized. >>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( ... "google/vit-base-patch16-224", "bert-base-uncased" ... ) >>> # saving model after fine-tuning >>> model.save_pretrained("./vit-bert") >>> # load fine-tuned model >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("./vit-bert") ```""" kwargs_vision = { argument[len("vision_") :]: value for argument, value in kwargs.items() if argument.startswith("vision_") } kwargs_text = { argument[len("text_") :]: value for argument, value in kwargs.items() if argument.startswith("text_") } # remove text, vision kwargs from kwargs for key in kwargs_vision.keys(): del kwargs["vision_" + key] for key in kwargs_text.keys(): del kwargs["text_" + key] # Load and initialize the text and vision model vision_model = kwargs_vision.pop("model", None) if vision_model is None: if vision_model_name_or_path is None: raise ValueError( "If `vision_model` is not defined as an argument, a `vision_model_name_or_path` has to be defined" ) if "config" not in kwargs_vision: vision_config = AutoConfig.from_pretrained(vision_model_name_or_path) if vision_config.model_type == "clip": kwargs_vision["config"] = vision_config.vision_config vision_model = FlaxCLIPVisionModel.from_pretrained( vision_model_name_or_path, *model_args, **kwargs_vision ) else: kwargs_vision["config"] = vision_config vision_model = FlaxAutoModel.from_pretrained(vision_model_name_or_path, *model_args, **kwargs_vision) text_model = kwargs_text.pop("model", None) if text_model is None: if text_model_name_or_path is None: raise ValueError( "If `text_model` is not defined as an argument, a `text_model_name_or_path` has to be defined" ) if "config" not in kwargs_text: text_config = AutoConfig.from_pretrained(text_model_name_or_path) kwargs_text["config"] = text_config text_model = FlaxAutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text) # instantiate config with corresponding kwargs dtype = kwargs.pop("dtype", jnp.float32) config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config, **kwargs) # init model model = cls(config, *model_args, dtype=dtype, **kwargs) model.params["vision_model"] = vision_model.params model.params["text_model"] = text_model.params # the projection layers are always newly initialized when loading the model # using pre-trained vision and text model. logger.warning( "The projection layer and logit scale weights `[('visual_projection', 'kernel'), ('text_projection'," " 'kernel'), ('logit_scale',)]` are newly initialized. You should probably TRAIN this model on a" " down-stream task to be able to use it for predictions and inference." ) return model VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING = r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> import jax >>> from transformers import ( ... FlaxVisionTextDualEncoderModel, ... VisionTextDualEncoderProcessor, ... ViTFeatureExtractor, ... BertTokenizer, ... ) >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224") >>> processor = VisionTextDualEncoderProcessor(feature_extractor, tokenizer) >>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( ... "google/vit-base-patch16-224", "bert-base-uncased" ... ) >>> # contrastive training >>> urls = [ ... "http://images.cocodataset.org/val2017/000000039769.jpg", ... "https://farm3.staticflickr.com/2674/5850229113_4fe05d5265_z.jpg", ... ] >>> images = [Image.open(requests.get(url, stream=True).raw) for url in urls] >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=images, return_tensors="np", padding=True ... ) >>> outputs = model( ... input_ids=inputs.input_ids, ... attention_mask=inputs.attention_mask, ... pixel_values=inputs.pixel_values, ... ) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> # save and load from pretrained >>> model.save_pretrained("vit-bert") >>> model = FlaxVisionTextDualEncoderModel.from_pretrained("vit-bert") >>> # inference >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = jax.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities ``` """ overwrite_call_docstring( FlaxVisionTextDualEncoderModel, VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING + VISION_TEXT_DUAL_ENCODER_MODEL_DOCSTRING, ) append_replace_return_docstrings( FlaxVisionTextDualEncoderModel, output_type=FlaxCLIPOutput, config_class=_CONFIG_FOR_DOC )

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./utils/check_dummies.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import re # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_dummies.py PATH_TO_TRANSFORMERS = "src/transformers" # Matches is_xxx_available() _re_backend = re.compile(r"is\_([a-z_]*)_available()") # Matches from xxx import bla _re_single_line_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n") _re_test_backend = re.compile(r"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)") DUMMY_CONSTANT = """ {0} = None """ DUMMY_CLASS = """ class {0}(metaclass=DummyObject): _backends = {1} def __init__(self, *args, **kwargs): requires_backends(self, {1}) """ DUMMY_FUNCTION = """ def {0}(*args, **kwargs): requires_backends({0}, {1}) """ def find_backend(line): """Find one (or multiple) backend in a code line of the init.""" if _re_test_backend.search(line) is None: return None backends = [b[0] for b in _re_backend.findall(line)] backends.sort() return "_and_".join(backends) def read_init(): """Read the init and extracts PyTorch, TensorFlow, SentencePiece and Tokenizers objects.""" with open(os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Get to the point we do the actual imports for type checking line_index = 0 while not lines[line_index].startswith("if TYPE_CHECKING"): line_index += 1 backend_specific_objects = {} # Go through the end of the file while line_index < len(lines): # If the line is an if is_backend_available, we grab all objects associated. backend = find_backend(lines[line_index]) if backend is not None: while not lines[line_index].startswith(" else:"): line_index += 1 line_index += 1 objects = [] # Until we unindent, add backend objects to the list while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 8): line = lines[line_index] single_line_import_search = _re_single_line_import.search(line) if single_line_import_search is not None: objects.extend(single_line_import_search.groups()[0].split(", ")) elif line.startswith(" " * 12): objects.append(line[12:-2]) line_index += 1 backend_specific_objects[backend] = objects else: line_index += 1 return backend_specific_objects def create_dummy_object(name, backend_name): """Create the code for the dummy object corresponding to `name`.""" if name.isupper(): return DUMMY_CONSTANT.format(name) elif name.islower(): return DUMMY_FUNCTION.format(name, backend_name) else: return DUMMY_CLASS.format(name, backend_name) def create_dummy_files(backend_specific_objects=None): """Create the content of the dummy files.""" if backend_specific_objects is None: backend_specific_objects = read_init() # For special correspondence backend to module name as used in the function requires_modulename dummy_files = {} for backend, objects in backend_specific_objects.items(): backend_name = "[" + ", ".join(f'"{b}"' for b in backend.split("_and_")) + "]" dummy_file = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n" dummy_file += "# flake8: noqa\n" dummy_file += "from ..utils import DummyObject, requires_backends\n\n" dummy_file += "\n".join([create_dummy_object(o, backend_name) for o in objects]) dummy_files[backend] = dummy_file return dummy_files def check_dummies(overwrite=False): """Check if the dummy files are up to date and maybe `overwrite` with the right content.""" dummy_files = create_dummy_files() # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py short_names = {"torch": "pt"} # Locate actual dummy modules and read their content. path = os.path.join(PATH_TO_TRANSFORMERS, "utils") dummy_file_paths = { backend: os.path.join(path, f"dummy_{short_names.get(backend, backend)}_objects.py") for backend in dummy_files.keys() } actual_dummies = {} for backend, file_path in dummy_file_paths.items(): if os.path.isfile(file_path): with open(file_path, "r", encoding="utf-8", newline="\n") as f: actual_dummies[backend] = f.read() else: actual_dummies[backend] = "" for backend in dummy_files.keys(): if dummy_files[backend] != actual_dummies[backend]: if overwrite: print( f"Updating transformers.utils.dummy_{short_names.get(backend, backend)}_objects.py as the main " "__init__ has new objects." ) with open(dummy_file_paths[backend], "w", encoding="utf-8", newline="\n") as f: f.write(dummy_files[backend]) else: raise ValueError( "The main __init__ has objects that are not present in " f"transformers.utils.dummy_{short_names.get(backend, backend)}_objects.py. Run `make fix-copies` " "to fix this." ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") args = parser.parse_args() check_dummies(args.fix_and_overwrite)

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import re # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_dummies.py PATH_TO_TRANSFORMERS = "src/transformers" # Matches is_xxx_available() _re_backend = re.compile(r"is\_([a-z_]*)_available()") # Matches from xxx import bla _re_single_line_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n") _re_test_backend = re.compile(r"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)") DUMMY_CONSTANT = """ {0} = None """ DUMMY_CLASS = """ class {0}(metaclass=DummyObject): _backends = {1} def __init__(self, *args, **kwargs): requires_backends(self, {1}) """ DUMMY_FUNCTION = """ def {0}(*args, **kwargs): requires_backends({0}, {1}) """ def find_backend(line): """Find one (or multiple) backend in a code line of the init.""" if _re_test_backend.search(line) is None: return None backends = [b[0] for b in _re_backend.findall(line)] backends.sort() return "_and_".join(backends) def read_init(): """Read the init and extracts PyTorch, TensorFlow, SentencePiece and Tokenizers objects.""" with open(os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Get to the point we do the actual imports for type checking line_index = 0 while not lines[line_index].startswith("if TYPE_CHECKING"): line_index += 1 backend_specific_objects = {} # Go through the end of the file while line_index < len(lines): # If the line is an if is_backend_available, we grab all objects associated. backend = find_backend(lines[line_index]) if backend is not None: while not lines[line_index].startswith(" else:"): line_index += 1 line_index += 1 objects = [] # Until we unindent, add backend objects to the list while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 8): line = lines[line_index] single_line_import_search = _re_single_line_import.search(line) if single_line_import_search is not None: objects.extend(single_line_import_search.groups()[0].split(", ")) elif line.startswith(" " * 12): objects.append(line[12:-2]) line_index += 1 backend_specific_objects[backend] = objects else: line_index += 1 return backend_specific_objects def create_dummy_object(name, backend_name): """Create the code for the dummy object corresponding to `name`.""" if name.isupper(): return DUMMY_CONSTANT.format(name) elif name.islower(): return DUMMY_FUNCTION.format(name, backend_name) else: return DUMMY_CLASS.format(name, backend_name) def create_dummy_files(backend_specific_objects=None): """Create the content of the dummy files.""" if backend_specific_objects is None: backend_specific_objects = read_init() # For special correspondence backend to module name as used in the function requires_modulename dummy_files = {} for backend, objects in backend_specific_objects.items(): backend_name = "[" + ", ".join(f'"{b}"' for b in backend.split("_and_")) + "]" dummy_file = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n" dummy_file += "# flake8: noqa\n" dummy_file += "from ..utils import DummyObject, requires_backends\n\n" dummy_file += "\n".join([create_dummy_object(o, backend_name) for o in objects]) dummy_files[backend] = dummy_file return dummy_files def check_dummies(overwrite=False): """Check if the dummy files are up to date and maybe `overwrite` with the right content.""" dummy_files = create_dummy_files() # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py short_names = {"torch": "pt"} # Locate actual dummy modules and read their content. path = os.path.join(PATH_TO_TRANSFORMERS, "utils") dummy_file_paths = { backend: os.path.join(path, f"dummy_{short_names.get(backend, backend)}_objects.py") for backend in dummy_files.keys() } actual_dummies = {} for backend, file_path in dummy_file_paths.items(): if os.path.isfile(file_path): with open(file_path, "r", encoding="utf-8", newline="\n") as f: actual_dummies[backend] = f.read() else: actual_dummies[backend] = "" for backend in dummy_files.keys(): if dummy_files[backend] != actual_dummies[backend]: if overwrite: print( f"Updating transformers.utils.dummy_{short_names.get(backend, backend)}_objects.py as the main " "__init__ has new objects." ) with open(dummy_file_paths[backend], "w", encoding="utf-8", newline="\n") as f: f.write(dummy_files[backend]) else: raise ValueError( "The main __init__ has objects that are not present in " f"transformers.utils.dummy_{short_names.get(backend, backend)}_objects.py. Run `make fix-copies` " "to fix this." ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") args = parser.parse_args() check_dummies(args.fix_and_overwrite)

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/models/auto/test_modeling_tf_auto.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import tempfile import unittest from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPT2Config, T5Config, TapasConfig, is_tf_available from transformers.testing_utils import ( DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, RequestCounter, require_tensorflow_probability, require_tf, slow, ) from ..bert.test_modeling_bert import BertModelTester if is_tf_available(): from transformers import ( TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelWithLMHead, TFBertForMaskedLM, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFBertModel, TFFunnelBaseModel, TFFunnelModel, TFGPT2LMHeadModel, TFRobertaForMaskedLM, TFT5ForConditionalGeneration, TFTapasForQuestionAnswering, ) from transformers.models.auto.modeling_tf_auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_MAPPING, ) from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST class NewModelConfig(BertConfig): model_type = "new-model" if is_tf_available(): class TFNewModel(TFBertModel): config_class = NewModelConfig @require_tf class TFAutoModelTest(unittest.TestCase): @slow def test_model_from_pretrained(self): model_name = "bert-base-cased" config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModel.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertModel) @slow def test_model_for_pretraining_from_pretrained(self): model_name = "bert-base-cased" config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForPreTraining.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForPreTraining) @slow def test_model_for_causal_lm(self): for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, GPT2Config) model = TFAutoModelForCausalLM.from_pretrained(model_name) model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFGPT2LMHeadModel) @slow def test_lmhead_model_from_pretrained(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelWithLMHead.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_masked_lm(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForMaskedLM.from_pretrained(model_name) model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_encoder_decoder_lm(self): for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, T5Config) model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name) model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFT5ForConditionalGeneration) @slow def test_sequence_classification_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForSequenceClassification.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForSequenceClassification) @slow def test_question_answering_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForQuestionAnswering.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForQuestionAnswering) @slow @require_tensorflow_probability def test_table_question_answering_model_from_pretrained(self): for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, TapasConfig) model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_name) model, loading_info = TFAutoModelForTableQuestionAnswering.from_pretrained( model_name, output_loading_info=True ) self.assertIsNotNone(model) self.assertIsInstance(model, TFTapasForQuestionAnswering) def test_from_pretrained_identifier(self): model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER) self.assertIsInstance(model, TFBertForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_identifier_from_model_type(self): model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER) self.assertIsInstance(model, TFRobertaForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_pretrained_with_tuple_values(self): # For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel model = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny") self.assertIsInstance(model, TFFunnelModel) config = copy.deepcopy(model.config) config.architectures = ["FunnelBaseModel"] model = TFAutoModel.from_config(config) self.assertIsInstance(model, TFFunnelBaseModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) model = TFAutoModel.from_pretrained(tmp_dir) self.assertIsInstance(model, TFFunnelBaseModel) def test_new_model_registration(self): try: AutoConfig.register("new-model", NewModelConfig) auto_classes = [ TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSequenceClassification, TFAutoModelForTokenClassification, ] for auto_class in auto_classes: with self.subTest(auto_class.__name__): # Wrong config class will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFNewModel) auto_class.register(NewModelConfig, TFNewModel) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFBertModel) # Now that the config is registered, it can be used as any other config with the auto-API tiny_config = BertModelTester(self).get_config() config = NewModelConfig(**tiny_config.to_dict()) model = auto_class.from_config(config) self.assertIsInstance(model, TFNewModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = auto_class.from_pretrained(tmp_dir) self.assertIsInstance(new_model, TFNewModel) finally: if "new-model" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["new-model"] for mapping in ( TF_MODEL_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, ): if NewModelConfig in mapping._extra_content: del mapping._extra_content[NewModelConfig] def test_repo_not_found(self): with self.assertRaisesRegex( EnvironmentError, "bert-base is not a local folder and is not a valid model identifier" ): _ = TFAutoModel.from_pretrained("bert-base") def test_revision_not_found(self): with self.assertRaisesRegex( EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ): _ = TFAutoModel.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa") def test_model_file_not_found(self): with self.assertRaisesRegex( EnvironmentError, "hf-internal-testing/config-no-model does not appear to have a file named tf_model.h5", ): _ = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model") def test_model_from_pt_suggestion(self): with self.assertRaisesRegex(EnvironmentError, "Use `from_pt=True` to load this model"): _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only") def test_cached_model_has_minimum_calls_to_head(self): # Make sure we have cached the model. _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0) # With a sharded checkpoint _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0)

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import tempfile import unittest from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPT2Config, T5Config, TapasConfig, is_tf_available from transformers.testing_utils import ( DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, RequestCounter, require_tensorflow_probability, require_tf, slow, ) from ..bert.test_modeling_bert import BertModelTester if is_tf_available(): from transformers import ( TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelWithLMHead, TFBertForMaskedLM, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFBertModel, TFFunnelBaseModel, TFFunnelModel, TFGPT2LMHeadModel, TFRobertaForMaskedLM, TFT5ForConditionalGeneration, TFTapasForQuestionAnswering, ) from transformers.models.auto.modeling_tf_auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_MAPPING, ) from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST class NewModelConfig(BertConfig): model_type = "new-model" if is_tf_available(): class TFNewModel(TFBertModel): config_class = NewModelConfig @require_tf class TFAutoModelTest(unittest.TestCase): @slow def test_model_from_pretrained(self): model_name = "bert-base-cased" config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModel.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertModel) @slow def test_model_for_pretraining_from_pretrained(self): model_name = "bert-base-cased" config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForPreTraining.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForPreTraining) @slow def test_model_for_causal_lm(self): for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, GPT2Config) model = TFAutoModelForCausalLM.from_pretrained(model_name) model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFGPT2LMHeadModel) @slow def test_lmhead_model_from_pretrained(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelWithLMHead.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_masked_lm(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForMaskedLM.from_pretrained(model_name) model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_encoder_decoder_lm(self): for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, T5Config) model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name) model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFT5ForConditionalGeneration) @slow def test_sequence_classification_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForSequenceClassification.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForSequenceClassification) @slow def test_question_answering_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForQuestionAnswering.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForQuestionAnswering) @slow @require_tensorflow_probability def test_table_question_answering_model_from_pretrained(self): for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, TapasConfig) model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_name) model, loading_info = TFAutoModelForTableQuestionAnswering.from_pretrained( model_name, output_loading_info=True ) self.assertIsNotNone(model) self.assertIsInstance(model, TFTapasForQuestionAnswering) def test_from_pretrained_identifier(self): model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER) self.assertIsInstance(model, TFBertForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_identifier_from_model_type(self): model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER) self.assertIsInstance(model, TFRobertaForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_pretrained_with_tuple_values(self): # For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel model = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny") self.assertIsInstance(model, TFFunnelModel) config = copy.deepcopy(model.config) config.architectures = ["FunnelBaseModel"] model = TFAutoModel.from_config(config) self.assertIsInstance(model, TFFunnelBaseModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) model = TFAutoModel.from_pretrained(tmp_dir) self.assertIsInstance(model, TFFunnelBaseModel) def test_new_model_registration(self): try: AutoConfig.register("new-model", NewModelConfig) auto_classes = [ TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSequenceClassification, TFAutoModelForTokenClassification, ] for auto_class in auto_classes: with self.subTest(auto_class.__name__): # Wrong config class will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFNewModel) auto_class.register(NewModelConfig, TFNewModel) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFBertModel) # Now that the config is registered, it can be used as any other config with the auto-API tiny_config = BertModelTester(self).get_config() config = NewModelConfig(**tiny_config.to_dict()) model = auto_class.from_config(config) self.assertIsInstance(model, TFNewModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = auto_class.from_pretrained(tmp_dir) self.assertIsInstance(new_model, TFNewModel) finally: if "new-model" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["new-model"] for mapping in ( TF_MODEL_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, ): if NewModelConfig in mapping._extra_content: del mapping._extra_content[NewModelConfig] def test_repo_not_found(self): with self.assertRaisesRegex( EnvironmentError, "bert-base is not a local folder and is not a valid model identifier" ): _ = TFAutoModel.from_pretrained("bert-base") def test_revision_not_found(self): with self.assertRaisesRegex( EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ): _ = TFAutoModel.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa") def test_model_file_not_found(self): with self.assertRaisesRegex( EnvironmentError, "hf-internal-testing/config-no-model does not appear to have a file named tf_model.h5", ): _ = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model") def test_model_from_pt_suggestion(self): with self.assertRaisesRegex(EnvironmentError, "Use `from_pt=True` to load this model"): _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only") def test_cached_model_has_minimum_calls_to_head(self): # Make sure we have cached the model. _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0) # With a sharded checkpoint _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0)

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/bert-loses-patience/run_glue_with_pabee.py

# coding=utf-8 # Copyright 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and Microsoft Corporation. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Training and inference using the library models for sequence classification on GLUE (Bert, Albert) with PABEE.""" import argparse import glob import json import logging import os import random import numpy as np import torch from torch import nn from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange import transformers from pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee from transformers import ( WEIGHTS_NAME, AdamW, AlbertConfig, AlbertTokenizer, BertConfig, BertTokenizer, get_linear_schedule_with_warmup, ) from transformers import glue_compute_metrics as compute_metrics from transformers import glue_convert_examples_to_features as convert_examples_to_features from transformers import glue_output_modes as output_modes from transformers import glue_processors as processors from transformers.trainer_utils import is_main_process try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) MODEL_CLASSES = { "bert": (BertConfig, BertForSequenceClassificationWithPabee, BertTokenizer), "albert": (AlbertConfig, AlbertForSequenceClassificationWithPabee, AlbertTokenizer), } def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def train(args, train_dataset, model, tokenizer): """Train the model""" if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer and schedule (linear warmup and decay) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) # Check if saved optimizer or scheduler states exist if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile( os.path.join(args.model_name_or_path, "scheduler.pt") ): # Load in optimizer and scheduler states optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"))) scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"))) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True, ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) global_step = 0 epochs_trained = 0 steps_trained_in_current_epoch = 0 # Check if continuing training from a checkpoint if os.path.exists(args.model_name_or_path): # set global_step to gobal_step of last saved checkpoint from model path global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0]) epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) logger.info(" Continuing training from checkpoint, will skip to saved global_step") logger.info(" Continuing training from epoch %d", epochs_trained) logger.info(" Continuing training from global step %d", global_step) logger.info( " Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch, ) tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange( epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0], ) set_seed(args) # Added here for reproductibility for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): # Skip past any already trained steps if resuming training if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 continue model.train() batch = tuple(t.to(args.device) for t in batch) inputs = { "input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3], } inputs["token_type_ids"] = batch[2] outputs = model(**inputs) loss = outputs[0] # model outputs are always tuple in transformers (see doc) if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: logs = {} if ( args.local_rank == -1 and args.evaluate_during_training ): # Only evaluate when single GPU otherwise metrics may not average well results = evaluate(args, model, tokenizer) for key, value in results.items(): eval_key = "eval_{}".format(key) logs[eval_key] = value loss_scalar = (tr_loss - logging_loss) / args.logging_steps learning_rate_scalar = scheduler.get_lr()[0] logs["learning_rate"] = learning_rate_scalar logs["loss"] = loss_scalar logging_loss = tr_loss for key, value in logs.items(): tb_writer.add_scalar(key, value, global_step) print(json.dumps({**logs, **{"step": global_step}})) if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) logger.info("Saving optimizer and scheduler states to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, prefix="", patience=0): if args.model_type == "albert": model.albert.set_regression_threshold(args.regression_threshold) model.albert.set_patience(patience) model.albert.reset_stats() elif args.model_type == "bert": model.bert.set_regression_threshold(args.regression_threshold) model.bert.set_patience(patience) model.bert.reset_stats() else: raise NotImplementedError() # Loop to handle MNLI double evaluation (matched, mis-matched) eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,) eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,) results = {} for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs): eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True) if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: os.makedirs(eval_output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu eval if args.n_gpu > 1 and not isinstance(model, nn.DataParallel): model = nn.DataParallel(model) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(eval_dataset)) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss = 0.0 nb_eval_steps = 0 preds = None out_label_ids = None for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = { "input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3], } inputs["token_type_ids"] = batch[2] outputs = model(**inputs) tmp_eval_loss, logits = outputs[:2] eval_loss += tmp_eval_loss.mean().item() nb_eval_steps += 1 if preds is None: preds = logits.detach().cpu().numpy() out_label_ids = inputs["labels"].detach().cpu().numpy() else: preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0) eval_loss = eval_loss / nb_eval_steps if args.output_mode == "classification": preds = np.argmax(preds, axis=1) elif args.output_mode == "regression": preds = np.squeeze(preds) result = compute_metrics(eval_task, preds, out_label_ids) results.update(result) output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results {} *****".format(prefix)) for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) print(" %s = %s" % (key, str(result[key]))) writer.write("%s = %s\n" % (key, str(result[key]))) if args.eval_all_checkpoints and patience != 0: if args.model_type == "albert": model.albert.log_stats() elif args.model_type == "bert": model.bert.log_stats() else: raise NotImplementedError() return results def load_and_cache_examples(args, task, tokenizer, evaluate=False): if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache processor = processors[task]() output_mode = output_modes[task] # Load data features from cache or dataset file cached_features_file = os.path.join( args.data_dir, "cached_{}_{}_{}_{}".format( "dev" if evaluate else "train", list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length), str(task), ), ) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", args.data_dir) label_list = processor.get_labels() if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta", "xlmroberta"]: # HACK(label indices are swapped in RoBERTa pretrained model) label_list[1], label_list[2] = label_list[2], label_list[1] examples = ( processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir) ) features = convert_examples_to_features( examples, tokenizer, label_list=label_list, max_length=args.max_seq_length, output_mode=output_mode, ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0 and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long) all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long) if output_mode == "classification": all_labels = torch.tensor([f.label for f in features], dtype=torch.long) elif output_mode == "regression": all_labels = torch.tensor([f.label for f in features], dtype=torch.float) dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels) return dataset def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--data_dir", default=None, type=str, required=True, help="The input data dir. Should contain the .tsv files (or other data files) for the task.", ) parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name.", ) parser.add_argument( "--task_name", default=None, type=str, required=True, help="The name of the task to train selected in the list: " + ", ".join(processors.keys()), ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--patience", default="0", type=str, required=False, ) parser.add_argument( "--regression_threshold", default=0, type=float, required=False, ) # Other parameters parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from huggingface.co", ) parser.add_argument( "--max_seq_length", default=128, type=int, help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ), ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step.", ) parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model.", ) parser.add_argument( "--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.", ) parser.add_argument( "--per_gpu_eval_batch_size", default=1, type=int, help="Batch size per GPU/CPU for evaluation.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.", ) parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.", ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.") parser.add_argument( "--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.", ) parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets", ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help=( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html" ), ) parser.add_argument( "--local_rank", type=int, default=-1, help="For distributed training: local_rank", ) parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.") parser.add_argument("--server_port", type=str, default="", help="For distant debugging.") args = parser.parse_args() if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir ): raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir ) ) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = torch.cuda.device_count() else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(args.local_rank): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Set seed set_seed(args) # Prepare GLUE task args.task_name = args.task_name.lower() if args.task_name not in processors: raise ValueError("Task not found: %s" % (args.task_name)) processor = processors[args.task_name]() args.output_mode = output_modes[args.task_name] label_list = processor.get_labels() num_labels = len(label_list) if args.patience != "0" and args.per_gpu_eval_batch_size != 1: raise ValueError("The eval batch size must be 1 with PABEE inference on.") # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name, cache_dir=args.cache_dir if args.cache_dir else None, ) tokenizer = tokenizer_class.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None, ) model = model_class.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) if args.local_rank == 0: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab model.to(args.device) print("Total Model Parameters:", sum(param.numel() for param in model.parameters())) output_layers_param_num = sum(param.numel() for param in model.classifiers.parameters()) print("Output Layers Parameters:", output_layers_param_num) single_output_layer_param_num = sum(param.numel() for param in model.classifiers[0].parameters()) print( "Added Output Layers Parameters:", output_layers_param_num - single_output_layer_param_num, ) logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Load a trained model and vocabulary that you have fine-tuned model = model_class.from_pretrained(args.output_dir) tokenizer = tokenizer_class.from_pretrained(args.output_dir) model.to(args.device) # Evaluation results = {} if args.do_eval and args.local_rank in [-1, 0]: patience_list = [int(x) for x in args.patience.split(",")] tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = list( os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ) logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else "" model = model_class.from_pretrained(checkpoint) model.to(args.device) print(f"Evaluation for checkpoint {prefix}") for patience in patience_list: result = evaluate(args, model, tokenizer, prefix=prefix, patience=patience) result = dict((k + "_{}".format(global_step), v) for k, v in result.items()) results.update(result) return results if __name__ == "__main__": main()

# coding=utf-8 # Copyright 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and Microsoft Corporation. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Training and inference using the library models for sequence classification on GLUE (Bert, Albert) with PABEE.""" import argparse import glob import json import logging import os import random import numpy as np import torch from torch import nn from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange import transformers from pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee from transformers import ( WEIGHTS_NAME, AdamW, AlbertConfig, AlbertTokenizer, BertConfig, BertTokenizer, get_linear_schedule_with_warmup, ) from transformers import glue_compute_metrics as compute_metrics from transformers import glue_convert_examples_to_features as convert_examples_to_features from transformers import glue_output_modes as output_modes from transformers import glue_processors as processors from transformers.trainer_utils import is_main_process try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) MODEL_CLASSES = { "bert": (BertConfig, BertForSequenceClassificationWithPabee, BertTokenizer), "albert": (AlbertConfig, AlbertForSequenceClassificationWithPabee, AlbertTokenizer), } def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def train(args, train_dataset, model, tokenizer): """Train the model""" if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs # Prepare optimizer and schedule (linear warmup and decay) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) # Check if saved optimizer or scheduler states exist if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile( os.path.join(args.model_name_or_path, "scheduler.pt") ): # Load in optimizer and scheduler states optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"))) scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"))) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True, ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) global_step = 0 epochs_trained = 0 steps_trained_in_current_epoch = 0 # Check if continuing training from a checkpoint if os.path.exists(args.model_name_or_path): # set global_step to gobal_step of last saved checkpoint from model path global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0]) epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) logger.info(" Continuing training from checkpoint, will skip to saved global_step") logger.info(" Continuing training from epoch %d", epochs_trained) logger.info(" Continuing training from global step %d", global_step) logger.info( " Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch, ) tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange( epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0], ) set_seed(args) # Added here for reproductibility for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): # Skip past any already trained steps if resuming training if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 continue model.train() batch = tuple(t.to(args.device) for t in batch) inputs = { "input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3], } inputs["token_type_ids"] = batch[2] outputs = model(**inputs) loss = outputs[0] # model outputs are always tuple in transformers (see doc) if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: logs = {} if ( args.local_rank == -1 and args.evaluate_during_training ): # Only evaluate when single GPU otherwise metrics may not average well results = evaluate(args, model, tokenizer) for key, value in results.items(): eval_key = "eval_{}".format(key) logs[eval_key] = value loss_scalar = (tr_loss - logging_loss) / args.logging_steps learning_rate_scalar = scheduler.get_lr()[0] logs["learning_rate"] = learning_rate_scalar logs["loss"] = loss_scalar logging_loss = tr_loss for key, value in logs.items(): tb_writer.add_scalar(key, value, global_step) print(json.dumps({**logs, **{"step": global_step}})) if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) logger.info("Saving optimizer and scheduler states to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, prefix="", patience=0): if args.model_type == "albert": model.albert.set_regression_threshold(args.regression_threshold) model.albert.set_patience(patience) model.albert.reset_stats() elif args.model_type == "bert": model.bert.set_regression_threshold(args.regression_threshold) model.bert.set_patience(patience) model.bert.reset_stats() else: raise NotImplementedError() # Loop to handle MNLI double evaluation (matched, mis-matched) eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,) eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,) results = {} for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs): eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True) if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]: os.makedirs(eval_output_dir) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu eval if args.n_gpu > 1 and not isinstance(model, nn.DataParallel): model = nn.DataParallel(model) # Eval! logger.info("***** Running evaluation {} *****".format(prefix)) logger.info(" Num examples = %d", len(eval_dataset)) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss = 0.0 nb_eval_steps = 0 preds = None out_label_ids = None for batch in tqdm(eval_dataloader, desc="Evaluating"): model.eval() batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = { "input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3], } inputs["token_type_ids"] = batch[2] outputs = model(**inputs) tmp_eval_loss, logits = outputs[:2] eval_loss += tmp_eval_loss.mean().item() nb_eval_steps += 1 if preds is None: preds = logits.detach().cpu().numpy() out_label_ids = inputs["labels"].detach().cpu().numpy() else: preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0) eval_loss = eval_loss / nb_eval_steps if args.output_mode == "classification": preds = np.argmax(preds, axis=1) elif args.output_mode == "regression": preds = np.squeeze(preds) result = compute_metrics(eval_task, preds, out_label_ids) results.update(result) output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results {} *****".format(prefix)) for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) print(" %s = %s" % (key, str(result[key]))) writer.write("%s = %s\n" % (key, str(result[key]))) if args.eval_all_checkpoints and patience != 0: if args.model_type == "albert": model.albert.log_stats() elif args.model_type == "bert": model.bert.log_stats() else: raise NotImplementedError() return results def load_and_cache_examples(args, task, tokenizer, evaluate=False): if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache processor = processors[task]() output_mode = output_modes[task] # Load data features from cache or dataset file cached_features_file = os.path.join( args.data_dir, "cached_{}_{}_{}_{}".format( "dev" if evaluate else "train", list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length), str(task), ), ) if os.path.exists(cached_features_file) and not args.overwrite_cache: logger.info("Loading features from cached file %s", cached_features_file) features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", args.data_dir) label_list = processor.get_labels() if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta", "xlmroberta"]: # HACK(label indices are swapped in RoBERTa pretrained model) label_list[1], label_list[2] = label_list[2], label_list[1] examples = ( processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir) ) features = convert_examples_to_features( examples, tokenizer, label_list=label_list, max_length=args.max_seq_length, output_mode=output_mode, ) if args.local_rank in [-1, 0]: logger.info("Saving features into cached file %s", cached_features_file) torch.save(features, cached_features_file) if args.local_rank == 0 and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long) all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long) if output_mode == "classification": all_labels = torch.tensor([f.label for f in features], dtype=torch.long) elif output_mode == "regression": all_labels = torch.tensor([f.label for f in features], dtype=torch.float) dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels) return dataset def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--data_dir", default=None, type=str, required=True, help="The input data dir. Should contain the .tsv files (or other data files) for the task.", ) parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name.", ) parser.add_argument( "--task_name", default=None, type=str, required=True, help="The name of the task to train selected in the list: " + ", ".join(processors.keys()), ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--patience", default="0", type=str, required=False, ) parser.add_argument( "--regression_threshold", default=0, type=float, required=False, ) # Other parameters parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from huggingface.co", ) parser.add_argument( "--max_seq_length", default=128, type=int, help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ), ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step.", ) parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model.", ) parser.add_argument( "--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.", ) parser.add_argument( "--per_gpu_eval_batch_size", default=1, type=int, help="Batch size per GPU/CPU for evaluation.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.", ) parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.", ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.") parser.add_argument( "--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.", ) parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory", ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets", ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help=( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html" ), ) parser.add_argument( "--local_rank", type=int, default=-1, help="For distributed training: local_rank", ) parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.") parser.add_argument("--server_port", type=str, default="", help="For distant debugging.") args = parser.parse_args() if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir ): raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir ) ) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = torch.cuda.device_count() else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(args.local_rank): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Set seed set_seed(args) # Prepare GLUE task args.task_name = args.task_name.lower() if args.task_name not in processors: raise ValueError("Task not found: %s" % (args.task_name)) processor = processors[args.task_name]() args.output_mode = output_modes[args.task_name] label_list = processor.get_labels() num_labels = len(label_list) if args.patience != "0" and args.per_gpu_eval_batch_size != 1: raise ValueError("The eval batch size must be 1 with PABEE inference on.") # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab args.model_type = args.model_type.lower() config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type] config = config_class.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name, cache_dir=args.cache_dir if args.cache_dir else None, ) tokenizer = tokenizer_class.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case, cache_dir=args.cache_dir if args.cache_dir else None, ) model = model_class.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) if args.local_rank == 0: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab model.to(args.device) print("Total Model Parameters:", sum(param.numel() for param in model.parameters())) output_layers_param_num = sum(param.numel() for param in model.classifiers.parameters()) print("Output Layers Parameters:", output_layers_param_num) single_output_layer_param_num = sum(param.numel() for param in model.classifiers[0].parameters()) print( "Added Output Layers Parameters:", output_layers_param_num - single_output_layer_param_num, ) logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False) global_step, tr_loss = train(args, train_dataset, model, tokenizer) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Load a trained model and vocabulary that you have fine-tuned model = model_class.from_pretrained(args.output_dir) tokenizer = tokenizer_class.from_pretrained(args.output_dir) model.to(args.device) # Evaluation results = {} if args.do_eval and args.local_rank in [-1, 0]: patience_list = [int(x) for x in args.patience.split(",")] tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = list( os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ) logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else "" model = model_class.from_pretrained(checkpoint) model.to(args.device) print(f"Evaluation for checkpoint {prefix}") for patience in patience_list: result = evaluate(args, model, tokenizer, prefix=prefix, patience=patience) result = dict((k + "_{}".format(global_step), v) for k, v in result.items()) results.update(result) return results if __name__ == "__main__": main()

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./docs/source/en/model_doc/electra.mdx

<!--Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # ELECTRA ## Overview The ELECTRA model was proposed in the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). ELECTRA is a new pretraining approach which trains two transformer models: the generator and the discriminator. The generator's role is to replace tokens in a sequence, and is therefore trained as a masked language model. The discriminator, which is the model we're interested in, tries to identify which tokens were replaced by the generator in the sequence. The abstract from the paper is the following: *Masked language modeling (MLM) pretraining methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a more sample-efficient pretraining task called replaced token detection. Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments demonstrate this new pretraining task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out. As a result, the contextual representations learned by our approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained using 30x more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale, where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when using the same amount of compute.* Tips: - ELECTRA is the pretraining approach, therefore there is nearly no changes done to the underlying model: BERT. The only change is the separation of the embedding size and the hidden size: the embedding size is generally smaller, while the hidden size is larger. An additional projection layer (linear) is used to project the embeddings from their embedding size to the hidden size. In the case where the embedding size is the same as the hidden size, no projection layer is used. - The ELECTRA checkpoints saved using [Google Research's implementation](https://github.com/google-research/electra) contain both the generator and discriminator. The conversion script requires the user to name which model to export into the correct architecture. Once converted to the HuggingFace format, these checkpoints may be loaded into all available ELECTRA models, however. This means that the discriminator may be loaded in the [`ElectraForMaskedLM`] model, and the generator may be loaded in the [`ElectraForPreTraining`] model (the classification head will be randomly initialized as it doesn't exist in the generator). This model was contributed by [lysandre](https://huggingface.co/lysandre). The original code can be found [here](https://github.com/google-research/electra). ## ElectraConfig [[autodoc]] ElectraConfig ## ElectraTokenizer [[autodoc]] ElectraTokenizer ## ElectraTokenizerFast [[autodoc]] ElectraTokenizerFast ## Electra specific outputs [[autodoc]] models.electra.modeling_electra.ElectraForPreTrainingOutput [[autodoc]] models.electra.modeling_tf_electra.TFElectraForPreTrainingOutput ## ElectraModel [[autodoc]] ElectraModel - forward ## ElectraForPreTraining [[autodoc]] ElectraForPreTraining - forward ## ElectraForCausalLM [[autodoc]] ElectraForCausalLM - forward ## ElectraForMaskedLM [[autodoc]] ElectraForMaskedLM - forward ## ElectraForSequenceClassification [[autodoc]] ElectraForSequenceClassification - forward ## ElectraForMultipleChoice [[autodoc]] ElectraForMultipleChoice - forward ## ElectraForTokenClassification [[autodoc]] ElectraForTokenClassification - forward ## ElectraForQuestionAnswering [[autodoc]] ElectraForQuestionAnswering - forward ## TFElectraModel [[autodoc]] TFElectraModel - call ## TFElectraForPreTraining [[autodoc]] TFElectraForPreTraining - call ## TFElectraForMaskedLM [[autodoc]] TFElectraForMaskedLM - call ## TFElectraForSequenceClassification [[autodoc]] TFElectraForSequenceClassification - call ## TFElectraForMultipleChoice [[autodoc]] TFElectraForMultipleChoice - call ## TFElectraForTokenClassification [[autodoc]] TFElectraForTokenClassification - call ## TFElectraForQuestionAnswering [[autodoc]] TFElectraForQuestionAnswering - call ## FlaxElectraModel [[autodoc]] FlaxElectraModel - __call__ ## FlaxElectraForPreTraining [[autodoc]] FlaxElectraForPreTraining - __call__ ## FlaxElectraForCausalLM [[autodoc]] FlaxElectraForCausalLM - __call__ ## FlaxElectraForMaskedLM [[autodoc]] FlaxElectraForMaskedLM - __call__ ## FlaxElectraForSequenceClassification [[autodoc]] FlaxElectraForSequenceClassification - __call__ ## FlaxElectraForMultipleChoice [[autodoc]] FlaxElectraForMultipleChoice - __call__ ## FlaxElectraForTokenClassification [[autodoc]] FlaxElectraForTokenClassification - __call__ ## FlaxElectraForQuestionAnswering [[autodoc]] FlaxElectraForQuestionAnswering - __call__

<!--Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # ELECTRA ## Overview The ELECTRA model was proposed in the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB). ELECTRA is a new pretraining approach which trains two transformer models: the generator and the discriminator. The generator's role is to replace tokens in a sequence, and is therefore trained as a masked language model. The discriminator, which is the model we're interested in, tries to identify which tokens were replaced by the generator in the sequence. The abstract from the paper is the following: *Masked language modeling (MLM) pretraining methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a more sample-efficient pretraining task called replaced token detection. Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments demonstrate this new pretraining task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out. As a result, the contextual representations learned by our approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained using 30x more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale, where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when using the same amount of compute.* Tips: - ELECTRA is the pretraining approach, therefore there is nearly no changes done to the underlying model: BERT. The only change is the separation of the embedding size and the hidden size: the embedding size is generally smaller, while the hidden size is larger. An additional projection layer (linear) is used to project the embeddings from their embedding size to the hidden size. In the case where the embedding size is the same as the hidden size, no projection layer is used. - The ELECTRA checkpoints saved using [Google Research's implementation](https://github.com/google-research/electra) contain both the generator and discriminator. The conversion script requires the user to name which model to export into the correct architecture. Once converted to the HuggingFace format, these checkpoints may be loaded into all available ELECTRA models, however. This means that the discriminator may be loaded in the [`ElectraForMaskedLM`] model, and the generator may be loaded in the [`ElectraForPreTraining`] model (the classification head will be randomly initialized as it doesn't exist in the generator). This model was contributed by [lysandre](https://huggingface.co/lysandre). The original code can be found [here](https://github.com/google-research/electra). ## ElectraConfig [[autodoc]] ElectraConfig ## ElectraTokenizer [[autodoc]] ElectraTokenizer ## ElectraTokenizerFast [[autodoc]] ElectraTokenizerFast ## Electra specific outputs [[autodoc]] models.electra.modeling_electra.ElectraForPreTrainingOutput [[autodoc]] models.electra.modeling_tf_electra.TFElectraForPreTrainingOutput ## ElectraModel [[autodoc]] ElectraModel - forward ## ElectraForPreTraining [[autodoc]] ElectraForPreTraining - forward ## ElectraForCausalLM [[autodoc]] ElectraForCausalLM - forward ## ElectraForMaskedLM [[autodoc]] ElectraForMaskedLM - forward ## ElectraForSequenceClassification [[autodoc]] ElectraForSequenceClassification - forward ## ElectraForMultipleChoice [[autodoc]] ElectraForMultipleChoice - forward ## ElectraForTokenClassification [[autodoc]] ElectraForTokenClassification - forward ## ElectraForQuestionAnswering [[autodoc]] ElectraForQuestionAnswering - forward ## TFElectraModel [[autodoc]] TFElectraModel - call ## TFElectraForPreTraining [[autodoc]] TFElectraForPreTraining - call ## TFElectraForMaskedLM [[autodoc]] TFElectraForMaskedLM - call ## TFElectraForSequenceClassification [[autodoc]] TFElectraForSequenceClassification - call ## TFElectraForMultipleChoice [[autodoc]] TFElectraForMultipleChoice - call ## TFElectraForTokenClassification [[autodoc]] TFElectraForTokenClassification - call ## TFElectraForQuestionAnswering [[autodoc]] TFElectraForQuestionAnswering - call ## FlaxElectraModel [[autodoc]] FlaxElectraModel - __call__ ## FlaxElectraForPreTraining [[autodoc]] FlaxElectraForPreTraining - __call__ ## FlaxElectraForCausalLM [[autodoc]] FlaxElectraForCausalLM - __call__ ## FlaxElectraForMaskedLM [[autodoc]] FlaxElectraForMaskedLM - __call__ ## FlaxElectraForSequenceClassification [[autodoc]] FlaxElectraForSequenceClassification - __call__ ## FlaxElectraForMultipleChoice [[autodoc]] FlaxElectraForMultipleChoice - __call__ ## FlaxElectraForTokenClassification [[autodoc]] FlaxElectraForTokenClassification - __call__ ## FlaxElectraForQuestionAnswering [[autodoc]] FlaxElectraForQuestionAnswering - __call__

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./src/transformers/models/canine/modeling_canine.py

# coding=utf-8 # Copyright 2021 Google AI The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch CANINE model.""" import copy import math import os from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, ModelOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_canine import CanineConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/canine-s" _CONFIG_FOR_DOC = "CanineConfig" _TOKENIZER_FOR_DOC = "CanineTokenizer" CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/canine-s", "google/canine-r" # See all CANINE models at https://huggingface.co/models?filter=canine ] # Support up to 16 hash functions. _PRIMES = [31, 43, 59, 61, 73, 97, 103, 113, 137, 149, 157, 173, 181, 193, 211, 223] @dataclass class CanineModelOutputWithPooling(ModelOutput): """ Output type of [`CanineModel`]. Based on [`~modeling_outputs.BaseModelOutputWithPooling`], but with slightly different `hidden_states` and `attentions`, as these also include the hidden states and attentions of the shallow Transformer encoders. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model (i.e. the output of the final shallow Transformer encoder). pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Hidden-state of the first token of the sequence (classification token) at the last layer of the deep Transformer encoder, further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the input to each encoder + one for the output of each layer of each encoder) of shape `(batch_size, sequence_length, hidden_size)` and `(batch_size, sequence_length // config.downsampling_rate, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial input to each Transformer encoder. The hidden states of the shallow encoders have length `sequence_length`, but the hidden states of the deep encoder have length `sequence_length` // `config.downsampling_rate`. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of the 3 Transformer encoders of shape `(batch_size, num_heads, sequence_length, sequence_length)` and `(batch_size, num_heads, sequence_length // config.downsampling_rate, sequence_length // config.downsampling_rate)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None def load_tf_weights_in_canine(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model # also discard the cls weights (which were used for the next sentence prediction pre-training task) if any( n in [ "adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step", "cls", "autoregressive_decoder", "char_output_weights", ] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue # if first scope name starts with "bert", change it to "encoder" if name[0] == "bert": name[0] = "encoder" # remove "embeddings" middle name of HashBucketCodepointEmbedders elif name[1] == "embeddings": name.remove(name[1]) # rename segment_embeddings to token_type_embeddings elif name[1] == "segment_embeddings": name[1] = "token_type_embeddings" # rename initial convolutional projection layer elif name[1] == "initial_char_encoder": name = ["chars_to_molecules"] + name[-2:] # rename final convolutional projection layer elif name[0] == "final_char_encoder" and name[1] in ["LayerNorm", "conv"]: name = ["projection"] + name[1:] pointer = model for m_name in name: if (re.fullmatch(r"[A-Za-z]+_\d+", m_name)) and "Embedder" not in m_name: scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name[-10:] in [f"Embedder_{i}" for i in range(8)]: pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class CanineEmbeddings(nn.Module): """Construct the character, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.config = config # character embeddings shard_embedding_size = config.hidden_size // config.num_hash_functions for i in range(config.num_hash_functions): name = f"HashBucketCodepointEmbedder_{i}" setattr(self, name, nn.Embedding(config.num_hash_buckets, shard_embedding_size)) self.char_position_embeddings = nn.Embedding(config.num_hash_buckets, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") def _hash_bucket_tensors(self, input_ids, num_hashes: int, num_buckets: int): """ Converts ids to hash bucket ids via multiple hashing. Args: input_ids: The codepoints or other IDs to be hashed. num_hashes: The number of hash functions to use. num_buckets: The number of hash buckets (i.e. embeddings in each table). Returns: A list of tensors, each of which is the hash bucket IDs from one hash function. """ if num_hashes > len(_PRIMES): raise ValueError(f"`num_hashes` must be <= {len(_PRIMES)}") primes = _PRIMES[:num_hashes] result_tensors = [] for prime in primes: hashed = ((input_ids + 1) * prime) % num_buckets result_tensors.append(hashed) return result_tensors def _embed_hash_buckets(self, input_ids, embedding_size: int, num_hashes: int, num_buckets: int): """Converts IDs (e.g. codepoints) into embeddings via multiple hashing.""" if embedding_size % num_hashes != 0: raise ValueError(f"Expected `embedding_size` ({embedding_size}) % `num_hashes` ({num_hashes}) == 0") hash_bucket_tensors = self._hash_bucket_tensors(input_ids, num_hashes=num_hashes, num_buckets=num_buckets) embedding_shards = [] for i, hash_bucket_ids in enumerate(hash_bucket_tensors): name = f"HashBucketCodepointEmbedder_{i}" shard_embeddings = getattr(self, name)(hash_bucket_ids) embedding_shards.append(shard_embeddings) return torch.cat(embedding_shards, dim=-1) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self._embed_hash_buckets( input_ids, self.config.hidden_size, self.config.num_hash_functions, self.config.num_hash_buckets ) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.char_position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class CharactersToMolecules(nn.Module): """Convert character sequence to initial molecule sequence (i.e. downsample) using strided convolutions.""" def __init__(self, config): super().__init__() self.conv = nn.Conv1d( in_channels=config.hidden_size, out_channels=config.hidden_size, kernel_size=config.downsampling_rate, stride=config.downsampling_rate, ) self.activation = ACT2FN[config.hidden_act] # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, char_encoding: torch.Tensor) -> torch.Tensor: # `cls_encoding`: [batch, 1, hidden_size] cls_encoding = char_encoding[:, 0:1, :] # char_encoding has shape [batch, char_seq, hidden_size] # We transpose it to be [batch, hidden_size, char_seq] char_encoding = torch.transpose(char_encoding, 1, 2) downsampled = self.conv(char_encoding) downsampled = torch.transpose(downsampled, 1, 2) downsampled = self.activation(downsampled) # Truncate the last molecule in order to reserve a position for [CLS]. # Often, the last position is never used (unless we completely fill the # text buffer). This is important in order to maintain alignment on TPUs # (i.e. a multiple of 128). downsampled_truncated = downsampled[:, 0:-1, :] # We also keep [CLS] as a separate sequence position since we always # want to reserve a position (and the model capacity that goes along # with that) in the deep BERT stack. # `result`: [batch, molecule_seq, molecule_dim] result = torch.cat([cls_encoding, downsampled_truncated], dim=1) result = self.LayerNorm(result) return result class ConvProjection(nn.Module): """ Project representations from hidden_size*2 back to hidden_size across a window of w = config.upsampling_kernel_size characters. """ def __init__(self, config): super().__init__() self.config = config self.conv = nn.Conv1d( in_channels=config.hidden_size * 2, out_channels=config.hidden_size, kernel_size=config.upsampling_kernel_size, stride=1, ) self.activation = ACT2FN[config.hidden_act] # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, inputs: torch.Tensor, final_seq_char_positions: Optional[torch.Tensor] = None, ) -> torch.Tensor: # inputs has shape [batch, mol_seq, molecule_hidden_size+char_hidden_final] # we transpose it to be [batch, molecule_hidden_size+char_hidden_final, mol_seq] inputs = torch.transpose(inputs, 1, 2) # PyTorch < 1.9 does not support padding="same" (which is used in the original implementation), # so we pad the tensor manually before passing it to the conv layer # based on https://github.com/google-research/big_transfer/blob/49afe42338b62af9fbe18f0258197a33ee578a6b/bit_tf2/models.py#L36-L38 pad_total = self.config.upsampling_kernel_size - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg pad = nn.ConstantPad1d((pad_beg, pad_end), 0) # `result`: shape (batch_size, char_seq_len, hidden_size) result = self.conv(pad(inputs)) result = torch.transpose(result, 1, 2) result = self.activation(result) result = self.LayerNorm(result) result = self.dropout(result) final_char_seq = result if final_seq_char_positions is not None: # Limit transformer query seq and attention mask to these character # positions to greatly reduce the compute cost. Typically, this is just # done for the MLM training task. # TODO add support for MLM raise NotImplementedError("CanineForMaskedLM is currently not supported") else: query_seq = final_char_seq return query_seq class CanineSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, from_tensor: torch.Tensor, to_tensor: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: mixed_query_layer = self.query(from_tensor) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. key_layer = self.transpose_for_scores(self.key(to_tensor)) value_layer = self.transpose_for_scores(self.value(to_tensor)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": seq_length = from_tensor.size()[1] position_ids_l = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(-1, 1) position_ids_r = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: if attention_mask.ndim == 3: # if attention_mask is 3D, do the following: attention_mask = torch.unsqueeze(attention_mask, dim=1) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and the dtype's smallest value for masked positions. attention_mask = (1.0 - attention_mask.float()) * torch.finfo(attention_scores.dtype).min # Apply the attention mask (precomputed for all layers in CanineModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class CanineSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class CanineAttention(nn.Module): """ Additional arguments related to local attention: - **local** (`bool`, *optional*, defaults to `False`) -- Whether to apply local attention. - **always_attend_to_first_position** (`bool`, *optional*, defaults to `False`) -- Should all blocks be able to attend to the `to_tensor`'s first position (e.g. a [CLS] position)? - **first_position_attends_to_all** (`bool`, *optional*, defaults to `False`) -- Should the *from_tensor*'s first position be able to attend to all positions within the *from_tensor*? - **attend_from_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in `from_tensor`. - **attend_from_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to skip when moving to the next block in `from_tensor`. - **attend_to_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in *to_tensor*. - **attend_to_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to skip when moving to the next block in `to_tensor`. """ def __init__( self, config, local=False, always_attend_to_first_position: bool = False, first_position_attends_to_all: bool = False, attend_from_chunk_width: int = 128, attend_from_chunk_stride: int = 128, attend_to_chunk_width: int = 128, attend_to_chunk_stride: int = 128, ): super().__init__() self.self = CanineSelfAttention(config) self.output = CanineSelfOutput(config) self.pruned_heads = set() # additional arguments related to local attention self.local = local if attend_from_chunk_width < attend_from_chunk_stride: raise ValueError( "`attend_from_chunk_width` < `attend_from_chunk_stride` would cause sequence positions to get skipped." ) if attend_to_chunk_width < attend_to_chunk_stride: raise ValueError( "`attend_to_chunk_width` < `attend_to_chunk_stride`would cause sequence positions to get skipped." ) self.always_attend_to_first_position = always_attend_to_first_position self.first_position_attends_to_all = first_position_attends_to_all self.attend_from_chunk_width = attend_from_chunk_width self.attend_from_chunk_stride = attend_from_chunk_stride self.attend_to_chunk_width = attend_to_chunk_width self.attend_to_chunk_stride = attend_to_chunk_stride def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: if not self.local: self_outputs = self.self(hidden_states, hidden_states, attention_mask, head_mask, output_attentions) attention_output = self_outputs[0] else: from_seq_length = to_seq_length = hidden_states.shape[1] from_tensor = to_tensor = hidden_states # Create chunks (windows) that we will attend *from* and then concatenate them. from_chunks = [] if self.first_position_attends_to_all: from_chunks.append((0, 1)) # We must skip this first position so that our output sequence is the # correct length (this matters in the *from* sequence only). from_start = 1 else: from_start = 0 for chunk_start in range(from_start, from_seq_length, self.attend_from_chunk_stride): chunk_end = min(from_seq_length, chunk_start + self.attend_from_chunk_width) from_chunks.append((chunk_start, chunk_end)) # Determine the chunks (windows) that will will attend *to*. to_chunks = [] if self.first_position_attends_to_all: to_chunks.append((0, to_seq_length)) for chunk_start in range(0, to_seq_length, self.attend_to_chunk_stride): chunk_end = min(to_seq_length, chunk_start + self.attend_to_chunk_width) to_chunks.append((chunk_start, chunk_end)) if len(from_chunks) != len(to_chunks): raise ValueError( f"Expected to have same number of `from_chunks` ({from_chunks}) and " f"`to_chunks` ({from_chunks}). Check strides." ) # next, compute attention scores for each pair of windows and concatenate attention_output_chunks = [] attention_probs_chunks = [] for (from_start, from_end), (to_start, to_end) in zip(from_chunks, to_chunks): from_tensor_chunk = from_tensor[:, from_start:from_end, :] to_tensor_chunk = to_tensor[:, to_start:to_end, :] # `attention_mask`: <float>[batch_size, from_seq, to_seq] # `attention_mask_chunk`: <float>[batch_size, from_seq_chunk, to_seq_chunk] attention_mask_chunk = attention_mask[:, from_start:from_end, to_start:to_end] if self.always_attend_to_first_position: cls_attention_mask = attention_mask[:, from_start:from_end, 0:1] attention_mask_chunk = torch.cat([cls_attention_mask, attention_mask_chunk], dim=2) cls_position = to_tensor[:, 0:1, :] to_tensor_chunk = torch.cat([cls_position, to_tensor_chunk], dim=1) attention_outputs_chunk = self.self( from_tensor_chunk, to_tensor_chunk, attention_mask_chunk, head_mask, output_attentions ) attention_output_chunks.append(attention_outputs_chunk[0]) if output_attentions: attention_probs_chunks.append(attention_outputs_chunk[1]) attention_output = torch.cat(attention_output_chunks, dim=1) attention_output = self.output(attention_output, hidden_states) outputs = (attention_output,) if not self.local: outputs = outputs + self_outputs[1:] # add attentions if we output them else: outputs = outputs + tuple(attention_probs_chunks) # add attentions if we output them return outputs class CanineIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class CanineOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class CanineLayer(nn.Module): def __init__( self, config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = CanineAttention( config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ) self.intermediate = CanineIntermediate(config) self.output = CanineOutput(config) def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class CanineEncoder(nn.Module): def __init__( self, config, local=False, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=128, attend_from_chunk_stride=128, attend_to_chunk_width=128, attend_to_chunk_stride=128, ): super().__init__() self.config = config self.layer = nn.ModuleList( [ CanineLayer( config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ) for _ in range(config.num_hidden_layers) ] ) self.gradient_checkpointing = False def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class CaninePooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class CaninePredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class CanineLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = CaninePredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class CanineOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = CanineLMPredictionHead(config) def forward( self, sequence_output: Tuple[torch.Tensor], ) -> Tuple[torch.Tensor]: prediction_scores = self.predictions(sequence_output) return prediction_scores class CaninePreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CanineConfig load_tf_weights = load_tf_weights_in_canine base_model_prefix = "canine" supports_gradient_checkpointing = True _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv1d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, CanineEncoder): module.gradient_checkpointing = value CANINE_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CanineConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ CANINE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`CanineTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare CANINE Model transformer outputting raw hidden-states without any specific head on top.", CANINE_START_DOCSTRING, ) class CanineModel(CaninePreTrainedModel): def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config shallow_config = copy.deepcopy(config) shallow_config.num_hidden_layers = 1 self.char_embeddings = CanineEmbeddings(config) # shallow/low-dim transformer encoder to get a initial character encoding self.initial_char_encoder = CanineEncoder( shallow_config, local=True, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=config.local_transformer_stride, attend_from_chunk_stride=config.local_transformer_stride, attend_to_chunk_width=config.local_transformer_stride, attend_to_chunk_stride=config.local_transformer_stride, ) self.chars_to_molecules = CharactersToMolecules(config) # deep transformer encoder self.encoder = CanineEncoder(config) self.projection = ConvProjection(config) # shallow/low-dim transformer encoder to get a final character encoding self.final_char_encoder = CanineEncoder(shallow_config) self.pooler = CaninePooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) def _create_3d_attention_mask_from_input_mask(self, from_tensor, to_mask): """ Create 3D attention mask from a 2D tensor mask. Args: from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...]. to_mask: int32 Tensor of shape [batch_size, to_seq_length]. Returns: float Tensor of shape [batch_size, from_seq_length, to_seq_length]. """ batch_size, from_seq_length = from_tensor.shape[0], from_tensor.shape[1] to_seq_length = to_mask.shape[1] to_mask = torch.reshape(to_mask, (batch_size, 1, to_seq_length)).float() # We don't assume that `from_tensor` is a mask (although it could be). We # don't actually care if we attend *from* padding tokens (only *to* padding) # tokens so we create a tensor of all ones. broadcast_ones = torch.ones(size=(batch_size, from_seq_length, 1), dtype=torch.float32, device=to_mask.device) # Here we broadcast along two dimensions to create the mask. mask = broadcast_ones * to_mask return mask def _downsample_attention_mask(self, char_attention_mask: torch.Tensor, downsampling_rate: int): """Downsample 2D character attention mask to 2D molecule attention mask using MaxPool1d layer.""" # first, make char_attention_mask 3D by adding a channel dim batch_size, char_seq_len = char_attention_mask.shape poolable_char_mask = torch.reshape(char_attention_mask, (batch_size, 1, char_seq_len)) # next, apply MaxPool1d to get pooled_molecule_mask of shape (batch_size, 1, mol_seq_len) pooled_molecule_mask = torch.nn.MaxPool1d(kernel_size=downsampling_rate, stride=downsampling_rate)( poolable_char_mask.float() ) # finally, squeeze to get tensor of shape (batch_size, mol_seq_len) molecule_attention_mask = torch.squeeze(pooled_molecule_mask, dim=-1) return molecule_attention_mask def _repeat_molecules(self, molecules: torch.Tensor, char_seq_length: torch.Tensor) -> torch.Tensor: """Repeats molecules to make them the same length as the char sequence.""" rate = self.config.downsampling_rate molecules_without_extra_cls = molecules[:, 1:, :] # `repeated`: [batch_size, almost_char_seq_len, molecule_hidden_size] repeated = torch.repeat_interleave(molecules_without_extra_cls, repeats=rate, dim=-2) # So far, we've repeated the elements sufficient for any `char_seq_length` # that's a multiple of `downsampling_rate`. Now we account for the last # n elements (n < `downsampling_rate`), i.e. the remainder of floor # division. We do this by repeating the last molecule a few extra times. last_molecule = molecules[:, -1:, :] remainder_length = torch.fmod(torch.tensor(char_seq_length), torch.tensor(rate)).item() remainder_repeated = torch.repeat_interleave( last_molecule, # +1 molecule to compensate for truncation. repeats=remainder_length + rate, dim=-2, ) # `repeated`: [batch_size, char_seq_len, molecule_hidden_size] return torch.cat([repeated, remainder_repeated], dim=-2) @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=CanineModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CanineModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) molecule_attention_mask = self._downsample_attention_mask( attention_mask, downsampling_rate=self.config.downsampling_rate ) extended_molecule_attention_mask: torch.Tensor = self.get_extended_attention_mask( molecule_attention_mask, (batch_size, molecule_attention_mask.shape[-1]) ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) # `input_char_embeddings`: shape (batch_size, char_seq, char_dim) input_char_embeddings = self.char_embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) # Contextualize character embeddings using shallow Transformer. # We use a 3D attention mask for the local attention. # `input_char_encoding`: shape (batch_size, char_seq_len, char_dim) char_attention_mask = self._create_3d_attention_mask_from_input_mask(input_ids, attention_mask) init_chars_encoder_outputs = self.initial_char_encoder( input_char_embeddings, attention_mask=char_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) input_char_encoding = init_chars_encoder_outputs.last_hidden_state # Downsample chars to molecules. # The following lines have dimensions: [batch, molecule_seq, molecule_dim]. # In this transformation, we change the dimensionality from `char_dim` to # `molecule_dim`, but do *NOT* add a resnet connection. Instead, we rely on # the resnet connections (a) from the final char transformer stack back into # the original char transformer stack and (b) the resnet connections from # the final char transformer stack back into the deep BERT stack of # molecules. # # Empirically, it is critical to use a powerful enough transformation here: # mean pooling causes training to diverge with huge gradient norms in this # region of the model; using a convolution here resolves this issue. From # this, it seems that molecules and characters require a very different # feature space; intuitively, this makes sense. init_molecule_encoding = self.chars_to_molecules(input_char_encoding) # Deep BERT encoder # `molecule_sequence_output`: shape (batch_size, mol_seq_len, mol_dim) encoder_outputs = self.encoder( init_molecule_encoding, attention_mask=extended_molecule_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) molecule_sequence_output = encoder_outputs[0] pooled_output = self.pooler(molecule_sequence_output) if self.pooler is not None else None # Upsample molecules back to characters. # `repeated_molecules`: shape (batch_size, char_seq_len, mol_hidden_size) repeated_molecules = self._repeat_molecules(molecule_sequence_output, char_seq_length=input_shape[-1]) # Concatenate representations (contextualized char embeddings and repeated molecules): # `concat`: shape [batch_size, char_seq_len, molecule_hidden_size+char_hidden_final] concat = torch.cat([input_char_encoding, repeated_molecules], dim=-1) # Project representation dimension back to hidden_size # `sequence_output`: shape (batch_size, char_seq_len, hidden_size]) sequence_output = self.projection(concat) # Apply final shallow Transformer # `sequence_output`: shape (batch_size, char_seq_len, hidden_size]) final_chars_encoder_outputs = self.final_char_encoder( sequence_output, attention_mask=extended_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) sequence_output = final_chars_encoder_outputs.last_hidden_state if output_hidden_states: deep_encoder_hidden_states = encoder_outputs.hidden_states if return_dict else encoder_outputs[1] all_hidden_states = ( all_hidden_states + init_chars_encoder_outputs.hidden_states + deep_encoder_hidden_states + final_chars_encoder_outputs.hidden_states ) if output_attentions: deep_encoder_self_attentions = encoder_outputs.attentions if return_dict else encoder_outputs[-1] all_self_attentions = ( all_self_attentions + init_chars_encoder_outputs.attentions + deep_encoder_self_attentions + final_chars_encoder_outputs.attentions ) if not return_dict: output = (sequence_output, pooled_output) output += tuple(v for v in [all_hidden_states, all_self_attentions] if v is not None) return output return CanineModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=all_hidden_states, attentions=all_self_attentions, ) @add_start_docstrings( """ CANINE Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, CANINE_START_DOCSTRING, ) class CanineForSequenceClassification(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, CANINE_START_DOCSTRING, ) class CanineForMultipleChoice(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, CANINE_START_DOCSTRING, ) class CanineForTokenClassification(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, CANINE_START_DOCSTRING, ) class CanineForQuestionAnswering(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions.clamp_(0, ignored_index) end_positions.clamp_(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

# coding=utf-8 # Copyright 2021 Google AI The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch CANINE model.""" import copy import math import os from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, ModelOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_canine import CanineConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/canine-s" _CONFIG_FOR_DOC = "CanineConfig" _TOKENIZER_FOR_DOC = "CanineTokenizer" CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/canine-s", "google/canine-r" # See all CANINE models at https://huggingface.co/models?filter=canine ] # Support up to 16 hash functions. _PRIMES = [31, 43, 59, 61, 73, 97, 103, 113, 137, 149, 157, 173, 181, 193, 211, 223] @dataclass class CanineModelOutputWithPooling(ModelOutput): """ Output type of [`CanineModel`]. Based on [`~modeling_outputs.BaseModelOutputWithPooling`], but with slightly different `hidden_states` and `attentions`, as these also include the hidden states and attentions of the shallow Transformer encoders. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model (i.e. the output of the final shallow Transformer encoder). pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Hidden-state of the first token of the sequence (classification token) at the last layer of the deep Transformer encoder, further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the input to each encoder + one for the output of each layer of each encoder) of shape `(batch_size, sequence_length, hidden_size)` and `(batch_size, sequence_length // config.downsampling_rate, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial input to each Transformer encoder. The hidden states of the shallow encoders have length `sequence_length`, but the hidden states of the deep encoder have length `sequence_length` // `config.downsampling_rate`. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of the 3 Transformer encoders of shape `(batch_size, num_heads, sequence_length, sequence_length)` and `(batch_size, num_heads, sequence_length // config.downsampling_rate, sequence_length // config.downsampling_rate)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None def load_tf_weights_in_canine(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model # also discard the cls weights (which were used for the next sentence prediction pre-training task) if any( n in [ "adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step", "cls", "autoregressive_decoder", "char_output_weights", ] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue # if first scope name starts with "bert", change it to "encoder" if name[0] == "bert": name[0] = "encoder" # remove "embeddings" middle name of HashBucketCodepointEmbedders elif name[1] == "embeddings": name.remove(name[1]) # rename segment_embeddings to token_type_embeddings elif name[1] == "segment_embeddings": name[1] = "token_type_embeddings" # rename initial convolutional projection layer elif name[1] == "initial_char_encoder": name = ["chars_to_molecules"] + name[-2:] # rename final convolutional projection layer elif name[0] == "final_char_encoder" and name[1] in ["LayerNorm", "conv"]: name = ["projection"] + name[1:] pointer = model for m_name in name: if (re.fullmatch(r"[A-Za-z]+_\d+", m_name)) and "Embedder" not in m_name: scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name[-10:] in [f"Embedder_{i}" for i in range(8)]: pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class CanineEmbeddings(nn.Module): """Construct the character, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.config = config # character embeddings shard_embedding_size = config.hidden_size // config.num_hash_functions for i in range(config.num_hash_functions): name = f"HashBucketCodepointEmbedder_{i}" setattr(self, name, nn.Embedding(config.num_hash_buckets, shard_embedding_size)) self.char_position_embeddings = nn.Embedding(config.num_hash_buckets, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") def _hash_bucket_tensors(self, input_ids, num_hashes: int, num_buckets: int): """ Converts ids to hash bucket ids via multiple hashing. Args: input_ids: The codepoints or other IDs to be hashed. num_hashes: The number of hash functions to use. num_buckets: The number of hash buckets (i.e. embeddings in each table). Returns: A list of tensors, each of which is the hash bucket IDs from one hash function. """ if num_hashes > len(_PRIMES): raise ValueError(f"`num_hashes` must be <= {len(_PRIMES)}") primes = _PRIMES[:num_hashes] result_tensors = [] for prime in primes: hashed = ((input_ids + 1) * prime) % num_buckets result_tensors.append(hashed) return result_tensors def _embed_hash_buckets(self, input_ids, embedding_size: int, num_hashes: int, num_buckets: int): """Converts IDs (e.g. codepoints) into embeddings via multiple hashing.""" if embedding_size % num_hashes != 0: raise ValueError(f"Expected `embedding_size` ({embedding_size}) % `num_hashes` ({num_hashes}) == 0") hash_bucket_tensors = self._hash_bucket_tensors(input_ids, num_hashes=num_hashes, num_buckets=num_buckets) embedding_shards = [] for i, hash_bucket_ids in enumerate(hash_bucket_tensors): name = f"HashBucketCodepointEmbedder_{i}" shard_embeddings = getattr(self, name)(hash_bucket_ids) embedding_shards.append(shard_embeddings) return torch.cat(embedding_shards, dim=-1) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self._embed_hash_buckets( input_ids, self.config.hidden_size, self.config.num_hash_functions, self.config.num_hash_buckets ) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.char_position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class CharactersToMolecules(nn.Module): """Convert character sequence to initial molecule sequence (i.e. downsample) using strided convolutions.""" def __init__(self, config): super().__init__() self.conv = nn.Conv1d( in_channels=config.hidden_size, out_channels=config.hidden_size, kernel_size=config.downsampling_rate, stride=config.downsampling_rate, ) self.activation = ACT2FN[config.hidden_act] # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, char_encoding: torch.Tensor) -> torch.Tensor: # `cls_encoding`: [batch, 1, hidden_size] cls_encoding = char_encoding[:, 0:1, :] # char_encoding has shape [batch, char_seq, hidden_size] # We transpose it to be [batch, hidden_size, char_seq] char_encoding = torch.transpose(char_encoding, 1, 2) downsampled = self.conv(char_encoding) downsampled = torch.transpose(downsampled, 1, 2) downsampled = self.activation(downsampled) # Truncate the last molecule in order to reserve a position for [CLS]. # Often, the last position is never used (unless we completely fill the # text buffer). This is important in order to maintain alignment on TPUs # (i.e. a multiple of 128). downsampled_truncated = downsampled[:, 0:-1, :] # We also keep [CLS] as a separate sequence position since we always # want to reserve a position (and the model capacity that goes along # with that) in the deep BERT stack. # `result`: [batch, molecule_seq, molecule_dim] result = torch.cat([cls_encoding, downsampled_truncated], dim=1) result = self.LayerNorm(result) return result class ConvProjection(nn.Module): """ Project representations from hidden_size*2 back to hidden_size across a window of w = config.upsampling_kernel_size characters. """ def __init__(self, config): super().__init__() self.config = config self.conv = nn.Conv1d( in_channels=config.hidden_size * 2, out_channels=config.hidden_size, kernel_size=config.upsampling_kernel_size, stride=1, ) self.activation = ACT2FN[config.hidden_act] # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, inputs: torch.Tensor, final_seq_char_positions: Optional[torch.Tensor] = None, ) -> torch.Tensor: # inputs has shape [batch, mol_seq, molecule_hidden_size+char_hidden_final] # we transpose it to be [batch, molecule_hidden_size+char_hidden_final, mol_seq] inputs = torch.transpose(inputs, 1, 2) # PyTorch < 1.9 does not support padding="same" (which is used in the original implementation), # so we pad the tensor manually before passing it to the conv layer # based on https://github.com/google-research/big_transfer/blob/49afe42338b62af9fbe18f0258197a33ee578a6b/bit_tf2/models.py#L36-L38 pad_total = self.config.upsampling_kernel_size - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg pad = nn.ConstantPad1d((pad_beg, pad_end), 0) # `result`: shape (batch_size, char_seq_len, hidden_size) result = self.conv(pad(inputs)) result = torch.transpose(result, 1, 2) result = self.activation(result) result = self.LayerNorm(result) result = self.dropout(result) final_char_seq = result if final_seq_char_positions is not None: # Limit transformer query seq and attention mask to these character # positions to greatly reduce the compute cost. Typically, this is just # done for the MLM training task. # TODO add support for MLM raise NotImplementedError("CanineForMaskedLM is currently not supported") else: query_seq = final_char_seq return query_seq class CanineSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, from_tensor: torch.Tensor, to_tensor: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: mixed_query_layer = self.query(from_tensor) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. key_layer = self.transpose_for_scores(self.key(to_tensor)) value_layer = self.transpose_for_scores(self.value(to_tensor)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": seq_length = from_tensor.size()[1] position_ids_l = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(-1, 1) position_ids_r = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: if attention_mask.ndim == 3: # if attention_mask is 3D, do the following: attention_mask = torch.unsqueeze(attention_mask, dim=1) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and the dtype's smallest value for masked positions. attention_mask = (1.0 - attention_mask.float()) * torch.finfo(attention_scores.dtype).min # Apply the attention mask (precomputed for all layers in CanineModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class CanineSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class CanineAttention(nn.Module): """ Additional arguments related to local attention: - **local** (`bool`, *optional*, defaults to `False`) -- Whether to apply local attention. - **always_attend_to_first_position** (`bool`, *optional*, defaults to `False`) -- Should all blocks be able to attend to the `to_tensor`'s first position (e.g. a [CLS] position)? - **first_position_attends_to_all** (`bool`, *optional*, defaults to `False`) -- Should the *from_tensor*'s first position be able to attend to all positions within the *from_tensor*? - **attend_from_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in `from_tensor`. - **attend_from_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to skip when moving to the next block in `from_tensor`. - **attend_to_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in *to_tensor*. - **attend_to_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to skip when moving to the next block in `to_tensor`. """ def __init__( self, config, local=False, always_attend_to_first_position: bool = False, first_position_attends_to_all: bool = False, attend_from_chunk_width: int = 128, attend_from_chunk_stride: int = 128, attend_to_chunk_width: int = 128, attend_to_chunk_stride: int = 128, ): super().__init__() self.self = CanineSelfAttention(config) self.output = CanineSelfOutput(config) self.pruned_heads = set() # additional arguments related to local attention self.local = local if attend_from_chunk_width < attend_from_chunk_stride: raise ValueError( "`attend_from_chunk_width` < `attend_from_chunk_stride` would cause sequence positions to get skipped." ) if attend_to_chunk_width < attend_to_chunk_stride: raise ValueError( "`attend_to_chunk_width` < `attend_to_chunk_stride`would cause sequence positions to get skipped." ) self.always_attend_to_first_position = always_attend_to_first_position self.first_position_attends_to_all = first_position_attends_to_all self.attend_from_chunk_width = attend_from_chunk_width self.attend_from_chunk_stride = attend_from_chunk_stride self.attend_to_chunk_width = attend_to_chunk_width self.attend_to_chunk_stride = attend_to_chunk_stride def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: if not self.local: self_outputs = self.self(hidden_states, hidden_states, attention_mask, head_mask, output_attentions) attention_output = self_outputs[0] else: from_seq_length = to_seq_length = hidden_states.shape[1] from_tensor = to_tensor = hidden_states # Create chunks (windows) that we will attend *from* and then concatenate them. from_chunks = [] if self.first_position_attends_to_all: from_chunks.append((0, 1)) # We must skip this first position so that our output sequence is the # correct length (this matters in the *from* sequence only). from_start = 1 else: from_start = 0 for chunk_start in range(from_start, from_seq_length, self.attend_from_chunk_stride): chunk_end = min(from_seq_length, chunk_start + self.attend_from_chunk_width) from_chunks.append((chunk_start, chunk_end)) # Determine the chunks (windows) that will will attend *to*. to_chunks = [] if self.first_position_attends_to_all: to_chunks.append((0, to_seq_length)) for chunk_start in range(0, to_seq_length, self.attend_to_chunk_stride): chunk_end = min(to_seq_length, chunk_start + self.attend_to_chunk_width) to_chunks.append((chunk_start, chunk_end)) if len(from_chunks) != len(to_chunks): raise ValueError( f"Expected to have same number of `from_chunks` ({from_chunks}) and " f"`to_chunks` ({from_chunks}). Check strides." ) # next, compute attention scores for each pair of windows and concatenate attention_output_chunks = [] attention_probs_chunks = [] for (from_start, from_end), (to_start, to_end) in zip(from_chunks, to_chunks): from_tensor_chunk = from_tensor[:, from_start:from_end, :] to_tensor_chunk = to_tensor[:, to_start:to_end, :] # `attention_mask`: <float>[batch_size, from_seq, to_seq] # `attention_mask_chunk`: <float>[batch_size, from_seq_chunk, to_seq_chunk] attention_mask_chunk = attention_mask[:, from_start:from_end, to_start:to_end] if self.always_attend_to_first_position: cls_attention_mask = attention_mask[:, from_start:from_end, 0:1] attention_mask_chunk = torch.cat([cls_attention_mask, attention_mask_chunk], dim=2) cls_position = to_tensor[:, 0:1, :] to_tensor_chunk = torch.cat([cls_position, to_tensor_chunk], dim=1) attention_outputs_chunk = self.self( from_tensor_chunk, to_tensor_chunk, attention_mask_chunk, head_mask, output_attentions ) attention_output_chunks.append(attention_outputs_chunk[0]) if output_attentions: attention_probs_chunks.append(attention_outputs_chunk[1]) attention_output = torch.cat(attention_output_chunks, dim=1) attention_output = self.output(attention_output, hidden_states) outputs = (attention_output,) if not self.local: outputs = outputs + self_outputs[1:] # add attentions if we output them else: outputs = outputs + tuple(attention_probs_chunks) # add attentions if we output them return outputs class CanineIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class CanineOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class CanineLayer(nn.Module): def __init__( self, config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = CanineAttention( config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ) self.intermediate = CanineIntermediate(config) self.output = CanineOutput(config) def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class CanineEncoder(nn.Module): def __init__( self, config, local=False, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=128, attend_from_chunk_stride=128, attend_to_chunk_width=128, attend_to_chunk_stride=128, ): super().__init__() self.config = config self.layer = nn.ModuleList( [ CanineLayer( config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ) for _ in range(config.num_hidden_layers) ] ) self.gradient_checkpointing = False def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class CaninePooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class CaninePredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class CanineLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = CaninePredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class CanineOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = CanineLMPredictionHead(config) def forward( self, sequence_output: Tuple[torch.Tensor], ) -> Tuple[torch.Tensor]: prediction_scores = self.predictions(sequence_output) return prediction_scores class CaninePreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CanineConfig load_tf_weights = load_tf_weights_in_canine base_model_prefix = "canine" supports_gradient_checkpointing = True _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv1d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, CanineEncoder): module.gradient_checkpointing = value CANINE_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CanineConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ CANINE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`CanineTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare CANINE Model transformer outputting raw hidden-states without any specific head on top.", CANINE_START_DOCSTRING, ) class CanineModel(CaninePreTrainedModel): def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config shallow_config = copy.deepcopy(config) shallow_config.num_hidden_layers = 1 self.char_embeddings = CanineEmbeddings(config) # shallow/low-dim transformer encoder to get a initial character encoding self.initial_char_encoder = CanineEncoder( shallow_config, local=True, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=config.local_transformer_stride, attend_from_chunk_stride=config.local_transformer_stride, attend_to_chunk_width=config.local_transformer_stride, attend_to_chunk_stride=config.local_transformer_stride, ) self.chars_to_molecules = CharactersToMolecules(config) # deep transformer encoder self.encoder = CanineEncoder(config) self.projection = ConvProjection(config) # shallow/low-dim transformer encoder to get a final character encoding self.final_char_encoder = CanineEncoder(shallow_config) self.pooler = CaninePooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) def _create_3d_attention_mask_from_input_mask(self, from_tensor, to_mask): """ Create 3D attention mask from a 2D tensor mask. Args: from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...]. to_mask: int32 Tensor of shape [batch_size, to_seq_length]. Returns: float Tensor of shape [batch_size, from_seq_length, to_seq_length]. """ batch_size, from_seq_length = from_tensor.shape[0], from_tensor.shape[1] to_seq_length = to_mask.shape[1] to_mask = torch.reshape(to_mask, (batch_size, 1, to_seq_length)).float() # We don't assume that `from_tensor` is a mask (although it could be). We # don't actually care if we attend *from* padding tokens (only *to* padding) # tokens so we create a tensor of all ones. broadcast_ones = torch.ones(size=(batch_size, from_seq_length, 1), dtype=torch.float32, device=to_mask.device) # Here we broadcast along two dimensions to create the mask. mask = broadcast_ones * to_mask return mask def _downsample_attention_mask(self, char_attention_mask: torch.Tensor, downsampling_rate: int): """Downsample 2D character attention mask to 2D molecule attention mask using MaxPool1d layer.""" # first, make char_attention_mask 3D by adding a channel dim batch_size, char_seq_len = char_attention_mask.shape poolable_char_mask = torch.reshape(char_attention_mask, (batch_size, 1, char_seq_len)) # next, apply MaxPool1d to get pooled_molecule_mask of shape (batch_size, 1, mol_seq_len) pooled_molecule_mask = torch.nn.MaxPool1d(kernel_size=downsampling_rate, stride=downsampling_rate)( poolable_char_mask.float() ) # finally, squeeze to get tensor of shape (batch_size, mol_seq_len) molecule_attention_mask = torch.squeeze(pooled_molecule_mask, dim=-1) return molecule_attention_mask def _repeat_molecules(self, molecules: torch.Tensor, char_seq_length: torch.Tensor) -> torch.Tensor: """Repeats molecules to make them the same length as the char sequence.""" rate = self.config.downsampling_rate molecules_without_extra_cls = molecules[:, 1:, :] # `repeated`: [batch_size, almost_char_seq_len, molecule_hidden_size] repeated = torch.repeat_interleave(molecules_without_extra_cls, repeats=rate, dim=-2) # So far, we've repeated the elements sufficient for any `char_seq_length` # that's a multiple of `downsampling_rate`. Now we account for the last # n elements (n < `downsampling_rate`), i.e. the remainder of floor # division. We do this by repeating the last molecule a few extra times. last_molecule = molecules[:, -1:, :] remainder_length = torch.fmod(torch.tensor(char_seq_length), torch.tensor(rate)).item() remainder_repeated = torch.repeat_interleave( last_molecule, # +1 molecule to compensate for truncation. repeats=remainder_length + rate, dim=-2, ) # `repeated`: [batch_size, char_seq_len, molecule_hidden_size] return torch.cat([repeated, remainder_repeated], dim=-2) @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=CanineModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CanineModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) molecule_attention_mask = self._downsample_attention_mask( attention_mask, downsampling_rate=self.config.downsampling_rate ) extended_molecule_attention_mask: torch.Tensor = self.get_extended_attention_mask( molecule_attention_mask, (batch_size, molecule_attention_mask.shape[-1]) ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) # `input_char_embeddings`: shape (batch_size, char_seq, char_dim) input_char_embeddings = self.char_embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) # Contextualize character embeddings using shallow Transformer. # We use a 3D attention mask for the local attention. # `input_char_encoding`: shape (batch_size, char_seq_len, char_dim) char_attention_mask = self._create_3d_attention_mask_from_input_mask(input_ids, attention_mask) init_chars_encoder_outputs = self.initial_char_encoder( input_char_embeddings, attention_mask=char_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) input_char_encoding = init_chars_encoder_outputs.last_hidden_state # Downsample chars to molecules. # The following lines have dimensions: [batch, molecule_seq, molecule_dim]. # In this transformation, we change the dimensionality from `char_dim` to # `molecule_dim`, but do *NOT* add a resnet connection. Instead, we rely on # the resnet connections (a) from the final char transformer stack back into # the original char transformer stack and (b) the resnet connections from # the final char transformer stack back into the deep BERT stack of # molecules. # # Empirically, it is critical to use a powerful enough transformation here: # mean pooling causes training to diverge with huge gradient norms in this # region of the model; using a convolution here resolves this issue. From # this, it seems that molecules and characters require a very different # feature space; intuitively, this makes sense. init_molecule_encoding = self.chars_to_molecules(input_char_encoding) # Deep BERT encoder # `molecule_sequence_output`: shape (batch_size, mol_seq_len, mol_dim) encoder_outputs = self.encoder( init_molecule_encoding, attention_mask=extended_molecule_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) molecule_sequence_output = encoder_outputs[0] pooled_output = self.pooler(molecule_sequence_output) if self.pooler is not None else None # Upsample molecules back to characters. # `repeated_molecules`: shape (batch_size, char_seq_len, mol_hidden_size) repeated_molecules = self._repeat_molecules(molecule_sequence_output, char_seq_length=input_shape[-1]) # Concatenate representations (contextualized char embeddings and repeated molecules): # `concat`: shape [batch_size, char_seq_len, molecule_hidden_size+char_hidden_final] concat = torch.cat([input_char_encoding, repeated_molecules], dim=-1) # Project representation dimension back to hidden_size # `sequence_output`: shape (batch_size, char_seq_len, hidden_size]) sequence_output = self.projection(concat) # Apply final shallow Transformer # `sequence_output`: shape (batch_size, char_seq_len, hidden_size]) final_chars_encoder_outputs = self.final_char_encoder( sequence_output, attention_mask=extended_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) sequence_output = final_chars_encoder_outputs.last_hidden_state if output_hidden_states: deep_encoder_hidden_states = encoder_outputs.hidden_states if return_dict else encoder_outputs[1] all_hidden_states = ( all_hidden_states + init_chars_encoder_outputs.hidden_states + deep_encoder_hidden_states + final_chars_encoder_outputs.hidden_states ) if output_attentions: deep_encoder_self_attentions = encoder_outputs.attentions if return_dict else encoder_outputs[-1] all_self_attentions = ( all_self_attentions + init_chars_encoder_outputs.attentions + deep_encoder_self_attentions + final_chars_encoder_outputs.attentions ) if not return_dict: output = (sequence_output, pooled_output) output += tuple(v for v in [all_hidden_states, all_self_attentions] if v is not None) return output return CanineModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=all_hidden_states, attentions=all_self_attentions, ) @add_start_docstrings( """ CANINE Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, CANINE_START_DOCSTRING, ) class CanineForSequenceClassification(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, CANINE_START_DOCSTRING, ) class CanineForMultipleChoice(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, CANINE_START_DOCSTRING, ) class CanineForTokenClassification(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, CANINE_START_DOCSTRING, ) class CanineForQuestionAnswering(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions.clamp_(0, ignored_index) end_positions.clamp_(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./docs/source/en/perf_infer_gpu_many.mdx

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the --> # Efficient Inference on a Multiple GPUs This document contains information on how to efficiently infer on a multiple GPUs. <Tip> Note: A multi GPU setup can use the majority of the strategies described in the [single GPU section](./perf_infer_gpu_one). You must be aware of simple techniques, though, that can be used for a better usage. </Tip>

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the --> # Efficient Inference on a Multiple GPUs This document contains information on how to efficiently infer on a multiple GPUs. <Tip> Note: A multi GPU setup can use the majority of the strategies described in the [single GPU section](./perf_infer_gpu_one). You must be aware of simple techniques, though, that can be used for a better usage. </Tip>

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/research_projects/seq2seq-distillation/run_eval.py

#!/usr/bin/env python import argparse import datetime import json import time import warnings from logging import getLogger from pathlib import Path from typing import Dict, List import torch from tqdm import tqdm from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params logger = getLogger(__name__) DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu" def generate_summaries_or_translations( examples: List[str], out_file: str, model_name: str, batch_size: int = 8, device: str = DEFAULT_DEVICE, fp16=False, task="summarization", prefix=None, **generate_kwargs, ) -> Dict: """Save model.generate results to <out_file>, and return how long it took.""" fout = Path(out_file).open("w", encoding="utf-8") model_name = str(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device) if fp16: model = model.half() tokenizer = AutoTokenizer.from_pretrained(model_name) logger.info(f"Inferred tokenizer type: {tokenizer.__class__}") # if this is wrong, check config.model_type. start_time = time.time() # update config with task specific params use_task_specific_params(model, task) if prefix is None: prefix = prefix or getattr(model.config, "prefix", "") or "" for examples_chunk in tqdm(list(chunks(examples, batch_size))): examples_chunk = [prefix + text for text in examples_chunk] batch = tokenizer(examples_chunk, return_tensors="pt", truncation=True, padding="longest").to(device) summaries = model.generate( input_ids=batch.input_ids, attention_mask=batch.attention_mask, **generate_kwargs, ) dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False) for hypothesis in dec: fout.write(hypothesis + "\n") fout.flush() fout.close() runtime = int(time.time() - start_time) # seconds n_obs = len(examples) return dict(n_obs=n_obs, runtime=runtime, seconds_per_sample=round(runtime / n_obs, 4)) def datetime_now(): return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") def run_generate(verbose=True): """ Takes input text, generates output, and then using reference calculates the BLEU scores. The results are saved to a file and returned to the caller, and printed out unless ``verbose=False`` is passed. Args: verbose (:obj:`bool`, `optional`, defaults to :obj:`True`): print results to stdout Returns: a tuple: ``(scores, params}`` - ``scores``: a dict of scores data ``{'bleu': 39.6501, 'n_obs': 2000, 'runtime': 186, 'seconds_per_sample': 0.093}`` - ``params``: a dict of custom params, e.g. ``{'num_beams': 5, 'length_penalty': 0.8}`` """ parser = argparse.ArgumentParser() parser.add_argument("model_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.") parser.add_argument("input_path", type=str, help="like cnn_dm/test.source") parser.add_argument("save_path", type=str, help="where to save summaries") parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test.target") parser.add_argument("--score_path", type=str, required=False, default="metrics.json", help="where to save metrics") parser.add_argument("--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.") parser.add_argument( "--prefix", type=str, required=False, default=None, help="will be added to the begininng of src examples" ) parser.add_argument("--task", type=str, default="summarization", help="used for task_specific_params + metrics") parser.add_argument("--bs", type=int, default=8, required=False, help="batch size") parser.add_argument( "--n_obs", type=int, default=-1, required=False, help="How many observations. Defaults to all." ) parser.add_argument("--fp16", action="store_true") parser.add_argument("--dump-args", action="store_true", help="print the custom hparams with the results") parser.add_argument( "--info", nargs="?", type=str, const=datetime_now(), help=( "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g." " lang=en-ru. If no value is passed, the current datetime string will be used." ), ) # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate args, rest = parser.parse_known_args() parsed_args = parse_numeric_n_bool_cl_kwargs(rest) if parsed_args and verbose: print(f"parsed the following generate kwargs: {parsed_args}") with open(args.input_path) as f: examples = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()] if args.n_obs > 0: examples = examples[: args.n_obs] Path(args.save_path).parent.mkdir(exist_ok=True) if args.reference_path is None and Path(args.score_path).exists(): warnings.warn(f"score_path {args.score_path} will be overwritten unless you type ctrl-c.") runtime_metrics = generate_summaries_or_translations( examples, args.save_path, args.model_name, batch_size=args.bs, device=args.device, fp16=args.fp16, task=args.task, prefix=args.prefix, **parsed_args, ) if args.reference_path is None: return {} # Compute scores score_fn = calculate_bleu if "translation" in args.task else calculate_rouge output_lns = [x.rstrip() for x in open(args.save_path).readlines()] reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()][: len(output_lns)] scores: dict = score_fn(output_lns, reference_lns) scores.update(runtime_metrics) if args.dump_args: scores.update(parsed_args) if args.info: scores["info"] = args.info if verbose: print(scores) if args.score_path is not None: json.dump(scores, open(args.score_path, "w")) return scores if __name__ == "__main__": # Usage for MT: # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@ run_generate(verbose=True)

#!/usr/bin/env python import argparse import datetime import json import time import warnings from logging import getLogger from pathlib import Path from typing import Dict, List import torch from tqdm import tqdm from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params logger = getLogger(__name__) DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu" def generate_summaries_or_translations( examples: List[str], out_file: str, model_name: str, batch_size: int = 8, device: str = DEFAULT_DEVICE, fp16=False, task="summarization", prefix=None, **generate_kwargs, ) -> Dict: """Save model.generate results to <out_file>, and return how long it took.""" fout = Path(out_file).open("w", encoding="utf-8") model_name = str(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device) if fp16: model = model.half() tokenizer = AutoTokenizer.from_pretrained(model_name) logger.info(f"Inferred tokenizer type: {tokenizer.__class__}") # if this is wrong, check config.model_type. start_time = time.time() # update config with task specific params use_task_specific_params(model, task) if prefix is None: prefix = prefix or getattr(model.config, "prefix", "") or "" for examples_chunk in tqdm(list(chunks(examples, batch_size))): examples_chunk = [prefix + text for text in examples_chunk] batch = tokenizer(examples_chunk, return_tensors="pt", truncation=True, padding="longest").to(device) summaries = model.generate( input_ids=batch.input_ids, attention_mask=batch.attention_mask, **generate_kwargs, ) dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False) for hypothesis in dec: fout.write(hypothesis + "\n") fout.flush() fout.close() runtime = int(time.time() - start_time) # seconds n_obs = len(examples) return dict(n_obs=n_obs, runtime=runtime, seconds_per_sample=round(runtime / n_obs, 4)) def datetime_now(): return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") def run_generate(verbose=True): """ Takes input text, generates output, and then using reference calculates the BLEU scores. The results are saved to a file and returned to the caller, and printed out unless ``verbose=False`` is passed. Args: verbose (:obj:`bool`, `optional`, defaults to :obj:`True`): print results to stdout Returns: a tuple: ``(scores, params}`` - ``scores``: a dict of scores data ``{'bleu': 39.6501, 'n_obs': 2000, 'runtime': 186, 'seconds_per_sample': 0.093}`` - ``params``: a dict of custom params, e.g. ``{'num_beams': 5, 'length_penalty': 0.8}`` """ parser = argparse.ArgumentParser() parser.add_argument("model_name", type=str, help="like facebook/bart-large-cnn,t5-base, etc.") parser.add_argument("input_path", type=str, help="like cnn_dm/test.source") parser.add_argument("save_path", type=str, help="where to save summaries") parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test.target") parser.add_argument("--score_path", type=str, required=False, default="metrics.json", help="where to save metrics") parser.add_argument("--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.") parser.add_argument( "--prefix", type=str, required=False, default=None, help="will be added to the begininng of src examples" ) parser.add_argument("--task", type=str, default="summarization", help="used for task_specific_params + metrics") parser.add_argument("--bs", type=int, default=8, required=False, help="batch size") parser.add_argument( "--n_obs", type=int, default=-1, required=False, help="How many observations. Defaults to all." ) parser.add_argument("--fp16", action="store_true") parser.add_argument("--dump-args", action="store_true", help="print the custom hparams with the results") parser.add_argument( "--info", nargs="?", type=str, const=datetime_now(), help=( "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g." " lang=en-ru. If no value is passed, the current datetime string will be used." ), ) # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate args, rest = parser.parse_known_args() parsed_args = parse_numeric_n_bool_cl_kwargs(rest) if parsed_args and verbose: print(f"parsed the following generate kwargs: {parsed_args}") with open(args.input_path) as f: examples = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()] if args.n_obs > 0: examples = examples[: args.n_obs] Path(args.save_path).parent.mkdir(exist_ok=True) if args.reference_path is None and Path(args.score_path).exists(): warnings.warn(f"score_path {args.score_path} will be overwritten unless you type ctrl-c.") runtime_metrics = generate_summaries_or_translations( examples, args.save_path, args.model_name, batch_size=args.bs, device=args.device, fp16=args.fp16, task=args.task, prefix=args.prefix, **parsed_args, ) if args.reference_path is None: return {} # Compute scores score_fn = calculate_bleu if "translation" in args.task else calculate_rouge output_lns = [x.rstrip() for x in open(args.save_path).readlines()] reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()][: len(output_lns)] scores: dict = score_fn(output_lns, reference_lns) scores.update(runtime_metrics) if args.dump_args: scores.update(parsed_args) if args.info: scores["info"] = args.info if verbose: print(scores) if args.score_path is not None: json.dump(scores, open(args.score_path, "w")) return scores if __name__ == "__main__": # Usage for MT: # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@ run_generate(verbose=True)

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./src/transformers/models/esm/openfold_utils/residue_constants.py

# Copyright 2021 AlQuraishi Laboratory # Copyright 2021 DeepMind Technologies Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Constants used in AlphaFold.""" import collections import copy import functools from importlib import resources from typing import List, Mapping, Tuple import numpy as np # Internal import (35fd). # Distance from one CA to next CA [trans configuration: omega = 180]. ca_ca = 3.80209737096 # Format: The list for each AA type contains chi1, chi2, chi3, chi4 in # this order (or a relevant subset from chi1 onwards). ALA and GLY don't have # chi angles so their chi angle lists are empty. chi_angles_atoms = { "ALA": [], # Chi5 in arginine is always 0 +- 5 degrees, so ignore it. "ARG": [ ["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "NE"], ["CG", "CD", "NE", "CZ"], ], "ASN": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "OD1"]], "ASP": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "OD1"]], "CYS": [["N", "CA", "CB", "SG"]], "GLN": [ ["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "OE1"], ], "GLU": [ ["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "OE1"], ], "GLY": [], "HIS": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "ND1"]], "ILE": [["N", "CA", "CB", "CG1"], ["CA", "CB", "CG1", "CD1"]], "LEU": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], "LYS": [ ["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "CE"], ["CG", "CD", "CE", "NZ"], ], "MET": [ ["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "SD"], ["CB", "CG", "SD", "CE"], ], "PHE": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], "PRO": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"]], "SER": [["N", "CA", "CB", "OG"]], "THR": [["N", "CA", "CB", "OG1"]], "TRP": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], "TYR": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], "VAL": [["N", "CA", "CB", "CG1"]], } # If chi angles given in fixed-length array, this matrix determines how to mask # them for each AA type. The order is as per restype_order (see below). chi_angles_mask = [ [0.0, 0.0, 0.0, 0.0], # ALA [1.0, 1.0, 1.0, 1.0], # ARG [1.0, 1.0, 0.0, 0.0], # ASN [1.0, 1.0, 0.0, 0.0], # ASP [1.0, 0.0, 0.0, 0.0], # CYS [1.0, 1.0, 1.0, 0.0], # GLN [1.0, 1.0, 1.0, 0.0], # GLU [0.0, 0.0, 0.0, 0.0], # GLY [1.0, 1.0, 0.0, 0.0], # HIS [1.0, 1.0, 0.0, 0.0], # ILE [1.0, 1.0, 0.0, 0.0], # LEU [1.0, 1.0, 1.0, 1.0], # LYS [1.0, 1.0, 1.0, 0.0], # MET [1.0, 1.0, 0.0, 0.0], # PHE [1.0, 1.0, 0.0, 0.0], # PRO [1.0, 0.0, 0.0, 0.0], # SER [1.0, 0.0, 0.0, 0.0], # THR [1.0, 1.0, 0.0, 0.0], # TRP [1.0, 1.0, 0.0, 0.0], # TYR [1.0, 0.0, 0.0, 0.0], # VAL ] # The following chi angles are pi periodic: they can be rotated by a multiple # of pi without affecting the structure. chi_pi_periodic = [ [0.0, 0.0, 0.0, 0.0], # ALA [0.0, 0.0, 0.0, 0.0], # ARG [0.0, 0.0, 0.0, 0.0], # ASN [0.0, 1.0, 0.0, 0.0], # ASP [0.0, 0.0, 0.0, 0.0], # CYS [0.0, 0.0, 0.0, 0.0], # GLN [0.0, 0.0, 1.0, 0.0], # GLU [0.0, 0.0, 0.0, 0.0], # GLY [0.0, 0.0, 0.0, 0.0], # HIS [0.0, 0.0, 0.0, 0.0], # ILE [0.0, 0.0, 0.0, 0.0], # LEU [0.0, 0.0, 0.0, 0.0], # LYS [0.0, 0.0, 0.0, 0.0], # MET [0.0, 1.0, 0.0, 0.0], # PHE [0.0, 0.0, 0.0, 0.0], # PRO [0.0, 0.0, 0.0, 0.0], # SER [0.0, 0.0, 0.0, 0.0], # THR [0.0, 0.0, 0.0, 0.0], # TRP [0.0, 1.0, 0.0, 0.0], # TYR [0.0, 0.0, 0.0, 0.0], # VAL [0.0, 0.0, 0.0, 0.0], # UNK ] # Atoms positions relative to the 8 rigid groups, defined by the pre-omega, phi, # psi and chi angles: # 0: 'backbone group', # 1: 'pre-omega-group', (empty) # 2: 'phi-group', (currently empty, because it defines only hydrogens) # 3: 'psi-group', # 4,5,6,7: 'chi1,2,3,4-group' # The atom positions are relative to the axis-end-atom of the corresponding # rotation axis. The x-axis is in direction of the rotation axis, and the y-axis # is defined such that the dihedral-angle-definiting atom (the last entry in # chi_angles_atoms above) is in the xy-plane (with a positive y-coordinate). # format: [atomname, group_idx, rel_position] rigid_group_atom_positions = { "ALA": [ ["N", 0, (-0.525, 1.363, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.526, -0.000, -0.000)], ["CB", 0, (-0.529, -0.774, -1.205)], ["O", 3, (0.627, 1.062, 0.000)], ], "ARG": [ ["N", 0, (-0.524, 1.362, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.525, -0.000, -0.000)], ["CB", 0, (-0.524, -0.778, -1.209)], ["O", 3, (0.626, 1.062, 0.000)], ["CG", 4, (0.616, 1.390, -0.000)], ["CD", 5, (0.564, 1.414, 0.000)], ["NE", 6, (0.539, 1.357, -0.000)], ["NH1", 7, (0.206, 2.301, 0.000)], ["NH2", 7, (2.078, 0.978, -0.000)], ["CZ", 7, (0.758, 1.093, -0.000)], ], "ASN": [ ["N", 0, (-0.536, 1.357, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.526, -0.000, -0.000)], ["CB", 0, (-0.531, -0.787, -1.200)], ["O", 3, (0.625, 1.062, 0.000)], ["CG", 4, (0.584, 1.399, 0.000)], ["ND2", 5, (0.593, -1.188, 0.001)], ["OD1", 5, (0.633, 1.059, 0.000)], ], "ASP": [ ["N", 0, (-0.525, 1.362, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.527, 0.000, -0.000)], ["CB", 0, (-0.526, -0.778, -1.208)], ["O", 3, (0.626, 1.062, -0.000)], ["CG", 4, (0.593, 1.398, -0.000)], ["OD1", 5, (0.610, 1.091, 0.000)], ["OD2", 5, (0.592, -1.101, -0.003)], ], "CYS": [ ["N", 0, (-0.522, 1.362, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.524, 0.000, 0.000)], ["CB", 0, (-0.519, -0.773, -1.212)], ["O", 3, (0.625, 1.062, -0.000)], ["SG", 4, (0.728, 1.653, 0.000)], ], "GLN": [ ["N", 0, (-0.526, 1.361, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.526, 0.000, 0.000)], ["CB", 0, (-0.525, -0.779, -1.207)], ["O", 3, (0.626, 1.062, -0.000)], ["CG", 4, (0.615, 1.393, 0.000)], ["CD", 5, (0.587, 1.399, -0.000)], ["NE2", 6, (0.593, -1.189, -0.001)], ["OE1", 6, (0.634, 1.060, 0.000)], ], "GLU": [ ["N", 0, (-0.528, 1.361, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.526, -0.000, -0.000)], ["CB", 0, (-0.526, -0.781, -1.207)], ["O", 3, (0.626, 1.062, 0.000)], ["CG", 4, (0.615, 1.392, 0.000)], ["CD", 5, (0.600, 1.397, 0.000)], ["OE1", 6, (0.607, 1.095, -0.000)], ["OE2", 6, (0.589, -1.104, -0.001)], ], "GLY": [ ["N", 0, (-0.572, 1.337, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.517, -0.000, -0.000)], ["O", 3, (0.626, 1.062, -0.000)], ], "HIS": [ ["N", 0, (-0.527, 1.360, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.525, 0.000, 0.000)], ["CB", 0, (-0.525, -0.778, -1.208)], ["O", 3, (0.625, 1.063, 0.000)], ["CG", 4, (0.600, 1.370, -0.000)], ["CD2", 5, (0.889, -1.021, 0.003)], ["ND1", 5, (0.744, 1.160, -0.000)], ["CE1", 5, (2.030, 0.851, 0.002)], ["NE2", 5, (2.145, -0.466, 0.004)], ], "ILE": [ ["N", 0, (-0.493, 1.373, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.527, -0.000, -0.000)], ["CB", 0, (-0.536, -0.793, -1.213)], ["O", 3, (0.627, 1.062, -0.000)], ["CG1", 4, (0.534, 1.437, -0.000)], ["CG2", 4, (0.540, -0.785, -1.199)], ["CD1", 5, (0.619, 1.391, 0.000)], ], "LEU": [ ["N", 0, (-0.520, 1.363, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.525, -0.000, -0.000)], ["CB", 0, (-0.522, -0.773, -1.214)], ["O", 3, (0.625, 1.063, -0.000)], ["CG", 4, (0.678, 1.371, 0.000)], ["CD1", 5, (0.530, 1.430, -0.000)], ["CD2", 5, (0.535, -0.774, 1.200)], ], "LYS": [ ["N", 0, (-0.526, 1.362, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.526, 0.000, 0.000)], ["CB", 0, (-0.524, -0.778, -1.208)], ["O", 3, (0.626, 1.062, -0.000)], ["CG", 4, (0.619, 1.390, 0.000)], ["CD", 5, (0.559, 1.417, 0.000)], ["CE", 6, (0.560, 1.416, 0.000)], ["NZ", 7, (0.554, 1.387, 0.000)], ], "MET": [ ["N", 0, (-0.521, 1.364, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.525, 0.000, 0.000)], ["CB", 0, (-0.523, -0.776, -1.210)], ["O", 3, (0.625, 1.062, -0.000)], ["CG", 4, (0.613, 1.391, -0.000)], ["SD", 5, (0.703, 1.695, 0.000)], ["CE", 6, (0.320, 1.786, -0.000)], ], "PHE": [ ["N", 0, (-0.518, 1.363, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.524, 0.000, -0.000)], ["CB", 0, (-0.525, -0.776, -1.212)], ["O", 3, (0.626, 1.062, -0.000)], ["CG", 4, (0.607, 1.377, 0.000)], ["CD1", 5, (0.709, 1.195, -0.000)], ["CD2", 5, (0.706, -1.196, 0.000)], ["CE1", 5, (2.102, 1.198, -0.000)], ["CE2", 5, (2.098, -1.201, -0.000)], ["CZ", 5, (2.794, -0.003, -0.001)], ], "PRO": [ ["N", 0, (-0.566, 1.351, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.527, -0.000, 0.000)], ["CB", 0, (-0.546, -0.611, -1.293)], ["O", 3, (0.621, 1.066, 0.000)], ["CG", 4, (0.382, 1.445, 0.0)], # ['CD', 5, (0.427, 1.440, 0.0)], ["CD", 5, (0.477, 1.424, 0.0)], # manually made angle 2 degrees larger ], "SER": [ ["N", 0, (-0.529, 1.360, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.525, -0.000, -0.000)], ["CB", 0, (-0.518, -0.777, -1.211)], ["O", 3, (0.626, 1.062, -0.000)], ["OG", 4, (0.503, 1.325, 0.000)], ], "THR": [ ["N", 0, (-0.517, 1.364, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.526, 0.000, -0.000)], ["CB", 0, (-0.516, -0.793, -1.215)], ["O", 3, (0.626, 1.062, 0.000)], ["CG2", 4, (0.550, -0.718, -1.228)], ["OG1", 4, (0.472, 1.353, 0.000)], ], "TRP": [ ["N", 0, (-0.521, 1.363, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.525, -0.000, 0.000)], ["CB", 0, (-0.523, -0.776, -1.212)], ["O", 3, (0.627, 1.062, 0.000)], ["CG", 4, (0.609, 1.370, -0.000)], ["CD1", 5, (0.824, 1.091, 0.000)], ["CD2", 5, (0.854, -1.148, -0.005)], ["CE2", 5, (2.186, -0.678, -0.007)], ["CE3", 5, (0.622, -2.530, -0.007)], ["NE1", 5, (2.140, 0.690, -0.004)], ["CH2", 5, (3.028, -2.890, -0.013)], ["CZ2", 5, (3.283, -1.543, -0.011)], ["CZ3", 5, (1.715, -3.389, -0.011)], ], "TYR": [ ["N", 0, (-0.522, 1.362, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.524, -0.000, -0.000)], ["CB", 0, (-0.522, -0.776, -1.213)], ["O", 3, (0.627, 1.062, -0.000)], ["CG", 4, (0.607, 1.382, -0.000)], ["CD1", 5, (0.716, 1.195, -0.000)], ["CD2", 5, (0.713, -1.194, -0.001)], ["CE1", 5, (2.107, 1.200, -0.002)], ["CE2", 5, (2.104, -1.201, -0.003)], ["OH", 5, (4.168, -0.002, -0.005)], ["CZ", 5, (2.791, -0.001, -0.003)], ], "VAL": [ ["N", 0, (-0.494, 1.373, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.527, -0.000, -0.000)], ["CB", 0, (-0.533, -0.795, -1.213)], ["O", 3, (0.627, 1.062, -0.000)], ["CG1", 4, (0.540, 1.429, -0.000)], ["CG2", 4, (0.533, -0.776, 1.203)], ], } # A list of atoms (excluding hydrogen) for each AA type. PDB naming convention. residue_atoms = { "ALA": ["C", "CA", "CB", "N", "O"], "ARG": ["C", "CA", "CB", "CG", "CD", "CZ", "N", "NE", "O", "NH1", "NH2"], "ASP": ["C", "CA", "CB", "CG", "N", "O", "OD1", "OD2"], "ASN": ["C", "CA", "CB", "CG", "N", "ND2", "O", "OD1"], "CYS": ["C", "CA", "CB", "N", "O", "SG"], "GLU": ["C", "CA", "CB", "CG", "CD", "N", "O", "OE1", "OE2"], "GLN": ["C", "CA", "CB", "CG", "CD", "N", "NE2", "O", "OE1"], "GLY": ["C", "CA", "N", "O"], "HIS": ["C", "CA", "CB", "CG", "CD2", "CE1", "N", "ND1", "NE2", "O"], "ILE": ["C", "CA", "CB", "CG1", "CG2", "CD1", "N", "O"], "LEU": ["C", "CA", "CB", "CG", "CD1", "CD2", "N", "O"], "LYS": ["C", "CA", "CB", "CG", "CD", "CE", "N", "NZ", "O"], "MET": ["C", "CA", "CB", "CG", "CE", "N", "O", "SD"], "PHE": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "N", "O"], "PRO": ["C", "CA", "CB", "CG", "CD", "N", "O"], "SER": ["C", "CA", "CB", "N", "O", "OG"], "THR": ["C", "CA", "CB", "CG2", "N", "O", "OG1"], "TRP": [ "C", "CA", "CB", "CG", "CD1", "CD2", "CE2", "CE3", "CZ2", "CZ3", "CH2", "N", "NE1", "O", ], "TYR": [ "C", "CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "N", "O", "OH", ], "VAL": ["C", "CA", "CB", "CG1", "CG2", "N", "O"], } # Naming swaps for ambiguous atom names. # Due to symmetries in the amino acids the naming of atoms is ambiguous in # 4 of the 20 amino acids. # (The LDDT paper lists 7 amino acids as ambiguous, but the naming ambiguities # in LEU, VAL and ARG can be resolved by using the 3d constellations of # the 'ambiguous' atoms and their neighbours) # TODO: ^ interpret this residue_atom_renaming_swaps = { "ASP": {"OD1": "OD2"}, "GLU": {"OE1": "OE2"}, "PHE": {"CD1": "CD2", "CE1": "CE2"}, "TYR": {"CD1": "CD2", "CE1": "CE2"}, } # Van der Waals radii [Angstroem] of the atoms (from Wikipedia) van_der_waals_radius = { "C": 1.7, "N": 1.55, "O": 1.52, "S": 1.8, } Bond = collections.namedtuple("Bond", ["atom1_name", "atom2_name", "length", "stddev"]) BondAngle = collections.namedtuple( "BondAngle", ["atom1_name", "atom2_name", "atom3name", "angle_rad", "stddev"], ) def map_structure_with_atom_order(in_list: List, first_call: bool = True): # Maps strings in a nested list structure to their corresponding index in atom_order if first_call: in_list = copy.deepcopy(in_list) for i in range(len(in_list)): if isinstance(in_list[i], list): in_list[i] = map_structure_with_atom_order(in_list[i], first_call=False) elif isinstance(in_list[i], str): in_list[i] = atom_order[in_list[i]] else: raise ValueError("Unexpected type when mapping nested lists!") return in_list @functools.lru_cache(maxsize=None) def load_stereo_chemical_props() -> Tuple[ Mapping[str, List[Bond]], Mapping[str, List[Bond]], Mapping[str, List[BondAngle]], ]: """Load stereo_chemical_props.txt into a nice structure. Load literature values for bond lengths and bond angles and translate bond angles into the length of the opposite edge of the triangle ("residue_virtual_bonds"). Returns: residue_bonds: dict that maps resname --> list of Bond tuples residue_virtual_bonds: dict that maps resname --> list of Bond tuples residue_bond_angles: dict that maps resname --> list of BondAngle tuples """ # TODO: this file should be downloaded in a setup script stereo_chemical_props = resources.read_text("openfold.resources", "stereo_chemical_props.txt") lines_iter = iter(stereo_chemical_props.splitlines()) # Load bond lengths. residue_bonds = {} next(lines_iter) # Skip header line. for line in lines_iter: if line.strip() == "-": break bond, resname, length, stddev = line.split() atom1, atom2 = bond.split("-") if resname not in residue_bonds: residue_bonds[resname] = [] residue_bonds[resname].append(Bond(atom1, atom2, float(length), float(stddev))) residue_bonds["UNK"] = [] # Load bond angles. residue_bond_angles = {} next(lines_iter) # Skip empty line. next(lines_iter) # Skip header line. for line in lines_iter: if line.strip() == "-": break bond, resname, angle_degree, stddev_degree = line.split() atom1, atom2, atom3 = bond.split("-") if resname not in residue_bond_angles: residue_bond_angles[resname] = [] residue_bond_angles[resname].append( BondAngle( atom1, atom2, atom3, float(angle_degree) / 180.0 * np.pi, float(stddev_degree) / 180.0 * np.pi, ) ) residue_bond_angles["UNK"] = [] def make_bond_key(atom1_name, atom2_name): """Unique key to lookup bonds.""" return "-".join(sorted([atom1_name, atom2_name])) # Translate bond angles into distances ("virtual bonds"). residue_virtual_bonds = {} for resname, bond_angles in residue_bond_angles.items(): # Create a fast lookup dict for bond lengths. bond_cache = {} for b in residue_bonds[resname]: bond_cache[make_bond_key(b.atom1_name, b.atom2_name)] = b residue_virtual_bonds[resname] = [] for ba in bond_angles: bond1 = bond_cache[make_bond_key(ba.atom1_name, ba.atom2_name)] bond2 = bond_cache[make_bond_key(ba.atom2_name, ba.atom3name)] # Compute distance between atom1 and atom3 using the law of cosines # c^2 = a^2 + b^2 - 2ab*cos(gamma). gamma = ba.angle_rad length = np.sqrt(bond1.length**2 + bond2.length**2 - 2 * bond1.length * bond2.length * np.cos(gamma)) # Propagation of uncertainty assuming uncorrelated errors. dl_outer = 0.5 / length dl_dgamma = (2 * bond1.length * bond2.length * np.sin(gamma)) * dl_outer dl_db1 = (2 * bond1.length - 2 * bond2.length * np.cos(gamma)) * dl_outer dl_db2 = (2 * bond2.length - 2 * bond1.length * np.cos(gamma)) * dl_outer stddev = np.sqrt( (dl_dgamma * ba.stddev) ** 2 + (dl_db1 * bond1.stddev) ** 2 + (dl_db2 * bond2.stddev) ** 2 ) residue_virtual_bonds[resname].append(Bond(ba.atom1_name, ba.atom3name, length, stddev)) return (residue_bonds, residue_virtual_bonds, residue_bond_angles) # Between-residue bond lengths for general bonds (first element) and for Proline # (second element). between_res_bond_length_c_n = [1.329, 1.341] between_res_bond_length_stddev_c_n = [0.014, 0.016] # Between-residue cos_angles. between_res_cos_angles_c_n_ca = [-0.5203, 0.0353] # degrees: 121.352 +- 2.315 between_res_cos_angles_ca_c_n = [-0.4473, 0.0311] # degrees: 116.568 +- 1.995 # This mapping is used when we need to store atom data in a format that requires # fixed atom data size for every residue (e.g. a numpy array). atom_types = [ "N", "CA", "C", "CB", "O", "CG", "CG1", "CG2", "OG", "OG1", "SG", "CD", "CD1", "CD2", "ND1", "ND2", "OD1", "OD2", "SD", "CE", "CE1", "CE2", "CE3", "NE", "NE1", "NE2", "OE1", "OE2", "CH2", "NH1", "NH2", "OH", "CZ", "CZ2", "CZ3", "NZ", "OXT", ] atom_order = {atom_type: i for i, atom_type in enumerate(atom_types)} atom_type_num = len(atom_types) # := 37. # A compact atom encoding with 14 columns # pylint: disable=line-too-long # pylint: disable=bad-whitespace restype_name_to_atom14_names = { "ALA": ["N", "CA", "C", "O", "CB", "", "", "", "", "", "", "", "", ""], "ARG": [ "N", "CA", "C", "O", "CB", "CG", "CD", "NE", "CZ", "NH1", "NH2", "", "", "", ], "ASN": [ "N", "CA", "C", "O", "CB", "CG", "OD1", "ND2", "", "", "", "", "", "", ], "ASP": [ "N", "CA", "C", "O", "CB", "CG", "OD1", "OD2", "", "", "", "", "", "", ], "CYS": ["N", "CA", "C", "O", "CB", "SG", "", "", "", "", "", "", "", ""], "GLN": [ "N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "NE2", "", "", "", "", "", ], "GLU": [ "N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "OE2", "", "", "", "", "", ], "GLY": ["N", "CA", "C", "O", "", "", "", "", "", "", "", "", "", ""], "HIS": [ "N", "CA", "C", "O", "CB", "CG", "ND1", "CD2", "CE1", "NE2", "", "", "", "", ], "ILE": [ "N", "CA", "C", "O", "CB", "CG1", "CG2", "CD1", "", "", "", "", "", "", ], "LEU": [ "N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "", "", "", "", "", "", ], "LYS": [ "N", "CA", "C", "O", "CB", "CG", "CD", "CE", "NZ", "", "", "", "", "", ], "MET": [ "N", "CA", "C", "O", "CB", "CG", "SD", "CE", "", "", "", "", "", "", ], "PHE": [ "N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "", "", "", ], "PRO": ["N", "CA", "C", "O", "CB", "CG", "CD", "", "", "", "", "", "", ""], "SER": ["N", "CA", "C", "O", "CB", "OG", "", "", "", "", "", "", "", ""], "THR": [ "N", "CA", "C", "O", "CB", "OG1", "CG2", "", "", "", "", "", "", "", ], "TRP": [ "N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "NE1", "CE2", "CE3", "CZ2", "CZ3", "CH2", ], "TYR": [ "N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "OH", "", "", ], "VAL": [ "N", "CA", "C", "O", "CB", "CG1", "CG2", "", "", "", "", "", "", "", ], "UNK": ["", "", "", "", "", "", "", "", "", "", "", "", "", ""], } # pylint: enable=line-too-long # pylint: enable=bad-whitespace # This is the standard residue order when coding AA type as a number. # Reproduce it by taking 3-letter AA codes and sorting them alphabetically. restypes = [ "A", "R", "N", "D", "C", "Q", "E", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V", ] restype_order = {restype: i for i, restype in enumerate(restypes)} restype_num = len(restypes) # := 20. unk_restype_index = restype_num # Catch-all index for unknown restypes. restypes_with_x = restypes + ["X"] restype_order_with_x = {restype: i for i, restype in enumerate(restypes_with_x)} def sequence_to_onehot(sequence: str, mapping: Mapping[str, int], map_unknown_to_x: bool = False) -> np.ndarray: """Maps the given sequence into a one-hot encoded matrix. Args: sequence: An amino acid sequence. mapping: A dictionary mapping amino acids to integers. map_unknown_to_x: If True, any amino acid that is not in the mapping will be mapped to the unknown amino acid 'X'. If the mapping doesn't contain amino acid 'X', an error will be thrown. If False, any amino acid not in the mapping will throw an error. Returns: A numpy array of shape (seq_len, num_unique_aas) with one-hot encoding of the sequence. Raises: ValueError: If the mapping doesn't contain values from 0 to num_unique_aas - 1 without any gaps. """ num_entries = max(mapping.values()) + 1 if sorted(set(mapping.values())) != list(range(num_entries)): raise ValueError( "The mapping must have values from 0 to num_unique_aas-1 without any gaps. Got: %s" % sorted(mapping.values()) ) one_hot_arr = np.zeros((len(sequence), num_entries), dtype=np.int32) for aa_index, aa_type in enumerate(sequence): if map_unknown_to_x: if aa_type.isalpha() and aa_type.isupper(): aa_id = mapping.get(aa_type, mapping["X"]) else: raise ValueError(f"Invalid character in the sequence: {aa_type}") else: aa_id = mapping[aa_type] one_hot_arr[aa_index, aa_id] = 1 return one_hot_arr restype_1to3 = { "A": "ALA", "R": "ARG", "N": "ASN", "D": "ASP", "C": "CYS", "Q": "GLN", "E": "GLU", "G": "GLY", "H": "HIS", "I": "ILE", "L": "LEU", "K": "LYS", "M": "MET", "F": "PHE", "P": "PRO", "S": "SER", "T": "THR", "W": "TRP", "Y": "TYR", "V": "VAL", } # NB: restype_3to1 differs from Bio.PDB.protein_letters_3to1 by being a simple # 1-to-1 mapping of 3 letter names to one letter names. The latter contains # many more, and less common, three letter names as keys and maps many of these # to the same one letter name (including 'X' and 'U' which we don't use here). restype_3to1 = {v: k for k, v in restype_1to3.items()} # Define a restype name for all unknown residues. unk_restype = "UNK" resnames = [restype_1to3[r] for r in restypes] + [unk_restype] resname_to_idx = {resname: i for i, resname in enumerate(resnames)} # The mapping here uses hhblits convention, so that B is mapped to D, J and O # are mapped to X, U is mapped to C, and Z is mapped to E. Other than that the # remaining 20 amino acids are kept in alphabetical order. # There are 2 non-amino acid codes, X (representing any amino acid) and # "-" representing a missing amino acid in an alignment. The id for these # codes is put at the end (20 and 21) so that they can easily be ignored if # desired. HHBLITS_AA_TO_ID = { "A": 0, "B": 2, "C": 1, "D": 2, "E": 3, "F": 4, "G": 5, "H": 6, "I": 7, "J": 20, "K": 8, "L": 9, "M": 10, "N": 11, "O": 20, "P": 12, "Q": 13, "R": 14, "S": 15, "T": 16, "U": 1, "V": 17, "W": 18, "X": 20, "Y": 19, "Z": 3, "-": 21, } # Partial inversion of HHBLITS_AA_TO_ID. ID_TO_HHBLITS_AA = { 0: "A", 1: "C", # Also U. 2: "D", # Also B. 3: "E", # Also Z. 4: "F", 5: "G", 6: "H", 7: "I", 8: "K", 9: "L", 10: "M", 11: "N", 12: "P", 13: "Q", 14: "R", 15: "S", 16: "T", 17: "V", 18: "W", 19: "Y", 20: "X", # Includes J and O. 21: "-", } restypes_with_x_and_gap = restypes + ["X", "-"] MAP_HHBLITS_AATYPE_TO_OUR_AATYPE = tuple( restypes_with_x_and_gap.index(ID_TO_HHBLITS_AA[i]) for i in range(len(restypes_with_x_and_gap)) ) def _make_standard_atom_mask() -> np.ndarray: """Returns [num_res_types, num_atom_types] mask array.""" # +1 to account for unknown (all 0s). mask = np.zeros([restype_num + 1, atom_type_num], dtype=np.int32) for restype, restype_letter in enumerate(restypes): restype_name = restype_1to3[restype_letter] atom_names = residue_atoms[restype_name] for atom_name in atom_names: atom_type = atom_order[atom_name] mask[restype, atom_type] = 1 return mask STANDARD_ATOM_MASK = _make_standard_atom_mask() # A one hot representation for the first and second atoms defining the axis # of rotation for each chi-angle in each residue. def chi_angle_atom(atom_index: int) -> np.ndarray: """Define chi-angle rigid groups via one-hot representations.""" chi_angles_index = {} one_hots = [] for k, v in chi_angles_atoms.items(): indices = [atom_types.index(s[atom_index]) for s in v] indices.extend([-1] * (4 - len(indices))) chi_angles_index[k] = indices for r in restypes: res3 = restype_1to3[r] one_hot = np.eye(atom_type_num)[chi_angles_index[res3]] one_hots.append(one_hot) one_hots.append(np.zeros([4, atom_type_num])) # Add zeros for residue `X`. one_hot = np.stack(one_hots, axis=0) one_hot = np.transpose(one_hot, [0, 2, 1]) return one_hot chi_atom_1_one_hot = chi_angle_atom(1) chi_atom_2_one_hot = chi_angle_atom(2) # An array like chi_angles_atoms but using indices rather than names. chi_angles_atom_indices = [chi_angles_atoms[restype_1to3[r]] for r in restypes] chi_angles_atom_indices_ours = map_structure_with_atom_order(chi_angles_atom_indices) chi_angles_atom_indices = np.array( [chi_atoms + ([[0, 0, 0, 0]] * (4 - len(chi_atoms))) for chi_atoms in chi_angles_atom_indices] ) # Mapping from (res_name, atom_name) pairs to the atom's chi group index # and atom index within that group. chi_groups_for_atom = collections.defaultdict(list) for res_name, chi_angle_atoms_for_res in chi_angles_atoms.items(): for chi_group_i, chi_group in enumerate(chi_angle_atoms_for_res): for atom_i, atom in enumerate(chi_group): chi_groups_for_atom[(res_name, atom)].append((chi_group_i, atom_i)) chi_groups_for_atom = dict(chi_groups_for_atom) def _make_rigid_transformation_4x4(ex, ey, translation): """Create a rigid 4x4 transformation matrix from two axes and transl.""" # Normalize ex. ex_normalized = ex / np.linalg.norm(ex) # make ey perpendicular to ex ey_normalized = ey - np.dot(ey, ex_normalized) * ex_normalized ey_normalized /= np.linalg.norm(ey_normalized) # compute ez as cross product eznorm = np.cross(ex_normalized, ey_normalized) m = np.stack([ex_normalized, ey_normalized, eznorm, translation]).transpose() m = np.concatenate([m, [[0.0, 0.0, 0.0, 1.0]]], axis=0) return m # create an array with (restype, atomtype) --> rigid_group_idx # and an array with (restype, atomtype, coord) for the atom positions # and compute affine transformation matrices (4,4) from one rigid group to the # previous group restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=int) restype_atom37_mask = np.zeros([21, 37], dtype=np.float32) restype_atom37_rigid_group_positions = np.zeros([21, 37, 3], dtype=np.float32) restype_atom14_to_rigid_group = np.zeros([21, 14], dtype=int) restype_atom14_mask = np.zeros([21, 14], dtype=np.float32) restype_atom14_rigid_group_positions = np.zeros([21, 14, 3], dtype=np.float32) restype_rigid_group_default_frame = np.zeros([21, 8, 4, 4], dtype=np.float32) def _make_rigid_group_constants(): """Fill the arrays above.""" for restype, restype_letter in enumerate(restypes): resname = restype_1to3[restype_letter] for atomname, group_idx, atom_position in rigid_group_atom_positions[resname]: atomtype = atom_order[atomname] restype_atom37_to_rigid_group[restype, atomtype] = group_idx restype_atom37_mask[restype, atomtype] = 1 restype_atom37_rigid_group_positions[restype, atomtype, :] = atom_position atom14idx = restype_name_to_atom14_names[resname].index(atomname) restype_atom14_to_rigid_group[restype, atom14idx] = group_idx restype_atom14_mask[restype, atom14idx] = 1 restype_atom14_rigid_group_positions[restype, atom14idx, :] = atom_position for restype, restype_letter in enumerate(restypes): resname = restype_1to3[restype_letter] atom_positions = {name: np.array(pos) for name, _, pos in rigid_group_atom_positions[resname]} # backbone to backbone is the identity transform restype_rigid_group_default_frame[restype, 0, :, :] = np.eye(4) # pre-omega-frame to backbone (currently dummy identity matrix) restype_rigid_group_default_frame[restype, 1, :, :] = np.eye(4) # phi-frame to backbone mat = _make_rigid_transformation_4x4( ex=atom_positions["N"] - atom_positions["CA"], ey=np.array([1.0, 0.0, 0.0]), translation=atom_positions["N"], ) restype_rigid_group_default_frame[restype, 2, :, :] = mat # psi-frame to backbone mat = _make_rigid_transformation_4x4( ex=atom_positions["C"] - atom_positions["CA"], ey=atom_positions["CA"] - atom_positions["N"], translation=atom_positions["C"], ) restype_rigid_group_default_frame[restype, 3, :, :] = mat # chi1-frame to backbone if chi_angles_mask[restype][0]: base_atom_names = chi_angles_atoms[resname][0] base_atom_positions = [atom_positions[name] for name in base_atom_names] mat = _make_rigid_transformation_4x4( ex=base_atom_positions[2] - base_atom_positions[1], ey=base_atom_positions[0] - base_atom_positions[1], translation=base_atom_positions[2], ) restype_rigid_group_default_frame[restype, 4, :, :] = mat # chi2-frame to chi1-frame # chi3-frame to chi2-frame # chi4-frame to chi3-frame # luckily all rotation axes for the next frame start at (0,0,0) of the # previous frame for chi_idx in range(1, 4): if chi_angles_mask[restype][chi_idx]: axis_end_atom_name = chi_angles_atoms[resname][chi_idx][2] axis_end_atom_position = atom_positions[axis_end_atom_name] mat = _make_rigid_transformation_4x4( ex=axis_end_atom_position, ey=np.array([-1.0, 0.0, 0.0]), translation=axis_end_atom_position, ) restype_rigid_group_default_frame[restype, 4 + chi_idx, :, :] = mat _make_rigid_group_constants() def make_atom14_dists_bounds(overlap_tolerance=1.5, bond_length_tolerance_factor=15): """compute upper and lower bounds for bonds to assess violations.""" restype_atom14_bond_lower_bound = np.zeros([21, 14, 14], np.float32) restype_atom14_bond_upper_bound = np.zeros([21, 14, 14], np.float32) restype_atom14_bond_stddev = np.zeros([21, 14, 14], np.float32) residue_bonds, residue_virtual_bonds, _ = load_stereo_chemical_props() for restype, restype_letter in enumerate(restypes): resname = restype_1to3[restype_letter] atom_list = restype_name_to_atom14_names[resname] # create lower and upper bounds for clashes for atom1_idx, atom1_name in enumerate(atom_list): if not atom1_name: continue atom1_radius = van_der_waals_radius[atom1_name[0]] for atom2_idx, atom2_name in enumerate(atom_list): if (not atom2_name) or atom1_idx == atom2_idx: continue atom2_radius = van_der_waals_radius[atom2_name[0]] lower = atom1_radius + atom2_radius - overlap_tolerance upper = 1e10 restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper # overwrite lower and upper bounds for bonds and angles for b in residue_bonds[resname] + residue_virtual_bonds[resname]: atom1_idx = atom_list.index(b.atom1_name) atom2_idx = atom_list.index(b.atom2_name) lower = b.length - bond_length_tolerance_factor * b.stddev upper = b.length + bond_length_tolerance_factor * b.stddev restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper restype_atom14_bond_stddev[restype, atom1_idx, atom2_idx] = b.stddev restype_atom14_bond_stddev[restype, atom2_idx, atom1_idx] = b.stddev return { "lower_bound": restype_atom14_bond_lower_bound, # shape (21,14,14) "upper_bound": restype_atom14_bond_upper_bound, # shape (21,14,14) "stddev": restype_atom14_bond_stddev, # shape (21,14,14) } restype_atom14_ambiguous_atoms = np.zeros((21, 14), dtype=np.float32) restype_atom14_ambiguous_atoms_swap_idx = np.tile(np.arange(14, dtype=int), (21, 1)) def _make_atom14_ambiguity_feats(): for res, pairs in residue_atom_renaming_swaps.items(): res_idx = restype_order[restype_3to1[res]] for atom1, atom2 in pairs.items(): atom1_idx = restype_name_to_atom14_names[res].index(atom1) atom2_idx = restype_name_to_atom14_names[res].index(atom2) restype_atom14_ambiguous_atoms[res_idx, atom1_idx] = 1 restype_atom14_ambiguous_atoms[res_idx, atom2_idx] = 1 restype_atom14_ambiguous_atoms_swap_idx[res_idx, atom1_idx] = atom2_idx restype_atom14_ambiguous_atoms_swap_idx[res_idx, atom2_idx] = atom1_idx _make_atom14_ambiguity_feats() def aatype_to_str_sequence(aatype): return "".join([restypes_with_x[aatype[i]] for i in range(len(aatype))])

# Copyright 2021 AlQuraishi Laboratory # Copyright 2021 DeepMind Technologies Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Constants used in AlphaFold.""" import collections import copy import functools from importlib import resources from typing import List, Mapping, Tuple import numpy as np # Internal import (35fd). # Distance from one CA to next CA [trans configuration: omega = 180]. ca_ca = 3.80209737096 # Format: The list for each AA type contains chi1, chi2, chi3, chi4 in # this order (or a relevant subset from chi1 onwards). ALA and GLY don't have # chi angles so their chi angle lists are empty. chi_angles_atoms = { "ALA": [], # Chi5 in arginine is always 0 +- 5 degrees, so ignore it. "ARG": [ ["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "NE"], ["CG", "CD", "NE", "CZ"], ], "ASN": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "OD1"]], "ASP": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "OD1"]], "CYS": [["N", "CA", "CB", "SG"]], "GLN": [ ["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "OE1"], ], "GLU": [ ["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "OE1"], ], "GLY": [], "HIS": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "ND1"]], "ILE": [["N", "CA", "CB", "CG1"], ["CA", "CB", "CG1", "CD1"]], "LEU": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], "LYS": [ ["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"], ["CB", "CG", "CD", "CE"], ["CG", "CD", "CE", "NZ"], ], "MET": [ ["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "SD"], ["CB", "CG", "SD", "CE"], ], "PHE": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], "PRO": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD"]], "SER": [["N", "CA", "CB", "OG"]], "THR": [["N", "CA", "CB", "OG1"]], "TRP": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], "TYR": [["N", "CA", "CB", "CG"], ["CA", "CB", "CG", "CD1"]], "VAL": [["N", "CA", "CB", "CG1"]], } # If chi angles given in fixed-length array, this matrix determines how to mask # them for each AA type. The order is as per restype_order (see below). chi_angles_mask = [ [0.0, 0.0, 0.0, 0.0], # ALA [1.0, 1.0, 1.0, 1.0], # ARG [1.0, 1.0, 0.0, 0.0], # ASN [1.0, 1.0, 0.0, 0.0], # ASP [1.0, 0.0, 0.0, 0.0], # CYS [1.0, 1.0, 1.0, 0.0], # GLN [1.0, 1.0, 1.0, 0.0], # GLU [0.0, 0.0, 0.0, 0.0], # GLY [1.0, 1.0, 0.0, 0.0], # HIS [1.0, 1.0, 0.0, 0.0], # ILE [1.0, 1.0, 0.0, 0.0], # LEU [1.0, 1.0, 1.0, 1.0], # LYS [1.0, 1.0, 1.0, 0.0], # MET [1.0, 1.0, 0.0, 0.0], # PHE [1.0, 1.0, 0.0, 0.0], # PRO [1.0, 0.0, 0.0, 0.0], # SER [1.0, 0.0, 0.0, 0.0], # THR [1.0, 1.0, 0.0, 0.0], # TRP [1.0, 1.0, 0.0, 0.0], # TYR [1.0, 0.0, 0.0, 0.0], # VAL ] # The following chi angles are pi periodic: they can be rotated by a multiple # of pi without affecting the structure. chi_pi_periodic = [ [0.0, 0.0, 0.0, 0.0], # ALA [0.0, 0.0, 0.0, 0.0], # ARG [0.0, 0.0, 0.0, 0.0], # ASN [0.0, 1.0, 0.0, 0.0], # ASP [0.0, 0.0, 0.0, 0.0], # CYS [0.0, 0.0, 0.0, 0.0], # GLN [0.0, 0.0, 1.0, 0.0], # GLU [0.0, 0.0, 0.0, 0.0], # GLY [0.0, 0.0, 0.0, 0.0], # HIS [0.0, 0.0, 0.0, 0.0], # ILE [0.0, 0.0, 0.0, 0.0], # LEU [0.0, 0.0, 0.0, 0.0], # LYS [0.0, 0.0, 0.0, 0.0], # MET [0.0, 1.0, 0.0, 0.0], # PHE [0.0, 0.0, 0.0, 0.0], # PRO [0.0, 0.0, 0.0, 0.0], # SER [0.0, 0.0, 0.0, 0.0], # THR [0.0, 0.0, 0.0, 0.0], # TRP [0.0, 1.0, 0.0, 0.0], # TYR [0.0, 0.0, 0.0, 0.0], # VAL [0.0, 0.0, 0.0, 0.0], # UNK ] # Atoms positions relative to the 8 rigid groups, defined by the pre-omega, phi, # psi and chi angles: # 0: 'backbone group', # 1: 'pre-omega-group', (empty) # 2: 'phi-group', (currently empty, because it defines only hydrogens) # 3: 'psi-group', # 4,5,6,7: 'chi1,2,3,4-group' # The atom positions are relative to the axis-end-atom of the corresponding # rotation axis. The x-axis is in direction of the rotation axis, and the y-axis # is defined such that the dihedral-angle-definiting atom (the last entry in # chi_angles_atoms above) is in the xy-plane (with a positive y-coordinate). # format: [atomname, group_idx, rel_position] rigid_group_atom_positions = { "ALA": [ ["N", 0, (-0.525, 1.363, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.526, -0.000, -0.000)], ["CB", 0, (-0.529, -0.774, -1.205)], ["O", 3, (0.627, 1.062, 0.000)], ], "ARG": [ ["N", 0, (-0.524, 1.362, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.525, -0.000, -0.000)], ["CB", 0, (-0.524, -0.778, -1.209)], ["O", 3, (0.626, 1.062, 0.000)], ["CG", 4, (0.616, 1.390, -0.000)], ["CD", 5, (0.564, 1.414, 0.000)], ["NE", 6, (0.539, 1.357, -0.000)], ["NH1", 7, (0.206, 2.301, 0.000)], ["NH2", 7, (2.078, 0.978, -0.000)], ["CZ", 7, (0.758, 1.093, -0.000)], ], "ASN": [ ["N", 0, (-0.536, 1.357, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.526, -0.000, -0.000)], ["CB", 0, (-0.531, -0.787, -1.200)], ["O", 3, (0.625, 1.062, 0.000)], ["CG", 4, (0.584, 1.399, 0.000)], ["ND2", 5, (0.593, -1.188, 0.001)], ["OD1", 5, (0.633, 1.059, 0.000)], ], "ASP": [ ["N", 0, (-0.525, 1.362, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.527, 0.000, -0.000)], ["CB", 0, (-0.526, -0.778, -1.208)], ["O", 3, (0.626, 1.062, -0.000)], ["CG", 4, (0.593, 1.398, -0.000)], ["OD1", 5, (0.610, 1.091, 0.000)], ["OD2", 5, (0.592, -1.101, -0.003)], ], "CYS": [ ["N", 0, (-0.522, 1.362, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.524, 0.000, 0.000)], ["CB", 0, (-0.519, -0.773, -1.212)], ["O", 3, (0.625, 1.062, -0.000)], ["SG", 4, (0.728, 1.653, 0.000)], ], "GLN": [ ["N", 0, (-0.526, 1.361, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.526, 0.000, 0.000)], ["CB", 0, (-0.525, -0.779, -1.207)], ["O", 3, (0.626, 1.062, -0.000)], ["CG", 4, (0.615, 1.393, 0.000)], ["CD", 5, (0.587, 1.399, -0.000)], ["NE2", 6, (0.593, -1.189, -0.001)], ["OE1", 6, (0.634, 1.060, 0.000)], ], "GLU": [ ["N", 0, (-0.528, 1.361, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.526, -0.000, -0.000)], ["CB", 0, (-0.526, -0.781, -1.207)], ["O", 3, (0.626, 1.062, 0.000)], ["CG", 4, (0.615, 1.392, 0.000)], ["CD", 5, (0.600, 1.397, 0.000)], ["OE1", 6, (0.607, 1.095, -0.000)], ["OE2", 6, (0.589, -1.104, -0.001)], ], "GLY": [ ["N", 0, (-0.572, 1.337, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.517, -0.000, -0.000)], ["O", 3, (0.626, 1.062, -0.000)], ], "HIS": [ ["N", 0, (-0.527, 1.360, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.525, 0.000, 0.000)], ["CB", 0, (-0.525, -0.778, -1.208)], ["O", 3, (0.625, 1.063, 0.000)], ["CG", 4, (0.600, 1.370, -0.000)], ["CD2", 5, (0.889, -1.021, 0.003)], ["ND1", 5, (0.744, 1.160, -0.000)], ["CE1", 5, (2.030, 0.851, 0.002)], ["NE2", 5, (2.145, -0.466, 0.004)], ], "ILE": [ ["N", 0, (-0.493, 1.373, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.527, -0.000, -0.000)], ["CB", 0, (-0.536, -0.793, -1.213)], ["O", 3, (0.627, 1.062, -0.000)], ["CG1", 4, (0.534, 1.437, -0.000)], ["CG2", 4, (0.540, -0.785, -1.199)], ["CD1", 5, (0.619, 1.391, 0.000)], ], "LEU": [ ["N", 0, (-0.520, 1.363, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.525, -0.000, -0.000)], ["CB", 0, (-0.522, -0.773, -1.214)], ["O", 3, (0.625, 1.063, -0.000)], ["CG", 4, (0.678, 1.371, 0.000)], ["CD1", 5, (0.530, 1.430, -0.000)], ["CD2", 5, (0.535, -0.774, 1.200)], ], "LYS": [ ["N", 0, (-0.526, 1.362, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.526, 0.000, 0.000)], ["CB", 0, (-0.524, -0.778, -1.208)], ["O", 3, (0.626, 1.062, -0.000)], ["CG", 4, (0.619, 1.390, 0.000)], ["CD", 5, (0.559, 1.417, 0.000)], ["CE", 6, (0.560, 1.416, 0.000)], ["NZ", 7, (0.554, 1.387, 0.000)], ], "MET": [ ["N", 0, (-0.521, 1.364, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.525, 0.000, 0.000)], ["CB", 0, (-0.523, -0.776, -1.210)], ["O", 3, (0.625, 1.062, -0.000)], ["CG", 4, (0.613, 1.391, -0.000)], ["SD", 5, (0.703, 1.695, 0.000)], ["CE", 6, (0.320, 1.786, -0.000)], ], "PHE": [ ["N", 0, (-0.518, 1.363, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.524, 0.000, -0.000)], ["CB", 0, (-0.525, -0.776, -1.212)], ["O", 3, (0.626, 1.062, -0.000)], ["CG", 4, (0.607, 1.377, 0.000)], ["CD1", 5, (0.709, 1.195, -0.000)], ["CD2", 5, (0.706, -1.196, 0.000)], ["CE1", 5, (2.102, 1.198, -0.000)], ["CE2", 5, (2.098, -1.201, -0.000)], ["CZ", 5, (2.794, -0.003, -0.001)], ], "PRO": [ ["N", 0, (-0.566, 1.351, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.527, -0.000, 0.000)], ["CB", 0, (-0.546, -0.611, -1.293)], ["O", 3, (0.621, 1.066, 0.000)], ["CG", 4, (0.382, 1.445, 0.0)], # ['CD', 5, (0.427, 1.440, 0.0)], ["CD", 5, (0.477, 1.424, 0.0)], # manually made angle 2 degrees larger ], "SER": [ ["N", 0, (-0.529, 1.360, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.525, -0.000, -0.000)], ["CB", 0, (-0.518, -0.777, -1.211)], ["O", 3, (0.626, 1.062, -0.000)], ["OG", 4, (0.503, 1.325, 0.000)], ], "THR": [ ["N", 0, (-0.517, 1.364, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.526, 0.000, -0.000)], ["CB", 0, (-0.516, -0.793, -1.215)], ["O", 3, (0.626, 1.062, 0.000)], ["CG2", 4, (0.550, -0.718, -1.228)], ["OG1", 4, (0.472, 1.353, 0.000)], ], "TRP": [ ["N", 0, (-0.521, 1.363, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.525, -0.000, 0.000)], ["CB", 0, (-0.523, -0.776, -1.212)], ["O", 3, (0.627, 1.062, 0.000)], ["CG", 4, (0.609, 1.370, -0.000)], ["CD1", 5, (0.824, 1.091, 0.000)], ["CD2", 5, (0.854, -1.148, -0.005)], ["CE2", 5, (2.186, -0.678, -0.007)], ["CE3", 5, (0.622, -2.530, -0.007)], ["NE1", 5, (2.140, 0.690, -0.004)], ["CH2", 5, (3.028, -2.890, -0.013)], ["CZ2", 5, (3.283, -1.543, -0.011)], ["CZ3", 5, (1.715, -3.389, -0.011)], ], "TYR": [ ["N", 0, (-0.522, 1.362, 0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.524, -0.000, -0.000)], ["CB", 0, (-0.522, -0.776, -1.213)], ["O", 3, (0.627, 1.062, -0.000)], ["CG", 4, (0.607, 1.382, -0.000)], ["CD1", 5, (0.716, 1.195, -0.000)], ["CD2", 5, (0.713, -1.194, -0.001)], ["CE1", 5, (2.107, 1.200, -0.002)], ["CE2", 5, (2.104, -1.201, -0.003)], ["OH", 5, (4.168, -0.002, -0.005)], ["CZ", 5, (2.791, -0.001, -0.003)], ], "VAL": [ ["N", 0, (-0.494, 1.373, -0.000)], ["CA", 0, (0.000, 0.000, 0.000)], ["C", 0, (1.527, -0.000, -0.000)], ["CB", 0, (-0.533, -0.795, -1.213)], ["O", 3, (0.627, 1.062, -0.000)], ["CG1", 4, (0.540, 1.429, -0.000)], ["CG2", 4, (0.533, -0.776, 1.203)], ], } # A list of atoms (excluding hydrogen) for each AA type. PDB naming convention. residue_atoms = { "ALA": ["C", "CA", "CB", "N", "O"], "ARG": ["C", "CA", "CB", "CG", "CD", "CZ", "N", "NE", "O", "NH1", "NH2"], "ASP": ["C", "CA", "CB", "CG", "N", "O", "OD1", "OD2"], "ASN": ["C", "CA", "CB", "CG", "N", "ND2", "O", "OD1"], "CYS": ["C", "CA", "CB", "N", "O", "SG"], "GLU": ["C", "CA", "CB", "CG", "CD", "N", "O", "OE1", "OE2"], "GLN": ["C", "CA", "CB", "CG", "CD", "N", "NE2", "O", "OE1"], "GLY": ["C", "CA", "N", "O"], "HIS": ["C", "CA", "CB", "CG", "CD2", "CE1", "N", "ND1", "NE2", "O"], "ILE": ["C", "CA", "CB", "CG1", "CG2", "CD1", "N", "O"], "LEU": ["C", "CA", "CB", "CG", "CD1", "CD2", "N", "O"], "LYS": ["C", "CA", "CB", "CG", "CD", "CE", "N", "NZ", "O"], "MET": ["C", "CA", "CB", "CG", "CE", "N", "O", "SD"], "PHE": ["C", "CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "N", "O"], "PRO": ["C", "CA", "CB", "CG", "CD", "N", "O"], "SER": ["C", "CA", "CB", "N", "O", "OG"], "THR": ["C", "CA", "CB", "CG2", "N", "O", "OG1"], "TRP": [ "C", "CA", "CB", "CG", "CD1", "CD2", "CE2", "CE3", "CZ2", "CZ3", "CH2", "N", "NE1", "O", ], "TYR": [ "C", "CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "N", "O", "OH", ], "VAL": ["C", "CA", "CB", "CG1", "CG2", "N", "O"], } # Naming swaps for ambiguous atom names. # Due to symmetries in the amino acids the naming of atoms is ambiguous in # 4 of the 20 amino acids. # (The LDDT paper lists 7 amino acids as ambiguous, but the naming ambiguities # in LEU, VAL and ARG can be resolved by using the 3d constellations of # the 'ambiguous' atoms and their neighbours) # TODO: ^ interpret this residue_atom_renaming_swaps = { "ASP": {"OD1": "OD2"}, "GLU": {"OE1": "OE2"}, "PHE": {"CD1": "CD2", "CE1": "CE2"}, "TYR": {"CD1": "CD2", "CE1": "CE2"}, } # Van der Waals radii [Angstroem] of the atoms (from Wikipedia) van_der_waals_radius = { "C": 1.7, "N": 1.55, "O": 1.52, "S": 1.8, } Bond = collections.namedtuple("Bond", ["atom1_name", "atom2_name", "length", "stddev"]) BondAngle = collections.namedtuple( "BondAngle", ["atom1_name", "atom2_name", "atom3name", "angle_rad", "stddev"], ) def map_structure_with_atom_order(in_list: List, first_call: bool = True): # Maps strings in a nested list structure to their corresponding index in atom_order if first_call: in_list = copy.deepcopy(in_list) for i in range(len(in_list)): if isinstance(in_list[i], list): in_list[i] = map_structure_with_atom_order(in_list[i], first_call=False) elif isinstance(in_list[i], str): in_list[i] = atom_order[in_list[i]] else: raise ValueError("Unexpected type when mapping nested lists!") return in_list @functools.lru_cache(maxsize=None) def load_stereo_chemical_props() -> Tuple[ Mapping[str, List[Bond]], Mapping[str, List[Bond]], Mapping[str, List[BondAngle]], ]: """Load stereo_chemical_props.txt into a nice structure. Load literature values for bond lengths and bond angles and translate bond angles into the length of the opposite edge of the triangle ("residue_virtual_bonds"). Returns: residue_bonds: dict that maps resname --> list of Bond tuples residue_virtual_bonds: dict that maps resname --> list of Bond tuples residue_bond_angles: dict that maps resname --> list of BondAngle tuples """ # TODO: this file should be downloaded in a setup script stereo_chemical_props = resources.read_text("openfold.resources", "stereo_chemical_props.txt") lines_iter = iter(stereo_chemical_props.splitlines()) # Load bond lengths. residue_bonds = {} next(lines_iter) # Skip header line. for line in lines_iter: if line.strip() == "-": break bond, resname, length, stddev = line.split() atom1, atom2 = bond.split("-") if resname not in residue_bonds: residue_bonds[resname] = [] residue_bonds[resname].append(Bond(atom1, atom2, float(length), float(stddev))) residue_bonds["UNK"] = [] # Load bond angles. residue_bond_angles = {} next(lines_iter) # Skip empty line. next(lines_iter) # Skip header line. for line in lines_iter: if line.strip() == "-": break bond, resname, angle_degree, stddev_degree = line.split() atom1, atom2, atom3 = bond.split("-") if resname not in residue_bond_angles: residue_bond_angles[resname] = [] residue_bond_angles[resname].append( BondAngle( atom1, atom2, atom3, float(angle_degree) / 180.0 * np.pi, float(stddev_degree) / 180.0 * np.pi, ) ) residue_bond_angles["UNK"] = [] def make_bond_key(atom1_name, atom2_name): """Unique key to lookup bonds.""" return "-".join(sorted([atom1_name, atom2_name])) # Translate bond angles into distances ("virtual bonds"). residue_virtual_bonds = {} for resname, bond_angles in residue_bond_angles.items(): # Create a fast lookup dict for bond lengths. bond_cache = {} for b in residue_bonds[resname]: bond_cache[make_bond_key(b.atom1_name, b.atom2_name)] = b residue_virtual_bonds[resname] = [] for ba in bond_angles: bond1 = bond_cache[make_bond_key(ba.atom1_name, ba.atom2_name)] bond2 = bond_cache[make_bond_key(ba.atom2_name, ba.atom3name)] # Compute distance between atom1 and atom3 using the law of cosines # c^2 = a^2 + b^2 - 2ab*cos(gamma). gamma = ba.angle_rad length = np.sqrt(bond1.length**2 + bond2.length**2 - 2 * bond1.length * bond2.length * np.cos(gamma)) # Propagation of uncertainty assuming uncorrelated errors. dl_outer = 0.5 / length dl_dgamma = (2 * bond1.length * bond2.length * np.sin(gamma)) * dl_outer dl_db1 = (2 * bond1.length - 2 * bond2.length * np.cos(gamma)) * dl_outer dl_db2 = (2 * bond2.length - 2 * bond1.length * np.cos(gamma)) * dl_outer stddev = np.sqrt( (dl_dgamma * ba.stddev) ** 2 + (dl_db1 * bond1.stddev) ** 2 + (dl_db2 * bond2.stddev) ** 2 ) residue_virtual_bonds[resname].append(Bond(ba.atom1_name, ba.atom3name, length, stddev)) return (residue_bonds, residue_virtual_bonds, residue_bond_angles) # Between-residue bond lengths for general bonds (first element) and for Proline # (second element). between_res_bond_length_c_n = [1.329, 1.341] between_res_bond_length_stddev_c_n = [0.014, 0.016] # Between-residue cos_angles. between_res_cos_angles_c_n_ca = [-0.5203, 0.0353] # degrees: 121.352 +- 2.315 between_res_cos_angles_ca_c_n = [-0.4473, 0.0311] # degrees: 116.568 +- 1.995 # This mapping is used when we need to store atom data in a format that requires # fixed atom data size for every residue (e.g. a numpy array). atom_types = [ "N", "CA", "C", "CB", "O", "CG", "CG1", "CG2", "OG", "OG1", "SG", "CD", "CD1", "CD2", "ND1", "ND2", "OD1", "OD2", "SD", "CE", "CE1", "CE2", "CE3", "NE", "NE1", "NE2", "OE1", "OE2", "CH2", "NH1", "NH2", "OH", "CZ", "CZ2", "CZ3", "NZ", "OXT", ] atom_order = {atom_type: i for i, atom_type in enumerate(atom_types)} atom_type_num = len(atom_types) # := 37. # A compact atom encoding with 14 columns # pylint: disable=line-too-long # pylint: disable=bad-whitespace restype_name_to_atom14_names = { "ALA": ["N", "CA", "C", "O", "CB", "", "", "", "", "", "", "", "", ""], "ARG": [ "N", "CA", "C", "O", "CB", "CG", "CD", "NE", "CZ", "NH1", "NH2", "", "", "", ], "ASN": [ "N", "CA", "C", "O", "CB", "CG", "OD1", "ND2", "", "", "", "", "", "", ], "ASP": [ "N", "CA", "C", "O", "CB", "CG", "OD1", "OD2", "", "", "", "", "", "", ], "CYS": ["N", "CA", "C", "O", "CB", "SG", "", "", "", "", "", "", "", ""], "GLN": [ "N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "NE2", "", "", "", "", "", ], "GLU": [ "N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "OE2", "", "", "", "", "", ], "GLY": ["N", "CA", "C", "O", "", "", "", "", "", "", "", "", "", ""], "HIS": [ "N", "CA", "C", "O", "CB", "CG", "ND1", "CD2", "CE1", "NE2", "", "", "", "", ], "ILE": [ "N", "CA", "C", "O", "CB", "CG1", "CG2", "CD1", "", "", "", "", "", "", ], "LEU": [ "N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "", "", "", "", "", "", ], "LYS": [ "N", "CA", "C", "O", "CB", "CG", "CD", "CE", "NZ", "", "", "", "", "", ], "MET": [ "N", "CA", "C", "O", "CB", "CG", "SD", "CE", "", "", "", "", "", "", ], "PHE": [ "N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "", "", "", ], "PRO": ["N", "CA", "C", "O", "CB", "CG", "CD", "", "", "", "", "", "", ""], "SER": ["N", "CA", "C", "O", "CB", "OG", "", "", "", "", "", "", "", ""], "THR": [ "N", "CA", "C", "O", "CB", "OG1", "CG2", "", "", "", "", "", "", "", ], "TRP": [ "N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "NE1", "CE2", "CE3", "CZ2", "CZ3", "CH2", ], "TYR": [ "N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "OH", "", "", ], "VAL": [ "N", "CA", "C", "O", "CB", "CG1", "CG2", "", "", "", "", "", "", "", ], "UNK": ["", "", "", "", "", "", "", "", "", "", "", "", "", ""], } # pylint: enable=line-too-long # pylint: enable=bad-whitespace # This is the standard residue order when coding AA type as a number. # Reproduce it by taking 3-letter AA codes and sorting them alphabetically. restypes = [ "A", "R", "N", "D", "C", "Q", "E", "G", "H", "I", "L", "K", "M", "F", "P", "S", "T", "W", "Y", "V", ] restype_order = {restype: i for i, restype in enumerate(restypes)} restype_num = len(restypes) # := 20. unk_restype_index = restype_num # Catch-all index for unknown restypes. restypes_with_x = restypes + ["X"] restype_order_with_x = {restype: i for i, restype in enumerate(restypes_with_x)} def sequence_to_onehot(sequence: str, mapping: Mapping[str, int], map_unknown_to_x: bool = False) -> np.ndarray: """Maps the given sequence into a one-hot encoded matrix. Args: sequence: An amino acid sequence. mapping: A dictionary mapping amino acids to integers. map_unknown_to_x: If True, any amino acid that is not in the mapping will be mapped to the unknown amino acid 'X'. If the mapping doesn't contain amino acid 'X', an error will be thrown. If False, any amino acid not in the mapping will throw an error. Returns: A numpy array of shape (seq_len, num_unique_aas) with one-hot encoding of the sequence. Raises: ValueError: If the mapping doesn't contain values from 0 to num_unique_aas - 1 without any gaps. """ num_entries = max(mapping.values()) + 1 if sorted(set(mapping.values())) != list(range(num_entries)): raise ValueError( "The mapping must have values from 0 to num_unique_aas-1 without any gaps. Got: %s" % sorted(mapping.values()) ) one_hot_arr = np.zeros((len(sequence), num_entries), dtype=np.int32) for aa_index, aa_type in enumerate(sequence): if map_unknown_to_x: if aa_type.isalpha() and aa_type.isupper(): aa_id = mapping.get(aa_type, mapping["X"]) else: raise ValueError(f"Invalid character in the sequence: {aa_type}") else: aa_id = mapping[aa_type] one_hot_arr[aa_index, aa_id] = 1 return one_hot_arr restype_1to3 = { "A": "ALA", "R": "ARG", "N": "ASN", "D": "ASP", "C": "CYS", "Q": "GLN", "E": "GLU", "G": "GLY", "H": "HIS", "I": "ILE", "L": "LEU", "K": "LYS", "M": "MET", "F": "PHE", "P": "PRO", "S": "SER", "T": "THR", "W": "TRP", "Y": "TYR", "V": "VAL", } # NB: restype_3to1 differs from Bio.PDB.protein_letters_3to1 by being a simple # 1-to-1 mapping of 3 letter names to one letter names. The latter contains # many more, and less common, three letter names as keys and maps many of these # to the same one letter name (including 'X' and 'U' which we don't use here). restype_3to1 = {v: k for k, v in restype_1to3.items()} # Define a restype name for all unknown residues. unk_restype = "UNK" resnames = [restype_1to3[r] for r in restypes] + [unk_restype] resname_to_idx = {resname: i for i, resname in enumerate(resnames)} # The mapping here uses hhblits convention, so that B is mapped to D, J and O # are mapped to X, U is mapped to C, and Z is mapped to E. Other than that the # remaining 20 amino acids are kept in alphabetical order. # There are 2 non-amino acid codes, X (representing any amino acid) and # "-" representing a missing amino acid in an alignment. The id for these # codes is put at the end (20 and 21) so that they can easily be ignored if # desired. HHBLITS_AA_TO_ID = { "A": 0, "B": 2, "C": 1, "D": 2, "E": 3, "F": 4, "G": 5, "H": 6, "I": 7, "J": 20, "K": 8, "L": 9, "M": 10, "N": 11, "O": 20, "P": 12, "Q": 13, "R": 14, "S": 15, "T": 16, "U": 1, "V": 17, "W": 18, "X": 20, "Y": 19, "Z": 3, "-": 21, } # Partial inversion of HHBLITS_AA_TO_ID. ID_TO_HHBLITS_AA = { 0: "A", 1: "C", # Also U. 2: "D", # Also B. 3: "E", # Also Z. 4: "F", 5: "G", 6: "H", 7: "I", 8: "K", 9: "L", 10: "M", 11: "N", 12: "P", 13: "Q", 14: "R", 15: "S", 16: "T", 17: "V", 18: "W", 19: "Y", 20: "X", # Includes J and O. 21: "-", } restypes_with_x_and_gap = restypes + ["X", "-"] MAP_HHBLITS_AATYPE_TO_OUR_AATYPE = tuple( restypes_with_x_and_gap.index(ID_TO_HHBLITS_AA[i]) for i in range(len(restypes_with_x_and_gap)) ) def _make_standard_atom_mask() -> np.ndarray: """Returns [num_res_types, num_atom_types] mask array.""" # +1 to account for unknown (all 0s). mask = np.zeros([restype_num + 1, atom_type_num], dtype=np.int32) for restype, restype_letter in enumerate(restypes): restype_name = restype_1to3[restype_letter] atom_names = residue_atoms[restype_name] for atom_name in atom_names: atom_type = atom_order[atom_name] mask[restype, atom_type] = 1 return mask STANDARD_ATOM_MASK = _make_standard_atom_mask() # A one hot representation for the first and second atoms defining the axis # of rotation for each chi-angle in each residue. def chi_angle_atom(atom_index: int) -> np.ndarray: """Define chi-angle rigid groups via one-hot representations.""" chi_angles_index = {} one_hots = [] for k, v in chi_angles_atoms.items(): indices = [atom_types.index(s[atom_index]) for s in v] indices.extend([-1] * (4 - len(indices))) chi_angles_index[k] = indices for r in restypes: res3 = restype_1to3[r] one_hot = np.eye(atom_type_num)[chi_angles_index[res3]] one_hots.append(one_hot) one_hots.append(np.zeros([4, atom_type_num])) # Add zeros for residue `X`. one_hot = np.stack(one_hots, axis=0) one_hot = np.transpose(one_hot, [0, 2, 1]) return one_hot chi_atom_1_one_hot = chi_angle_atom(1) chi_atom_2_one_hot = chi_angle_atom(2) # An array like chi_angles_atoms but using indices rather than names. chi_angles_atom_indices = [chi_angles_atoms[restype_1to3[r]] for r in restypes] chi_angles_atom_indices_ours = map_structure_with_atom_order(chi_angles_atom_indices) chi_angles_atom_indices = np.array( [chi_atoms + ([[0, 0, 0, 0]] * (4 - len(chi_atoms))) for chi_atoms in chi_angles_atom_indices] ) # Mapping from (res_name, atom_name) pairs to the atom's chi group index # and atom index within that group. chi_groups_for_atom = collections.defaultdict(list) for res_name, chi_angle_atoms_for_res in chi_angles_atoms.items(): for chi_group_i, chi_group in enumerate(chi_angle_atoms_for_res): for atom_i, atom in enumerate(chi_group): chi_groups_for_atom[(res_name, atom)].append((chi_group_i, atom_i)) chi_groups_for_atom = dict(chi_groups_for_atom) def _make_rigid_transformation_4x4(ex, ey, translation): """Create a rigid 4x4 transformation matrix from two axes and transl.""" # Normalize ex. ex_normalized = ex / np.linalg.norm(ex) # make ey perpendicular to ex ey_normalized = ey - np.dot(ey, ex_normalized) * ex_normalized ey_normalized /= np.linalg.norm(ey_normalized) # compute ez as cross product eznorm = np.cross(ex_normalized, ey_normalized) m = np.stack([ex_normalized, ey_normalized, eznorm, translation]).transpose() m = np.concatenate([m, [[0.0, 0.0, 0.0, 1.0]]], axis=0) return m # create an array with (restype, atomtype) --> rigid_group_idx # and an array with (restype, atomtype, coord) for the atom positions # and compute affine transformation matrices (4,4) from one rigid group to the # previous group restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=int) restype_atom37_mask = np.zeros([21, 37], dtype=np.float32) restype_atom37_rigid_group_positions = np.zeros([21, 37, 3], dtype=np.float32) restype_atom14_to_rigid_group = np.zeros([21, 14], dtype=int) restype_atom14_mask = np.zeros([21, 14], dtype=np.float32) restype_atom14_rigid_group_positions = np.zeros([21, 14, 3], dtype=np.float32) restype_rigid_group_default_frame = np.zeros([21, 8, 4, 4], dtype=np.float32) def _make_rigid_group_constants(): """Fill the arrays above.""" for restype, restype_letter in enumerate(restypes): resname = restype_1to3[restype_letter] for atomname, group_idx, atom_position in rigid_group_atom_positions[resname]: atomtype = atom_order[atomname] restype_atom37_to_rigid_group[restype, atomtype] = group_idx restype_atom37_mask[restype, atomtype] = 1 restype_atom37_rigid_group_positions[restype, atomtype, :] = atom_position atom14idx = restype_name_to_atom14_names[resname].index(atomname) restype_atom14_to_rigid_group[restype, atom14idx] = group_idx restype_atom14_mask[restype, atom14idx] = 1 restype_atom14_rigid_group_positions[restype, atom14idx, :] = atom_position for restype, restype_letter in enumerate(restypes): resname = restype_1to3[restype_letter] atom_positions = {name: np.array(pos) for name, _, pos in rigid_group_atom_positions[resname]} # backbone to backbone is the identity transform restype_rigid_group_default_frame[restype, 0, :, :] = np.eye(4) # pre-omega-frame to backbone (currently dummy identity matrix) restype_rigid_group_default_frame[restype, 1, :, :] = np.eye(4) # phi-frame to backbone mat = _make_rigid_transformation_4x4( ex=atom_positions["N"] - atom_positions["CA"], ey=np.array([1.0, 0.0, 0.0]), translation=atom_positions["N"], ) restype_rigid_group_default_frame[restype, 2, :, :] = mat # psi-frame to backbone mat = _make_rigid_transformation_4x4( ex=atom_positions["C"] - atom_positions["CA"], ey=atom_positions["CA"] - atom_positions["N"], translation=atom_positions["C"], ) restype_rigid_group_default_frame[restype, 3, :, :] = mat # chi1-frame to backbone if chi_angles_mask[restype][0]: base_atom_names = chi_angles_atoms[resname][0] base_atom_positions = [atom_positions[name] for name in base_atom_names] mat = _make_rigid_transformation_4x4( ex=base_atom_positions[2] - base_atom_positions[1], ey=base_atom_positions[0] - base_atom_positions[1], translation=base_atom_positions[2], ) restype_rigid_group_default_frame[restype, 4, :, :] = mat # chi2-frame to chi1-frame # chi3-frame to chi2-frame # chi4-frame to chi3-frame # luckily all rotation axes for the next frame start at (0,0,0) of the # previous frame for chi_idx in range(1, 4): if chi_angles_mask[restype][chi_idx]: axis_end_atom_name = chi_angles_atoms[resname][chi_idx][2] axis_end_atom_position = atom_positions[axis_end_atom_name] mat = _make_rigid_transformation_4x4( ex=axis_end_atom_position, ey=np.array([-1.0, 0.0, 0.0]), translation=axis_end_atom_position, ) restype_rigid_group_default_frame[restype, 4 + chi_idx, :, :] = mat _make_rigid_group_constants() def make_atom14_dists_bounds(overlap_tolerance=1.5, bond_length_tolerance_factor=15): """compute upper and lower bounds for bonds to assess violations.""" restype_atom14_bond_lower_bound = np.zeros([21, 14, 14], np.float32) restype_atom14_bond_upper_bound = np.zeros([21, 14, 14], np.float32) restype_atom14_bond_stddev = np.zeros([21, 14, 14], np.float32) residue_bonds, residue_virtual_bonds, _ = load_stereo_chemical_props() for restype, restype_letter in enumerate(restypes): resname = restype_1to3[restype_letter] atom_list = restype_name_to_atom14_names[resname] # create lower and upper bounds for clashes for atom1_idx, atom1_name in enumerate(atom_list): if not atom1_name: continue atom1_radius = van_der_waals_radius[atom1_name[0]] for atom2_idx, atom2_name in enumerate(atom_list): if (not atom2_name) or atom1_idx == atom2_idx: continue atom2_radius = van_der_waals_radius[atom2_name[0]] lower = atom1_radius + atom2_radius - overlap_tolerance upper = 1e10 restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper # overwrite lower and upper bounds for bonds and angles for b in residue_bonds[resname] + residue_virtual_bonds[resname]: atom1_idx = atom_list.index(b.atom1_name) atom2_idx = atom_list.index(b.atom2_name) lower = b.length - bond_length_tolerance_factor * b.stddev upper = b.length + bond_length_tolerance_factor * b.stddev restype_atom14_bond_lower_bound[restype, atom1_idx, atom2_idx] = lower restype_atom14_bond_lower_bound[restype, atom2_idx, atom1_idx] = lower restype_atom14_bond_upper_bound[restype, atom1_idx, atom2_idx] = upper restype_atom14_bond_upper_bound[restype, atom2_idx, atom1_idx] = upper restype_atom14_bond_stddev[restype, atom1_idx, atom2_idx] = b.stddev restype_atom14_bond_stddev[restype, atom2_idx, atom1_idx] = b.stddev return { "lower_bound": restype_atom14_bond_lower_bound, # shape (21,14,14) "upper_bound": restype_atom14_bond_upper_bound, # shape (21,14,14) "stddev": restype_atom14_bond_stddev, # shape (21,14,14) } restype_atom14_ambiguous_atoms = np.zeros((21, 14), dtype=np.float32) restype_atom14_ambiguous_atoms_swap_idx = np.tile(np.arange(14, dtype=int), (21, 1)) def _make_atom14_ambiguity_feats(): for res, pairs in residue_atom_renaming_swaps.items(): res_idx = restype_order[restype_3to1[res]] for atom1, atom2 in pairs.items(): atom1_idx = restype_name_to_atom14_names[res].index(atom1) atom2_idx = restype_name_to_atom14_names[res].index(atom2) restype_atom14_ambiguous_atoms[res_idx, atom1_idx] = 1 restype_atom14_ambiguous_atoms[res_idx, atom2_idx] = 1 restype_atom14_ambiguous_atoms_swap_idx[res_idx, atom1_idx] = atom2_idx restype_atom14_ambiguous_atoms_swap_idx[res_idx, atom2_idx] = atom1_idx _make_atom14_ambiguity_feats() def aatype_to_str_sequence(aatype): return "".join([restypes_with_x[aatype[i]] for i in range(len(aatype))])

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./examples/tensorflow/question-answering/README.md

<!--- Copyright 2021 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Question answering example This folder contains the `run_qa.py` script, demonstrating *question answering* with the 🤗 Transformers library. For straightforward use-cases you may be able to use this script without modification, although we have also included comments in the code to indicate areas that you may need to adapt to your own projects. ### Usage notes Note that when contexts are long they may be split into multiple training cases, not all of which may contain the answer span. As-is, the example script will train on SQuAD or any other question-answering dataset formatted the same way, and can handle user inputs as well. ### Multi-GPU and TPU usage By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs can also be used by passing the name of the TPU resource with the `--tpu` argument. There are some issues surrounding these strategies and our models right now, which are most likely to appear in the evaluation/prediction steps. We're actively working on better support for multi-GPU and TPU training in TF, but if you encounter problems a quick workaround is to train in the multi-GPU or TPU context and then perform predictions outside of it. ### Memory usage and data loading One thing to note is that all data is loaded into memory in this script. Most question answering datasets are small enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and README, but for more information you can see the 'Input Datasets' section of [this document](https://www.tensorflow.org/guide/tpu). ### Example command ``` python run_qa.py \ --model_name_or_path distilbert-base-cased \ --output_dir output \ --dataset_name squad \ --do_train \ --do_eval \ ```

<!--- Copyright 2021 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Question answering example This folder contains the `run_qa.py` script, demonstrating *question answering* with the 🤗 Transformers library. For straightforward use-cases you may be able to use this script without modification, although we have also included comments in the code to indicate areas that you may need to adapt to your own projects. ### Usage notes Note that when contexts are long they may be split into multiple training cases, not all of which may contain the answer span. As-is, the example script will train on SQuAD or any other question-answering dataset formatted the same way, and can handle user inputs as well. ### Multi-GPU and TPU usage By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs can also be used by passing the name of the TPU resource with the `--tpu` argument. There are some issues surrounding these strategies and our models right now, which are most likely to appear in the evaluation/prediction steps. We're actively working on better support for multi-GPU and TPU training in TF, but if you encounter problems a quick workaround is to train in the multi-GPU or TPU context and then perform predictions outside of it. ### Memory usage and data loading One thing to note is that all data is loaded into memory in this script. Most question answering datasets are small enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and README, but for more information you can see the 'Input Datasets' section of [this document](https://www.tensorflow.org/guide/tpu). ### Example command ``` python run_qa.py \ --model_name_or_path distilbert-base-cased \ --output_dir output \ --dataset_name squad \ --do_train \ --do_eval \ ```

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./docs/source/it/multilingual.mdx

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Modelli multilingue per l'inferenza [[open-in-colab]] Ci sono diversi modelli multilingue in 🤗 Transformers, e il loro utilizzo per l'inferenza differisce da quello dei modelli monolingua. Non *tutti* gli utilizzi dei modelli multilingue sono però diversi. Alcuni modelli, come [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased), possono essere usati come un modello monolingua. Questa guida ti mostrerà come utilizzare modelli multilingue che utilizzano un modo diverso per fare l'inferenza. ## XLM XLM ha dieci diversi checkpoint, di cui solo uno è monolingua. I nove checkpoint rimanenti possono essere suddivisi in due categorie: i checkpoint che utilizzano i language embeddings e quelli che non li utilizzano. ### XLM con language embeddings I seguenti modelli XLM utilizzano gli embeddings linguistici per specificare la lingua utilizzata per l'inferenza: - `xlm-mlm-ende-1024` (Modellazione mascherata del linguaggio (Masked language modeling, in inglese), Inglese-Tedesco) - `xlm-mlm-enfr-1024` (Modellazione mascherata del linguaggio, Inglese-Francese) - `xlm-mlm-enro-1024` (Modellazione mascherata del linguaggio, Inglese-Rumeno) - `xlm-mlm-xnli15-1024` (Modellazione mascherata del linguaggio, lingue XNLI) - `xlm-mlm-tlm-xnli15-1024` (Modellazione mascherata del linguaggio + traduzione, lingue XNLI) - `xlm-clm-enfr-1024` (Modellazione causale del linguaggio, Inglese-Francese) - `xlm-clm-ende-1024` (Modellazione causale del linguaggio, Inglese-Tedesco) Gli embeddings linguistici sono rappresentati come un tensore delle stesse dimensioni dell' `input_ids` passato al modello. I valori in questi tensori dipendono dal linguaggio usato e sono identificati dagli attributi `lang2id` e `id2lang` del tokenizer. In questo esempio, carica il checkpoint `xlm-clm-enfr-1024` (Modellazione causale del linguaggio, Inglese-Francese): ```py >>> import torch >>> from transformers import XLMTokenizer, XLMWithLMHeadModel >>> tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024") >>> model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024") ``` L'attributo `lang2id` del tokenizer mostra il linguaggio del modello e il suo ids: ```py >>> print(tokenizer.lang2id) {'en': 0, 'fr': 1} ``` Poi, crea un esempio di input: ```py >>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1 ``` Imposta l'id del linguaggio a `"en"` e usalo per definire il language embedding. Il language embedding è un tensore riempito con `0` perché questo è il language id per l'inglese. Questo tensore dovrebbe avere la stessa dimensione di `input_ids`. ```py >>> language_id = tokenizer.lang2id["en"] # 0 >>> langs = torch.tensor([language_id] * input_ids.shape[1]) # torch.tensor([0, 0, 0, ..., 0]) >>> # We reshape it to be of size (batch_size, sequence_length) >>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1) ``` Adesso puoi inserire `input_ids` e language embedding nel modello: ```py >>> outputs = model(input_ids, langs=langs) ``` Lo script [run_generation.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-generation/run_generation.py) può generare testo tramite i language embeddings usando i checkpoints `xlm-clm`. ### XLM senza language embeddings I seguenti modelli XLM non richiedono l'utilizzo dei language embeddings per fare inferenza: - `xlm-mlm-17-1280` (Modellazione mascherata del linguaggio, 17 lingue) - `xlm-mlm-100-1280` (Modellazione mascherata del linguaggio, 100 lingue) Questi modelli sono utilizzati per rappresentazioni generiche di frasi, a differenza dei precedenti checkpoints XML. ## BERT Il seguente modello BERT può essere usato per compiti multilingue: - `bert-base-multilingual-uncased` (Modellazione mascherata del linguaggio + Previsione della prossima frase, 102 lingue) - `bert-base-multilingual-cased` (Modellazione mascherata del linguaggio + Previsione della prossima frase, 104 lingue) Questi modelli non richiedono language embeddings per fare inferenza. Riescono ad identificare il linguaggio dal contesto e inferire di conseguenza. ## XLM-RoBERTa Il seguente modello XLM-RoBERTa può essere usato per compiti multilingue: - `xlm-roberta-base` (Modellazione mascherata del linguaggio, 100 lingue) - `xlm-roberta-large` (Modellazione mascherata del linguaggio, 100 lingue) XLM-RoBERTa è stato addestrato su 2.5TB di dati CommonCrawl appena creati e puliti in 100 lingue. Offre notevoli vantaggi rispetto ai modelli multilingue rilasciati in precedenza, come mBERT o XLM, in compiti come la classificazione, l'etichettatura delle sequenze e la risposta alle domande. ## M2M100 Il seguente modello M2M100 può essere usato per compiti multilingue: - `facebook/m2m100_418M` (Traduzione) - `facebook/m2m100_1.2B` (Traduzione) In questo esempio, carica il checkpoint `facebook/m2m100_418M` per tradurre dal cinese all'inglese. Puoi impostare la lingua di partenza nel tokenizer: ```py >>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer >>> en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger." >>> chinese_text = "不要插手巫師的事務, 因為他們是微妙的, 很快就會發怒." >>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="zh") >>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M") ``` Applica il tokenizer al testo: ```py >>> encoded_zh = tokenizer(chinese_text, return_tensors="pt") ``` M2M100 forza l'id della lingua obiettivo come primo token generato per tradurre nella lingua obiettivo. Imposta il parametro `forced_bos_token_id` a `en` nel metodo `generate` per tradurre in inglese: ```py >>> generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en")) >>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) 'Do not interfere with the matters of the witches, because they are delicate and will soon be angry.' ``` ## MBart Il seguente modello MBart può essere usato per compiti multilingue: - `facebook/mbart-large-50-one-to-many-mmt` (Traduzione automatica multilingue uno-a-molti, 50 lingue) - `facebook/mbart-large-50-many-to-many-mmt` (Traduzione automatica multilingue molti-a-molti, 50 lingue) - `facebook/mbart-large-50-many-to-one-mmt` (Traduzione automatica multilingue molti-a-uno, 50 lingue) - `facebook/mbart-large-50` (Traduzione multilingue, 50 lingue) - `facebook/mbart-large-cc25` In questo esempio, carica il checkpoint `facebook/mbart-large-50-many-to-many-mmt` per tradurre dal finlandese all'inglese. Puoi impostare la lingua di partenza nel tokenizer: ```py >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger." >>> fi_text = "Älä sekaannu velhojen asioihin, sillä ne ovat hienovaraisia ja nopeasti vihaisia." >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", src_lang="fi_FI") >>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") ``` Applica il tokenizer sul testo: ```py >>> encoded_en = tokenizer(en_text, return_tensors="pt") ``` MBart forza l'id della lingua obiettivo come primo token generato per tradurre nella lingua obiettivo. Imposta il parametro `forced_bos_token_id` a `en` nel metodo `generate` per tradurre in inglese: ```py >>> generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id("en_XX")) >>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) "Don't interfere with the wizard's affairs, because they are subtle, will soon get angry." ``` Se stai usando il checkpoint `facebook/mbart-large-50-many-to-one-mmt`, non hai bisogno di forzare l'id della lingua obiettivo come primo token generato altrimenti l'uso è lo stesso.

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Modelli multilingue per l'inferenza [[open-in-colab]] Ci sono diversi modelli multilingue in 🤗 Transformers, e il loro utilizzo per l'inferenza differisce da quello dei modelli monolingua. Non *tutti* gli utilizzi dei modelli multilingue sono però diversi. Alcuni modelli, come [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased), possono essere usati come un modello monolingua. Questa guida ti mostrerà come utilizzare modelli multilingue che utilizzano un modo diverso per fare l'inferenza. ## XLM XLM ha dieci diversi checkpoint, di cui solo uno è monolingua. I nove checkpoint rimanenti possono essere suddivisi in due categorie: i checkpoint che utilizzano i language embeddings e quelli che non li utilizzano. ### XLM con language embeddings I seguenti modelli XLM utilizzano gli embeddings linguistici per specificare la lingua utilizzata per l'inferenza: - `xlm-mlm-ende-1024` (Modellazione mascherata del linguaggio (Masked language modeling, in inglese), Inglese-Tedesco) - `xlm-mlm-enfr-1024` (Modellazione mascherata del linguaggio, Inglese-Francese) - `xlm-mlm-enro-1024` (Modellazione mascherata del linguaggio, Inglese-Rumeno) - `xlm-mlm-xnli15-1024` (Modellazione mascherata del linguaggio, lingue XNLI) - `xlm-mlm-tlm-xnli15-1024` (Modellazione mascherata del linguaggio + traduzione, lingue XNLI) - `xlm-clm-enfr-1024` (Modellazione causale del linguaggio, Inglese-Francese) - `xlm-clm-ende-1024` (Modellazione causale del linguaggio, Inglese-Tedesco) Gli embeddings linguistici sono rappresentati come un tensore delle stesse dimensioni dell' `input_ids` passato al modello. I valori in questi tensori dipendono dal linguaggio usato e sono identificati dagli attributi `lang2id` e `id2lang` del tokenizer. In questo esempio, carica il checkpoint `xlm-clm-enfr-1024` (Modellazione causale del linguaggio, Inglese-Francese): ```py >>> import torch >>> from transformers import XLMTokenizer, XLMWithLMHeadModel >>> tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024") >>> model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024") ``` L'attributo `lang2id` del tokenizer mostra il linguaggio del modello e il suo ids: ```py >>> print(tokenizer.lang2id) {'en': 0, 'fr': 1} ``` Poi, crea un esempio di input: ```py >>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1 ``` Imposta l'id del linguaggio a `"en"` e usalo per definire il language embedding. Il language embedding è un tensore riempito con `0` perché questo è il language id per l'inglese. Questo tensore dovrebbe avere la stessa dimensione di `input_ids`. ```py >>> language_id = tokenizer.lang2id["en"] # 0 >>> langs = torch.tensor([language_id] * input_ids.shape[1]) # torch.tensor([0, 0, 0, ..., 0]) >>> # We reshape it to be of size (batch_size, sequence_length) >>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1) ``` Adesso puoi inserire `input_ids` e language embedding nel modello: ```py >>> outputs = model(input_ids, langs=langs) ``` Lo script [run_generation.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-generation/run_generation.py) può generare testo tramite i language embeddings usando i checkpoints `xlm-clm`. ### XLM senza language embeddings I seguenti modelli XLM non richiedono l'utilizzo dei language embeddings per fare inferenza: - `xlm-mlm-17-1280` (Modellazione mascherata del linguaggio, 17 lingue) - `xlm-mlm-100-1280` (Modellazione mascherata del linguaggio, 100 lingue) Questi modelli sono utilizzati per rappresentazioni generiche di frasi, a differenza dei precedenti checkpoints XML. ## BERT Il seguente modello BERT può essere usato per compiti multilingue: - `bert-base-multilingual-uncased` (Modellazione mascherata del linguaggio + Previsione della prossima frase, 102 lingue) - `bert-base-multilingual-cased` (Modellazione mascherata del linguaggio + Previsione della prossima frase, 104 lingue) Questi modelli non richiedono language embeddings per fare inferenza. Riescono ad identificare il linguaggio dal contesto e inferire di conseguenza. ## XLM-RoBERTa Il seguente modello XLM-RoBERTa può essere usato per compiti multilingue: - `xlm-roberta-base` (Modellazione mascherata del linguaggio, 100 lingue) - `xlm-roberta-large` (Modellazione mascherata del linguaggio, 100 lingue) XLM-RoBERTa è stato addestrato su 2.5TB di dati CommonCrawl appena creati e puliti in 100 lingue. Offre notevoli vantaggi rispetto ai modelli multilingue rilasciati in precedenza, come mBERT o XLM, in compiti come la classificazione, l'etichettatura delle sequenze e la risposta alle domande. ## M2M100 Il seguente modello M2M100 può essere usato per compiti multilingue: - `facebook/m2m100_418M` (Traduzione) - `facebook/m2m100_1.2B` (Traduzione) In questo esempio, carica il checkpoint `facebook/m2m100_418M` per tradurre dal cinese all'inglese. Puoi impostare la lingua di partenza nel tokenizer: ```py >>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer >>> en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger." >>> chinese_text = "不要插手巫師的事務, 因為他們是微妙的, 很快就會發怒." >>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="zh") >>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M") ``` Applica il tokenizer al testo: ```py >>> encoded_zh = tokenizer(chinese_text, return_tensors="pt") ``` M2M100 forza l'id della lingua obiettivo come primo token generato per tradurre nella lingua obiettivo. Imposta il parametro `forced_bos_token_id` a `en` nel metodo `generate` per tradurre in inglese: ```py >>> generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en")) >>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) 'Do not interfere with the matters of the witches, because they are delicate and will soon be angry.' ``` ## MBart Il seguente modello MBart può essere usato per compiti multilingue: - `facebook/mbart-large-50-one-to-many-mmt` (Traduzione automatica multilingue uno-a-molti, 50 lingue) - `facebook/mbart-large-50-many-to-many-mmt` (Traduzione automatica multilingue molti-a-molti, 50 lingue) - `facebook/mbart-large-50-many-to-one-mmt` (Traduzione automatica multilingue molti-a-uno, 50 lingue) - `facebook/mbart-large-50` (Traduzione multilingue, 50 lingue) - `facebook/mbart-large-cc25` In questo esempio, carica il checkpoint `facebook/mbart-large-50-many-to-many-mmt` per tradurre dal finlandese all'inglese. Puoi impostare la lingua di partenza nel tokenizer: ```py >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger." >>> fi_text = "Älä sekaannu velhojen asioihin, sillä ne ovat hienovaraisia ja nopeasti vihaisia." >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", src_lang="fi_FI") >>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") ``` Applica il tokenizer sul testo: ```py >>> encoded_en = tokenizer(en_text, return_tensors="pt") ``` MBart forza l'id della lingua obiettivo come primo token generato per tradurre nella lingua obiettivo. Imposta il parametro `forced_bos_token_id` a `en` nel metodo `generate` per tradurre in inglese: ```py >>> generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id("en_XX")) >>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) "Don't interfere with the wizard's affairs, because they are subtle, will soon get angry." ``` Se stai usando il checkpoint `facebook/mbart-large-50-many-to-one-mmt`, non hai bisogno di forzare l'id della lingua obiettivo come primo token generato altrimenti l'uso è lo stesso.

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./tests/models/bart/test_modeling_flax_bart.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np import timeout_decorator # noqa from transformers import BartConfig, BartTokenizer, is_flax_available from transformers.testing_utils import require_flax, slow from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform" import jax import jax.numpy as jnp from transformers.models.bart.modeling_flax_bart import ( FlaxBartForConditionalGeneration, FlaxBartForQuestionAnswering, FlaxBartForSequenceClassification, FlaxBartModel, shift_tokens_right, ) def prepare_bart_inputs_dict( config, input_ids, decoder_input_ids=None, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = np.where(input_ids != config.pad_token_id, 1, 0) if decoder_attention_mask is None: decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0) if head_mask is None: head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, } class FlaxBartModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=32, eos_token_id=2, pad_token_id=1, bos_token_id=0, initializer_range=0.02, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.initializer_range = initializer_range def prepare_config_and_inputs(self): input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size) input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1) decoder_input_ids = shift_tokens_right(input_ids, 1, 2) config = BartConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, initializer_range=self.initializer_range, use_cache=False, ) inputs_dict = prepare_bart_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def check_use_cache_forward(self, model_class_name, config, inputs_dict): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(inputs_dict["input_ids"]) decoder_input_ids, decoder_attention_mask = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4") decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), ) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=past_key_values, decoder_position_ids=decoder_position_ids, ) decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=outputs_cache.past_key_values, decoder_position_ids=decoder_position_ids, ) outputs = model.decode(decoder_input_ids, encoder_outputs) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(inputs_dict["input_ids"]) decoder_input_ids, decoder_attention_mask = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) decoder_attention_mask_cache = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])), ], axis=-1, ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), ) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask_cache, past_key_values=past_key_values, decoder_position_ids=decoder_position_ids, ) decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, past_key_values=outputs_cache.past_key_values, decoder_attention_mask=decoder_attention_mask_cache, decoder_position_ids=decoder_position_ids, ) outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") @require_flax class BartHeadTests(unittest.TestCase): vocab_size = 99 def _get_config_and_data(self): input_ids = np.array( [ [71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 82, 2], [5, 97, 17, 39, 94, 40, 2], [76, 83, 94, 25, 70, 78, 2], [87, 59, 41, 35, 48, 66, 2], [55, 13, 16, 58, 5, 2, 1], # note padding [64, 27, 31, 51, 12, 75, 2], [52, 64, 86, 17, 83, 39, 2], [48, 61, 9, 24, 71, 82, 2], [26, 1, 60, 48, 22, 13, 2], [21, 5, 62, 28, 14, 76, 2], [45, 98, 37, 86, 59, 48, 2], [70, 70, 50, 9, 28, 0, 2], ], dtype=np.int64, ) batch_size = input_ids.shape[0] config = BartConfig( vocab_size=self.vocab_size, d_model=24, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=32, decoder_ffn_dim=32, max_position_embeddings=48, eos_token_id=2, pad_token_id=1, bos_token_id=0, ) return config, input_ids, batch_size def test_sequence_classification_forward(self): config, input_ids, batch_size = self._get_config_and_data() model = FlaxBartForSequenceClassification(config) outputs = model(input_ids=input_ids, decoder_input_ids=input_ids) expected_shape = (batch_size, config.num_labels) self.assertEqual(outputs["logits"].shape, expected_shape) def test_question_answering_forward(self): config, input_ids, batch_size = self._get_config_and_data() model = FlaxBartForQuestionAnswering(config) outputs = model(input_ids=input_ids) self.assertEqual(outputs["start_logits"].shape, input_ids.shape) self.assertEqual(outputs["end_logits"].shape, input_ids.shape) # @timeout_decorator.timeout(1) # not working with the decorator so far def test_lm_forward(self): config, input_ids, batch_size = self._get_config_and_data() lm_model = FlaxBartForConditionalGeneration(config) outputs = lm_model(input_ids=input_ids) expected_shape = (batch_size, input_ids.shape[1], config.vocab_size) self.assertEqual(outputs["logits"].shape, expected_shape) def test_lm_uneven_forward(self): config = BartConfig( vocab_size=self.vocab_size, d_model=14, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=8, decoder_ffn_dim=8, max_position_embeddings=48, ) lm_model = FlaxBartForConditionalGeneration(config) context = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype=np.int64) summary = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype=np.int64) outputs = lm_model(input_ids=context, decoder_input_ids=summary) expected_shape = (*summary.shape, config.vocab_size) self.assertEqual(outputs["logits"].shape, expected_shape) def test_shift_tokens_right(self): input_ids = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=np.int64) shifted = shift_tokens_right(input_ids, 1, 2) n_pad_before = np.equal(input_ids, 1).astype(np.float32).sum() n_pad_after = np.equal(shifted, 1).astype(np.float32).sum() self.assertEqual(shifted.shape, input_ids.shape) self.assertEqual(n_pad_after, n_pad_before - 1) self.assertTrue(np.equal(shifted[:, 0], 2).all()) @require_flax class FlaxBartModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin): is_encoder_decoder = True all_model_classes = ( ( FlaxBartModel, FlaxBartForConditionalGeneration, FlaxBartForSequenceClassification, FlaxBartForQuestionAnswering, ) if is_flax_available() else () ) all_generative_model_classes = (FlaxBartForConditionalGeneration,) if is_flax_available() else () def setUp(self): self.model_tester = FlaxBartModelTester(self) def test_use_cache_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(model_class, config, inputs_dict) def test_use_cache_forward_with_attn_mask(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict) def test_encode(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class) model = model_class(config) @jax.jit def encode_jitted(input_ids, attention_mask=None, **kwargs): return model.encode(input_ids=input_ids, attention_mask=attention_mask) with self.subTest("JIT Enabled"): jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = encode_jitted(**prepared_inputs_dict).to_tuple() self.assertEqual(len(outputs), len(jitted_outputs)) for jitted_output, output in zip(jitted_outputs, outputs): self.assertEqual(jitted_output.shape, output.shape) def test_decode(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): model = model_class(config) encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"]) prepared_inputs_dict = { "decoder_input_ids": inputs_dict["decoder_input_ids"], "decoder_attention_mask": inputs_dict["decoder_attention_mask"], "encoder_outputs": encoder_outputs, } @jax.jit def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs): return model.decode( decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, ) with self.subTest("JIT Enabled"): jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = decode_jitted(**prepared_inputs_dict).to_tuple() self.assertEqual(len(outputs), len(jitted_outputs)) for jitted_output, output in zip(jitted_outputs, outputs): self.assertEqual(jitted_output.shape, output.shape) @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("facebook/bart-base", from_pt=True) # FlaxBartForSequenceClassification expects eos token in input_ids input_ids = np.ones((1, 1)) * model.config.eos_token_id outputs = model(input_ids) self.assertIsNotNone(outputs) @slow def test_summarization_fast(self): model = FlaxBartForConditionalGeneration.from_pretrained("sshleifer/distilbart-cnn-6-6") tokenizer = BartTokenizer.from_pretrained("sshleifer/distilbart-cnn-6-6") input_str = ( "This sentence is made of three parts. Each part is important on its own. One part is about animals, the" " other part about planes, and the last part about housing." ) input_ids = tokenizer(input_str, return_tensors="np").input_ids sequences = model.generate(input_ids, num_beams=2, max_length=20).sequences output_str = tokenizer.batch_decode(sequences)[0] assert ( output_str == "</s><s>This sentence is made of three parts. One part is about animals, the other part</s>" ) @slow def test_cnn_summarization_same_as_fairseq(self): model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn") FRANCE_ARTICLE = ( # @noq " Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings" " Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane." ' Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation."' ' He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s' " comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video" " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French" " Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a" " phone at the wreckage site. The two publications described the supposed video, but did not post it on" " their websites. The publications said that they watched the video, which was found by a source close to" " the investigation. \"One can hear cries of 'My God' in several languages,\" Paris Match reported." ' "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the' " cockpit door with a heavy object. Towards the end, after a heavy shake, stronger than the others, the" ' screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt,' " editor-in-chief of Bild online. An official with France's accident investigation agency, the BEA, said" " the agency is not aware of any such video. Lt. Col. Jean-Marc Menichini, a French Gendarmerie spokesman" " in charge of communications on rescue efforts around the Germanwings crash site, told CNN that the" ' reports were "completely wrong" and "unwarranted." Cell phones have been collected at the site, he said,' ' but that they "hadn\'t been exploited yet." Menichini said he believed the cell phones would need to be' " sent to the Criminal Research Institute in Rosny sous-Bois, near Paris, in order to be analyzed by" " specialized technicians working hand-in-hand with investigators. But none of the cell phones found so" " far have been sent to the institute, Menichini said. Asked whether staff involved in the search could" ' have leaked a memory card to the media, Menichini answered with a categorical "no." Reichelt told "Erin' ' Burnett: Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match' ' are "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered' ' cell phones from the crash site after Bild and Paris Match published their reports. "That is something' " we did not know before. ... Overall we can say many things of the investigation weren't revealed by the" ' investigation at the beginning," he said. What was mental state of Germanwings co-pilot? German airline' " Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled depression years before he took the" " controls of Germanwings Flight 9525, which he's accused of deliberately crashing last week in the" ' French Alps. Lubitz told his Lufthansa flight training school in 2009 that he had a "previous episode of' ' severe depression," the airline said Tuesday. Email correspondence between Lubitz and the school' " discovered in an internal investigation, Lufthansa said, included medical documents he submitted in" " connection with resuming his flight training. The announcement indicates that Lufthansa, the parent" " company of Germanwings, knew of Lubitz's battle with depression, allowed him to continue training and" " ultimately put him in the cockpit. Lufthansa, whose CEO Carsten Spohr previously said Lubitz was 100%" ' fit to fly, described its statement Tuesday as a "swift and seamless clarification" and said it was' " sharing the information and documents -- including training and medical records -- with public" " prosecutors. Spohr traveled to the crash site Wednesday, where recovery teams have been working for the" " past week to recover human remains and plane debris scattered across a steep mountainside. He saw the" " crisis center set up in Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash" " site, where grieving families have left flowers at a simple stone memorial. Menichini told CNN late" " Tuesday that no visible human remains were left at the site but recovery teams would keep searching." " French President Francois Hollande, speaking Tuesday, said that it should be possible to identify all" " the victims using DNA analysis by the end of the week, sooner than authorities had previously suggested." " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini said." " Among those personal belongings could be more cell phones belonging to the 144 passengers and six crew" " on board. Check out the latest from our correspondents . The details about Lubitz's correspondence with" " the flight school during his training were among several developments as investigators continued to" " delve into what caused the crash and Lubitz's possible motive for downing the jet. A Lufthansa" " spokesperson told CNN on Tuesday that Lubitz had a valid medical certificate, had passed all his" ' examinations and "held all the licenses required." Earlier, a spokesman for the prosecutor\'s office in' " Dusseldorf, Christoph Kumpa, said medical records reveal Lubitz suffered from suicidal tendencies at" " some point before his aviation career and underwent psychotherapy before he got his pilot's license." " Kumpa emphasized there's no evidence suggesting Lubitz was suicidal or acting aggressively before the" " crash. Investigators are looking into whether Lubitz feared his medical condition would cause him to" " lose his pilot's license, a European government official briefed on the investigation told CNN on" ' Tuesday. While flying was "a big part of his life," the source said, it\'s only one theory being' " considered. Another source, a law enforcement official briefed on the investigation, also told CNN that" " authorities believe the primary motive for Lubitz to bring down the plane was that he feared he would" " not be allowed to fly because of his medical problems. Lubitz's girlfriend told investigators he had" " seen an eye doctor and a neuropsychologist, both of whom deemed him unfit to work recently and concluded" " he had psychological issues, the European government official said. But no matter what details emerge" " about his previous mental health struggles, there's more to the story, said Brian Russell, a forensic" ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the fact' " that maybe they weren't going to keep doing their job and they're upset about that and so they're" ' suicidal," he said. "But there is no mental illness that explains why somebody then feels entitled to' " also take that rage and turn it outward on 149 other people who had nothing to do with the person's" ' problems." Germanwings crash compensation: What we know . Who was the captain of Germanwings Flight' " 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from Dusseldorf, while Laura" " Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff, Antonia Mortensen, Sandrine" " Amiel and Anna-Maja Rappard contributed to this report." ) SHORTER_ARTICLE = ( " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The" " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based." " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its" ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East' ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the' " situation in Palestinian territories, paving the way for possible war crimes investigations against" " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and" " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the" " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a" ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the' ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an' ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge' " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the" ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine' " acquires all the rights as well as responsibilities that come with being a State Party to the Statute." ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights' ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should' " immediately end their pressure, and countries that support universal acceptance of the court's treaty" ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the' " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's" ' decision to join a treaty to which over 100 countries around the world are members." In January, when' " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an" ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"' " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a" ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in' ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We' ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"' " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the' " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou" ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war' " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry" " will include alleged war crimes committed since June. The International Criminal Court was set up in" " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder" " and Faith Karimi contributed to this report." ) # The below article tests that we don't add any hypotheses outside of the top n_beams IRAN_ARTICLE = ( " (CNN)The United States and its negotiating partners reached a very strong framework agreement with Iran" " in Lausanne, Switzerland, on Thursday that limits Iran's nuclear program in such a way as to effectively" " block it from building a nuclear weapon. Expect pushback anyway, if the recent past is any harbinger." " Just last month, in an attempt to head off such an agreement, House Speaker John Boehner invited Israeli" " Prime Minister Benjamin Netanyahu to preemptively blast it before Congress, and 47 senators sent a" " letter to the Iranian leadership warning them away from a deal. The debate that has already begun since" " the announcement of the new framework will likely result in more heat than light. It will not be helped" " by the gathering swirl of dubious assumptions and doubtful assertions. Let us address some of these: ." " The most misleading assertion, despite universal rejection by experts, is that the negotiations'" " objective at the outset was the total elimination of any nuclear program in Iran. That is the position" " of Netanyahu and his acolytes in the U.S. Congress. But that is not and never was the objective. If it" " had been, there would have been no Iranian team at the negotiating table. Rather, the objective has" " always been to structure an agreement or series of agreements so that Iran could not covertly develop a" " nuclear arsenal before the United States and its allies could respond. The new framework has exceeded" " expectations in achieving that goal. It would reduce Iran's low-enriched uranium stockpile, cut by" " two-thirds its number of installed centrifuges and implement a rigorous inspection regime. Another" " dubious assumption of opponents is that the Iranian nuclear program is a covert weapons program. Despite" " sharp accusations by some in the United States and its allies, Iran denies having such a program, and" " U.S. intelligence contends that Iran has not yet made the decision to build a nuclear weapon. Iran's" " continued cooperation with International Atomic Energy Agency inspections is further evidence on this" " point, and we'll know even more about Iran's program in the coming months and years because of the deal." " In fact, the inspections provisions that are part of this agreement are designed to protect against any" " covert action by the Iranians. What's more, the rhetoric of some members of Congress has implied that" " the negotiations have been between only the United States and Iran (i.e., the 47 senators' letter" " warning that a deal might be killed by Congress or a future president). This of course is not the case." " The talks were between Iran and the five permanent members of the U.N. Security Council (United States," " United Kingdom, France, China and Russia) plus Germany, dubbed the P5+1. While the United States has" " played a leading role in the effort, it negotiated the terms alongside its partners. If the agreement" " reached by the P5+1 is rejected by Congress, it could result in an unraveling of the sanctions on Iran" " and threaten NATO cohesion in other areas. Another questionable assertion is that this agreement" " contains a sunset clause, after which Iran will be free to do as it pleases. Again, this is not the" " case. Some of the restrictions on Iran's nuclear activities, such as uranium enrichment, will be eased" " or eliminated over time, as long as 15 years. But most importantly, the framework agreement includes" " Iran's ratification of the Additional Protocol, which allows IAEA inspectors expanded access to nuclear" " sites both declared and nondeclared. This provision will be permanent. It does not sunset. Thus, going" " forward, if Iran decides to enrich uranium to weapons-grade levels, monitors will be able to detect such" " a move in a matter of days and alert the U.N. Security Council. Many in Congress have said that the" ' agreement should be a formal treaty requiring the Senate to "advise and consent." But the issue is not' " suited for a treaty. Treaties impose equivalent obligations on all signatories. For example, the New" " START treaty limits Russia and the United States to 1,550 deployed strategic warheads. But any agreement" " with Iran will not be so balanced. The restrictions and obligations in the final framework agreement" " will be imposed almost exclusively on Iran. The P5+1 are obligated only to ease and eventually remove" " most but not all economic sanctions, which were imposed as leverage to gain this final deal. Finally" " some insist that any agreement must address Iranian missile programs, human rights violations or support" " for Hamas or Hezbollah. As important as these issues are, and they must indeed be addressed, they are" " unrelated to the most important aim of a nuclear deal: preventing a nuclear Iran. To include them in" " the negotiations would be a poison pill. This agreement should be judged on its merits and on how it" " affects the security of our negotiating partners and allies, including Israel. Those judgments should be" " fact-based, not based on questionable assertions or dubious assumptions." ) ARTICLE_SUBWAY = ( " New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A" " year later, she got married again in Westchester County, but to a different man and without divorcing" " her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos" ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married' " once more, this time in the Bronx. In an application for a marriage license, she stated it was her" ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false' ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage' " license application, according to court documents. Prosecutors said the marriages were part of an" " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to" " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was" " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New" " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total," " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All" " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be" " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors" " said the immigration scam involved some of her husbands, who filed for permanent residence status" " shortly after the marriages. Any divorces happened only after such filings were approved. It was" " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District" " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's" ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,' " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his" " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces" " up to four years in prison. Her next court appearance is scheduled for May 18." ) dct = tokenizer.batch_encode_plus( [FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY], max_length=1024, padding="max_length", truncation_strategy="only_first", truncation=True, return_tensors="np", ) self.assertEqual(1024, dct["input_ids"].shape[1]) hypotheses_batch = model.generate( input_ids=dct["input_ids"], attention_mask=dct["attention_mask"], num_beams=2, ).sequences assert (hypotheses_batch[:, 1] == 0).all().item() EXPECTED = [ "A French prosecutor says he is not aware of any video footage from on board the plane. Two German" " magazines claim to have found a cell phone video showing the crash. The publications say they watched" " the video, which was found by a source close to the investigation. All 150 on board the Germanwings" " flight were killed.", "Palestinian Authority becomes 123rd member of the International Criminal Court. The move gives the court" " jurisdiction over alleged crimes in Palestinian territories. Israel and the United States opposed the" " Palestinians' efforts to join the body. But Palestinian Foreign Minister Riad al-Malki said it was a" " move toward greater justice.", "U.S. and its negotiating partners reached a strong framework agreement with Iran. Peter Bergen: The" " debate that has already begun will likely result in more heat than light. Bergen: The most misleading" " assertion is that the negotiations' objective at the outset was the total elimination of any nuclear" " program.", "Liana Barrientos, 39, has been married 10 times, sometimes within two weeks of each other. Prosecutors" " say the marriages were part of an immigration scam. She pleaded not guilty at State Supreme Court in the" " Bronx on Friday. If convicted, Barrientos faces up to four years in prison.", ] generated_summaries = tokenizer.batch_decode( hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True ) assert generated_summaries == EXPECTED class FlaxBartStandaloneDecoderModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_attention_mask=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=32, eos_token_id=2, pad_token_id=1, bos_token_id=0, initializer_range=0.02, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.initializer_range = initializer_range def prepare_config_and_inputs(self): input_ids = jnp.clip(ids_tensor([self.batch_size, self.seq_length], self.vocab_size), 3, self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = BartConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, initializer_range=self.initializer_range, use_cache=False, ) return config, input_ids, attention_mask def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask} return config, inputs_dict def prepare_config_and_inputs_for_decoder(self): config, input_ids, attention_mask = self.prepare_config_and_inputs() encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, attention_mask, encoder_hidden_states, encoder_attention_mask, )

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np import timeout_decorator # noqa from transformers import BartConfig, BartTokenizer, is_flax_available from transformers.testing_utils import require_flax, slow from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform" import jax import jax.numpy as jnp from transformers.models.bart.modeling_flax_bart import ( FlaxBartForConditionalGeneration, FlaxBartForQuestionAnswering, FlaxBartForSequenceClassification, FlaxBartModel, shift_tokens_right, ) def prepare_bart_inputs_dict( config, input_ids, decoder_input_ids=None, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = np.where(input_ids != config.pad_token_id, 1, 0) if decoder_attention_mask is None: decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0) if head_mask is None: head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, } class FlaxBartModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=32, eos_token_id=2, pad_token_id=1, bos_token_id=0, initializer_range=0.02, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.initializer_range = initializer_range def prepare_config_and_inputs(self): input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size) input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1) decoder_input_ids = shift_tokens_right(input_ids, 1, 2) config = BartConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, initializer_range=self.initializer_range, use_cache=False, ) inputs_dict = prepare_bart_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def check_use_cache_forward(self, model_class_name, config, inputs_dict): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(inputs_dict["input_ids"]) decoder_input_ids, decoder_attention_mask = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4") decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), ) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=past_key_values, decoder_position_ids=decoder_position_ids, ) decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=outputs_cache.past_key_values, decoder_position_ids=decoder_position_ids, ) outputs = model.decode(decoder_input_ids, encoder_outputs) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(inputs_dict["input_ids"]) decoder_input_ids, decoder_attention_mask = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) decoder_attention_mask_cache = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])), ], axis=-1, ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), ) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask_cache, past_key_values=past_key_values, decoder_position_ids=decoder_position_ids, ) decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, past_key_values=outputs_cache.past_key_values, decoder_attention_mask=decoder_attention_mask_cache, decoder_position_ids=decoder_position_ids, ) outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") @require_flax class BartHeadTests(unittest.TestCase): vocab_size = 99 def _get_config_and_data(self): input_ids = np.array( [ [71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 82, 2], [5, 97, 17, 39, 94, 40, 2], [76, 83, 94, 25, 70, 78, 2], [87, 59, 41, 35, 48, 66, 2], [55, 13, 16, 58, 5, 2, 1], # note padding [64, 27, 31, 51, 12, 75, 2], [52, 64, 86, 17, 83, 39, 2], [48, 61, 9, 24, 71, 82, 2], [26, 1, 60, 48, 22, 13, 2], [21, 5, 62, 28, 14, 76, 2], [45, 98, 37, 86, 59, 48, 2], [70, 70, 50, 9, 28, 0, 2], ], dtype=np.int64, ) batch_size = input_ids.shape[0] config = BartConfig( vocab_size=self.vocab_size, d_model=24, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=32, decoder_ffn_dim=32, max_position_embeddings=48, eos_token_id=2, pad_token_id=1, bos_token_id=0, ) return config, input_ids, batch_size def test_sequence_classification_forward(self): config, input_ids, batch_size = self._get_config_and_data() model = FlaxBartForSequenceClassification(config) outputs = model(input_ids=input_ids, decoder_input_ids=input_ids) expected_shape = (batch_size, config.num_labels) self.assertEqual(outputs["logits"].shape, expected_shape) def test_question_answering_forward(self): config, input_ids, batch_size = self._get_config_and_data() model = FlaxBartForQuestionAnswering(config) outputs = model(input_ids=input_ids) self.assertEqual(outputs["start_logits"].shape, input_ids.shape) self.assertEqual(outputs["end_logits"].shape, input_ids.shape) # @timeout_decorator.timeout(1) # not working with the decorator so far def test_lm_forward(self): config, input_ids, batch_size = self._get_config_and_data() lm_model = FlaxBartForConditionalGeneration(config) outputs = lm_model(input_ids=input_ids) expected_shape = (batch_size, input_ids.shape[1], config.vocab_size) self.assertEqual(outputs["logits"].shape, expected_shape) def test_lm_uneven_forward(self): config = BartConfig( vocab_size=self.vocab_size, d_model=14, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=8, decoder_ffn_dim=8, max_position_embeddings=48, ) lm_model = FlaxBartForConditionalGeneration(config) context = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype=np.int64) summary = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype=np.int64) outputs = lm_model(input_ids=context, decoder_input_ids=summary) expected_shape = (*summary.shape, config.vocab_size) self.assertEqual(outputs["logits"].shape, expected_shape) def test_shift_tokens_right(self): input_ids = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=np.int64) shifted = shift_tokens_right(input_ids, 1, 2) n_pad_before = np.equal(input_ids, 1).astype(np.float32).sum() n_pad_after = np.equal(shifted, 1).astype(np.float32).sum() self.assertEqual(shifted.shape, input_ids.shape) self.assertEqual(n_pad_after, n_pad_before - 1) self.assertTrue(np.equal(shifted[:, 0], 2).all()) @require_flax class FlaxBartModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin): is_encoder_decoder = True all_model_classes = ( ( FlaxBartModel, FlaxBartForConditionalGeneration, FlaxBartForSequenceClassification, FlaxBartForQuestionAnswering, ) if is_flax_available() else () ) all_generative_model_classes = (FlaxBartForConditionalGeneration,) if is_flax_available() else () def setUp(self): self.model_tester = FlaxBartModelTester(self) def test_use_cache_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(model_class, config, inputs_dict) def test_use_cache_forward_with_attn_mask(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict) def test_encode(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class) model = model_class(config) @jax.jit def encode_jitted(input_ids, attention_mask=None, **kwargs): return model.encode(input_ids=input_ids, attention_mask=attention_mask) with self.subTest("JIT Enabled"): jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = encode_jitted(**prepared_inputs_dict).to_tuple() self.assertEqual(len(outputs), len(jitted_outputs)) for jitted_output, output in zip(jitted_outputs, outputs): self.assertEqual(jitted_output.shape, output.shape) def test_decode(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): model = model_class(config) encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"]) prepared_inputs_dict = { "decoder_input_ids": inputs_dict["decoder_input_ids"], "decoder_attention_mask": inputs_dict["decoder_attention_mask"], "encoder_outputs": encoder_outputs, } @jax.jit def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs): return model.decode( decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, ) with self.subTest("JIT Enabled"): jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = decode_jitted(**prepared_inputs_dict).to_tuple() self.assertEqual(len(outputs), len(jitted_outputs)) for jitted_output, output in zip(jitted_outputs, outputs): self.assertEqual(jitted_output.shape, output.shape) @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("facebook/bart-base", from_pt=True) # FlaxBartForSequenceClassification expects eos token in input_ids input_ids = np.ones((1, 1)) * model.config.eos_token_id outputs = model(input_ids) self.assertIsNotNone(outputs) @slow def test_summarization_fast(self): model = FlaxBartForConditionalGeneration.from_pretrained("sshleifer/distilbart-cnn-6-6") tokenizer = BartTokenizer.from_pretrained("sshleifer/distilbart-cnn-6-6") input_str = ( "This sentence is made of three parts. Each part is important on its own. One part is about animals, the" " other part about planes, and the last part about housing." ) input_ids = tokenizer(input_str, return_tensors="np").input_ids sequences = model.generate(input_ids, num_beams=2, max_length=20).sequences output_str = tokenizer.batch_decode(sequences)[0] assert ( output_str == "</s><s>This sentence is made of three parts. One part is about animals, the other part</s>" ) @slow def test_cnn_summarization_same_as_fairseq(self): model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn") FRANCE_ARTICLE = ( # @noq " Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings" " Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane." ' Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation."' ' He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s' " comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video" " showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French" " Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a" " phone at the wreckage site. The two publications described the supposed video, but did not post it on" " their websites. The publications said that they watched the video, which was found by a source close to" " the investigation. \"One can hear cries of 'My God' in several languages,\" Paris Match reported." ' "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the' " cockpit door with a heavy object. Towards the end, after a heavy shake, stronger than the others, the" ' screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt,' " editor-in-chief of Bild online. An official with France's accident investigation agency, the BEA, said" " the agency is not aware of any such video. Lt. Col. Jean-Marc Menichini, a French Gendarmerie spokesman" " in charge of communications on rescue efforts around the Germanwings crash site, told CNN that the" ' reports were "completely wrong" and "unwarranted." Cell phones have been collected at the site, he said,' ' but that they "hadn\'t been exploited yet." Menichini said he believed the cell phones would need to be' " sent to the Criminal Research Institute in Rosny sous-Bois, near Paris, in order to be analyzed by" " specialized technicians working hand-in-hand with investigators. But none of the cell phones found so" " far have been sent to the institute, Menichini said. Asked whether staff involved in the search could" ' have leaked a memory card to the media, Menichini answered with a categorical "no." Reichelt told "Erin' ' Burnett: Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match' ' are "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered' ' cell phones from the crash site after Bild and Paris Match published their reports. "That is something' " we did not know before. ... Overall we can say many things of the investigation weren't revealed by the" ' investigation at the beginning," he said. What was mental state of Germanwings co-pilot? German airline' " Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled depression years before he took the" " controls of Germanwings Flight 9525, which he's accused of deliberately crashing last week in the" ' French Alps. Lubitz told his Lufthansa flight training school in 2009 that he had a "previous episode of' ' severe depression," the airline said Tuesday. Email correspondence between Lubitz and the school' " discovered in an internal investigation, Lufthansa said, included medical documents he submitted in" " connection with resuming his flight training. The announcement indicates that Lufthansa, the parent" " company of Germanwings, knew of Lubitz's battle with depression, allowed him to continue training and" " ultimately put him in the cockpit. Lufthansa, whose CEO Carsten Spohr previously said Lubitz was 100%" ' fit to fly, described its statement Tuesday as a "swift and seamless clarification" and said it was' " sharing the information and documents -- including training and medical records -- with public" " prosecutors. Spohr traveled to the crash site Wednesday, where recovery teams have been working for the" " past week to recover human remains and plane debris scattered across a steep mountainside. He saw the" " crisis center set up in Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash" " site, where grieving families have left flowers at a simple stone memorial. Menichini told CNN late" " Tuesday that no visible human remains were left at the site but recovery teams would keep searching." " French President Francois Hollande, speaking Tuesday, said that it should be possible to identify all" " the victims using DNA analysis by the end of the week, sooner than authorities had previously suggested." " In the meantime, the recovery of the victims' personal belongings will start Wednesday, Menichini said." " Among those personal belongings could be more cell phones belonging to the 144 passengers and six crew" " on board. Check out the latest from our correspondents . The details about Lubitz's correspondence with" " the flight school during his training were among several developments as investigators continued to" " delve into what caused the crash and Lubitz's possible motive for downing the jet. A Lufthansa" " spokesperson told CNN on Tuesday that Lubitz had a valid medical certificate, had passed all his" ' examinations and "held all the licenses required." Earlier, a spokesman for the prosecutor\'s office in' " Dusseldorf, Christoph Kumpa, said medical records reveal Lubitz suffered from suicidal tendencies at" " some point before his aviation career and underwent psychotherapy before he got his pilot's license." " Kumpa emphasized there's no evidence suggesting Lubitz was suicidal or acting aggressively before the" " crash. Investigators are looking into whether Lubitz feared his medical condition would cause him to" " lose his pilot's license, a European government official briefed on the investigation told CNN on" ' Tuesday. While flying was "a big part of his life," the source said, it\'s only one theory being' " considered. Another source, a law enforcement official briefed on the investigation, also told CNN that" " authorities believe the primary motive for Lubitz to bring down the plane was that he feared he would" " not be allowed to fly because of his medical problems. Lubitz's girlfriend told investigators he had" " seen an eye doctor and a neuropsychologist, both of whom deemed him unfit to work recently and concluded" " he had psychological issues, the European government official said. But no matter what details emerge" " about his previous mental health struggles, there's more to the story, said Brian Russell, a forensic" ' psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the fact' " that maybe they weren't going to keep doing their job and they're upset about that and so they're" ' suicidal," he said. "But there is no mental illness that explains why somebody then feels entitled to' " also take that rage and turn it outward on 149 other people who had nothing to do with the person's" ' problems." Germanwings crash compensation: What we know . Who was the captain of Germanwings Flight' " 9525? CNN's Margot Haddad reported from Marseille and Pamela Brown from Dusseldorf, while Laura" " Smith-Spark wrote from London. CNN's Frederik Pleitgen, Pamela Boykoff, Antonia Mortensen, Sandrine" " Amiel and Anna-Maja Rappard contributed to this report." ) SHORTER_ARTICLE = ( " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on" " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The" " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based." " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its" ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East' ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the' " situation in Palestinian territories, paving the way for possible war crimes investigations against" " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and" " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the" " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a" ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the' ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an' ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge' " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the" ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine' " acquires all the rights as well as responsibilities that come with being a State Party to the Statute." ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights' ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should' " immediately end their pressure, and countries that support universal acceptance of the court's treaty" ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the' " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's" ' decision to join a treaty to which over 100 countries around the world are members." In January, when' " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an" ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"' " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a" ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in' ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We' ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"' " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the" ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the' " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou" ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war' " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry" " will include alleged war crimes committed since June. The International Criminal Court was set up in" " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder" " and Faith Karimi contributed to this report." ) # The below article tests that we don't add any hypotheses outside of the top n_beams IRAN_ARTICLE = ( " (CNN)The United States and its negotiating partners reached a very strong framework agreement with Iran" " in Lausanne, Switzerland, on Thursday that limits Iran's nuclear program in such a way as to effectively" " block it from building a nuclear weapon. Expect pushback anyway, if the recent past is any harbinger." " Just last month, in an attempt to head off such an agreement, House Speaker John Boehner invited Israeli" " Prime Minister Benjamin Netanyahu to preemptively blast it before Congress, and 47 senators sent a" " letter to the Iranian leadership warning them away from a deal. The debate that has already begun since" " the announcement of the new framework will likely result in more heat than light. It will not be helped" " by the gathering swirl of dubious assumptions and doubtful assertions. Let us address some of these: ." " The most misleading assertion, despite universal rejection by experts, is that the negotiations'" " objective at the outset was the total elimination of any nuclear program in Iran. That is the position" " of Netanyahu and his acolytes in the U.S. Congress. But that is not and never was the objective. If it" " had been, there would have been no Iranian team at the negotiating table. Rather, the objective has" " always been to structure an agreement or series of agreements so that Iran could not covertly develop a" " nuclear arsenal before the United States and its allies could respond. The new framework has exceeded" " expectations in achieving that goal. It would reduce Iran's low-enriched uranium stockpile, cut by" " two-thirds its number of installed centrifuges and implement a rigorous inspection regime. Another" " dubious assumption of opponents is that the Iranian nuclear program is a covert weapons program. Despite" " sharp accusations by some in the United States and its allies, Iran denies having such a program, and" " U.S. intelligence contends that Iran has not yet made the decision to build a nuclear weapon. Iran's" " continued cooperation with International Atomic Energy Agency inspections is further evidence on this" " point, and we'll know even more about Iran's program in the coming months and years because of the deal." " In fact, the inspections provisions that are part of this agreement are designed to protect against any" " covert action by the Iranians. What's more, the rhetoric of some members of Congress has implied that" " the negotiations have been between only the United States and Iran (i.e., the 47 senators' letter" " warning that a deal might be killed by Congress or a future president). This of course is not the case." " The talks were between Iran and the five permanent members of the U.N. Security Council (United States," " United Kingdom, France, China and Russia) plus Germany, dubbed the P5+1. While the United States has" " played a leading role in the effort, it negotiated the terms alongside its partners. If the agreement" " reached by the P5+1 is rejected by Congress, it could result in an unraveling of the sanctions on Iran" " and threaten NATO cohesion in other areas. Another questionable assertion is that this agreement" " contains a sunset clause, after which Iran will be free to do as it pleases. Again, this is not the" " case. Some of the restrictions on Iran's nuclear activities, such as uranium enrichment, will be eased" " or eliminated over time, as long as 15 years. But most importantly, the framework agreement includes" " Iran's ratification of the Additional Protocol, which allows IAEA inspectors expanded access to nuclear" " sites both declared and nondeclared. This provision will be permanent. It does not sunset. Thus, going" " forward, if Iran decides to enrich uranium to weapons-grade levels, monitors will be able to detect such" " a move in a matter of days and alert the U.N. Security Council. Many in Congress have said that the" ' agreement should be a formal treaty requiring the Senate to "advise and consent." But the issue is not' " suited for a treaty. Treaties impose equivalent obligations on all signatories. For example, the New" " START treaty limits Russia and the United States to 1,550 deployed strategic warheads. But any agreement" " with Iran will not be so balanced. The restrictions and obligations in the final framework agreement" " will be imposed almost exclusively on Iran. The P5+1 are obligated only to ease and eventually remove" " most but not all economic sanctions, which were imposed as leverage to gain this final deal. Finally" " some insist that any agreement must address Iranian missile programs, human rights violations or support" " for Hamas or Hezbollah. As important as these issues are, and they must indeed be addressed, they are" " unrelated to the most important aim of a nuclear deal: preventing a nuclear Iran. To include them in" " the negotiations would be a poison pill. This agreement should be judged on its merits and on how it" " affects the security of our negotiating partners and allies, including Israel. Those judgments should be" " fact-based, not based on questionable assertions or dubious assumptions." ) ARTICLE_SUBWAY = ( " New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A" " year later, she got married again in Westchester County, but to a different man and without divorcing" " her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos" ' declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married' " once more, this time in the Bronx. In an application for a marriage license, she stated it was her" ' "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false' ' instrument for filing in the first degree," referring to her false statements on the 2010 marriage' " license application, according to court documents. Prosecutors said the marriages were part of an" " immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to" " her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was" " arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New" " York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total," " Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All" " occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be" " married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors" " said the immigration scam involved some of her husbands, who filed for permanent residence status" " shortly after the marriages. Any divorces happened only after such filings were approved. It was" " unclear whether any of the men will be prosecuted. The case was referred to the Bronx District" " Attorney's Office by Immigration and Customs Enforcement and the Department of Homeland Security's" ' Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt,' " Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his" " native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces" " up to four years in prison. Her next court appearance is scheduled for May 18." ) dct = tokenizer.batch_encode_plus( [FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY], max_length=1024, padding="max_length", truncation_strategy="only_first", truncation=True, return_tensors="np", ) self.assertEqual(1024, dct["input_ids"].shape[1]) hypotheses_batch = model.generate( input_ids=dct["input_ids"], attention_mask=dct["attention_mask"], num_beams=2, ).sequences assert (hypotheses_batch[:, 1] == 0).all().item() EXPECTED = [ "A French prosecutor says he is not aware of any video footage from on board the plane. Two German" " magazines claim to have found a cell phone video showing the crash. The publications say they watched" " the video, which was found by a source close to the investigation. All 150 on board the Germanwings" " flight were killed.", "Palestinian Authority becomes 123rd member of the International Criminal Court. The move gives the court" " jurisdiction over alleged crimes in Palestinian territories. Israel and the United States opposed the" " Palestinians' efforts to join the body. But Palestinian Foreign Minister Riad al-Malki said it was a" " move toward greater justice.", "U.S. and its negotiating partners reached a strong framework agreement with Iran. Peter Bergen: The" " debate that has already begun will likely result in more heat than light. Bergen: The most misleading" " assertion is that the negotiations' objective at the outset was the total elimination of any nuclear" " program.", "Liana Barrientos, 39, has been married 10 times, sometimes within two weeks of each other. Prosecutors" " say the marriages were part of an immigration scam. She pleaded not guilty at State Supreme Court in the" " Bronx on Friday. If convicted, Barrientos faces up to four years in prison.", ] generated_summaries = tokenizer.batch_decode( hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True ) assert generated_summaries == EXPECTED class FlaxBartStandaloneDecoderModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_attention_mask=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=32, eos_token_id=2, pad_token_id=1, bos_token_id=0, initializer_range=0.02, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.initializer_range = initializer_range def prepare_config_and_inputs(self): input_ids = jnp.clip(ids_tensor([self.batch_size, self.seq_length], self.vocab_size), 3, self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = BartConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, initializer_range=self.initializer_range, use_cache=False, ) return config, input_ids, attention_mask def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask} return config, inputs_dict def prepare_config_and_inputs_for_decoder(self): config, input_ids, attention_mask = self.prepare_config_and_inputs() encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, attention_mask, encoder_hidden_states, encoder_attention_mask, )

huggingface/transformers

Update reqs to include min gather_for_metrics Accelerate version

# What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

muellerzr

c19aa7accef713d0df95cc2d02156b43d461aa89

822ae69c1b1c486b6ed277964906e273888221a3

Update reqs to include min gather_for_metrics Accelerate version. # What does this PR do? Update all the PyTorch examples using `accelerate` and `gather_for_metrics` to include a minimum accelerate version of 0.12.0 since this introduced `gather_for_metrics` Related to https://github.com/huggingface/accelerate/issues/854 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @sgugger

./src/transformers/models/gptj/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) _import_structure = {"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_gptj"] = [ "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTJForCausalLM", "GPTJForQuestionAnswering", "GPTJForSequenceClassification", "GPTJModel", "GPTJPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_gptj"] = [ "TFGPTJForCausalLM", "TFGPTJForQuestionAnswering", "TFGPTJForSequenceClassification", "TFGPTJModel", "TFGPTJPreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flax_gptj"] = [ "FlaxGPTJForCausalLM", "FlaxGPTJModel", "FlaxGPTJPreTrainedModel", ] if TYPE_CHECKING: from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_gptj import ( GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST, GPTJForCausalLM, GPTJForQuestionAnswering, GPTJForSequenceClassification, GPTJModel, GPTJPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_gptj import ( TFGPTJForCausalLM, TFGPTJForQuestionAnswering, TFGPTJForSequenceClassification, TFGPTJModel, TFGPTJPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) _import_structure = {"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_gptj"] = [ "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTJForCausalLM", "GPTJForQuestionAnswering", "GPTJForSequenceClassification", "GPTJModel", "GPTJPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_gptj"] = [ "TFGPTJForCausalLM", "TFGPTJForQuestionAnswering", "TFGPTJForSequenceClassification", "TFGPTJModel", "TFGPTJPreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flax_gptj"] = [ "FlaxGPTJForCausalLM", "FlaxGPTJModel", "FlaxGPTJPreTrainedModel", ] if TYPE_CHECKING: from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_gptj import ( GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST, GPTJForCausalLM, GPTJForQuestionAnswering, GPTJForSequenceClassification, GPTJModel, GPTJPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_gptj import ( TFGPTJForCausalLM, TFGPTJForQuestionAnswering, TFGPTJForSequenceClassification, TFGPTJModel, TFGPTJPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/keras_callbacks.py

import logging import os from pathlib import Path from time import sleep from typing import Callable, List, Optional, Union import numpy as np import tensorflow as tf from packaging.version import parse from tensorflow.keras.callbacks import Callback from huggingface_hub import Repository from . import IntervalStrategy, PreTrainedTokenizerBase from .modelcard import TrainingSummary from .utils import get_full_repo_name logger = logging.getLogger(__name__) class KerasMetricCallback(Callback): """ Callback to compute metrics at the end of every epoch. Unlike normal Keras metrics, these do not need to be compilable by TF. It is particularly useful for common NLP metrics like BLEU and ROUGE that require string operations or generation loops that cannot be compiled. Predictions (or generations) will be computed on the `eval_dataset` before being passed to the `metric_fn` in `np.ndarray` format. The `metric_fn` should compute metrics and return a dict mapping metric names to metric values. We provide an example of a suitable metric_fn that computes ROUGE scores for a summarization model below. Note that this example skips some post-processing for readability and simplicity, and should probably not be used as-is! ```py from datasets import load_metric rouge_metric = load_metric("rouge") def rouge_fn(predictions, labels): decoded_predictions = tokenizer.batch_decode(predictions, skip_special_tokens=True) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) result = rouge_metric.compute(predictions=decoded_predictions, references=decoded_labels) return {key: value.mid.fmeasure * 100 for key, value in result.items()} ``` The above function will return a dict containing values which will be logged like any other Keras metric: ``` {'rouge1': 37.4199, 'rouge2': 13.9768, 'rougeL': 34.361, 'rougeLsum': 35.0781 ``` Args: metric_fn (`Callable`): Metric function provided by the user. It will be called with two arguments - `predictions` and `labels`. These contain the model's outputs and matching labels from the dataset. It should return a dict mapping metric names to numerical values. eval_dataset (`tf.data.Dataset` or `dict` or `tuple` or `np.ndarray` or `tf.Tensor`): Validation data to be used to generate predictions for the `metric_fn`. output_cols (`List[str], *optional*): A list of columns to be retained from the model output as the predictions. Defaults to all. label_cols ('`List[str]`, *optional*'): A list of columns to be retained from the input dataset as the labels. Will be autodetected if this is not supplied. batch_size (`int`, *optional*): Batch size. Only used when the data is not a pre-batched `tf.data.Dataset`. predict_with_generate (`bool`, *optional*, defaults to `False`): Whether we should use `model.generate()` to get outputs for the model. use_xla_generation (`bool`, *optional*, defaults to `False`): If we're generating, whether to compile model generation with XLA. This can massively increase the speed of generation (up to 100X speedup) but will require a new XLA compilation for each input shape. When using XLA generation, it's a good idea to pad your inputs to the same size, or to use the `pad_to_multiple_of` argument in your `tokenizer` or `DataCollator`, which will reduce the number of unique input shapes and save a lot of compilation time. This option has no effect is `predict_with_generate` is `False`. generate_kwargs (`dict`, *optional*): Keyword arguments to pass to `model.generate()` when generating. Has no effect if `predict_with_generate` is `False`. """ def __init__( self, metric_fn: Callable, eval_dataset: Union[tf.data.Dataset, np.ndarray, tf.Tensor, tuple, dict], output_cols: Optional[List[str]] = None, label_cols: Optional[List[str]] = None, batch_size: Optional[int] = None, predict_with_generate: bool = False, use_xla_generation: bool = False, generate_kwargs: Optional[dict] = None, ): super().__init__() self.metric_fn = metric_fn self.batch_size = batch_size if not isinstance(eval_dataset, tf.data.Dataset): if batch_size is None: raise ValueError( "When passing data to KerasMetricCallback that is not a pre-batched tf.data.Dataset " "the batch_size argument must be set." ) # Wrap a tf.data.Dataset around it eval_dataset = tf.data.Dataset.from_tensor_slices(eval_dataset).batch(batch_size, drop_remainder=False) self.eval_dataset = eval_dataset self.predict_with_generate = predict_with_generate self.output_cols = output_cols # This next block attempts to parse out which elements of the dataset should be appended to the labels list # that is passed to the metric_fn if isinstance(eval_dataset.element_spec, tuple) and len(eval_dataset.element_spec) == 2: input_spec, label_spec = eval_dataset.element_spec else: input_spec = eval_dataset.element_spec label_spec = None if label_cols is not None: for label in label_cols: if label not in input_spec: raise ValueError(f"Label {label} is in label_cols but could not be found in the dataset inputs!") self.label_cols = label_cols self.use_keras_label = False elif label_spec is not None: # If the dataset inputs are split into a 2-tuple of inputs and labels, # assume the second element is the labels self.label_cols = None self.use_keras_label = True elif "labels" in input_spec: self.label_cols = ["labels"] self.use_keras_label = False logging.warning("No label_cols specified for KerasMetricCallback, assuming you want the 'labels' key.") elif "start_positions" in input_spec and "end_positions" in input_spec: self.label_cols = ["start_positions", "end_positions"] self.use_keras_label = False logging.warning( "No label_cols specified for KerasMetricCallback, assuming you want the " "start_positions and end_positions keys." ) else: raise ValueError("Could not autodetect label_cols for KerasMetricCallback, please specify them!") if parse(tf.__version__) < parse("2.7"): logging.warning("TF versions less than 2.7 may encounter issues with KerasMetricCallback!") self.use_xla_generation = use_xla_generation self.generate_kwargs = {} if generate_kwargs is None else generate_kwargs self.generation_function = None @staticmethod def _concatenate_batches(batches, padding_index=-100): # If all batches are unidimensional or same length, do a simple concatenation if batches[0].ndim == 1 or all([batch.shape[1] == batches[0].shape[1] for batch in batches]): return np.concatenate(batches, axis=0) # Welp, they're not the same length. Let's do some padding max_len = max([batch.shape[1] for batch in batches]) num_samples = sum([batch.shape[0] for batch in batches]) output = np.full_like( batches[0], fill_value=padding_index, shape=[num_samples, max_len] + list(batches[0].shape[2:]) ) # i keeps track of which part of the concatenated array we're writing the next batch to i = 0 for batch in batches: output[i : i + len(batch), : batch.shape[1]] = batch i += len(batch) return output def _postprocess_predictions_or_labels(self, inputs): if isinstance(inputs[0], dict): outputs = dict() for key in inputs[0].keys(): outputs[key] = self._concatenate_batches([batch[key] for batch in inputs]) # If it's a dict with only one key, just return the array if len(outputs) == 1: outputs = list(outputs.values())[0] elif isinstance(inputs[0], list) or isinstance(inputs[0], tuple): outputs = [] for input_list in zip(*inputs): outputs.append(self._concatenate_batches(input_list)) if len(outputs) == 1: outputs = outputs[0] # If it's a list with only one element, just return the array elif isinstance(inputs[0], np.ndarray): outputs = self._concatenate_batches(inputs) elif isinstance(inputs[0], tf.Tensor): outputs = self._concatenate_batches([tensor.numpy() for tensor in inputs]) else: raise TypeError(f"Couldn't handle batch of type {type(inputs[0])}!") return outputs def on_epoch_end(self, epoch, logs=None): if hasattr(self.model, "config"): ignore_keys = getattr(self.model.config, "keys_to_ignore_at_inference", []) else: ignore_keys = [] main_input_name = None if self.predict_with_generate: # This dense conditional recognizes the case where we have an encoder-decoder model, but # avoids getting tangled up when we just have a model with a layer called 'encoder' if hasattr(self.model, "encoder") and hasattr(self.model.encoder, "main_input_name"): if self.model.encoder.main_input_name != self.model.main_input_name: main_input_name = self.model.encoder.main_input_name else: main_input_name = getattr(self.model, "main_input_name", "input_ids") if self.use_xla_generation and self.generation_function is None: def generation_function(inputs, attention_mask): return self.model.generate(inputs, attention_mask=attention_mask, **self.generate_kwargs) self.generation_function = tf.function(generation_function, jit_compile=True) prediction_list = [] label_list = [] # The whole predict/generate loop is handled inside this method for batch in self.eval_dataset: if isinstance(batch, tuple): batch, labels = batch else: labels = None if self.predict_with_generate: if isinstance(batch, dict): generation_inputs = batch[main_input_name] attention_mask = batch.get("attention_mask", None) else: generation_inputs = batch attention_mask = None if self.use_xla_generation: predictions = self.generation_function(generation_inputs, attention_mask=attention_mask) else: predictions = self.model.generate(generation_inputs, attention_mask=attention_mask) else: predictions = self.model.predict_on_batch(batch) if isinstance(predictions, dict): # This converts any dict-subclass to a regular dict # Keras REALLY doesn't like it when we pass around a BatchEncoding or other derived class predictions = dict(predictions) if self.output_cols is not None: predictions = {key: predictions[key] for key in self.output_cols} else: predictions = {key: val for key, val in predictions.items() if key not in ignore_keys + ["loss"]} prediction_list.append(predictions) if not self.use_keras_label: labels = {key: batch[key].numpy() for key in self.label_cols} elif isinstance(labels, dict): labels = {key: array.numpy() for key, array in labels.items()} elif isinstance(labels, list) or isinstance(labels, tuple): labels = [array.numpy() for array in labels] elif isinstance(labels, tf.Tensor): labels = labels.numpy() else: raise TypeError(f"Confused by labels of type {type(labels)}") label_list.append(labels) all_preds = self._postprocess_predictions_or_labels(prediction_list) all_labels = self._postprocess_predictions_or_labels(label_list) metric_output = self.metric_fn((all_preds, all_labels)) if not isinstance(metric_output, dict): raise TypeError( f"metric_fn should return a dict mapping metric names to values but instead returned {metric_output}" ) # This is the critical bit - Keras passes a dict containing the loss and standard metric values for this epoch # in the logs argument. Ordinarily, this is so the callback can read them, but in this case we write a bunch of # new keys in there, which will then get read by the History callback and treated like any other metric value. # I promise that I have it in writing from Chollet that this is okay. logs.update(metric_output) class PushToHubCallback(Callback): """ Callback that will save and push the model to the Hub regularly. By default, it pushes once per epoch, but this can be changed with the `save_strategy` argument. Pushed models can be accessed like any other model on the hub, such as with the `from_pretrained` method. ```py from transformers.keras_callbacks import PushToHubCallback push_to_hub_callback = PushToHubCallback( output_dir="./model_save", tokenizer=tokenizer, hub_model_id="gpt5-7xlarge", ) model.fit(train_dataset, callbacks=[push_to_hub_callback]) ``` Args: output_dir (`str`): The output directory where the model predictions and checkpoints will be written and synced with the repository on the Hub. save_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"epoch"`): The checkpoint save strategy to adopt during training. Possible values are: - `"no"`: Save is done at the end of training. - `"epoch"`: Save is done at the end of each epoch. - `"steps"`: Save is done every `save_steps` save_steps (`int`, *optional*): The number of steps between saves when using the "steps" `save_strategy`. tokenizer (`PreTrainedTokenizerBase`, *optional*): The tokenizer used by the model. If supplied, will be uploaded to the repo alongside the weights. hub_model_id (`str`, *optional*): The name of the repository to keep in sync with the local `output_dir`. It can be a simple model ID in which case the model will be pushed in your namespace. Otherwise it should be the whole repository name, for instance `"user_name/model"`, which allows you to push to an organization you are a member of with `"organization_name/model"`. Will default to the name of `output_dir`. hub_token (`str`, *optional*): The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with `huggingface-cli login`. checkpoint (`bool`, *optional*, defaults to `False`): Whether to save full training checkpoints (including epoch and optimizer state) to allow training to be resumed. Only usable when `save_strategy` is `"epoch"`. """ def __init__( self, output_dir: Union[str, Path], save_strategy: Union[str, IntervalStrategy] = "epoch", save_steps: Optional[int] = None, tokenizer: Optional[PreTrainedTokenizerBase] = None, hub_model_id: Optional[str] = None, hub_token: Optional[str] = None, checkpoint: bool = False, **model_card_args ): super().__init__() if checkpoint and save_strategy != "epoch": raise ValueError("Cannot save checkpoints when save_strategy is not 'epoch'!") if isinstance(save_strategy, str): save_strategy = IntervalStrategy(save_strategy.lower()) self.save_strategy = save_strategy if self.save_strategy == IntervalStrategy.STEPS and (not isinstance(save_steps, int) or save_steps <= 0): raise ValueError("Please supply a positive integer argument for save_steps when save_strategy == 'steps'!") self.save_steps = save_steps output_dir = Path(output_dir) if hub_model_id is None: hub_model_id = output_dir.absolute().name if "/" not in hub_model_id: hub_model_id = get_full_repo_name(hub_model_id, token=hub_token) self.output_dir = output_dir self.hub_model_id = hub_model_id self.repo = Repository( str(self.output_dir), clone_from=self.hub_model_id, use_auth_token=hub_token if hub_token else True, ) self.tokenizer = tokenizer self.last_job = None self.checkpoint = checkpoint self.training_history = None self.model_card_args = model_card_args def on_train_begin(self, logs=None): # Although we can access model.history, we have no guarantees that the History callback will fire before this # one, so we keep track of it here too self.training_history = [] def on_train_batch_end(self, batch, logs=None): if self.save_strategy == IntervalStrategy.STEPS and (batch + 1) % self.save_steps == 0: if self.last_job is not None and not self.last_job.is_done: return # The last upload is still running, don't start another self.model.save_pretrained(self.output_dir) if self.tokenizer is not None: self.tokenizer.save_pretrained(self.output_dir) _, self.last_job = self.repo.push_to_hub( commit_message=f"Training in progress steps {batch}", blocking=False ) def on_epoch_end(self, epoch, logs=None): logs = logs.copy() # Don't accidentally write things that Keras will read later if "epoch" not in logs: logs["epoch"] = epoch self.training_history.append(logs) if self.save_strategy == IntervalStrategy.EPOCH: if self.last_job is not None and not self.last_job.is_done: return # The last upload is still running, don't start another self.model.save_pretrained(self.output_dir) if self.tokenizer is not None: self.tokenizer.save_pretrained(self.output_dir) if self.checkpoint: checkpoint_dir = os.path.join(self.output_dir, "checkpoint") self.model._save_checkpoint(checkpoint_dir, epoch) train_summary = TrainingSummary.from_keras( model=self.model, model_name=self.hub_model_id, keras_history=self.training_history, **self.model_card_args, ) model_card = train_summary.to_model_card() with (self.output_dir / "README.md").open("w") as f: f.write(model_card) _, self.last_job = self.repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False ) def on_train_end(self, logs=None): if self.last_job is not None and not self.last_job.is_done: self.last_job._process.terminate() # Gotta go fast while not self.last_job.is_done: sleep(1) self.model.save_pretrained(self.output_dir) if self.tokenizer is not None: self.tokenizer.save_pretrained(self.output_dir) train_summary = TrainingSummary.from_keras( model=self.model, model_name=self.hub_model_id, keras_history=self.training_history, **self.model_card_args ) model_card = train_summary.to_model_card() with (self.output_dir / "README.md").open("w") as f: f.write(model_card) self.repo.push_to_hub(commit_message="End of training", blocking=True)

import logging import os from pathlib import Path from time import sleep from typing import Callable, List, Optional, Union import numpy as np import tensorflow as tf from packaging.version import parse from tensorflow.keras.callbacks import Callback from huggingface_hub import Repository, create_repo from . import IntervalStrategy, PreTrainedTokenizerBase from .modelcard import TrainingSummary from .utils import get_full_repo_name logger = logging.getLogger(__name__) class KerasMetricCallback(Callback): """ Callback to compute metrics at the end of every epoch. Unlike normal Keras metrics, these do not need to be compilable by TF. It is particularly useful for common NLP metrics like BLEU and ROUGE that require string operations or generation loops that cannot be compiled. Predictions (or generations) will be computed on the `eval_dataset` before being passed to the `metric_fn` in `np.ndarray` format. The `metric_fn` should compute metrics and return a dict mapping metric names to metric values. We provide an example of a suitable metric_fn that computes ROUGE scores for a summarization model below. Note that this example skips some post-processing for readability and simplicity, and should probably not be used as-is! ```py from datasets import load_metric rouge_metric = load_metric("rouge") def rouge_fn(predictions, labels): decoded_predictions = tokenizer.batch_decode(predictions, skip_special_tokens=True) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) result = rouge_metric.compute(predictions=decoded_predictions, references=decoded_labels) return {key: value.mid.fmeasure * 100 for key, value in result.items()} ``` The above function will return a dict containing values which will be logged like any other Keras metric: ``` {'rouge1': 37.4199, 'rouge2': 13.9768, 'rougeL': 34.361, 'rougeLsum': 35.0781 ``` Args: metric_fn (`Callable`): Metric function provided by the user. It will be called with two arguments - `predictions` and `labels`. These contain the model's outputs and matching labels from the dataset. It should return a dict mapping metric names to numerical values. eval_dataset (`tf.data.Dataset` or `dict` or `tuple` or `np.ndarray` or `tf.Tensor`): Validation data to be used to generate predictions for the `metric_fn`. output_cols (`List[str], *optional*): A list of columns to be retained from the model output as the predictions. Defaults to all. label_cols ('`List[str]`, *optional*'): A list of columns to be retained from the input dataset as the labels. Will be autodetected if this is not supplied. batch_size (`int`, *optional*): Batch size. Only used when the data is not a pre-batched `tf.data.Dataset`. predict_with_generate (`bool`, *optional*, defaults to `False`): Whether we should use `model.generate()` to get outputs for the model. use_xla_generation (`bool`, *optional*, defaults to `False`): If we're generating, whether to compile model generation with XLA. This can massively increase the speed of generation (up to 100X speedup) but will require a new XLA compilation for each input shape. When using XLA generation, it's a good idea to pad your inputs to the same size, or to use the `pad_to_multiple_of` argument in your `tokenizer` or `DataCollator`, which will reduce the number of unique input shapes and save a lot of compilation time. This option has no effect is `predict_with_generate` is `False`. generate_kwargs (`dict`, *optional*): Keyword arguments to pass to `model.generate()` when generating. Has no effect if `predict_with_generate` is `False`. """ def __init__( self, metric_fn: Callable, eval_dataset: Union[tf.data.Dataset, np.ndarray, tf.Tensor, tuple, dict], output_cols: Optional[List[str]] = None, label_cols: Optional[List[str]] = None, batch_size: Optional[int] = None, predict_with_generate: bool = False, use_xla_generation: bool = False, generate_kwargs: Optional[dict] = None, ): super().__init__() self.metric_fn = metric_fn self.batch_size = batch_size if not isinstance(eval_dataset, tf.data.Dataset): if batch_size is None: raise ValueError( "When passing data to KerasMetricCallback that is not a pre-batched tf.data.Dataset " "the batch_size argument must be set." ) # Wrap a tf.data.Dataset around it eval_dataset = tf.data.Dataset.from_tensor_slices(eval_dataset).batch(batch_size, drop_remainder=False) self.eval_dataset = eval_dataset self.predict_with_generate = predict_with_generate self.output_cols = output_cols # This next block attempts to parse out which elements of the dataset should be appended to the labels list # that is passed to the metric_fn if isinstance(eval_dataset.element_spec, tuple) and len(eval_dataset.element_spec) == 2: input_spec, label_spec = eval_dataset.element_spec else: input_spec = eval_dataset.element_spec label_spec = None if label_cols is not None: for label in label_cols: if label not in input_spec: raise ValueError(f"Label {label} is in label_cols but could not be found in the dataset inputs!") self.label_cols = label_cols self.use_keras_label = False elif label_spec is not None: # If the dataset inputs are split into a 2-tuple of inputs and labels, # assume the second element is the labels self.label_cols = None self.use_keras_label = True elif "labels" in input_spec: self.label_cols = ["labels"] self.use_keras_label = False logging.warning("No label_cols specified for KerasMetricCallback, assuming you want the 'labels' key.") elif "start_positions" in input_spec and "end_positions" in input_spec: self.label_cols = ["start_positions", "end_positions"] self.use_keras_label = False logging.warning( "No label_cols specified for KerasMetricCallback, assuming you want the " "start_positions and end_positions keys." ) else: raise ValueError("Could not autodetect label_cols for KerasMetricCallback, please specify them!") if parse(tf.__version__) < parse("2.7"): logging.warning("TF versions less than 2.7 may encounter issues with KerasMetricCallback!") self.use_xla_generation = use_xla_generation self.generate_kwargs = {} if generate_kwargs is None else generate_kwargs self.generation_function = None @staticmethod def _concatenate_batches(batches, padding_index=-100): # If all batches are unidimensional or same length, do a simple concatenation if batches[0].ndim == 1 or all([batch.shape[1] == batches[0].shape[1] for batch in batches]): return np.concatenate(batches, axis=0) # Welp, they're not the same length. Let's do some padding max_len = max([batch.shape[1] for batch in batches]) num_samples = sum([batch.shape[0] for batch in batches]) output = np.full_like( batches[0], fill_value=padding_index, shape=[num_samples, max_len] + list(batches[0].shape[2:]) ) # i keeps track of which part of the concatenated array we're writing the next batch to i = 0 for batch in batches: output[i : i + len(batch), : batch.shape[1]] = batch i += len(batch) return output def _postprocess_predictions_or_labels(self, inputs): if isinstance(inputs[0], dict): outputs = dict() for key in inputs[0].keys(): outputs[key] = self._concatenate_batches([batch[key] for batch in inputs]) # If it's a dict with only one key, just return the array if len(outputs) == 1: outputs = list(outputs.values())[0] elif isinstance(inputs[0], list) or isinstance(inputs[0], tuple): outputs = [] for input_list in zip(*inputs): outputs.append(self._concatenate_batches(input_list)) if len(outputs) == 1: outputs = outputs[0] # If it's a list with only one element, just return the array elif isinstance(inputs[0], np.ndarray): outputs = self._concatenate_batches(inputs) elif isinstance(inputs[0], tf.Tensor): outputs = self._concatenate_batches([tensor.numpy() for tensor in inputs]) else: raise TypeError(f"Couldn't handle batch of type {type(inputs[0])}!") return outputs def on_epoch_end(self, epoch, logs=None): if hasattr(self.model, "config"): ignore_keys = getattr(self.model.config, "keys_to_ignore_at_inference", []) else: ignore_keys = [] main_input_name = None if self.predict_with_generate: # This dense conditional recognizes the case where we have an encoder-decoder model, but # avoids getting tangled up when we just have a model with a layer called 'encoder' if hasattr(self.model, "encoder") and hasattr(self.model.encoder, "main_input_name"): if self.model.encoder.main_input_name != self.model.main_input_name: main_input_name = self.model.encoder.main_input_name else: main_input_name = getattr(self.model, "main_input_name", "input_ids") if self.use_xla_generation and self.generation_function is None: def generation_function(inputs, attention_mask): return self.model.generate(inputs, attention_mask=attention_mask, **self.generate_kwargs) self.generation_function = tf.function(generation_function, jit_compile=True) prediction_list = [] label_list = [] # The whole predict/generate loop is handled inside this method for batch in self.eval_dataset: if isinstance(batch, tuple): batch, labels = batch else: labels = None if self.predict_with_generate: if isinstance(batch, dict): generation_inputs = batch[main_input_name] attention_mask = batch.get("attention_mask", None) else: generation_inputs = batch attention_mask = None if self.use_xla_generation: predictions = self.generation_function(generation_inputs, attention_mask=attention_mask) else: predictions = self.model.generate(generation_inputs, attention_mask=attention_mask) else: predictions = self.model.predict_on_batch(batch) if isinstance(predictions, dict): # This converts any dict-subclass to a regular dict # Keras REALLY doesn't like it when we pass around a BatchEncoding or other derived class predictions = dict(predictions) if self.output_cols is not None: predictions = {key: predictions[key] for key in self.output_cols} else: predictions = {key: val for key, val in predictions.items() if key not in ignore_keys + ["loss"]} prediction_list.append(predictions) if not self.use_keras_label: labels = {key: batch[key].numpy() for key in self.label_cols} elif isinstance(labels, dict): labels = {key: array.numpy() for key, array in labels.items()} elif isinstance(labels, list) or isinstance(labels, tuple): labels = [array.numpy() for array in labels] elif isinstance(labels, tf.Tensor): labels = labels.numpy() else: raise TypeError(f"Confused by labels of type {type(labels)}") label_list.append(labels) all_preds = self._postprocess_predictions_or_labels(prediction_list) all_labels = self._postprocess_predictions_or_labels(label_list) metric_output = self.metric_fn((all_preds, all_labels)) if not isinstance(metric_output, dict): raise TypeError( f"metric_fn should return a dict mapping metric names to values but instead returned {metric_output}" ) # This is the critical bit - Keras passes a dict containing the loss and standard metric values for this epoch # in the logs argument. Ordinarily, this is so the callback can read them, but in this case we write a bunch of # new keys in there, which will then get read by the History callback and treated like any other metric value. # I promise that I have it in writing from Chollet that this is okay. logs.update(metric_output) class PushToHubCallback(Callback): """ Callback that will save and push the model to the Hub regularly. By default, it pushes once per epoch, but this can be changed with the `save_strategy` argument. Pushed models can be accessed like any other model on the hub, such as with the `from_pretrained` method. ```py from transformers.keras_callbacks import PushToHubCallback push_to_hub_callback = PushToHubCallback( output_dir="./model_save", tokenizer=tokenizer, hub_model_id="gpt5-7xlarge", ) model.fit(train_dataset, callbacks=[push_to_hub_callback]) ``` Args: output_dir (`str`): The output directory where the model predictions and checkpoints will be written and synced with the repository on the Hub. save_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"epoch"`): The checkpoint save strategy to adopt during training. Possible values are: - `"no"`: Save is done at the end of training. - `"epoch"`: Save is done at the end of each epoch. - `"steps"`: Save is done every `save_steps` save_steps (`int`, *optional*): The number of steps between saves when using the "steps" `save_strategy`. tokenizer (`PreTrainedTokenizerBase`, *optional*): The tokenizer used by the model. If supplied, will be uploaded to the repo alongside the weights. hub_model_id (`str`, *optional*): The name of the repository to keep in sync with the local `output_dir`. It can be a simple model ID in which case the model will be pushed in your namespace. Otherwise it should be the whole repository name, for instance `"user_name/model"`, which allows you to push to an organization you are a member of with `"organization_name/model"`. Will default to the name of `output_dir`. hub_token (`str`, *optional*): The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with `huggingface-cli login`. checkpoint (`bool`, *optional*, defaults to `False`): Whether to save full training checkpoints (including epoch and optimizer state) to allow training to be resumed. Only usable when `save_strategy` is `"epoch"`. """ def __init__( self, output_dir: Union[str, Path], save_strategy: Union[str, IntervalStrategy] = "epoch", save_steps: Optional[int] = None, tokenizer: Optional[PreTrainedTokenizerBase] = None, hub_model_id: Optional[str] = None, hub_token: Optional[str] = None, checkpoint: bool = False, **model_card_args ): super().__init__() if checkpoint and save_strategy != "epoch": raise ValueError("Cannot save checkpoints when save_strategy is not 'epoch'!") if isinstance(save_strategy, str): save_strategy = IntervalStrategy(save_strategy.lower()) self.save_strategy = save_strategy if self.save_strategy == IntervalStrategy.STEPS and (not isinstance(save_steps, int) or save_steps <= 0): raise ValueError("Please supply a positive integer argument for save_steps when save_strategy == 'steps'!") self.save_steps = save_steps output_dir = Path(output_dir) if hub_model_id is None: hub_model_id = output_dir.absolute().name if "/" not in hub_model_id: hub_model_id = get_full_repo_name(hub_model_id, token=hub_token) self.output_dir = output_dir self.hub_model_id = hub_model_id create_repo(self.hub_model_id, exist_ok=True) self.repo = Repository( str(self.output_dir), clone_from=self.hub_model_id, use_auth_token=hub_token if hub_token else True, ) self.tokenizer = tokenizer self.last_job = None self.checkpoint = checkpoint self.training_history = None self.model_card_args = model_card_args def on_train_begin(self, logs=None): # Although we can access model.history, we have no guarantees that the History callback will fire before this # one, so we keep track of it here too self.training_history = [] def on_train_batch_end(self, batch, logs=None): if self.save_strategy == IntervalStrategy.STEPS and (batch + 1) % self.save_steps == 0: if self.last_job is not None and not self.last_job.is_done: return # The last upload is still running, don't start another self.model.save_pretrained(self.output_dir) if self.tokenizer is not None: self.tokenizer.save_pretrained(self.output_dir) _, self.last_job = self.repo.push_to_hub( commit_message=f"Training in progress steps {batch}", blocking=False ) def on_epoch_end(self, epoch, logs=None): logs = logs.copy() # Don't accidentally write things that Keras will read later if "epoch" not in logs: logs["epoch"] = epoch self.training_history.append(logs) if self.save_strategy == IntervalStrategy.EPOCH: if self.last_job is not None and not self.last_job.is_done: return # The last upload is still running, don't start another self.model.save_pretrained(self.output_dir) if self.tokenizer is not None: self.tokenizer.save_pretrained(self.output_dir) if self.checkpoint: checkpoint_dir = os.path.join(self.output_dir, "checkpoint") self.model._save_checkpoint(checkpoint_dir, epoch) train_summary = TrainingSummary.from_keras( model=self.model, model_name=self.hub_model_id, keras_history=self.training_history, **self.model_card_args, ) model_card = train_summary.to_model_card() with (self.output_dir / "README.md").open("w") as f: f.write(model_card) _, self.last_job = self.repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False ) def on_train_end(self, logs=None): # Makes sure the latest version of the model is uploaded if self.last_job is not None and not self.last_job.is_done: logging.info("Pushing the last epoch to the Hub, this may take a while...") while not self.last_job.is_done: sleep(1) else: self.model.save_pretrained(self.output_dir) if self.tokenizer is not None: self.tokenizer.save_pretrained(self.output_dir) train_summary = TrainingSummary.from_keras( model=self.model, model_name=self.hub_model_id, keras_history=self.training_history, **self.model_card_args, ) model_card = train_summary.to_model_card() with (self.output_dir / "README.md").open("w") as f: f.write(model_card) self.repo.push_to_hub(commit_message="End of training", blocking=True)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/blenderbot/tokenization_blenderbot.py

# coding=utf-8 # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization class for Blenderbot.""" import json import os from functools import lru_cache from typing import TYPE_CHECKING, List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging if TYPE_CHECKING: from transformers.pipelines.conversational import Conversation logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_config_file": "tokenizer_config.json", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"}, "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"}, "tokenizer_config_file": { "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json" }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/blenderbot-3B": 128} @lru_cache() # Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode def bytes_to_unicode(): """ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. """ bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) # Copied from transformers.models.roberta.tokenization_roberta.get_pairs def get_pairs(word): """ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class BlenderbotTokenizer(PreTrainedTokenizer): """ Constructs a Blenderbot tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ``` >>> from transformers import BlenderbotTokenizer >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B") >>> tokenizer("Hello world")['input_ids'] [6950, 1085, 2] >>> tokenizer(" Hello world")['input_ids'] [6950, 1085, 2] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. <Tip> When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one). </Tip> This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (Blenderbot tokenizer detect beginning of words by the preceding space). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.__init__ with Roberta->Blenderbot, RoBERTa->Blenderbot def __init__( self, vocab_file, merges_file, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, **kwargs ): bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token super().__init__( errors=errors, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, **kwargs, ) with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: bpe_merges = merges_handle.read().split("\n")[1:-1] bpe_merges = [tuple(merge.split()) for merge in bpe_merges] self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {} self.add_prefix_space = add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") @property # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot def vocab_size(self): return len(self.encoder) # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_vocab with Roberta->Blenderbot, RoBERTa->Blenderbot def get_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.bpe with Roberta->Blenderbot, RoBERTa->Blenderbot def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token) pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) self.cache[token] = word return word # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._tokenize with Roberta->Blenderbot, RoBERTa->Blenderbot def _tokenize(self, text): """Tokenize a string.""" bpe_tokens = [] for token in re.findall(self.pat, text): token = "".join( self.byte_encoder[b] for b in token.encode("utf-8") ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) return bpe_tokens # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_token_to_id with Roberta->Blenderbot, RoBERTa->Blenderbot def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_id_to_token with Roberta->Blenderbot, RoBERTa->Blenderbot def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index) # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.convert_tokens_to_string with Roberta->Blenderbot, RoBERTa->Blenderbot def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" text = "".join(tokens) text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) return text # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.save_vocabulary with Roberta->Blenderbot, RoBERTa->Blenderbot def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) merge_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") index = 0 with open(merge_file, "w", encoding="utf-8") as writer: writer.write("#version: 0.2\n") for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return vocab_file, merge_file # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_special_tokens_mask with Roberta->Blenderbot, RoBERTa->Blenderbot def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.create_token_type_ids_from_sequences with Roberta->Blenderbot, RoBERTa->Blenderbot def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. Blenderbot does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.prepare_for_tokenization with Roberta->Blenderbot, RoBERTa->Blenderbot def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space) if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()): text = " " + text return (text, kwargs) def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A Blenderbot sequence has the following format: - single sequence: ` X </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added token_ids_1 (`List[int]`, *optional*): Will be ignored Returns: `List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ return token_ids_0 + [self.eos_token_id] def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]: inputs = [] for is_user, text in conversation.iter_texts(): if is_user: # We need to space prefix as it's being done within blenderbot inputs.append(" " + text) else: # Generated responses should contain them already. inputs.append(text) full_string = " ".join(inputs) input_ids = self.encode(full_string) if len(input_ids) > self.model_max_length: input_ids = input_ids[-self.model_max_length :] logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens.") return input_ids

# coding=utf-8 # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization class for Blenderbot.""" import json import os from functools import lru_cache from typing import TYPE_CHECKING, List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging if TYPE_CHECKING: from transformers.pipelines.conversational import Conversation logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_config_file": "tokenizer_config.json", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"}, "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"}, "tokenizer_config_file": { "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json" }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/blenderbot-3B": 128} @lru_cache() # Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode def bytes_to_unicode(): """ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. """ bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) # Copied from transformers.models.roberta.tokenization_roberta.get_pairs def get_pairs(word): """ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class BlenderbotTokenizer(PreTrainedTokenizer): """ Constructs a Blenderbot tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ``` >>> from transformers import BlenderbotTokenizer >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B") >>> tokenizer("Hello world")['input_ids'] [6950, 1085, 2] >>> tokenizer(" Hello world")['input_ids'] [6950, 1085, 2] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. <Tip> When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one). </Tip> This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (Blenderbot tokenizer detect beginning of words by the preceding space). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.__init__ with Roberta->Blenderbot, RoBERTa->Blenderbot def __init__( self, vocab_file, merges_file, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, **kwargs ): bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token super().__init__( errors=errors, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, **kwargs, ) with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: bpe_merges = merges_handle.read().split("\n")[1:-1] bpe_merges = [tuple(merge.split()) for merge in bpe_merges] self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {} self.add_prefix_space = add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") @property # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot def vocab_size(self): return len(self.encoder) # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_vocab with Roberta->Blenderbot, RoBERTa->Blenderbot def get_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.bpe with Roberta->Blenderbot, RoBERTa->Blenderbot def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token) pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) self.cache[token] = word return word # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._tokenize with Roberta->Blenderbot, RoBERTa->Blenderbot def _tokenize(self, text): """Tokenize a string.""" bpe_tokens = [] for token in re.findall(self.pat, text): token = "".join( self.byte_encoder[b] for b in token.encode("utf-8") ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) return bpe_tokens # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_token_to_id with Roberta->Blenderbot, RoBERTa->Blenderbot def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_id_to_token with Roberta->Blenderbot, RoBERTa->Blenderbot def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index) # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.convert_tokens_to_string with Roberta->Blenderbot, RoBERTa->Blenderbot def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" text = "".join(tokens) text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) return text # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.save_vocabulary with Roberta->Blenderbot, RoBERTa->Blenderbot def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) merge_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") index = 0 with open(merge_file, "w", encoding="utf-8") as writer: writer.write("#version: 0.2\n") for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return vocab_file, merge_file # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_special_tokens_mask with Roberta->Blenderbot, RoBERTa->Blenderbot def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.create_token_type_ids_from_sequences with Roberta->Blenderbot, RoBERTa->Blenderbot def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. Blenderbot does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.prepare_for_tokenization with Roberta->Blenderbot, RoBERTa->Blenderbot def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space) if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()): text = " " + text return (text, kwargs) def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A Blenderbot sequence has the following format: - single sequence: ` X </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added token_ids_1 (`List[int]`, *optional*): Will be ignored Returns: `List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ return token_ids_0 + [self.eos_token_id] def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]: inputs = [] for is_user, text in conversation.iter_texts(): if is_user: # We need to space prefix as it's being done within blenderbot inputs.append(" " + text) else: # Generated responses should contain them already. inputs.append(text) full_string = " ".join(inputs) input_ids = self.encode(full_string) if len(input_ids) > self.model_max_length: input_ids = input_ids[-self.model_max_length :] logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens.") return input_ids

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/blenderbot/tokenization_blenderbot_fast.py

# coding=utf-8 # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Fast Tokenization class for Blenderbot.""" import json from typing import TYPE_CHECKING, List, Optional, Tuple from tokenizers import pre_tokenizers, processors from ...tokenization_utils_base import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_blenderbot import BlenderbotTokenizer if TYPE_CHECKING: from transformers.pipelines.conversational import Conversation logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_config_file": "tokenizer_config.json", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"}, "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"}, "tokenizer_config_file": { "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json" }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/blenderbot-3B": 128} class BlenderbotTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" Blenderbot tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ``` >>> from transformers import BlenderbotTokenizerFast >>> tokenizer = BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B") >>> tokenizer("Hello world")['input_ids'] [6950, 1085, 2] >>> tokenizer(" Hello world")['input_ids'] [6950, 1085, 2] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. <Tip> When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. </Tip> This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (Blenderbot tokenizer detect beginning of words by the preceding space). trim_offsets (`bool`, *optional*, defaults to `True`): Whether the post processing step should trim offsets to avoid including whitespaces. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] slow_tokenizer_class = BlenderbotTokenizer # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.__init__ with Roberta->Blenderbot, RoBERTa->Blenderbot def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, trim_offsets=True, **kwargs ): super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, errors=errors, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets, **kwargs, ) pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) pre_tok_state["add_prefix_space"] = add_prefix_space self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) self.add_prefix_space = add_prefix_space tokenizer_component = "post_processor" tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None) if tokenizer_component_instance: state = json.loads(tokenizer_component_instance.__getstate__()) # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` if "sep" in state: state["sep"] = tuple(state["sep"]) if "cls" in state: state["cls"] = tuple(state["cls"]) changes_to_apply = False if state.get("add_prefix_space", add_prefix_space) != add_prefix_space: state["add_prefix_space"] = add_prefix_space changes_to_apply = True if state.get("trim_offsets", trim_offsets) != trim_offsets: state["trim_offsets"] = trim_offsets changes_to_apply = True if changes_to_apply: component_class = getattr(processors, state.pop("type")) new_value = component_class(**state) setattr(self.backend_tokenizer, tokenizer_component, new_value) @property # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot def mask_token(self) -> str: """ `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not having been set. Blenderbot tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily comprise the space before the *<mask>*. """ if self._mask_token is None: if self.verbose: logger.error("Using mask_token, but it is not set yet.") return None return str(self._mask_token) @mask_token.setter def mask_token(self, value): """ Overriding the default behavior of the mask token to have it eat the space before it. This is needed to preserve backward compatibility with all the previously used models based on Roberta. """ # Mask token behave like a normal word, i.e. include the space before it # So we set lstrip to True value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value self._mask_token = value # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast._batch_encode_plus with Roberta->Blenderbot, RoBERTa->Blenderbot def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*args, **kwargs) # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast._encode_plus with Roberta->Blenderbot, RoBERTa->Blenderbot def _encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._encode_plus(*args, **kwargs) # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.save_vocabulary with Roberta->Blenderbot, RoBERTa->Blenderbot def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.create_token_type_ids_from_sequences with Roberta->Blenderbot, RoBERTa->Blenderbot def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. Blenderbot does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A Blenderbot sequence has the following format: - single sequence: ` X </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added token_ids_1 (`List[int]`, *optional*): Will be ignored Returns: `List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ return token_ids_0 + [self.eos_token_id] def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]: inputs = [] for is_user, text in conversation.iter_texts(): if is_user: # We need to space prefix as it's being done within blenderbot inputs.append(" " + text) else: # Generated responses should contain them already. inputs.append(text) full_string = " ".join(inputs) input_ids = self.encode(full_string) if len(input_ids) > self.model_max_length: input_ids = input_ids[-self.model_max_length :] logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens.") return input_ids

# coding=utf-8 # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Fast Tokenization class for Blenderbot.""" import json from typing import TYPE_CHECKING, List, Optional, Tuple from tokenizers import pre_tokenizers, processors from ...tokenization_utils_base import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_blenderbot import BlenderbotTokenizer if TYPE_CHECKING: from transformers.pipelines.conversational import Conversation logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_config_file": "tokenizer_config.json", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"}, "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"}, "tokenizer_config_file": { "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json" }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/blenderbot-3B": 128} class BlenderbotTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" Blenderbot tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ``` >>> from transformers import BlenderbotTokenizerFast >>> tokenizer = BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B") >>> tokenizer("Hello world")['input_ids'] [6950, 1085, 2] >>> tokenizer(" Hello world")['input_ids'] [6950, 1085, 2] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. <Tip> When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. </Tip> This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (Blenderbot tokenizer detect beginning of words by the preceding space). trim_offsets (`bool`, *optional*, defaults to `True`): Whether the post processing step should trim offsets to avoid including whitespaces. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] slow_tokenizer_class = BlenderbotTokenizer # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.__init__ with Roberta->Blenderbot, RoBERTa->Blenderbot def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, trim_offsets=True, **kwargs ): super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, errors=errors, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets, **kwargs, ) pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) pre_tok_state["add_prefix_space"] = add_prefix_space self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) self.add_prefix_space = add_prefix_space tokenizer_component = "post_processor" tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None) if tokenizer_component_instance: state = json.loads(tokenizer_component_instance.__getstate__()) # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` if "sep" in state: state["sep"] = tuple(state["sep"]) if "cls" in state: state["cls"] = tuple(state["cls"]) changes_to_apply = False if state.get("add_prefix_space", add_prefix_space) != add_prefix_space: state["add_prefix_space"] = add_prefix_space changes_to_apply = True if state.get("trim_offsets", trim_offsets) != trim_offsets: state["trim_offsets"] = trim_offsets changes_to_apply = True if changes_to_apply: component_class = getattr(processors, state.pop("type")) new_value = component_class(**state) setattr(self.backend_tokenizer, tokenizer_component, new_value) @property # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot def mask_token(self) -> str: """ `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not having been set. Blenderbot tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily comprise the space before the *<mask>*. """ if self._mask_token is None: if self.verbose: logger.error("Using mask_token, but it is not set yet.") return None return str(self._mask_token) @mask_token.setter def mask_token(self, value): """ Overriding the default behavior of the mask token to have it eat the space before it. This is needed to preserve backward compatibility with all the previously used models based on Roberta. """ # Mask token behave like a normal word, i.e. include the space before it # So we set lstrip to True value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value self._mask_token = value # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast._batch_encode_plus with Roberta->Blenderbot, RoBERTa->Blenderbot def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*args, **kwargs) # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast._encode_plus with Roberta->Blenderbot, RoBERTa->Blenderbot def _encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._encode_plus(*args, **kwargs) # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.save_vocabulary with Roberta->Blenderbot, RoBERTa->Blenderbot def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.create_token_type_ids_from_sequences with Roberta->Blenderbot, RoBERTa->Blenderbot def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. Blenderbot does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A Blenderbot sequence has the following format: - single sequence: ` X </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added token_ids_1 (`List[int]`, *optional*): Will be ignored Returns: `List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ return token_ids_0 + [self.eos_token_id] def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]: inputs = [] for is_user, text in conversation.iter_texts(): if is_user: # We need to space prefix as it's being done within blenderbot inputs.append(" " + text) else: # Generated responses should contain them already. inputs.append(text) full_string = " ".join(inputs) input_ids = self.encode(full_string) if len(input_ids) > self.model_max_length: input_ids = input_ids[-self.model_max_length :] logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens.") return input_ids

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/markuplm/tokenization_markuplm.py

# coding=utf-8 # Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization class for MarkupLM.""" import json import os from functools import lru_cache from typing import Dict, List, Optional, Tuple, Union import regex as re from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...tokenization_utils_base import ( ENCODE_KWARGS_DOCSTRING, BatchEncoding, EncodedInput, PreTokenizedInput, TextInput, TextInputPair, TruncationStrategy, ) from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/vocab.json", "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/vocab.json", }, "merges_file": { "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/merges.txt", "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/merges.txt", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "microsoft/markuplm-base": 512, "microsoft/markuplm-large": 512, } MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r""" add_special_tokens (`bool`, *optional*, defaults to `True`): Whether or not to encode the sequences with the special tokens relative to their model. padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): Activates and controls truncation. Accepts the following values: - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). max_length (`int`, *optional*): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. stride (`int`, *optional*, defaults to 0): If set to a number along with `max_length`, the overflowing tokens returned when `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). return_tensors (`str` or [`~file_utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. """ @lru_cache() def bytes_to_unicode(): """ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. """ bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): """ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class MarkupLMTokenizer(PreTrainedTokenizer): r""" Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). [`MarkupLMTokenizer`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (RoBERTa tokenizer detect beginning of words by the preceding space). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self, vocab_file, merges_file, tags_dict, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, max_depth=50, max_width=1000, pad_width=1001, pad_token_label=-100, only_label_first_subword=True, **kwargs ): bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token super().__init__( vocab_file=vocab_file, merges_file=merges_file, tags_dict=tags_dict, errors=errors, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, max_depth=max_depth, max_width=max_width, pad_width=pad_width, pad_token_label=pad_token_label, only_label_first_subword=only_label_first_subword, **kwargs, ) with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.tags_dict = tags_dict self.decoder = {v: k for k, v in self.encoder.items()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: bpe_merges = merges_handle.read().split("\n")[1:-1] bpe_merges = [tuple(merge.split()) for merge in bpe_merges] self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {} self.add_prefix_space = add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") # additional properties self.max_depth = max_depth self.max_width = max_width self.pad_width = pad_width self.unk_tag_id = len(self.tags_dict) self.pad_tag_id = self.unk_tag_id + 1 self.pad_xpath_tags_seq = [self.pad_tag_id] * self.max_depth self.pad_xpath_subs_seq = [self.pad_width] * self.max_depth self.pad_token_label = pad_token_label self.only_label_first_subword = only_label_first_subword def get_xpath_seq(self, xpath): """ Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of tag IDs and corresponding subscripts, taking into account max depth. """ xpath_tags_list = [] xpath_subs_list = [] xpath_units = xpath.split("/") for unit in xpath_units: if not unit.strip(): continue name_subs = unit.strip().split("[") tag_name = name_subs[0] sub = 0 if len(name_subs) == 1 else int(name_subs[1][:-1]) xpath_tags_list.append(self.tags_dict.get(tag_name, self.unk_tag_id)) xpath_subs_list.append(min(self.max_width, sub)) xpath_tags_list = xpath_tags_list[: self.max_depth] xpath_subs_list = xpath_tags_list[: self.max_depth] xpath_tags_list += [self.pad_tag_id] * (self.max_depth - len(xpath_tags_list)) xpath_subs_list += [self.pad_width] * (self.max_depth - len(xpath_subs_list)) return xpath_tags_list, xpath_subs_list @property def vocab_size(self): return len(self.encoder) def get_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token) pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) self.cache[token] = word return word def _tokenize(self, text): """Tokenize a string.""" bpe_tokens = [] for token in re.findall(self.pat, text): token = "".join( self.byte_encoder[b] for b in token.encode("utf-8") ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) return bpe_tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" logger.warning( "MarkupLM now does not support generative tasks, decoding is experimental and subject to change." ) text = "".join(tokens) text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) return text def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) merge_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) # save vocab_file with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") # save merge_file index = 0 with open(merge_file, "w", encoding="utf-8") as writer: writer.write("#version: 0.2\n") for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return vocab_file, merge_file def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space) if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()): text = " " + text return (text, kwargs) def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A RoBERTa sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + token_ids_1 + sep def build_xpath_tags_with_special_tokens( self, xpath_tags_0: List[int], xpath_tags_1: Optional[List[int]] = None ) -> List[int]: pad = [self.pad_xpath_tags_seq] if len(xpath_tags_1) == 0: return pad + xpath_tags_0 + pad return pad + xpath_tags_0 + pad + xpath_tags_1 + pad def build_xpath_subs_with_special_tokens( self, xpath_subs_0: List[int], xpath_subs_1: Optional[List[int]] = None ) -> List[int]: pad = [self.pad_xpath_subs_seq] if len(xpath_subs_1) == 0: return pad + xpath_subs_0 + pad return pad + xpath_subs_0 + pad + xpath_subs_1 + pad def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Args: Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + token_ids_1 + sep) * [0] @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def __call__( self, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None, xpaths: Union[List[List[int]], List[List[List[int]]]] = None, node_labels: Optional[Union[List[int], List[List[int]]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with node-level xpaths and optional labels. Args: text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings (nodes of a single example or questions of a batch of examples) or a list of list of strings (batch of nodes). text_pair (`List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence should be a list of strings (pretokenized string). xpaths (`List[List[int]]`, `List[List[List[int]]]`): Node-level xpaths. node_labels (`List[int]`, `List[List[int]]`, *optional*): Node-level integer labels (for token classification tasks). """ # Input type checking for clearer error def _is_valid_text_input(t): if isinstance(t, str): # Strings are fine return True elif isinstance(t, (list, tuple)): # List are fine as long as they are... if len(t) == 0: # ... empty return True elif isinstance(t[0], str): # ... list of strings return True elif isinstance(t[0], (list, tuple)): # ... list with an empty list or with a list of strings return len(t[0]) == 0 or isinstance(t[0][0], str) else: return False else: return False if text_pair is not None: # in case text + text_pair are provided, text = questions, text_pair = nodes if not _is_valid_text_input(text): raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ") if not isinstance(text_pair, (list, tuple)): raise ValueError( "Nodes must be of type `List[str]` (single pretokenized example), " "or `List[List[str]]` (batch of pretokenized examples)." ) else: # in case only text is provided => must be nodes if not isinstance(text, (list, tuple)): raise ValueError( "Nodes must be of type `List[str]` (single pretokenized example), " "or `List[List[str]]` (batch of pretokenized examples)." ) if text_pair is not None: is_batched = isinstance(text, (list, tuple)) else: is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple)) nodes = text if text_pair is None else text_pair assert xpaths is not None, "You must provide corresponding xpaths" if is_batched: assert len(nodes) == len(xpaths), "You must provide nodes and xpaths for an equal amount of examples" for nodes_example, xpaths_example in zip(nodes, xpaths): assert len(nodes_example) == len(xpaths_example), "You must provide as many nodes as there are xpaths" else: assert len(nodes) == len(xpaths), "You must provide as many nodes as there are xpaths" if is_batched: if text_pair is not None and len(text) != len(text_pair): raise ValueError( f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:" f" {len(text_pair)}." ) batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text is_pair = bool(text_pair is not None) return self.batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, is_pair=is_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: return self.encode_plus( text=text, text_pair=text_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def batch_encode_plus( self, batch_text_or_text_pairs: Union[ List[TextInput], List[TextInputPair], List[PreTokenizedInput], ], is_pair: bool = None, xpaths: Optional[List[List[List[int]]]] = None, node_labels: Optional[Union[List[int], List[List[int]]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, is_pair=is_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _batch_encode_plus( self, batch_text_or_text_pairs: Union[ List[TextInput], List[TextInputPair], List[PreTokenizedInput], ], is_pair: bool = None, xpaths: Optional[List[List[List[int]]]] = None, node_labels: Optional[List[List[int]]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast." ) batch_outputs = self._batch_prepare_for_model( batch_text_or_text_pairs=batch_text_or_text_pairs, is_pair=is_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=return_tensors, verbose=verbose, ) return BatchEncoding(batch_outputs) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def _batch_prepare_for_model( self, batch_text_or_text_pairs, is_pair: bool = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[List[int]]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[str] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_length: bool = False, verbose: bool = True, ) -> BatchEncoding: """ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Args: batch_ids_pairs: list of tokenized input ids or input ids pairs """ batch_outputs = {} for idx, example in enumerate(zip(batch_text_or_text_pairs, xpaths)): batch_text_or_text_pair, xpaths_example = example outputs = self.prepare_for_model( batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair, batch_text_or_text_pair[1] if is_pair else None, xpaths_example, node_labels=node_labels[idx] if node_labels is not None else None, add_special_tokens=add_special_tokens, padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward truncation=truncation_strategy.value, max_length=max_length, stride=stride, pad_to_multiple_of=None, # we pad in batch afterward return_attention_mask=False, # we pad in batch afterward return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=None, # We convert the whole batch to tensors at the end prepend_batch_axis=False, verbose=verbose, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) batch_outputs = self.pad( batch_outputs, padding=padding_strategy.value, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors) return batch_outputs @add_end_docstrings(ENCODE_KWARGS_DOCSTRING) def encode( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[PreTokenizedInput] = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[int]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> List[int]: encoded_inputs = self.encode_plus( text=text, text_pair=text_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) return encoded_inputs["input_ids"] @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def encode_plus( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[PreTokenizedInput] = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[int]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated, `__call__` should be used instead. Args: text (`str`, `List[str]`, `List[List[str]]`): The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings. text_pair (`List[str]` or `List[int]`, *optional*): Optional second sequence to be encoded. This can be a list of strings (nodes of a single example) or a list of list of strings (nodes of a batch of examples). """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._encode_plus( text=text, xpaths=xpaths, text_pair=text_pair, node_labels=node_labels, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _encode_plus( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[PreTokenizedInput] = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[int]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast. " "More information on available tokenizers at " "https://github.com/huggingface/transformers/pull/2674" ) return self.prepare_for_model( text=text, text_pair=text_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding_strategy.value, truncation=truncation_strategy.value, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, prepend_batch_axis=True, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, verbose=verbose, ) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def prepare_for_model( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[PreTokenizedInput] = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[int]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, prepend_batch_axis: bool = False, **kwargs ) -> BatchEncoding: """ Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Please Note, for *text_pair* different than `None` and *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an error. Node-level `xpaths` are turned into token-level `xpath_tags_seq` and `xpath_subs_seq`. If provided, node-level `node_labels` are turned into token-level `labels`. The node label is used for the first token of the node, while remaining tokens are labeled with -100, such that they will be ignored by the loss function. Args: text (`str`, `List[str]`, `List[List[str]]`): The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings. text_pair (`List[str]` or `List[int]`, *optional*): Optional second sequence to be encoded. This can be a list of strings (nodes of a single example) or a list of list of strings (nodes of a batch of examples). """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) tokens = [] pair_tokens = [] xpath_tags_seq = [] xpath_subs_seq = [] pair_xpath_tags_seq = [] pair_xpath_subs_seq = [] labels = [] if text_pair is None: if node_labels is None: # CASE 1: web page classification (training + inference) + CASE 2: token classification (inference) for word, xpath in zip(text, xpaths): if len(word) < 1: # skip empty nodes continue word_tokens = self.tokenize(word) tokens.extend(word_tokens) xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath) xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens)) xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens)) else: # CASE 2: token classification (training) for word, xpath, label in zip(text, xpaths, node_labels): if len(word) < 1: # skip empty nodes continue word_tokens = self.tokenize(word) tokens.extend(word_tokens) xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath) xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens)) xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens)) if self.only_label_first_subword: # Use the real label id for the first token of the word, and padding ids for the remaining tokens labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1)) else: labels.extend([label] * len(word_tokens)) else: # CASE 3: web page question answering (inference) # text = question # text_pair = nodes tokens = self.tokenize(text) xpath_tags_seq = [self.pad_xpath_tags_seq for _ in range(len(tokens))] xpath_subs_seq = [self.pad_xpath_subs_seq for _ in range(len(tokens))] for word, xpath in zip(text_pair, xpaths): if len(word) < 1: # skip empty nodes continue word_tokens = self.tokenize(word) pair_tokens.extend(word_tokens) xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath) pair_xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens)) pair_xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens)) # Create ids + pair_ids ids = self.convert_tokens_to_ids(tokens) pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None if ( return_overflowing_tokens and truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is not None ): raise ValueError( "Not possible to return overflowing tokens for pair of sequences with the " "`longest_first`. Please select another truncation strategy than `longest_first`, " "for instance `only_second` or `only_first`." ) # Compute the total size of the returned encodings pair = bool(pair_ids is not None) len_ids = len(ids) len_pair_ids = len(pair_ids) if pair else 0 total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0) # Truncation: Handle max sequence length overflowing_tokens = [] overflowing_xpath_tags_seq = [] overflowing_xpath_subs_seq = [] overflowing_labels = [] if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length: ( ids, xpath_tags_seq, xpath_subs_seq, pair_ids, pair_xpath_tags_seq, pair_xpath_subs_seq, labels, overflowing_tokens, overflowing_xpath_tags_seq, overflowing_xpath_subs_seq, overflowing_labels, ) = self.truncate_sequences( ids, xpath_tags_seq=xpath_tags_seq, xpath_subs_seq=xpath_subs_seq, pair_ids=pair_ids, pair_xpath_tags_seq=pair_xpath_tags_seq, pair_xpath_subs_seq=pair_xpath_subs_seq, labels=labels, num_tokens_to_remove=total_len - max_length, truncation_strategy=truncation_strategy, stride=stride, ) if return_token_type_ids and not add_special_tokens: raise ValueError( "Asking to return token_type_ids while setting add_special_tokens to False " "results in an undefined behavior. Please set add_special_tokens to True or " "set return_token_type_ids to None." ) # Load from model defaults if return_token_type_ids is None: return_token_type_ids = "token_type_ids" in self.model_input_names if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names encoded_inputs = {} if return_overflowing_tokens: encoded_inputs["overflowing_tokens"] = overflowing_tokens encoded_inputs["overflowing_xpath_tags_seq"] = overflowing_xpath_tags_seq encoded_inputs["overflowing_xpath_subs_seq"] = overflowing_xpath_subs_seq encoded_inputs["overflowing_labels"] = overflowing_labels encoded_inputs["num_truncated_tokens"] = total_len - max_length # Add special tokens if add_special_tokens: sequence = self.build_inputs_with_special_tokens(ids, pair_ids) token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids) xpath_tags_ids = self.build_xpath_tags_with_special_tokens(xpath_tags_seq, pair_xpath_tags_seq) xpath_subs_ids = self.build_xpath_subs_with_special_tokens(xpath_subs_seq, pair_xpath_subs_seq) if labels: labels = [self.pad_token_label] + labels + [self.pad_token_label] else: sequence = ids + pair_ids if pair else ids token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else []) xpath_tags_ids = xpath_tags_seq + pair_xpath_tags_seq if pair else xpath_tags_seq xpath_subs_ids = xpath_subs_seq + pair_xpath_subs_seq if pair else xpath_subs_seq # Build output dictionary encoded_inputs["input_ids"] = sequence encoded_inputs["xpath_tags_seq"] = xpath_tags_ids encoded_inputs["xpath_subs_seq"] = xpath_subs_ids if return_token_type_ids: encoded_inputs["token_type_ids"] = token_type_ids if return_special_tokens_mask: if add_special_tokens: encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids) else: encoded_inputs["special_tokens_mask"] = [0] * len(sequence) if labels: encoded_inputs["labels"] = labels # Check lengths self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose) # Padding if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask: encoded_inputs = self.pad( encoded_inputs, max_length=max_length, padding=padding_strategy.value, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) if return_length: encoded_inputs["length"] = len(encoded_inputs["input_ids"]) batch_outputs = BatchEncoding( encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis ) return batch_outputs def truncate_sequences( self, ids: List[int], xpath_tags_seq: List[List[int]], xpath_subs_seq: List[List[int]], pair_ids: Optional[List[int]] = None, pair_xpath_tags_seq: Optional[List[List[int]]] = None, pair_xpath_subs_seq: Optional[List[List[int]]] = None, labels: Optional[List[int]] = None, num_tokens_to_remove: int = 0, truncation_strategy: Union[str, TruncationStrategy] = "longest_first", stride: int = 0, ) -> Tuple[List[int], List[int], List[int]]: """ Args: Truncates a sequence pair in-place following the strategy. ids (`List[int]`): Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. xpath_tags_seq (`List[List[int]]`): XPath tag IDs of the first sequence. xpath_subs_seq (`List[List[int]]`): XPath sub IDs of the first sequence. pair_ids (`List[int]`, *optional*): Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. pair_xpath_tags_seq (`List[List[int]]`, *optional*): XPath tag IDs of the second sequence. pair_xpath_subs_seq (`List[List[int]]`, *optional*): XPath sub IDs of the second sequence. num_tokens_to_remove (`int`, *optional*, defaults to 0): Number of tokens to remove using the truncation strategy. truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): The strategy to follow for truncation. Can be: - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). stride (`int`, *optional*, defaults to 0): If set to a positive number, the overflowing tokens returned will contain some tokens from the main sequence returned. The value of this argument defines the number of additional tokens. Returns: `Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair of sequences (or a batch of pairs) is provided. """ if num_tokens_to_remove <= 0: return ids, xpath_tags_seq, xpath_subs_seq, pair_ids, pair_xpath_tags_seq, pair_xpath_subs_seq, [], [], [] if not isinstance(truncation_strategy, TruncationStrategy): truncation_strategy = TruncationStrategy(truncation_strategy) overflowing_tokens = [] overflowing_xpath_tags_seq = [] overflowing_xpath_subs_seq = [] overflowing_labels = [] if truncation_strategy == TruncationStrategy.ONLY_FIRST or ( truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None ): if len(ids) > num_tokens_to_remove: window_len = min(len(ids), stride + num_tokens_to_remove) overflowing_tokens = ids[-window_len:] overflowing_xpath_tags_seq = xpath_tags_seq[-window_len:] overflowing_xpath_subs_seq = xpath_subs_seq[-window_len:] ids = ids[:-num_tokens_to_remove] xpath_tags_seq = xpath_tags_seq[:-num_tokens_to_remove] xpath_subs_seq = xpath_subs_seq[:-num_tokens_to_remove] labels = labels[:-num_tokens_to_remove] else: error_msg = ( f"We need to remove {num_tokens_to_remove} to truncate the input " f"but the first sequence has a length {len(ids)}. " ) if truncation_strategy == TruncationStrategy.ONLY_FIRST: error_msg = ( error_msg + "Please select another truncation strategy than " f"{truncation_strategy}, for instance 'longest_first' or 'only_second'." ) logger.error(error_msg) elif truncation_strategy == TruncationStrategy.LONGEST_FIRST: logger.warning( "Be aware, overflowing tokens are not returned for the setting you have chosen," f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' " "truncation strategy. So the returned list will always be empty even if some " "tokens have been removed." ) for _ in range(num_tokens_to_remove): if pair_ids is None or len(ids) > len(pair_ids): ids = ids[:-1] xpath_tags_seq = xpath_tags_seq[:-1] xpath_subs_seq = xpath_subs_seq[:-1] labels = labels[:-1] else: pair_ids = pair_ids[:-1] pair_xpath_tags_seq = pair_xpath_tags_seq[:-1] pair_xpath_subs_seq = pair_xpath_subs_seq[:-1] elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None: if len(pair_ids) > num_tokens_to_remove: window_len = min(len(pair_ids), stride + num_tokens_to_remove) overflowing_tokens = pair_ids[-window_len:] overflowing_xpath_tags_seq = pair_xpath_tags_seq[-window_len:] overflowing_xpath_subs_seq = pair_xpath_subs_seq[-window_len:] pair_ids = pair_ids[:-num_tokens_to_remove] pair_xpath_tags_seq = pair_xpath_tags_seq[:-num_tokens_to_remove] pair_xpath_subs_seq = pair_xpath_subs_seq[:-num_tokens_to_remove] else: logger.error( f"We need to remove {num_tokens_to_remove} to truncate the input " f"but the second sequence has a length {len(pair_ids)}. " f"Please select another truncation strategy than {truncation_strategy}, " "for instance 'longest_first' or 'only_first'." ) return ( ids, xpath_tags_seq, xpath_subs_seq, pair_ids, pair_xpath_tags_seq, pair_xpath_subs_seq, labels, overflowing_tokens, overflowing_xpath_tags_seq, overflowing_xpath_subs_seq, overflowing_labels, ) def _pad( self, encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], max_length: Optional[int] = None, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, ) -> dict: """ Args: Pad encoded inputs (on left/right and up to predefined length or max length in the batch) encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). max_length: maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens. padding_strategy: PaddingStrategy to use for padding. - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - PaddingStrategy.DO_NOT_PAD: Do not pad The tokenizer padding sides are defined in self.padding_side: - 'left': pads on the left of the sequences - 'right': pads on the right of the sequences pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability >= 7.5 (Volta). return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) """ # Load from model defaults if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names required_input = encoded_inputs[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: max_length = len(required_input) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length # Initialize attention mask if not present. if return_attention_mask and "attention_mask" not in encoded_inputs: encoded_inputs["attention_mask"] = [1] * len(required_input) if needs_to_be_padded: difference = max_length - len(required_input) if self.padding_side == "right": if return_attention_mask: encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = ( encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference ) if "xpath_tags_seq" in encoded_inputs: encoded_inputs["xpath_tags_seq"] = ( encoded_inputs["xpath_tags_seq"] + [self.pad_xpath_tags_seq] * difference ) if "xpath_subs_seq" in encoded_inputs: encoded_inputs["xpath_subs_seq"] = ( encoded_inputs["xpath_subs_seq"] + [self.pad_xpath_subs_seq] * difference ) if "labels" in encoded_inputs: encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference elif self.padding_side == "left": if return_attention_mask: encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[ "token_type_ids" ] if "xpath_tags_seq" in encoded_inputs: encoded_inputs["xpath_tags_seq"] = [self.pad_xpath_tags_seq] * difference + encoded_inputs[ "xpath_tags_seq" ] if "xpath_subs_seq" in encoded_inputs: encoded_inputs["xpath_subs_seq"] = [self.pad_xpath_subs_seq] * difference + encoded_inputs[ "xpath_subs_seq" ] if "labels" in encoded_inputs: encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"] if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"] encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input else: raise ValueError("Invalid padding strategy:" + str(self.padding_side)) return encoded_inputs

# coding=utf-8 # Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization class for MarkupLM.""" import json import os from functools import lru_cache from typing import Dict, List, Optional, Tuple, Union import regex as re from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...tokenization_utils_base import ( ENCODE_KWARGS_DOCSTRING, BatchEncoding, EncodedInput, PreTokenizedInput, TextInput, TextInputPair, TruncationStrategy, ) from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/vocab.json", "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/vocab.json", }, "merges_file": { "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/merges.txt", "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/merges.txt", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "microsoft/markuplm-base": 512, "microsoft/markuplm-large": 512, } MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r""" add_special_tokens (`bool`, *optional*, defaults to `True`): Whether or not to encode the sequences with the special tokens relative to their model. padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): Activates and controls truncation. Accepts the following values: - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). max_length (`int`, *optional*): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. stride (`int`, *optional*, defaults to 0): If set to a number along with `max_length`, the overflowing tokens returned when `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). return_tensors (`str` or [`~file_utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. """ @lru_cache() def bytes_to_unicode(): """ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. """ bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): """ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class MarkupLMTokenizer(PreTrainedTokenizer): r""" Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). [`MarkupLMTokenizer`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (RoBERTa tokenizer detect beginning of words by the preceding space). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self, vocab_file, merges_file, tags_dict, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, max_depth=50, max_width=1000, pad_width=1001, pad_token_label=-100, only_label_first_subword=True, **kwargs ): bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token super().__init__( vocab_file=vocab_file, merges_file=merges_file, tags_dict=tags_dict, errors=errors, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, max_depth=max_depth, max_width=max_width, pad_width=pad_width, pad_token_label=pad_token_label, only_label_first_subword=only_label_first_subword, **kwargs, ) with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.tags_dict = tags_dict self.decoder = {v: k for k, v in self.encoder.items()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: bpe_merges = merges_handle.read().split("\n")[1:-1] bpe_merges = [tuple(merge.split()) for merge in bpe_merges] self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {} self.add_prefix_space = add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") # additional properties self.max_depth = max_depth self.max_width = max_width self.pad_width = pad_width self.unk_tag_id = len(self.tags_dict) self.pad_tag_id = self.unk_tag_id + 1 self.pad_xpath_tags_seq = [self.pad_tag_id] * self.max_depth self.pad_xpath_subs_seq = [self.pad_width] * self.max_depth self.pad_token_label = pad_token_label self.only_label_first_subword = only_label_first_subword def get_xpath_seq(self, xpath): """ Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of tag IDs and corresponding subscripts, taking into account max depth. """ xpath_tags_list = [] xpath_subs_list = [] xpath_units = xpath.split("/") for unit in xpath_units: if not unit.strip(): continue name_subs = unit.strip().split("[") tag_name = name_subs[0] sub = 0 if len(name_subs) == 1 else int(name_subs[1][:-1]) xpath_tags_list.append(self.tags_dict.get(tag_name, self.unk_tag_id)) xpath_subs_list.append(min(self.max_width, sub)) xpath_tags_list = xpath_tags_list[: self.max_depth] xpath_subs_list = xpath_tags_list[: self.max_depth] xpath_tags_list += [self.pad_tag_id] * (self.max_depth - len(xpath_tags_list)) xpath_subs_list += [self.pad_width] * (self.max_depth - len(xpath_subs_list)) return xpath_tags_list, xpath_subs_list @property def vocab_size(self): return len(self.encoder) def get_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token) pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) self.cache[token] = word return word def _tokenize(self, text): """Tokenize a string.""" bpe_tokens = [] for token in re.findall(self.pat, text): token = "".join( self.byte_encoder[b] for b in token.encode("utf-8") ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) return bpe_tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" logger.warning( "MarkupLM now does not support generative tasks, decoding is experimental and subject to change." ) text = "".join(tokens) text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) return text def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) merge_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) # save vocab_file with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") # save merge_file index = 0 with open(merge_file, "w", encoding="utf-8") as writer: writer.write("#version: 0.2\n") for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return vocab_file, merge_file def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space) if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()): text = " " + text return (text, kwargs) def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A RoBERTa sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + token_ids_1 + sep def build_xpath_tags_with_special_tokens( self, xpath_tags_0: List[int], xpath_tags_1: Optional[List[int]] = None ) -> List[int]: pad = [self.pad_xpath_tags_seq] if len(xpath_tags_1) == 0: return pad + xpath_tags_0 + pad return pad + xpath_tags_0 + pad + xpath_tags_1 + pad def build_xpath_subs_with_special_tokens( self, xpath_subs_0: List[int], xpath_subs_1: Optional[List[int]] = None ) -> List[int]: pad = [self.pad_xpath_subs_seq] if len(xpath_subs_1) == 0: return pad + xpath_subs_0 + pad return pad + xpath_subs_0 + pad + xpath_subs_1 + pad def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Args: Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + token_ids_1 + sep) * [0] @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def __call__( self, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None, xpaths: Union[List[List[int]], List[List[List[int]]]] = None, node_labels: Optional[Union[List[int], List[List[int]]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with node-level xpaths and optional labels. Args: text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings (nodes of a single example or questions of a batch of examples) or a list of list of strings (batch of nodes). text_pair (`List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence should be a list of strings (pretokenized string). xpaths (`List[List[int]]`, `List[List[List[int]]]`): Node-level xpaths. node_labels (`List[int]`, `List[List[int]]`, *optional*): Node-level integer labels (for token classification tasks). """ # Input type checking for clearer error def _is_valid_text_input(t): if isinstance(t, str): # Strings are fine return True elif isinstance(t, (list, tuple)): # List are fine as long as they are... if len(t) == 0: # ... empty return True elif isinstance(t[0], str): # ... list of strings return True elif isinstance(t[0], (list, tuple)): # ... list with an empty list or with a list of strings return len(t[0]) == 0 or isinstance(t[0][0], str) else: return False else: return False if text_pair is not None: # in case text + text_pair are provided, text = questions, text_pair = nodes if not _is_valid_text_input(text): raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ") if not isinstance(text_pair, (list, tuple)): raise ValueError( "Nodes must be of type `List[str]` (single pretokenized example), " "or `List[List[str]]` (batch of pretokenized examples)." ) else: # in case only text is provided => must be nodes if not isinstance(text, (list, tuple)): raise ValueError( "Nodes must be of type `List[str]` (single pretokenized example), " "or `List[List[str]]` (batch of pretokenized examples)." ) if text_pair is not None: is_batched = isinstance(text, (list, tuple)) else: is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple)) nodes = text if text_pair is None else text_pair assert xpaths is not None, "You must provide corresponding xpaths" if is_batched: assert len(nodes) == len(xpaths), "You must provide nodes and xpaths for an equal amount of examples" for nodes_example, xpaths_example in zip(nodes, xpaths): assert len(nodes_example) == len(xpaths_example), "You must provide as many nodes as there are xpaths" else: assert len(nodes) == len(xpaths), "You must provide as many nodes as there are xpaths" if is_batched: if text_pair is not None and len(text) != len(text_pair): raise ValueError( f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:" f" {len(text_pair)}." ) batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text is_pair = bool(text_pair is not None) return self.batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, is_pair=is_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: return self.encode_plus( text=text, text_pair=text_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def batch_encode_plus( self, batch_text_or_text_pairs: Union[ List[TextInput], List[TextInputPair], List[PreTokenizedInput], ], is_pair: bool = None, xpaths: Optional[List[List[List[int]]]] = None, node_labels: Optional[Union[List[int], List[List[int]]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, is_pair=is_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _batch_encode_plus( self, batch_text_or_text_pairs: Union[ List[TextInput], List[TextInputPair], List[PreTokenizedInput], ], is_pair: bool = None, xpaths: Optional[List[List[List[int]]]] = None, node_labels: Optional[List[List[int]]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast." ) batch_outputs = self._batch_prepare_for_model( batch_text_or_text_pairs=batch_text_or_text_pairs, is_pair=is_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=return_tensors, verbose=verbose, ) return BatchEncoding(batch_outputs) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def _batch_prepare_for_model( self, batch_text_or_text_pairs, is_pair: bool = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[List[int]]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[str] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_length: bool = False, verbose: bool = True, ) -> BatchEncoding: """ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Args: batch_ids_pairs: list of tokenized input ids or input ids pairs """ batch_outputs = {} for idx, example in enumerate(zip(batch_text_or_text_pairs, xpaths)): batch_text_or_text_pair, xpaths_example = example outputs = self.prepare_for_model( batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair, batch_text_or_text_pair[1] if is_pair else None, xpaths_example, node_labels=node_labels[idx] if node_labels is not None else None, add_special_tokens=add_special_tokens, padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward truncation=truncation_strategy.value, max_length=max_length, stride=stride, pad_to_multiple_of=None, # we pad in batch afterward return_attention_mask=False, # we pad in batch afterward return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=None, # We convert the whole batch to tensors at the end prepend_batch_axis=False, verbose=verbose, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) batch_outputs = self.pad( batch_outputs, padding=padding_strategy.value, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors) return batch_outputs @add_end_docstrings(ENCODE_KWARGS_DOCSTRING) def encode( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[PreTokenizedInput] = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[int]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> List[int]: encoded_inputs = self.encode_plus( text=text, text_pair=text_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) return encoded_inputs["input_ids"] @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def encode_plus( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[PreTokenizedInput] = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[int]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated, `__call__` should be used instead. Args: text (`str`, `List[str]`, `List[List[str]]`): The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings. text_pair (`List[str]` or `List[int]`, *optional*): Optional second sequence to be encoded. This can be a list of strings (nodes of a single example) or a list of list of strings (nodes of a batch of examples). """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._encode_plus( text=text, xpaths=xpaths, text_pair=text_pair, node_labels=node_labels, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _encode_plus( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[PreTokenizedInput] = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[int]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast. " "More information on available tokenizers at " "https://github.com/huggingface/transformers/pull/2674" ) return self.prepare_for_model( text=text, text_pair=text_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding_strategy.value, truncation=truncation_strategy.value, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, prepend_batch_axis=True, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, verbose=verbose, ) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def prepare_for_model( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[PreTokenizedInput] = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[int]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, prepend_batch_axis: bool = False, **kwargs ) -> BatchEncoding: """ Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Please Note, for *text_pair* different than `None` and *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an error. Node-level `xpaths` are turned into token-level `xpath_tags_seq` and `xpath_subs_seq`. If provided, node-level `node_labels` are turned into token-level `labels`. The node label is used for the first token of the node, while remaining tokens are labeled with -100, such that they will be ignored by the loss function. Args: text (`str`, `List[str]`, `List[List[str]]`): The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings. text_pair (`List[str]` or `List[int]`, *optional*): Optional second sequence to be encoded. This can be a list of strings (nodes of a single example) or a list of list of strings (nodes of a batch of examples). """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) tokens = [] pair_tokens = [] xpath_tags_seq = [] xpath_subs_seq = [] pair_xpath_tags_seq = [] pair_xpath_subs_seq = [] labels = [] if text_pair is None: if node_labels is None: # CASE 1: web page classification (training + inference) + CASE 2: token classification (inference) for word, xpath in zip(text, xpaths): if len(word) < 1: # skip empty nodes continue word_tokens = self.tokenize(word) tokens.extend(word_tokens) xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath) xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens)) xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens)) else: # CASE 2: token classification (training) for word, xpath, label in zip(text, xpaths, node_labels): if len(word) < 1: # skip empty nodes continue word_tokens = self.tokenize(word) tokens.extend(word_tokens) xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath) xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens)) xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens)) if self.only_label_first_subword: # Use the real label id for the first token of the word, and padding ids for the remaining tokens labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1)) else: labels.extend([label] * len(word_tokens)) else: # CASE 3: web page question answering (inference) # text = question # text_pair = nodes tokens = self.tokenize(text) xpath_tags_seq = [self.pad_xpath_tags_seq for _ in range(len(tokens))] xpath_subs_seq = [self.pad_xpath_subs_seq for _ in range(len(tokens))] for word, xpath in zip(text_pair, xpaths): if len(word) < 1: # skip empty nodes continue word_tokens = self.tokenize(word) pair_tokens.extend(word_tokens) xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath) pair_xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens)) pair_xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens)) # Create ids + pair_ids ids = self.convert_tokens_to_ids(tokens) pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None if ( return_overflowing_tokens and truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is not None ): raise ValueError( "Not possible to return overflowing tokens for pair of sequences with the " "`longest_first`. Please select another truncation strategy than `longest_first`, " "for instance `only_second` or `only_first`." ) # Compute the total size of the returned encodings pair = bool(pair_ids is not None) len_ids = len(ids) len_pair_ids = len(pair_ids) if pair else 0 total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0) # Truncation: Handle max sequence length overflowing_tokens = [] overflowing_xpath_tags_seq = [] overflowing_xpath_subs_seq = [] overflowing_labels = [] if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length: ( ids, xpath_tags_seq, xpath_subs_seq, pair_ids, pair_xpath_tags_seq, pair_xpath_subs_seq, labels, overflowing_tokens, overflowing_xpath_tags_seq, overflowing_xpath_subs_seq, overflowing_labels, ) = self.truncate_sequences( ids, xpath_tags_seq=xpath_tags_seq, xpath_subs_seq=xpath_subs_seq, pair_ids=pair_ids, pair_xpath_tags_seq=pair_xpath_tags_seq, pair_xpath_subs_seq=pair_xpath_subs_seq, labels=labels, num_tokens_to_remove=total_len - max_length, truncation_strategy=truncation_strategy, stride=stride, ) if return_token_type_ids and not add_special_tokens: raise ValueError( "Asking to return token_type_ids while setting add_special_tokens to False " "results in an undefined behavior. Please set add_special_tokens to True or " "set return_token_type_ids to None." ) # Load from model defaults if return_token_type_ids is None: return_token_type_ids = "token_type_ids" in self.model_input_names if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names encoded_inputs = {} if return_overflowing_tokens: encoded_inputs["overflowing_tokens"] = overflowing_tokens encoded_inputs["overflowing_xpath_tags_seq"] = overflowing_xpath_tags_seq encoded_inputs["overflowing_xpath_subs_seq"] = overflowing_xpath_subs_seq encoded_inputs["overflowing_labels"] = overflowing_labels encoded_inputs["num_truncated_tokens"] = total_len - max_length # Add special tokens if add_special_tokens: sequence = self.build_inputs_with_special_tokens(ids, pair_ids) token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids) xpath_tags_ids = self.build_xpath_tags_with_special_tokens(xpath_tags_seq, pair_xpath_tags_seq) xpath_subs_ids = self.build_xpath_subs_with_special_tokens(xpath_subs_seq, pair_xpath_subs_seq) if labels: labels = [self.pad_token_label] + labels + [self.pad_token_label] else: sequence = ids + pair_ids if pair else ids token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else []) xpath_tags_ids = xpath_tags_seq + pair_xpath_tags_seq if pair else xpath_tags_seq xpath_subs_ids = xpath_subs_seq + pair_xpath_subs_seq if pair else xpath_subs_seq # Build output dictionary encoded_inputs["input_ids"] = sequence encoded_inputs["xpath_tags_seq"] = xpath_tags_ids encoded_inputs["xpath_subs_seq"] = xpath_subs_ids if return_token_type_ids: encoded_inputs["token_type_ids"] = token_type_ids if return_special_tokens_mask: if add_special_tokens: encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids) else: encoded_inputs["special_tokens_mask"] = [0] * len(sequence) if labels: encoded_inputs["labels"] = labels # Check lengths self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose) # Padding if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask: encoded_inputs = self.pad( encoded_inputs, max_length=max_length, padding=padding_strategy.value, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) if return_length: encoded_inputs["length"] = len(encoded_inputs["input_ids"]) batch_outputs = BatchEncoding( encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis ) return batch_outputs def truncate_sequences( self, ids: List[int], xpath_tags_seq: List[List[int]], xpath_subs_seq: List[List[int]], pair_ids: Optional[List[int]] = None, pair_xpath_tags_seq: Optional[List[List[int]]] = None, pair_xpath_subs_seq: Optional[List[List[int]]] = None, labels: Optional[List[int]] = None, num_tokens_to_remove: int = 0, truncation_strategy: Union[str, TruncationStrategy] = "longest_first", stride: int = 0, ) -> Tuple[List[int], List[int], List[int]]: """ Args: Truncates a sequence pair in-place following the strategy. ids (`List[int]`): Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. xpath_tags_seq (`List[List[int]]`): XPath tag IDs of the first sequence. xpath_subs_seq (`List[List[int]]`): XPath sub IDs of the first sequence. pair_ids (`List[int]`, *optional*): Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. pair_xpath_tags_seq (`List[List[int]]`, *optional*): XPath tag IDs of the second sequence. pair_xpath_subs_seq (`List[List[int]]`, *optional*): XPath sub IDs of the second sequence. num_tokens_to_remove (`int`, *optional*, defaults to 0): Number of tokens to remove using the truncation strategy. truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): The strategy to follow for truncation. Can be: - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). stride (`int`, *optional*, defaults to 0): If set to a positive number, the overflowing tokens returned will contain some tokens from the main sequence returned. The value of this argument defines the number of additional tokens. Returns: `Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair of sequences (or a batch of pairs) is provided. """ if num_tokens_to_remove <= 0: return ids, xpath_tags_seq, xpath_subs_seq, pair_ids, pair_xpath_tags_seq, pair_xpath_subs_seq, [], [], [] if not isinstance(truncation_strategy, TruncationStrategy): truncation_strategy = TruncationStrategy(truncation_strategy) overflowing_tokens = [] overflowing_xpath_tags_seq = [] overflowing_xpath_subs_seq = [] overflowing_labels = [] if truncation_strategy == TruncationStrategy.ONLY_FIRST or ( truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None ): if len(ids) > num_tokens_to_remove: window_len = min(len(ids), stride + num_tokens_to_remove) overflowing_tokens = ids[-window_len:] overflowing_xpath_tags_seq = xpath_tags_seq[-window_len:] overflowing_xpath_subs_seq = xpath_subs_seq[-window_len:] ids = ids[:-num_tokens_to_remove] xpath_tags_seq = xpath_tags_seq[:-num_tokens_to_remove] xpath_subs_seq = xpath_subs_seq[:-num_tokens_to_remove] labels = labels[:-num_tokens_to_remove] else: error_msg = ( f"We need to remove {num_tokens_to_remove} to truncate the input " f"but the first sequence has a length {len(ids)}. " ) if truncation_strategy == TruncationStrategy.ONLY_FIRST: error_msg = ( error_msg + "Please select another truncation strategy than " f"{truncation_strategy}, for instance 'longest_first' or 'only_second'." ) logger.error(error_msg) elif truncation_strategy == TruncationStrategy.LONGEST_FIRST: logger.warning( "Be aware, overflowing tokens are not returned for the setting you have chosen," f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' " "truncation strategy. So the returned list will always be empty even if some " "tokens have been removed." ) for _ in range(num_tokens_to_remove): if pair_ids is None or len(ids) > len(pair_ids): ids = ids[:-1] xpath_tags_seq = xpath_tags_seq[:-1] xpath_subs_seq = xpath_subs_seq[:-1] labels = labels[:-1] else: pair_ids = pair_ids[:-1] pair_xpath_tags_seq = pair_xpath_tags_seq[:-1] pair_xpath_subs_seq = pair_xpath_subs_seq[:-1] elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None: if len(pair_ids) > num_tokens_to_remove: window_len = min(len(pair_ids), stride + num_tokens_to_remove) overflowing_tokens = pair_ids[-window_len:] overflowing_xpath_tags_seq = pair_xpath_tags_seq[-window_len:] overflowing_xpath_subs_seq = pair_xpath_subs_seq[-window_len:] pair_ids = pair_ids[:-num_tokens_to_remove] pair_xpath_tags_seq = pair_xpath_tags_seq[:-num_tokens_to_remove] pair_xpath_subs_seq = pair_xpath_subs_seq[:-num_tokens_to_remove] else: logger.error( f"We need to remove {num_tokens_to_remove} to truncate the input " f"but the second sequence has a length {len(pair_ids)}. " f"Please select another truncation strategy than {truncation_strategy}, " "for instance 'longest_first' or 'only_first'." ) return ( ids, xpath_tags_seq, xpath_subs_seq, pair_ids, pair_xpath_tags_seq, pair_xpath_subs_seq, labels, overflowing_tokens, overflowing_xpath_tags_seq, overflowing_xpath_subs_seq, overflowing_labels, ) def _pad( self, encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], max_length: Optional[int] = None, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, ) -> dict: """ Args: Pad encoded inputs (on left/right and up to predefined length or max length in the batch) encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). max_length: maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens. padding_strategy: PaddingStrategy to use for padding. - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - PaddingStrategy.DO_NOT_PAD: Do not pad The tokenizer padding sides are defined in self.padding_side: - 'left': pads on the left of the sequences - 'right': pads on the right of the sequences pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability >= 7.5 (Volta). return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) """ # Load from model defaults if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names required_input = encoded_inputs[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: max_length = len(required_input) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length # Initialize attention mask if not present. if return_attention_mask and "attention_mask" not in encoded_inputs: encoded_inputs["attention_mask"] = [1] * len(required_input) if needs_to_be_padded: difference = max_length - len(required_input) if self.padding_side == "right": if return_attention_mask: encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = ( encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference ) if "xpath_tags_seq" in encoded_inputs: encoded_inputs["xpath_tags_seq"] = ( encoded_inputs["xpath_tags_seq"] + [self.pad_xpath_tags_seq] * difference ) if "xpath_subs_seq" in encoded_inputs: encoded_inputs["xpath_subs_seq"] = ( encoded_inputs["xpath_subs_seq"] + [self.pad_xpath_subs_seq] * difference ) if "labels" in encoded_inputs: encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference elif self.padding_side == "left": if return_attention_mask: encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[ "token_type_ids" ] if "xpath_tags_seq" in encoded_inputs: encoded_inputs["xpath_tags_seq"] = [self.pad_xpath_tags_seq] * difference + encoded_inputs[ "xpath_tags_seq" ] if "xpath_subs_seq" in encoded_inputs: encoded_inputs["xpath_subs_seq"] = [self.pad_xpath_subs_seq] * difference + encoded_inputs[ "xpath_subs_seq" ] if "labels" in encoded_inputs: encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"] if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"] encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input else: raise ValueError("Invalid padding strategy:" + str(self.padding_side)) return encoded_inputs

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/markuplm/tokenization_markuplm_fast.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fast tokenization class for MarkupLM. It overwrites 2 methods of the slow tokenizer class, namely _batch_encode_plus and _encode_plus, in which the Rust tokenizer is used. """ import json from functools import lru_cache from typing import Dict, List, Optional, Tuple, Union from tokenizers import pre_tokenizers, processors from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings from ...tokenization_utils_base import ( ENCODE_KWARGS_DOCSTRING, BatchEncoding, EncodedInput, PreTokenizedInput, TextInput, TextInputPair, TruncationStrategy, ) from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_markuplm import MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING, MarkupLMTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/vocab.json", "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/vocab.json", }, "merges_file": { "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/merges.txt", "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/merges.txt", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "microsoft/markuplm-base": 512, "microsoft/markuplm-large": 512, } @lru_cache() def bytes_to_unicode(): """ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. """ bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): """ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class MarkupLMTokenizerFast(PreTrainedTokenizerFast): r""" Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). [`MarkupLMTokenizerFast`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (RoBERTa tokenizer detect beginning of words by the preceding space). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES slow_tokenizer_class = MarkupLMTokenizer def __init__( self, vocab_file, merges_file, tags_dict, tokenizer_file=None, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, max_depth=50, max_width=1000, pad_width=1001, pad_token_label=-100, only_label_first_subword=True, trim_offsets=False, **kwargs ): super().__init__( vocab_file=vocab_file, merges_file=merges_file, tags_dict=tags_dict, tokenizer_file=tokenizer_file, errors=errors, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets, max_depth=max_depth, max_width=max_width, pad_width=pad_width, pad_token_label=pad_token_label, only_label_first_subword=only_label_first_subword, **kwargs, ) if trim_offsets: # Not implemented yet, because we need to chain two post processors which is not possible yet # We need to wait for https://github.com/huggingface/tokenizers/pull/1005 # With `trim_offsets=False` we don't need to do add `processors.ByteLevel(trim_offsets=False)` # because it's not doing anything raise NotImplementedError( "`trim_offsets=True` is not implemented for MarkupLMTokenizerFast. Please set it to False." ) self.tags_dict = tags_dict pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) pre_tok_state["add_prefix_space"] = add_prefix_space self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) self.add_prefix_space = add_prefix_space tokenizer_component = "post_processor" tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None) if tokenizer_component_instance: state = json.loads(tokenizer_component_instance.__getstate__()) # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` if "sep" in state: state["sep"] = tuple(state["sep"]) if "cls" in state: state["cls"] = tuple(state["cls"]) changes_to_apply = False if state.get("add_prefix_space", add_prefix_space) != add_prefix_space: state["add_prefix_space"] = add_prefix_space changes_to_apply = True if changes_to_apply: component_class = getattr(processors, state.pop("type")) new_value = component_class(**state) setattr(self.backend_tokenizer, tokenizer_component, new_value) # additional properties self.max_depth = max_depth self.max_width = max_width self.pad_width = pad_width self.unk_tag_id = len(self.tags_dict) self.pad_tag_id = self.unk_tag_id + 1 self.pad_xpath_tags_seq = [self.pad_tag_id] * self.max_depth self.pad_xpath_subs_seq = [self.pad_width] * self.max_depth self.pad_token_label = pad_token_label self.only_label_first_subword = only_label_first_subword def get_xpath_seq(self, xpath): """ Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of tag IDs and corresponding subscripts, taking into account max depth. """ xpath_tags_list = [] xpath_subs_list = [] xpath_units = xpath.split("/") for unit in xpath_units: if not unit.strip(): continue name_subs = unit.strip().split("[") tag_name = name_subs[0] sub = 0 if len(name_subs) == 1 else int(name_subs[1][:-1]) xpath_tags_list.append(self.tags_dict.get(tag_name, self.unk_tag_id)) xpath_subs_list.append(min(self.max_width, sub)) xpath_tags_list = xpath_tags_list[: self.max_depth] xpath_subs_list = xpath_tags_list[: self.max_depth] xpath_tags_list += [self.pad_tag_id] * (self.max_depth - len(xpath_tags_list)) xpath_subs_list += [self.pad_width] * (self.max_depth - len(xpath_subs_list)) return xpath_tags_list, xpath_subs_list @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def __call__( self, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None, xpaths: Union[List[List[int]], List[List[List[int]]]] = None, node_labels: Optional[Union[List[int], List[List[int]]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with nodes, xpaths and optional labels. Args: text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings (words of a single example or questions of a batch of examples) or a list of list of strings (batch of words). text_pair (`List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence should be a list of strings (pretokenized string). xpaths (`List[List[int]]`, `List[List[List[int]]]`): Node-level xpaths. Each bounding box should be normalized to be on a 0-1000 scale. node_labels (`List[int]`, `List[List[int]]`, *optional*): Node-level integer labels (for token classification tasks). """ # Input type checking for clearer error def _is_valid_text_input(t): if isinstance(t, str): # Strings are fine return True elif isinstance(t, (list, tuple)): # List are fine as long as they are... if len(t) == 0: # ... empty return True elif isinstance(t[0], str): # ... list of strings return True elif isinstance(t[0], (list, tuple)): # ... list with an empty list or with a list of strings return len(t[0]) == 0 or isinstance(t[0][0], str) else: return False else: return False if text_pair is not None: # in case text + text_pair are provided, text = questions, text_pair = nodes if not _is_valid_text_input(text): raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ") if not isinstance(text_pair, (list, tuple)): raise ValueError( "Nodes must be of type `List[str]` (single pretokenized example), " "or `List[List[str]]` (batch of pretokenized examples)." ) else: # in case only text is provided => must be nodes if not isinstance(text, (list, tuple)): raise ValueError( "Nodes must be of type `List[str]` (single pretokenized example), " "or `List[List[str]]` (batch of pretokenized examples)." ) if text_pair is not None: is_batched = isinstance(text, (list, tuple)) else: is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple)) nodes = text if text_pair is None else text_pair assert xpaths is not None, "You must provide corresponding xpaths" if is_batched: assert len(nodes) == len(xpaths), "You must provide nodes and xpaths for an equal amount of examples" for nodes_example, xpaths_example in zip(nodes, xpaths): assert len(nodes_example) == len(xpaths_example), "You must provide as many nodes as there are xpaths" else: assert len(nodes) == len(xpaths), "You must provide as many nodes as there are xpaths" if is_batched: if text_pair is not None and len(text) != len(text_pair): raise ValueError( f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:" f" {len(text_pair)}." ) batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text is_pair = bool(text_pair is not None) return self.batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, is_pair=is_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: return self.encode_plus( text=text, text_pair=text_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def batch_encode_plus( self, batch_text_or_text_pairs: Union[ List[TextInput], List[TextInputPair], List[PreTokenizedInput], ], is_pair: bool = None, xpaths: Optional[List[List[List[int]]]] = None, node_labels: Optional[Union[List[int], List[List[int]]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, is_pair=is_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]: batched_input = [(text, pair)] if pair else [text] encodings = self._tokenizer.encode_batch( batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs ) return encodings[0].tokens @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def encode_plus( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[PreTokenizedInput] = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[int]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated, `__call__` should be used instead. Args: text (`str`, `List[str]`, `List[List[str]]`): The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings. text_pair (`List[str]` or `List[int]`, *optional*): Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a list of list of strings (words of a batch of examples). """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._encode_plus( text=text, xpaths=xpaths, text_pair=text_pair, node_labels=node_labels, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _batch_encode_plus( self, batch_text_or_text_pairs: Union[ List[TextInput], List[TextInputPair], List[PreTokenizedInput], ], is_pair: bool = None, xpaths: Optional[List[List[List[int]]]] = None, node_labels: Optional[List[List[int]]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[str] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, ) -> BatchEncoding: if not isinstance(batch_text_or_text_pairs, list): raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})") # Set the truncation and padding strategy and restore the initial configuration self.set_truncation_and_padding( padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, ) if is_pair: batch_text_or_text_pairs = [([text], text_pair) for text, text_pair in batch_text_or_text_pairs] encodings = self._tokenizer.encode_batch( batch_text_or_text_pairs, add_special_tokens=add_special_tokens, is_pretokenized=True, # we set this to True as MarkupLM always expects pretokenized inputs ) # Convert encoding to dict # `Tokens` is a tuple of (List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]], # List[EncodingFast]) with nested dimensions corresponding to batch, overflows, sequence length tokens_and_encodings = [ self._convert_encoding( encoding=encoding, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=True if node_labels is not None else return_offsets_mapping, # we use offsets to create the labels return_length=return_length, verbose=verbose, ) for encoding in encodings ] # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length) # (we say ~ because the number of overflow varies with the example in the batch) # # To match each overflowing sample with the original sample in the batch # we add an overflow_to_sample_mapping array (see below) sanitized_tokens = {} for key in tokens_and_encodings[0][0].keys(): stack = [e for item, _ in tokens_and_encodings for e in item[key]] sanitized_tokens[key] = stack sanitized_encodings = [e for _, item in tokens_and_encodings for e in item] # If returning overflowing tokens, we need to return a mapping # from the batch idx to the original sample if return_overflowing_tokens: overflow_to_sample_mapping = [] for i, (toks, _) in enumerate(tokens_and_encodings): overflow_to_sample_mapping += [i] * len(toks["input_ids"]) sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping for input_ids in sanitized_tokens["input_ids"]: self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose) # create the token-level xpaths tags and subscripts xpath_tags_seq = [] xpath_subs_seq = [] for batch_index in range(len(sanitized_tokens["input_ids"])): if return_overflowing_tokens: original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index] else: original_index = batch_index xpath_tags_seq_example = [] xpath_subs_seq_example = [] for id, sequence_id, word_id in zip( sanitized_tokens["input_ids"][batch_index], sanitized_encodings[batch_index].sequence_ids, sanitized_encodings[batch_index].word_ids, ): if word_id is not None: if is_pair and sequence_id == 0: xpath_tags_seq_example.append(self.pad_xpath_tags_seq) xpath_subs_seq_example.append(self.pad_xpath_subs_seq) else: xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpaths[original_index][word_id]) xpath_tags_seq_example.extend([xpath_tags_list]) xpath_subs_seq_example.extend([xpath_subs_list]) else: if id in [self.cls_token_id, self.sep_token_id, self.pad_token_id]: xpath_tags_seq_example.append(self.pad_xpath_tags_seq) xpath_subs_seq_example.append(self.pad_xpath_subs_seq) else: raise ValueError("Id not recognized") xpath_tags_seq.append(xpath_tags_seq_example) xpath_subs_seq.append(xpath_subs_seq_example) sanitized_tokens["xpath_tags_seq"] = xpath_tags_seq sanitized_tokens["xpath_subs_seq"] = xpath_subs_seq # optionally, create the labels if node_labels is not None: labels = [] for batch_index in range(len(sanitized_tokens["input_ids"])): if return_overflowing_tokens: original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index] else: original_index = batch_index labels_example = [] for id, offset, word_id in zip( sanitized_tokens["input_ids"][batch_index], sanitized_tokens["offset_mapping"][batch_index], sanitized_encodings[batch_index].word_ids, ): if word_id is not None: if self.only_label_first_subword: if offset[0] == 0: # Use the real label id for the first token of the word, and padding ids for the remaining tokens labels_example.append(node_labels[original_index][word_id]) else: labels_example.append(self.pad_token_label) else: labels_example.append(node_labels[original_index][word_id]) else: labels_example.append(self.pad_token_label) labels.append(labels_example) sanitized_tokens["labels"] = labels # finally, remove offsets if the user didn't want them if not return_offsets_mapping: del sanitized_tokens["offset_mapping"] return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors) def _encode_plus( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[PreTokenizedInput] = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[int]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[bool] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: # make it a batched input # 2 options: # 1) only text, in case text must be a list of str # 2) text + text_pair, in which case text = str and text_pair a list of str batched_input = [(text, text_pair)] if text_pair else [text] batched_xpaths = [xpaths] batched_node_labels = [node_labels] if node_labels is not None else None batched_output = self._batch_encode_plus( batched_input, is_pair=bool(text_pair is not None), xpaths=batched_xpaths, node_labels=batched_node_labels, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) # Return tensor is None, then we can remove the leading batch axis # Overflowing tokens are returned as a batch of output so we keep them in this case if return_tensors is None and not return_overflowing_tokens: batched_output = BatchEncoding( { key: value[0] if len(value) > 0 and isinstance(value[0], list) else value for key, value in batched_output.items() }, batched_output.encodings, ) self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose) return batched_output def _pad( self, encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], max_length: Optional[int] = None, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, ) -> dict: """ Args: Pad encoded inputs (on left/right and up to predefined length or max length in the batch) encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). max_length: maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens. padding_strategy: PaddingStrategy to use for padding. - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - PaddingStrategy.DO_NOT_PAD: Do not pad The tokenizer padding sides are defined in self.padding_side: - 'left': pads on the left of the sequences - 'right': pads on the right of the sequences pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability >= 7.5 (Volta). return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) """ # Load from model defaults if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names required_input = encoded_inputs[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: max_length = len(required_input) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length # Initialize attention mask if not present. if return_attention_mask and "attention_mask" not in encoded_inputs: encoded_inputs["attention_mask"] = [1] * len(required_input) if needs_to_be_padded: difference = max_length - len(required_input) if self.padding_side == "right": if return_attention_mask: encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = ( encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference ) if "xpath_tags_seq" in encoded_inputs: encoded_inputs["xpath_tags_seq"] = ( encoded_inputs["xpath_tags_seq"] + [self.pad_xpath_tags_seq] * difference ) if "xpath_subs_seq" in encoded_inputs: encoded_inputs["xpath_subs_seq"] = ( encoded_inputs["xpath_subs_seq"] + [self.pad_xpath_subs_seq] * difference ) if "labels" in encoded_inputs: encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference elif self.padding_side == "left": if return_attention_mask: encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[ "token_type_ids" ] if "xpath_tags_seq" in encoded_inputs: encoded_inputs["xpath_tags_seq"] = [self.pad_xpath_tags_seq] * difference + encoded_inputs[ "xpath_tags_seq" ] if "xpath_subs_seq" in encoded_inputs: encoded_inputs["xpath_subs_seq"] = [self.pad_xpath_subs_seq] * difference + encoded_inputs[ "xpath_subs_seq" ] if "labels" in encoded_inputs: encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"] if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"] encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input else: raise ValueError("Invalid padding strategy:" + str(self.padding_side)) return encoded_inputs def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A RoBERTa sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + token_ids_1 + sep def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + token_ids_1 + sep) * [0] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fast tokenization class for MarkupLM. It overwrites 2 methods of the slow tokenizer class, namely _batch_encode_plus and _encode_plus, in which the Rust tokenizer is used. """ import json from functools import lru_cache from typing import Dict, List, Optional, Tuple, Union from tokenizers import pre_tokenizers, processors from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings from ...tokenization_utils_base import ( ENCODE_KWARGS_DOCSTRING, BatchEncoding, EncodedInput, PreTokenizedInput, TextInput, TextInputPair, TruncationStrategy, ) from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_markuplm import MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING, MarkupLMTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/vocab.json", "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/vocab.json", }, "merges_file": { "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/merges.txt", "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/merges.txt", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "microsoft/markuplm-base": 512, "microsoft/markuplm-large": 512, } @lru_cache() def bytes_to_unicode(): """ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. """ bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): """ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class MarkupLMTokenizerFast(PreTrainedTokenizerFast): r""" Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). [`MarkupLMTokenizerFast`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (RoBERTa tokenizer detect beginning of words by the preceding space). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES slow_tokenizer_class = MarkupLMTokenizer def __init__( self, vocab_file, merges_file, tags_dict, tokenizer_file=None, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, max_depth=50, max_width=1000, pad_width=1001, pad_token_label=-100, only_label_first_subword=True, trim_offsets=False, **kwargs ): super().__init__( vocab_file=vocab_file, merges_file=merges_file, tags_dict=tags_dict, tokenizer_file=tokenizer_file, errors=errors, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets, max_depth=max_depth, max_width=max_width, pad_width=pad_width, pad_token_label=pad_token_label, only_label_first_subword=only_label_first_subword, **kwargs, ) if trim_offsets: # Not implemented yet, because we need to chain two post processors which is not possible yet # We need to wait for https://github.com/huggingface/tokenizers/pull/1005 # With `trim_offsets=False` we don't need to do add `processors.ByteLevel(trim_offsets=False)` # because it's not doing anything raise NotImplementedError( "`trim_offsets=True` is not implemented for MarkupLMTokenizerFast. Please set it to False." ) self.tags_dict = tags_dict pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) pre_tok_state["add_prefix_space"] = add_prefix_space self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) self.add_prefix_space = add_prefix_space tokenizer_component = "post_processor" tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None) if tokenizer_component_instance: state = json.loads(tokenizer_component_instance.__getstate__()) # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` if "sep" in state: state["sep"] = tuple(state["sep"]) if "cls" in state: state["cls"] = tuple(state["cls"]) changes_to_apply = False if state.get("add_prefix_space", add_prefix_space) != add_prefix_space: state["add_prefix_space"] = add_prefix_space changes_to_apply = True if changes_to_apply: component_class = getattr(processors, state.pop("type")) new_value = component_class(**state) setattr(self.backend_tokenizer, tokenizer_component, new_value) # additional properties self.max_depth = max_depth self.max_width = max_width self.pad_width = pad_width self.unk_tag_id = len(self.tags_dict) self.pad_tag_id = self.unk_tag_id + 1 self.pad_xpath_tags_seq = [self.pad_tag_id] * self.max_depth self.pad_xpath_subs_seq = [self.pad_width] * self.max_depth self.pad_token_label = pad_token_label self.only_label_first_subword = only_label_first_subword def get_xpath_seq(self, xpath): """ Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of tag IDs and corresponding subscripts, taking into account max depth. """ xpath_tags_list = [] xpath_subs_list = [] xpath_units = xpath.split("/") for unit in xpath_units: if not unit.strip(): continue name_subs = unit.strip().split("[") tag_name = name_subs[0] sub = 0 if len(name_subs) == 1 else int(name_subs[1][:-1]) xpath_tags_list.append(self.tags_dict.get(tag_name, self.unk_tag_id)) xpath_subs_list.append(min(self.max_width, sub)) xpath_tags_list = xpath_tags_list[: self.max_depth] xpath_subs_list = xpath_tags_list[: self.max_depth] xpath_tags_list += [self.pad_tag_id] * (self.max_depth - len(xpath_tags_list)) xpath_subs_list += [self.pad_width] * (self.max_depth - len(xpath_subs_list)) return xpath_tags_list, xpath_subs_list @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def __call__( self, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None, xpaths: Union[List[List[int]], List[List[List[int]]]] = None, node_labels: Optional[Union[List[int], List[List[int]]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences with nodes, xpaths and optional labels. Args: text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings (words of a single example or questions of a batch of examples) or a list of list of strings (batch of words). text_pair (`List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence should be a list of strings (pretokenized string). xpaths (`List[List[int]]`, `List[List[List[int]]]`): Node-level xpaths. Each bounding box should be normalized to be on a 0-1000 scale. node_labels (`List[int]`, `List[List[int]]`, *optional*): Node-level integer labels (for token classification tasks). """ # Input type checking for clearer error def _is_valid_text_input(t): if isinstance(t, str): # Strings are fine return True elif isinstance(t, (list, tuple)): # List are fine as long as they are... if len(t) == 0: # ... empty return True elif isinstance(t[0], str): # ... list of strings return True elif isinstance(t[0], (list, tuple)): # ... list with an empty list or with a list of strings return len(t[0]) == 0 or isinstance(t[0][0], str) else: return False else: return False if text_pair is not None: # in case text + text_pair are provided, text = questions, text_pair = nodes if not _is_valid_text_input(text): raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ") if not isinstance(text_pair, (list, tuple)): raise ValueError( "Nodes must be of type `List[str]` (single pretokenized example), " "or `List[List[str]]` (batch of pretokenized examples)." ) else: # in case only text is provided => must be nodes if not isinstance(text, (list, tuple)): raise ValueError( "Nodes must be of type `List[str]` (single pretokenized example), " "or `List[List[str]]` (batch of pretokenized examples)." ) if text_pair is not None: is_batched = isinstance(text, (list, tuple)) else: is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple)) nodes = text if text_pair is None else text_pair assert xpaths is not None, "You must provide corresponding xpaths" if is_batched: assert len(nodes) == len(xpaths), "You must provide nodes and xpaths for an equal amount of examples" for nodes_example, xpaths_example in zip(nodes, xpaths): assert len(nodes_example) == len(xpaths_example), "You must provide as many nodes as there are xpaths" else: assert len(nodes) == len(xpaths), "You must provide as many nodes as there are xpaths" if is_batched: if text_pair is not None and len(text) != len(text_pair): raise ValueError( f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:" f" {len(text_pair)}." ) batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text is_pair = bool(text_pair is not None) return self.batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, is_pair=is_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: return self.encode_plus( text=text, text_pair=text_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def batch_encode_plus( self, batch_text_or_text_pairs: Union[ List[TextInput], List[TextInputPair], List[PreTokenizedInput], ], is_pair: bool = None, xpaths: Optional[List[List[List[int]]]] = None, node_labels: Optional[Union[List[int], List[List[int]]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._batch_encode_plus( batch_text_or_text_pairs=batch_text_or_text_pairs, is_pair=is_pair, xpaths=xpaths, node_labels=node_labels, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]: batched_input = [(text, pair)] if pair else [text] encodings = self._tokenizer.encode_batch( batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs ) return encodings[0].tokens @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def encode_plus( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[PreTokenizedInput] = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[int]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated, `__call__` should be used instead. Args: text (`str`, `List[str]`, `List[List[str]]`): The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings. text_pair (`List[str]` or `List[int]`, *optional*): Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a list of list of strings (words of a batch of examples). """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._encode_plus( text=text, xpaths=xpaths, text_pair=text_pair, node_labels=node_labels, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _batch_encode_plus( self, batch_text_or_text_pairs: Union[ List[TextInput], List[TextInputPair], List[PreTokenizedInput], ], is_pair: bool = None, xpaths: Optional[List[List[List[int]]]] = None, node_labels: Optional[List[List[int]]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[str] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, ) -> BatchEncoding: if not isinstance(batch_text_or_text_pairs, list): raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})") # Set the truncation and padding strategy and restore the initial configuration self.set_truncation_and_padding( padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, ) if is_pair: batch_text_or_text_pairs = [([text], text_pair) for text, text_pair in batch_text_or_text_pairs] encodings = self._tokenizer.encode_batch( batch_text_or_text_pairs, add_special_tokens=add_special_tokens, is_pretokenized=True, # we set this to True as MarkupLM always expects pretokenized inputs ) # Convert encoding to dict # `Tokens` is a tuple of (List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]], # List[EncodingFast]) with nested dimensions corresponding to batch, overflows, sequence length tokens_and_encodings = [ self._convert_encoding( encoding=encoding, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=True if node_labels is not None else return_offsets_mapping, # we use offsets to create the labels return_length=return_length, verbose=verbose, ) for encoding in encodings ] # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length) # (we say ~ because the number of overflow varies with the example in the batch) # # To match each overflowing sample with the original sample in the batch # we add an overflow_to_sample_mapping array (see below) sanitized_tokens = {} for key in tokens_and_encodings[0][0].keys(): stack = [e for item, _ in tokens_and_encodings for e in item[key]] sanitized_tokens[key] = stack sanitized_encodings = [e for _, item in tokens_and_encodings for e in item] # If returning overflowing tokens, we need to return a mapping # from the batch idx to the original sample if return_overflowing_tokens: overflow_to_sample_mapping = [] for i, (toks, _) in enumerate(tokens_and_encodings): overflow_to_sample_mapping += [i] * len(toks["input_ids"]) sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping for input_ids in sanitized_tokens["input_ids"]: self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose) # create the token-level xpaths tags and subscripts xpath_tags_seq = [] xpath_subs_seq = [] for batch_index in range(len(sanitized_tokens["input_ids"])): if return_overflowing_tokens: original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index] else: original_index = batch_index xpath_tags_seq_example = [] xpath_subs_seq_example = [] for id, sequence_id, word_id in zip( sanitized_tokens["input_ids"][batch_index], sanitized_encodings[batch_index].sequence_ids, sanitized_encodings[batch_index].word_ids, ): if word_id is not None: if is_pair and sequence_id == 0: xpath_tags_seq_example.append(self.pad_xpath_tags_seq) xpath_subs_seq_example.append(self.pad_xpath_subs_seq) else: xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpaths[original_index][word_id]) xpath_tags_seq_example.extend([xpath_tags_list]) xpath_subs_seq_example.extend([xpath_subs_list]) else: if id in [self.cls_token_id, self.sep_token_id, self.pad_token_id]: xpath_tags_seq_example.append(self.pad_xpath_tags_seq) xpath_subs_seq_example.append(self.pad_xpath_subs_seq) else: raise ValueError("Id not recognized") xpath_tags_seq.append(xpath_tags_seq_example) xpath_subs_seq.append(xpath_subs_seq_example) sanitized_tokens["xpath_tags_seq"] = xpath_tags_seq sanitized_tokens["xpath_subs_seq"] = xpath_subs_seq # optionally, create the labels if node_labels is not None: labels = [] for batch_index in range(len(sanitized_tokens["input_ids"])): if return_overflowing_tokens: original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index] else: original_index = batch_index labels_example = [] for id, offset, word_id in zip( sanitized_tokens["input_ids"][batch_index], sanitized_tokens["offset_mapping"][batch_index], sanitized_encodings[batch_index].word_ids, ): if word_id is not None: if self.only_label_first_subword: if offset[0] == 0: # Use the real label id for the first token of the word, and padding ids for the remaining tokens labels_example.append(node_labels[original_index][word_id]) else: labels_example.append(self.pad_token_label) else: labels_example.append(node_labels[original_index][word_id]) else: labels_example.append(self.pad_token_label) labels.append(labels_example) sanitized_tokens["labels"] = labels # finally, remove offsets if the user didn't want them if not return_offsets_mapping: del sanitized_tokens["offset_mapping"] return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors) def _encode_plus( self, text: Union[TextInput, PreTokenizedInput], text_pair: Optional[PreTokenizedInput] = None, xpaths: Optional[List[List[int]]] = None, node_labels: Optional[List[int]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[bool] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: # make it a batched input # 2 options: # 1) only text, in case text must be a list of str # 2) text + text_pair, in which case text = str and text_pair a list of str batched_input = [(text, text_pair)] if text_pair else [text] batched_xpaths = [xpaths] batched_node_labels = [node_labels] if node_labels is not None else None batched_output = self._batch_encode_plus( batched_input, is_pair=bool(text_pair is not None), xpaths=batched_xpaths, node_labels=batched_node_labels, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) # Return tensor is None, then we can remove the leading batch axis # Overflowing tokens are returned as a batch of output so we keep them in this case if return_tensors is None and not return_overflowing_tokens: batched_output = BatchEncoding( { key: value[0] if len(value) > 0 and isinstance(value[0], list) else value for key, value in batched_output.items() }, batched_output.encodings, ) self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose) return batched_output def _pad( self, encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], max_length: Optional[int] = None, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, ) -> dict: """ Args: Pad encoded inputs (on left/right and up to predefined length or max length in the batch) encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). max_length: maximum length of the returned list and optionally padding length (see below). Will truncate by taking into account the special tokens. padding_strategy: PaddingStrategy to use for padding. - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - PaddingStrategy.DO_NOT_PAD: Do not pad The tokenizer padding sides are defined in self.padding_side: - 'left': pads on the left of the sequences - 'right': pads on the right of the sequences pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability >= 7.5 (Volta). return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics) """ # Load from model defaults if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names required_input = encoded_inputs[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: max_length = len(required_input) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length # Initialize attention mask if not present. if return_attention_mask and "attention_mask" not in encoded_inputs: encoded_inputs["attention_mask"] = [1] * len(required_input) if needs_to_be_padded: difference = max_length - len(required_input) if self.padding_side == "right": if return_attention_mask: encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = ( encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference ) if "xpath_tags_seq" in encoded_inputs: encoded_inputs["xpath_tags_seq"] = ( encoded_inputs["xpath_tags_seq"] + [self.pad_xpath_tags_seq] * difference ) if "xpath_subs_seq" in encoded_inputs: encoded_inputs["xpath_subs_seq"] = ( encoded_inputs["xpath_subs_seq"] + [self.pad_xpath_subs_seq] * difference ) if "labels" in encoded_inputs: encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference elif self.padding_side == "left": if return_attention_mask: encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] if "token_type_ids" in encoded_inputs: encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[ "token_type_ids" ] if "xpath_tags_seq" in encoded_inputs: encoded_inputs["xpath_tags_seq"] = [self.pad_xpath_tags_seq] * difference + encoded_inputs[ "xpath_tags_seq" ] if "xpath_subs_seq" in encoded_inputs: encoded_inputs["xpath_subs_seq"] = [self.pad_xpath_subs_seq] * difference + encoded_inputs[ "xpath_subs_seq" ] if "labels" in encoded_inputs: encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"] if "special_tokens_mask" in encoded_inputs: encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"] encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input else: raise ValueError("Invalid padding strategy:" + str(self.padding_side)) return encoded_inputs def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A RoBERTa sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + token_ids_1 + sep def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + token_ids_1 + sep) * [0] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/tapex/tokenization_tapex.py

# coding=utf-8 # Copyright 2022 Microsoft Research and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for TAPEX.""" import json import os import random from functools import lru_cache from typing import Dict, List, Optional, Tuple, Union import regex as re from ...file_utils import ExplicitEnum, PaddingStrategy, TensorType, add_end_docstrings, is_pandas_available from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...tokenization_utils_base import ENCODE_KWARGS_DOCSTRING, BatchEncoding, TextInput, TruncationStrategy from ...utils import logging if is_pandas_available(): import pandas as pd logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "microsoft/tapex-base": "https://huggingface.co/microsoft/tapex-base/resolve/main/vocab.json", }, "merges_file": { "microsoft/tapex-base": "https://huggingface.co/microsoft/tapex-base/resolve/main/merges.txt", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "microsoft/tapex-base": 512, } PRETRAINED_INIT_CONFIGURATION = { "microsoft/tapex-base": {"do_lower_case": True}, } class TapexTruncationStrategy(ExplicitEnum): """ Possible values for the `truncation` argument in [`~TapasTokenizer.__call__`]. Useful for tab-completion in an IDE. """ DROP_ROWS_TO_FIT = "drop_rows_to_fit" TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r""" add_special_tokens (`bool`, *optional*, defaults to `True`): Whether or not to encode the sequences with the special tokens relative to their model. padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (`bool`, `str`, [`TapexTruncationStrategy`] or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): Activates and controls truncation. Accepts the following values: - `'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate row by row, removing rows from the table. - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). max_length (`int`, *optional*): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. stride (`int`, *optional*, defaults to 0): If set to a number along with `max_length`, the overflowing tokens returned when `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). return_tensors (`str` or [`~file_utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. """ @lru_cache() def bytes_to_unicode(): """ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. """ bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): """ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class IndexedRowTableLinearize: """ FORMAT: col: col1 | col2 | col 3 row 1 : val1 | val2 | val3 row 2 : ... """ def process_table(self, table_content: Dict): """ Given a table, TableLinearize aims at converting it into a flatten sequence with special symbols. """ assert "header" in table_content and "rows" in table_content, self.PROMPT_MESSAGE # process header table_str = self.process_header(table_content["header"]) + " " # process rows for i, row_example in enumerate(table_content["rows"]): # NOTE: the row should start from row 1 instead of 0 table_str += self.process_row(row_example, row_index=i + 1) + " " return table_str.strip() def process_header(self, headers: List): """ Given a list of headers, TableLinearize aims at converting it into a flatten sequence with special symbols. """ return "col : " + " | ".join(headers) def process_row(self, row: List, row_index: int): """ Given a row, TableLinearize aims at converting it into a flatten sequence with special symbols. """ row_str = "" row_cell_values = [] for cell_value in row: if isinstance(cell_value, int): row_cell_values.append(str(cell_value)) else: row_cell_values.append(cell_value) row_str += " | ".join(row_cell_values) return "row " + str(row_index) + " : " + row_str class TapexTokenizer(PreTrainedTokenizer): r""" Construct a TAPEX tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). This tokenizer can be used to flatten one or more table(s) and concatenate them with one or more related sentences to be used by TAPEX models. The format that the TAPEX tokenizer creates is the following: sentence col: col1 | col2 | col 3 row 1 : val1 | val2 | val3 row 2 : ... The tokenizer supports a single table + single query, a single table and multiple queries (in which case the table will be duplicated for every query), a single query and multiple tables (in which case the query will be duplicated for every table), and multiple tables and queries. In other words, you can provide a batch of tables + questions to the tokenizer for instance to prepare them for the model. Tokenization itself is based on the BPE algorithm. It is identical to the one used by BART, RoBERTa and GPT-2. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. do_lower_case (`bool`, *optional*, defaults to `True`): Whether or not to lowercase the input when tokenizing. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (BART tokenizer detect beginning of words by the preceding space). max_cell_length (`int`, *optional*, defaults to 15): Maximum number of characters per cell when linearizing a table. If this number is exceeded, truncation takes place. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, merges_file, do_lower_case=True, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, max_cell_length=15, **kwargs ): bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token super().__init__( vocab_file=vocab_file, merges_file=merges_file, do_lower_case=do_lower_case, errors=errors, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, max_cell_length=max_cell_length, **kwargs, ) with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: bpe_merges = merges_handle.read().split("\n")[1:-1] bpe_merges = [tuple(merge.split()) for merge in bpe_merges] self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {} self.add_prefix_space = add_prefix_space self.do_lower_case = do_lower_case # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") # additional properties self.max_cell_length = max_cell_length self.table_linearize = IndexedRowTableLinearize() def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A TAPEX sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + sep + token_ids_1 + sep def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Args: Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Args: Create a mask from the two sequences passed to be used in a sequence-pair classification task. TAPEX does not: make use of token type ids, therefore a list of zeros is returned. token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space) if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()): text = " " + text return (text, kwargs) @property def vocab_size(self): return len(self.encoder) def get_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token) pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) self.cache[token] = word return word def _tokenize(self, text): """Tokenize a string.""" bpe_tokens = [] for token in re.findall(self.pat, text): token = "".join( self.byte_encoder[b] for b in token.encode("utf-8") ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) return bpe_tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" text = "".join(tokens) text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) return text def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) merge_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") index = 0 with open(merge_file, "w", encoding="utf-8") as writer: writer.write("#version: 0.2\n") for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return vocab_file, merge_file @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def __call__( self, table: Union["pd.DataFrame", List["pd.DataFrame"]] = None, query: Optional[Union[TextInput, List[TextInput]]] = None, answer: Union[str, List[str]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several table-sequence pair(s). Args: table (`pd.DataFrame`, `List[pd.DataFrame]`): Table(s) containing tabular data. query (`str` or `List[str]`, *optional*): Sentence or batch of sentences related to one or more table(s) to be encoded. Note that the number of sentences must match the number of tables. answer (`str` or `List[str]`, *optional*): Optionally, the corresponding answer to the questions as supervision. """ if table is not None: return self.source_call_func( table=table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) elif answer is not None: return self.target_call_func( answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: raise ValueError("You need to provide either a `table` or an `answer`.") def source_call_func( self, table: Union["pd.DataFrame", List["pd.DataFrame"]], query: Optional[Union[TextInput, List[TextInput]]] = None, answer: Union[str, List[str]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: # Input type checking for clearer error valid_table = False valid_query = False # Check that table have a valid type if isinstance(table, pd.DataFrame): valid_table = True elif isinstance(table, (list, tuple)) and isinstance(table[0], pd.DataFrame): valid_table = True # Check that query have a valid type if query is None or isinstance(query, str): valid_query = True elif isinstance(query, (list, tuple)): if len(query) == 0 or isinstance(query[0], str): valid_query = True if not valid_table: raise ValueError( "table input must of type `pd.DataFrame` (single example), `List[pd.DataFrame]` (batch of examples). " ) if not valid_query: raise ValueError("query input must of type `str` (single example), `List[str]` (batch of examples). ") is_batched = isinstance(table, (list, tuple)) or isinstance(query, (list, tuple)) if is_batched: return self.batch_encode_plus( table=table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: return self.encode_plus( table=table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def batch_encode_plus( self, table: Union["pd.DataFrame", List["pd.DataFrame"]], query: Optional[List[TextInput]] = None, answer: List[str] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str] = None, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ <Tip warning={true}> This method is deprecated, `__call__` should be used instead. </Tip> """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._batch_encode_plus( table=table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _batch_encode_plus( self, table: Union["pd.DataFrame", List["pd.DataFrame"]], query: Optional[List[TextInput]] = None, answer: Optional[List[str]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast." ) if isinstance(table, pd.DataFrame) and isinstance(query, (list, tuple)): # single table, many queries case # duplicate table for every query table = [table] * len(query) if isinstance(table, (list, tuple)) and isinstance(query, str): # many tables, single query case # duplicate query for every table query = [query] * len(table) batch_outputs = self._batch_prepare_for_model( table=table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=return_tensors, verbose=verbose, ) return BatchEncoding(batch_outputs) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def _batch_prepare_for_model( self, table: Union["pd.DataFrame", List["pd.DataFrame"]], query: Optional[Union[TextInput, List[TextInput]]] = None, answer: Optional[Union[str, List[str]]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[str] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_length: bool = False, verbose: bool = True, ) -> BatchEncoding: """ This method adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. """ batch_outputs = {} if answer is None: answer = [None] * len(table) for _table, _query, _answer in zip(table, query, answer): text = self.prepare_table_query( _table, _query, _answer, truncation_strategy=truncation_strategy, max_length=max_length ) if self.do_lower_case: text = text.lower() tokens = self.tokenize(text) outputs = self.prepare_for_model( ids=self.convert_tokens_to_ids(tokens), add_special_tokens=add_special_tokens, padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterwards truncation=truncation_strategy.value, max_length=max_length, stride=stride, pad_to_multiple_of=None, # we pad in batch afterwards return_attention_mask=False, # we pad in batch afterwards return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=None, # We convert the whole batch to tensors at the end prepend_batch_axis=False, verbose=verbose, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) batch_outputs = self.pad( batch_outputs, padding=padding_strategy.value, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors) return batch_outputs @add_end_docstrings(ENCODE_KWARGS_DOCSTRING) def encode( self, table: "pd.DataFrame", query: Optional[TextInput] = None, answer: Optional[str] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy, TapexTruncationStrategy] = None, max_length: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs ) -> List[int]: """ Prepare a table, a string and possible answer for the model. This method does not return token type IDs, attention masks, etc. which are necessary for the model to work correctly. Use this method if you want to build your processing on your own, otherwise refer to `__call__`. """ encoded_inputs = self.encode_plus( table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, return_tensors=return_tensors, **kwargs, ) return encoded_inputs["input_ids"] @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def encode_plus( self, table: "pd.DataFrame", query: Optional[TextInput] = None, answer: Optional[str] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str] = None, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._encode_plus( table=table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _encode_plus( self, table: "pd.DataFrame", query: Optional[TextInput] = None, answer: Optional[str] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast. " "More information on available tokenizers at " "https://github.com/huggingface/transformers/pull/2674" ) text = self.prepare_table_query( table, query, answer, truncation_strategy=truncation_strategy, max_length=max_length ) # if necessary, perform lower case if self.do_lower_case: text = text.lower() tokens = self.tokenize(text) return self.prepare_for_model( ids=self.convert_tokens_to_ids(tokens), add_special_tokens=add_special_tokens, padding=padding_strategy.value, truncation=truncation_strategy.value, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, prepend_batch_axis=True, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, verbose=verbose, ) def target_call_func( self, answer: Union[str, List[str]], add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ The method tokenizes and prepares the answer label for the model. Args: answer (`str` or `List[str]`): Corresponding answer supervision to the queries for training the model. """ is_batched = isinstance(answer, (list, tuple)) if is_batched: return self.target_batch_encode_plus( answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: return self.target_encode_plus( answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def target_batch_encode_plus( self, answer: List[str], add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str] = None, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Prepare answer strings for the model. Args: answer `List[str]`: Corresponding answer supervision to the queries for training the model. """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._target_batch_encode_plus( answer=answer, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _target_batch_encode_plus( self, answer: List[str], add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: batch_outputs = {} for text in answer: if self.do_lower_case: text = text.lower() tokens = self.tokenize(text) outputs = self.prepare_for_model( ids=self.convert_tokens_to_ids(tokens), add_special_tokens=add_special_tokens, padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterwards truncation=truncation_strategy.value, max_length=max_length, stride=stride, pad_to_multiple_of=None, # we pad in batch afterwards return_attention_mask=False, # we pad in batch afterwards return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=None, # We convert the whole batch to tensors at the end prepend_batch_axis=False, verbose=verbose, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) batch_outputs = self.pad( batch_outputs, padding=padding_strategy.value, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors) return BatchEncoding(batch_outputs) def target_encode( self, answer: str, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy, TapexTruncationStrategy] = None, max_length: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs ) -> List[int]: """ Prepare the answer string for the model. This method does not return token type IDs, attention masks, etc. which are necessary for the model to work correctly. Use this method if you want to build your processing on your own, otherwise refer to `__call__`. Args: answer `str`: Corresponding answer supervision to the queries for training the model """ encoded_outputs = self.target_encode_plus( answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, return_tensors=return_tensors, **kwargs, ) return encoded_outputs["input_ids"] def target_encode_plus( self, answer: str, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str] = None, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Prepare a answer string for the model. Args: answer `str`: Corresponding answer supervision to the queries for training the model. """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._target_encode_plus( answer=answer, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _target_encode_plus( self, answer: str, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast. " "More information on available tokenizers at " "https://github.com/huggingface/transformers/pull/2674" ) text = answer # if necessary, perform lower case if self.do_lower_case: text = text.lower() tokens = self.tokenize(text) return self.prepare_for_model( ids=self.convert_tokens_to_ids(tokens), add_special_tokens=add_special_tokens, padding=padding_strategy.value, truncation=truncation_strategy.value, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, prepend_batch_axis=True, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, verbose=verbose, ) def prepare_table_query( self, table, query, answer=None, truncation_strategy=Union[str, TruncationStrategy, TapexTruncationStrategy], max_length=None, ): """ This method can be used to linearize a table and add a corresponding query. Optionally, it also handles truncation of the table (cells). An answer can be provided for more precise truncation. """ if not table.empty: # step 1: create table dictionary table_content = {"header": list(table.columns), "rows": [list(row.values) for i, row in table.iterrows()]} # step 2: modify table internally # always truncate table cells based on self.max_cell_length # optionally truncate rows if truncation_strategy is set to it self.truncate_table_cells(table_content, query, answer) if truncation_strategy == TapexTruncationStrategy.DROP_ROWS_TO_FIT: self.truncate_table_rows(table_content, query, answer, max_length=max_length) # step 3: linearize table linear_table = self.table_linearize.process_table(table_content) else: linear_table = "" if linear_table == "": logger.warning( "You provide an empty table, or all cells contain much tokens (e.g., >= 1024 tokens). " + f"Please carefully check the corresponding table with the query : {query}." ) if query == "": logger.warning("You provide nothing to query with respect to the table.") # step 4: concatenate query with linear_table separator = " " if query and linear_table else "" joint_input = (query + separator + linear_table) if query else linear_table return joint_input def truncate_table_cells(self, table_content: Dict, question: str, answer: List): # TODO (Qian): is it possible to revert the original cell if it is in the final answer? cell_mapping = {} for row in table_content["rows"]: for i, cell in enumerate(row): truncate_cell = self.truncate_cell(cell) if truncate_cell is not None: cell_mapping[cell] = truncate_cell row[i] = truncate_cell # modify the answer list if answer is not None: for i, case in enumerate(answer): if case in cell_mapping.keys(): answer[i] = cell_mapping[case] def truncate_cell(self, cell_value): # do not process on these cases if isinstance(cell_value, int) or isinstance(cell_value, float): return cell_value if cell_value.strip() != "": try_tokens = self.tokenize(cell_value) if len(try_tokens) >= self.max_cell_length: retain_tokens = try_tokens[: self.max_cell_length] retain_cell_value = self.convert_tokens_to_string(retain_tokens) return retain_cell_value else: return None else: return cell_value def truncate_table_rows( self, table_content: Dict, question: str, answer: Optional[Union[str, List[str]]] = None, max_length=None ): """ Args: table_content: {"header": xxx, "rows": xxx, "id" (Optionally): xxx} question: natural language sentence answer: if for training, is the supervision; otherwise will be empty """ delete_ratio, remain_token_len = self.estimate_delete_ratio(table_content, question, max_length) # randomly delete unrelated rows self.delete_unrelated_rows(table_content, question, answer, delete_ratio) # guarantee the result < max_length maximum_keep_rows = 0 for ind, row_example in enumerate(table_content["rows"]): value_string = self.table_linearize.process_row(row_example, ind + 1) value_token_len = len(self.tokenize(value_string)) # over the size limit, and take action if value_token_len > remain_token_len: break remain_token_len -= value_token_len maximum_keep_rows += 1 del table_content["rows"][maximum_keep_rows:] def estimate_delete_ratio(self, table_content: Dict, question: str, max_length=None): if "header" not in table_content or "rows" not in table_content: raise ValueError("The table content should contain both 'header' and 'rows' keys.") # calculate the tokens of header, special tokens will only be pre-prepended into question question_tokens = self.tokenize(question, add_special_tokens=True) # calculate the tokens of header header_string = self.table_linearize.process_header(table_content["header"]) header_tokens = self.tokenize(header_string, add_special_tokens=False) # split all cell values into tokens and see how many can be accommodated used_token_len = len(question_tokens) + len(header_tokens) # remaining token space for rows remain_token_len = max_length - used_token_len value_string = "" for _, row_example in enumerate(table_content["rows"]): # use a general index to roughly estimate the overall token len value_string += self.table_linearize.process_row(row_example, 100) + " " value_token_len = len(self.tokenize(value_string)) if value_token_len < remain_token_len: # no row will be deleted return 0.0, remain_token_len else: # calc a roughly delete rate return 1.0 - remain_token_len / value_token_len, remain_token_len def delete_unrelated_rows(self, table_content: Dict, question: str, answer: List, delete_ratio: float): """ The argument answer is used only during training. """ truncated_unrelated_indices = [] related_indices = [] if answer is None or len(answer) == 0: answer_set = set([]) else: answer_set = set([ans_ex.lower() for ans_ex in answer]) # add question key words into answer set if question is not None: answer_set.update(question.split()) question_set = set(question.strip("?!.,").split(" ")) row_max_len = len(table_content["rows"]) for _row_idx, row in enumerate(table_content["rows"]): lower_row = set([str(cell).lower() for cell in row]) if len(lower_row & answer_set) == 0 and len(lower_row & question_set) == 0: truncated_unrelated_indices.append(_row_idx) else: # add neighbours to preserve information aggressively related_indices.extend([_row_idx - 2, _row_idx - 1, _row_idx, _row_idx + 1, _row_idx + 2]) # remove the neighbours truncated_unrelated_indices = [ _row_idx for _row_idx in truncated_unrelated_indices if _row_idx not in related_indices ] # select some cases to drop drop_items = min(len(truncated_unrelated_indices), int(len(table_content["rows"]) * delete_ratio)) drop_row_indices = random.choices(truncated_unrelated_indices, k=drop_items) for _row_idx in reversed(range(row_max_len)): if _row_idx in drop_row_indices: del table_content["rows"][_row_idx] # only when the drop ratio is too large, logging for warning. if "id" in table_content and len(drop_row_indices) > 0: logger.warning("Delete {:.2f} rows in table {}".format(len(drop_row_indices), table_content["id"]))

# coding=utf-8 # Copyright 2022 Microsoft Research and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for TAPEX.""" import json import os import random from functools import lru_cache from typing import Dict, List, Optional, Tuple, Union import regex as re from ...file_utils import ExplicitEnum, PaddingStrategy, TensorType, add_end_docstrings, is_pandas_available from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...tokenization_utils_base import ENCODE_KWARGS_DOCSTRING, BatchEncoding, TextInput, TruncationStrategy from ...utils import logging if is_pandas_available(): import pandas as pd logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "microsoft/tapex-base": "https://huggingface.co/microsoft/tapex-base/resolve/main/vocab.json", }, "merges_file": { "microsoft/tapex-base": "https://huggingface.co/microsoft/tapex-base/resolve/main/merges.txt", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "microsoft/tapex-base": 512, } PRETRAINED_INIT_CONFIGURATION = { "microsoft/tapex-base": {"do_lower_case": True}, } class TapexTruncationStrategy(ExplicitEnum): """ Possible values for the `truncation` argument in [`~TapasTokenizer.__call__`]. Useful for tab-completion in an IDE. """ DROP_ROWS_TO_FIT = "drop_rows_to_fit" TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r""" add_special_tokens (`bool`, *optional*, defaults to `True`): Whether or not to encode the sequences with the special tokens relative to their model. padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (`bool`, `str`, [`TapexTruncationStrategy`] or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): Activates and controls truncation. Accepts the following values: - `'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate row by row, removing rows from the table. - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). max_length (`int`, *optional*): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. stride (`int`, *optional*, defaults to 0): If set to a number along with `max_length`, the overflowing tokens returned when `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). return_tensors (`str` or [`~file_utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. """ @lru_cache() def bytes_to_unicode(): """ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. """ bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): """ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class IndexedRowTableLinearize: """ FORMAT: col: col1 | col2 | col 3 row 1 : val1 | val2 | val3 row 2 : ... """ def process_table(self, table_content: Dict): """ Given a table, TableLinearize aims at converting it into a flatten sequence with special symbols. """ assert "header" in table_content and "rows" in table_content, self.PROMPT_MESSAGE # process header table_str = self.process_header(table_content["header"]) + " " # process rows for i, row_example in enumerate(table_content["rows"]): # NOTE: the row should start from row 1 instead of 0 table_str += self.process_row(row_example, row_index=i + 1) + " " return table_str.strip() def process_header(self, headers: List): """ Given a list of headers, TableLinearize aims at converting it into a flatten sequence with special symbols. """ return "col : " + " | ".join(headers) def process_row(self, row: List, row_index: int): """ Given a row, TableLinearize aims at converting it into a flatten sequence with special symbols. """ row_str = "" row_cell_values = [] for cell_value in row: if isinstance(cell_value, int): row_cell_values.append(str(cell_value)) else: row_cell_values.append(cell_value) row_str += " | ".join(row_cell_values) return "row " + str(row_index) + " : " + row_str class TapexTokenizer(PreTrainedTokenizer): r""" Construct a TAPEX tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). This tokenizer can be used to flatten one or more table(s) and concatenate them with one or more related sentences to be used by TAPEX models. The format that the TAPEX tokenizer creates is the following: sentence col: col1 | col2 | col 3 row 1 : val1 | val2 | val3 row 2 : ... The tokenizer supports a single table + single query, a single table and multiple queries (in which case the table will be duplicated for every query), a single query and multiple tables (in which case the query will be duplicated for every table), and multiple tables and queries. In other words, you can provide a batch of tables + questions to the tokenizer for instance to prepare them for the model. Tokenization itself is based on the BPE algorithm. It is identical to the one used by BART, RoBERTa and GPT-2. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. do_lower_case (`bool`, *optional*, defaults to `True`): Whether or not to lowercase the input when tokenizing. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (BART tokenizer detect beginning of words by the preceding space). max_cell_length (`int`, *optional*, defaults to 15): Maximum number of characters per cell when linearizing a table. If this number is exceeded, truncation takes place. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, merges_file, do_lower_case=True, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, max_cell_length=15, **kwargs ): bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token super().__init__( vocab_file=vocab_file, merges_file=merges_file, do_lower_case=do_lower_case, errors=errors, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, max_cell_length=max_cell_length, **kwargs, ) with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: bpe_merges = merges_handle.read().split("\n")[1:-1] bpe_merges = [tuple(merge.split()) for merge in bpe_merges] self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {} self.add_prefix_space = add_prefix_space self.do_lower_case = do_lower_case # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") # additional properties self.max_cell_length = max_cell_length self.table_linearize = IndexedRowTableLinearize() def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A TAPEX sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + sep + token_ids_1 + sep def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Args: Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Args: Create a mask from the two sequences passed to be used in a sequence-pair classification task. TAPEX does not: make use of token type ids, therefore a list of zeros is returned. token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space) if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()): text = " " + text return (text, kwargs) @property def vocab_size(self): return len(self.encoder) def get_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token) pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) self.cache[token] = word return word def _tokenize(self, text): """Tokenize a string.""" bpe_tokens = [] for token in re.findall(self.pat, text): token = "".join( self.byte_encoder[b] for b in token.encode("utf-8") ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) return bpe_tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" text = "".join(tokens) text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) return text def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) merge_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") index = 0 with open(merge_file, "w", encoding="utf-8") as writer: writer.write("#version: 0.2\n") for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return vocab_file, merge_file @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def __call__( self, table: Union["pd.DataFrame", List["pd.DataFrame"]] = None, query: Optional[Union[TextInput, List[TextInput]]] = None, answer: Union[str, List[str]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several table-sequence pair(s). Args: table (`pd.DataFrame`, `List[pd.DataFrame]`): Table(s) containing tabular data. query (`str` or `List[str]`, *optional*): Sentence or batch of sentences related to one or more table(s) to be encoded. Note that the number of sentences must match the number of tables. answer (`str` or `List[str]`, *optional*): Optionally, the corresponding answer to the questions as supervision. """ if table is not None: return self.source_call_func( table=table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) elif answer is not None: return self.target_call_func( answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: raise ValueError("You need to provide either a `table` or an `answer`.") def source_call_func( self, table: Union["pd.DataFrame", List["pd.DataFrame"]], query: Optional[Union[TextInput, List[TextInput]]] = None, answer: Union[str, List[str]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: # Input type checking for clearer error valid_table = False valid_query = False # Check that table have a valid type if isinstance(table, pd.DataFrame): valid_table = True elif isinstance(table, (list, tuple)) and isinstance(table[0], pd.DataFrame): valid_table = True # Check that query have a valid type if query is None or isinstance(query, str): valid_query = True elif isinstance(query, (list, tuple)): if len(query) == 0 or isinstance(query[0], str): valid_query = True if not valid_table: raise ValueError( "table input must of type `pd.DataFrame` (single example), `List[pd.DataFrame]` (batch of examples). " ) if not valid_query: raise ValueError("query input must of type `str` (single example), `List[str]` (batch of examples). ") is_batched = isinstance(table, (list, tuple)) or isinstance(query, (list, tuple)) if is_batched: return self.batch_encode_plus( table=table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: return self.encode_plus( table=table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def batch_encode_plus( self, table: Union["pd.DataFrame", List["pd.DataFrame"]], query: Optional[List[TextInput]] = None, answer: List[str] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str] = None, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ <Tip warning={true}> This method is deprecated, `__call__` should be used instead. </Tip> """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._batch_encode_plus( table=table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _batch_encode_plus( self, table: Union["pd.DataFrame", List["pd.DataFrame"]], query: Optional[List[TextInput]] = None, answer: Optional[List[str]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast." ) if isinstance(table, pd.DataFrame) and isinstance(query, (list, tuple)): # single table, many queries case # duplicate table for every query table = [table] * len(query) if isinstance(table, (list, tuple)) and isinstance(query, str): # many tables, single query case # duplicate query for every table query = [query] * len(table) batch_outputs = self._batch_prepare_for_model( table=table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=return_tensors, verbose=verbose, ) return BatchEncoding(batch_outputs) @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def _batch_prepare_for_model( self, table: Union["pd.DataFrame", List["pd.DataFrame"]], query: Optional[Union[TextInput, List[TextInput]]] = None, answer: Optional[Union[str, List[str]]] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[str] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_length: bool = False, verbose: bool = True, ) -> BatchEncoding: """ This method adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. """ batch_outputs = {} if answer is None: answer = [None] * len(table) for _table, _query, _answer in zip(table, query, answer): text = self.prepare_table_query( _table, _query, _answer, truncation_strategy=truncation_strategy, max_length=max_length ) if self.do_lower_case: text = text.lower() tokens = self.tokenize(text) outputs = self.prepare_for_model( ids=self.convert_tokens_to_ids(tokens), add_special_tokens=add_special_tokens, padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterwards truncation=truncation_strategy.value, max_length=max_length, stride=stride, pad_to_multiple_of=None, # we pad in batch afterwards return_attention_mask=False, # we pad in batch afterwards return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=None, # We convert the whole batch to tensors at the end prepend_batch_axis=False, verbose=verbose, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) batch_outputs = self.pad( batch_outputs, padding=padding_strategy.value, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors) return batch_outputs @add_end_docstrings(ENCODE_KWARGS_DOCSTRING) def encode( self, table: "pd.DataFrame", query: Optional[TextInput] = None, answer: Optional[str] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy, TapexTruncationStrategy] = None, max_length: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs ) -> List[int]: """ Prepare a table, a string and possible answer for the model. This method does not return token type IDs, attention masks, etc. which are necessary for the model to work correctly. Use this method if you want to build your processing on your own, otherwise refer to `__call__`. """ encoded_inputs = self.encode_plus( table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, return_tensors=return_tensors, **kwargs, ) return encoded_inputs["input_ids"] @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) def encode_plus( self, table: "pd.DataFrame", query: Optional[TextInput] = None, answer: Optional[str] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str] = None, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._encode_plus( table=table, query=query, answer=answer, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _encode_plus( self, table: "pd.DataFrame", query: Optional[TextInput] = None, answer: Optional[str] = None, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast. " "More information on available tokenizers at " "https://github.com/huggingface/transformers/pull/2674" ) text = self.prepare_table_query( table, query, answer, truncation_strategy=truncation_strategy, max_length=max_length ) # if necessary, perform lower case if self.do_lower_case: text = text.lower() tokens = self.tokenize(text) return self.prepare_for_model( ids=self.convert_tokens_to_ids(tokens), add_special_tokens=add_special_tokens, padding=padding_strategy.value, truncation=truncation_strategy.value, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, prepend_batch_axis=True, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, verbose=verbose, ) def target_call_func( self, answer: Union[str, List[str]], add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = None, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ The method tokenizes and prepares the answer label for the model. Args: answer (`str` or `List[str]`): Corresponding answer supervision to the queries for training the model. """ is_batched = isinstance(answer, (list, tuple)) if is_batched: return self.target_batch_encode_plus( answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) else: return self.target_encode_plus( answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def target_batch_encode_plus( self, answer: List[str], add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str] = None, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Prepare answer strings for the model. Args: answer `List[str]`: Corresponding answer supervision to the queries for training the model. """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._target_batch_encode_plus( answer=answer, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _target_batch_encode_plus( self, answer: List[str], add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: batch_outputs = {} for text in answer: if self.do_lower_case: text = text.lower() tokens = self.tokenize(text) outputs = self.prepare_for_model( ids=self.convert_tokens_to_ids(tokens), add_special_tokens=add_special_tokens, padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterwards truncation=truncation_strategy.value, max_length=max_length, stride=stride, pad_to_multiple_of=None, # we pad in batch afterwards return_attention_mask=False, # we pad in batch afterwards return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=None, # We convert the whole batch to tensors at the end prepend_batch_axis=False, verbose=verbose, ) for key, value in outputs.items(): if key not in batch_outputs: batch_outputs[key] = [] batch_outputs[key].append(value) batch_outputs = self.pad( batch_outputs, padding=padding_strategy.value, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors) return BatchEncoding(batch_outputs) def target_encode( self, answer: str, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy, TapexTruncationStrategy] = None, max_length: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs ) -> List[int]: """ Prepare the answer string for the model. This method does not return token type IDs, attention masks, etc. which are necessary for the model to work correctly. Use this method if you want to build your processing on your own, otherwise refer to `__call__`. Args: answer `str`: Corresponding answer supervision to the queries for training the model """ encoded_outputs = self.target_encode_plus( answer=answer, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, return_tensors=return_tensors, **kwargs, ) return encoded_outputs["input_ids"] def target_encode_plus( self, answer: str, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str] = None, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Prepare a answer string for the model. Args: answer `str`: Corresponding answer supervision to the queries for training the model. """ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( padding=padding, truncation=truncation, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, verbose=verbose, **kwargs, ) return self._target_encode_plus( answer=answer, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs, ) def _target_encode_plus( self, answer: str, add_special_tokens: bool = True, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, **kwargs ) -> BatchEncoding: if return_offsets_mapping: raise NotImplementedError( "return_offset_mapping is not available when using Python tokenizers. " "To use this feature, change your tokenizer to one deriving from " "transformers.PreTrainedTokenizerFast. " "More information on available tokenizers at " "https://github.com/huggingface/transformers/pull/2674" ) text = answer # if necessary, perform lower case if self.do_lower_case: text = text.lower() tokens = self.tokenize(text) return self.prepare_for_model( ids=self.convert_tokens_to_ids(tokens), add_special_tokens=add_special_tokens, padding=padding_strategy.value, truncation=truncation_strategy.value, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_tensors=return_tensors, prepend_batch_axis=True, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, verbose=verbose, ) def prepare_table_query( self, table, query, answer=None, truncation_strategy=Union[str, TruncationStrategy, TapexTruncationStrategy], max_length=None, ): """ This method can be used to linearize a table and add a corresponding query. Optionally, it also handles truncation of the table (cells). An answer can be provided for more precise truncation. """ if not table.empty: # step 1: create table dictionary table_content = {"header": list(table.columns), "rows": [list(row.values) for i, row in table.iterrows()]} # step 2: modify table internally # always truncate table cells based on self.max_cell_length # optionally truncate rows if truncation_strategy is set to it self.truncate_table_cells(table_content, query, answer) if truncation_strategy == TapexTruncationStrategy.DROP_ROWS_TO_FIT: self.truncate_table_rows(table_content, query, answer, max_length=max_length) # step 3: linearize table linear_table = self.table_linearize.process_table(table_content) else: linear_table = "" if linear_table == "": logger.warning( "You provide an empty table, or all cells contain much tokens (e.g., >= 1024 tokens). " + f"Please carefully check the corresponding table with the query : {query}." ) if query == "": logger.warning("You provide nothing to query with respect to the table.") # step 4: concatenate query with linear_table separator = " " if query and linear_table else "" joint_input = (query + separator + linear_table) if query else linear_table return joint_input def truncate_table_cells(self, table_content: Dict, question: str, answer: List): # TODO (Qian): is it possible to revert the original cell if it is in the final answer? cell_mapping = {} for row in table_content["rows"]: for i, cell in enumerate(row): truncate_cell = self.truncate_cell(cell) if truncate_cell is not None: cell_mapping[cell] = truncate_cell row[i] = truncate_cell # modify the answer list if answer is not None: for i, case in enumerate(answer): if case in cell_mapping.keys(): answer[i] = cell_mapping[case] def truncate_cell(self, cell_value): # do not process on these cases if isinstance(cell_value, int) or isinstance(cell_value, float): return cell_value if cell_value.strip() != "": try_tokens = self.tokenize(cell_value) if len(try_tokens) >= self.max_cell_length: retain_tokens = try_tokens[: self.max_cell_length] retain_cell_value = self.convert_tokens_to_string(retain_tokens) return retain_cell_value else: return None else: return cell_value def truncate_table_rows( self, table_content: Dict, question: str, answer: Optional[Union[str, List[str]]] = None, max_length=None ): """ Args: table_content: {"header": xxx, "rows": xxx, "id" (Optionally): xxx} question: natural language sentence answer: if for training, is the supervision; otherwise will be empty """ delete_ratio, remain_token_len = self.estimate_delete_ratio(table_content, question, max_length) # randomly delete unrelated rows self.delete_unrelated_rows(table_content, question, answer, delete_ratio) # guarantee the result < max_length maximum_keep_rows = 0 for ind, row_example in enumerate(table_content["rows"]): value_string = self.table_linearize.process_row(row_example, ind + 1) value_token_len = len(self.tokenize(value_string)) # over the size limit, and take action if value_token_len > remain_token_len: break remain_token_len -= value_token_len maximum_keep_rows += 1 del table_content["rows"][maximum_keep_rows:] def estimate_delete_ratio(self, table_content: Dict, question: str, max_length=None): if "header" not in table_content or "rows" not in table_content: raise ValueError("The table content should contain both 'header' and 'rows' keys.") # calculate the tokens of header, special tokens will only be pre-prepended into question question_tokens = self.tokenize(question, add_special_tokens=True) # calculate the tokens of header header_string = self.table_linearize.process_header(table_content["header"]) header_tokens = self.tokenize(header_string, add_special_tokens=False) # split all cell values into tokens and see how many can be accommodated used_token_len = len(question_tokens) + len(header_tokens) # remaining token space for rows remain_token_len = max_length - used_token_len value_string = "" for _, row_example in enumerate(table_content["rows"]): # use a general index to roughly estimate the overall token len value_string += self.table_linearize.process_row(row_example, 100) + " " value_token_len = len(self.tokenize(value_string)) if value_token_len < remain_token_len: # no row will be deleted return 0.0, remain_token_len else: # calc a roughly delete rate return 1.0 - remain_token_len / value_token_len, remain_token_len def delete_unrelated_rows(self, table_content: Dict, question: str, answer: List, delete_ratio: float): """ The argument answer is used only during training. """ truncated_unrelated_indices = [] related_indices = [] if answer is None or len(answer) == 0: answer_set = set([]) else: answer_set = set([ans_ex.lower() for ans_ex in answer]) # add question key words into answer set if question is not None: answer_set.update(question.split()) question_set = set(question.strip("?!.,").split(" ")) row_max_len = len(table_content["rows"]) for _row_idx, row in enumerate(table_content["rows"]): lower_row = set([str(cell).lower() for cell in row]) if len(lower_row & answer_set) == 0 and len(lower_row & question_set) == 0: truncated_unrelated_indices.append(_row_idx) else: # add neighbours to preserve information aggressively related_indices.extend([_row_idx - 2, _row_idx - 1, _row_idx, _row_idx + 1, _row_idx + 2]) # remove the neighbours truncated_unrelated_indices = [ _row_idx for _row_idx in truncated_unrelated_indices if _row_idx not in related_indices ] # select some cases to drop drop_items = min(len(truncated_unrelated_indices), int(len(table_content["rows"]) * delete_ratio)) drop_row_indices = random.choices(truncated_unrelated_indices, k=drop_items) for _row_idx in reversed(range(row_max_len)): if _row_idx in drop_row_indices: del table_content["rows"][_row_idx] # only when the drop ratio is too large, logging for warning. if "id" in table_content and len(drop_row_indices) > 0: logger.warning("Delete {:.2f} rows in table {}".format(len(drop_row_indices), table_content["id"]))

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/test_modeling_tf_common.py

# coding=utf-8 # Copyright 2019 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import inspect import json import os import random import tempfile import unittest import unittest.mock as mock from dataclasses import fields from importlib import import_module from math import isnan from typing import List, Tuple, get_type_hints from datasets import Dataset from huggingface_hub import HfFolder, Repository, delete_repo, set_access_token from huggingface_hub.file_download import http_get from requests.exceptions import HTTPError from transformers import is_tf_available, is_torch_available from transformers.configuration_utils import PretrainedConfig from transformers.models.auto import get_values from transformers.testing_utils import ( # noqa: F401 TOKEN, USER, CaptureLogger, CaptureStdout, _tf_gpu_memory_limit, is_pt_tf_cross_test, is_staging_test, require_safetensors, require_tf, require_tf2onnx, slow, tooslow, torch_device, ) from transformers.utils import SAFE_WEIGHTS_NAME, TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging from transformers.utils.generic import ModelOutput logger = logging.get_logger(__name__) if is_tf_available(): import h5py import numpy as np import tensorflow as tf from transformers import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, BertConfig, RagRetriever, TFAutoModel, TFAutoModelForSequenceClassification, TFBertModel, TFRagModel, TFSharedEmbeddings, ) from transformers.generation import ( TFBeamSampleDecoderOnlyOutput, TFBeamSampleEncoderDecoderOutput, TFBeamSearchDecoderOnlyOutput, TFBeamSearchEncoderDecoderOutput, TFGreedySearchDecoderOnlyOutput, TFGreedySearchEncoderDecoderOutput, TFSampleDecoderOnlyOutput, TFSampleEncoderDecoderOutput, ) from transformers.modeling_tf_utils import tf_shard_checkpoint, unpack_inputs from transformers.tf_utils import stable_softmax if _tf_gpu_memory_limit is not None: gpus = tf.config.list_physical_devices("GPU") for gpu in gpus: # Restrict TensorFlow to only allocate x GB of memory on the GPUs try: tf.config.set_logical_device_configuration( gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)] ) logical_gpus = tf.config.list_logical_devices("GPU") print("Logical GPUs", logical_gpus) except RuntimeError as e: # Virtual devices must be set before GPUs have been initialized print(e) if is_torch_available(): import torch from transformers import BertModel def _config_zero_init(config): configs_no_init = copy.deepcopy(config) for key in configs_no_init.__dict__.keys(): if "_range" in key or "_std" in key: setattr(configs_no_init, key, 0.0) return configs_no_init def _return_type_has_loss(model): return_type = get_type_hints(model.call) if "return" not in return_type: return False return_type = return_type["return"] if hasattr(return_type, "__args__"): # Awkward check for union because UnionType only turns up in 3.10 for type_annotation in return_type.__args__: if inspect.isclass(type_annotation) and issubclass(type_annotation, ModelOutput): field_names = [field.name for field in fields(type_annotation)] if "loss" in field_names: return True return False elif isinstance(return_type, tuple): return False elif isinstance(return_type, ModelOutput): class_fields = fields(return_type) return "loss" in class_fields return False @require_tf class TFModelTesterMixin: model_tester = None all_model_classes = () all_generative_model_classes = () test_mismatched_shapes = True test_resize_embeddings = True test_head_masking = True is_encoder_decoder = False has_attentions = True def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict: inputs_dict = copy.deepcopy(inputs_dict) if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict = { k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)) if isinstance(v, tf.Tensor) and v.ndim > 0 else v for k, v in inputs_dict.items() } if return_labels: if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32) elif model_class in [ *get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING), *get_values(TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING), ]: inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) elif model_class in [ *get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING), *get_values(TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING), ]: inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) elif model_class in get_values(TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING): inputs_dict["next_sentence_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) elif model_class in [ *get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING), *get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING), *get_values(TF_MODEL_FOR_MASKED_LM_MAPPING), *get_values(TF_MODEL_FOR_PRETRAINING_MAPPING), *get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING), *get_values(TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING), ] and "labels" in dict(inspect.signature(model_class.call).parameters): inputs_dict["labels"] = tf.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32 ) elif model_class in get_values(TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING): num_patches = self.model_tester.image_size // self.model_tester.patch_size inputs_dict["bool_masked_pos"] = tf.zeros( (self.model_tester.batch_size, num_patches**2), dtype=tf.int32 ) elif model_class in get_values(TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING): batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape inputs_dict["labels"] = tf.zeros((self.model_tester.batch_size, height, width), dtype=tf.int32) elif model_class.__name__.endswith("ForCTC"): # When we have enough CTC models for an AutoClass, we should use their mapping instead of name checks inputs_dict["labels"] = tf.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32 ) return inputs_dict def test_initialization(self): pass def test_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=False) model = model_class.from_pretrained(tmpdirname) after_outputs = model(self._prepare_for_class(inputs_dict, model_class)) self.assert_outputs_same(after_outputs, outputs) def test_save_load_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) model_config = model.get_config() # make sure that returned config is jsonifiable, which is required by keras json.dumps(model_config) new_model = model_class.from_config(model.get_config()) # make sure it also accepts a normal config _ = model_class.from_config(model.config) _ = new_model(self._prepare_for_class(inputs_dict, model_class)) # Build model new_model.set_weights(model.get_weights()) after_outputs = new_model(self._prepare_for_class(inputs_dict, model_class)) self.assert_outputs_same(after_outputs, outputs) @slow def test_saved_model_creation(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = False config.output_attentions = False if hasattr(config, "use_cache"): config.use_cache = False model_class = self.all_model_classes[0] class_inputs_dict = self._prepare_for_class(inputs_dict, model_class) model = model_class(config) model(class_inputs_dict) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=True) saved_model_dir = os.path.join(tmpdirname, "saved_model", "1") self.assertTrue(os.path.exists(saved_model_dir)) def test_prepare_serving_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = self.has_attentions for model_class in self.all_model_classes: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(inputs) serving_outputs = model.serving_output(outputs) for k, v in serving_outputs.items(): # Check that we have one of three possible outputs: None, tuple of tensors or a tensor if isinstance(v, tuple): self.assertTrue(all(isinstance(elem, tf.Tensor) for elem in v)) elif v is not None: self.assertIsInstance(v, tf.Tensor) else: self.assertIsNone(v) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.call) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] if model.config.is_encoder_decoder: expected_arg_names = [ "input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend(["decoder_position_ids"] if "decoder_position_ids" in arg_names else []) expected_arg_names.extend( ["head_mask", "decoder_head_mask"] if "head_mask" and "decoder_head_mask" in arg_names else [] ) expected_arg_names.extend( ["cross_attn_head_mask", "encoder_outputs"] if "cross_attn_head_mask" in arg_names else ["encoder_outputs"] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) else: expected_arg_names = ["input_ids"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_onnx_compliancy(self): if not self.test_onnx: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() INTERNAL_OPS = [ "Assert", "AssignVariableOp", "EmptyTensorList", "ReadVariableOp", "ResourceGather", "TruncatedNormal", "VarHandleOp", "VarIsInitializedOp", ] onnx_ops = [] with open(os.path.join(".", "utils", "tf_ops", "onnx.json")) as f: onnx_opsets = json.load(f)["opsets"] for i in range(1, self.onnx_min_opset + 1): onnx_ops.extend(onnx_opsets[str(i)]) for model_class in self.all_model_classes: model_op_names = set() with tf.Graph().as_default() as g: model = model_class(config) model(model.dummy_inputs) for op in g.get_operations(): model_op_names.add(op.node_def.op) model_op_names = sorted(model_op_names) incompatible_ops = [] for op in model_op_names: if op not in onnx_ops and op not in INTERNAL_OPS: incompatible_ops.append(op) self.assertEqual(len(incompatible_ops), 0, incompatible_ops) @require_tf2onnx @slow def test_onnx_runtime_optimize(self): if not self.test_onnx: return import onnxruntime import tf2onnx config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model(model.dummy_inputs) onnx_model_proto, _ = tf2onnx.convert.from_keras(model, opset=self.onnx_min_opset) onnxruntime.InferenceSession(onnx_model_proto.SerializeToString()) def test_keras_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() tf_main_layer_classes = set( module_member for model_class in self.all_model_classes for module in (import_module(model_class.__module__),) for module_member_name in dir(module) if module_member_name.endswith("MainLayer") # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`. and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")] for module_member in (getattr(module, module_member_name),) if isinstance(module_member, type) and tf.keras.layers.Layer in module_member.__bases__ and getattr(module_member, "_keras_serializable", False) ) for main_layer_class in tf_main_layer_classes: # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter if "T5" in main_layer_class.__name__: # Take the same values than in TFT5ModelTester for this shared layer shared = TFSharedEmbeddings(99, 32, name="shared") config.use_cache = inputs_dict.pop("use_cache", None) main_layer = main_layer_class(config, embed_tokens=shared) else: main_layer = main_layer_class(config) symbolic_inputs = { name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items() } model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs)) outputs = model(inputs_dict) with tempfile.TemporaryDirectory() as tmpdirname: filepath = os.path.join(tmpdirname, "keras_model.h5") model.save(filepath) if "T5" in main_layer_class.__name__: model = tf.keras.models.load_model( filepath, custom_objects={ main_layer_class.__name__: main_layer_class, "TFSharedEmbeddings": TFSharedEmbeddings, }, ) else: model = tf.keras.models.load_model( filepath, custom_objects={main_layer_class.__name__: main_layer_class} ) assert isinstance(model, tf.keras.Model) after_outputs = model(inputs_dict) self.assert_outputs_same(after_outputs, outputs) def assert_outputs_same(self, after_outputs, outputs): # Make sure we don't have nans if isinstance(after_outputs, tf.Tensor): out_1 = after_outputs.numpy() elif isinstance(after_outputs, dict): out_1 = after_outputs[list(after_outputs.keys())[0]].numpy() else: out_1 = after_outputs[0].numpy() out_2 = outputs[0].numpy() self.assertEqual(out_1.shape, out_2.shape) out_1 = out_1[~np.isnan(out_1)] out_2 = out_2[~np.isnan(out_2)] max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) # Don't copy this method to model specific test file! # TODO: remove this method once the issues are all fixed! def _make_attention_mask_non_null(self, inputs_dict): """Make sure no sequence has all zeros as attention mask""" for k in ["attention_mask", "encoder_attention_mask", "decoder_attention_mask"]: if k in inputs_dict: attention_mask = inputs_dict[k] # Make sure no all 0s attention masks - to avoid failure at this moment. # Put `1` at the beginning of sequences to make it still work when combining causal attention masks. # TODO: remove this line once a fix regarding large negative values for attention mask is done. attention_mask = tf.concat( [tf.ones_like(attention_mask[:, :1], dtype=attention_mask.dtype), attention_mask[:, 1:]], axis=-1 ) # Here we make the first sequence with all 0s as attention mask. # Currently, this will fail for `TFWav2Vec2Model`. This is caused by the different large negative # values, like `1e-4`, `1e-9`, `1e-30` and `-inf` for attention mask across models/frameworks. # TODO: enable this block once the large negative values thing is cleaned up. # (see https://github.com/huggingface/transformers/issues/14859) # attention_mask = tf.concat( # [ # tf.zeros_like(attention_mask[:1], dtype=tf.int32), # tf.cast(attention_mask[1:], dtype=tf.int32) # ], # axis=0 # ) inputs_dict[k] = attention_mask # Don't copy this method to model specific test file! # TODO: remove this method once the issues are all fixed! def _postprocessing_to_ignore_test_cases(self, tf_outputs, pt_outputs, model_class): """For temporarily ignoring some failed test cases (issues to be fixed)""" tf_keys = set([k for k, v in tf_outputs.items() if v is not None]) pt_keys = set([k for k, v in pt_outputs.items() if v is not None]) key_differences = tf_keys.symmetric_difference(pt_keys) if model_class.__name__ in [ "TFFlaubertWithLMHeadModel", "TFFunnelForPreTraining", "TFElectraForPreTraining", "TFXLMWithLMHeadModel", "TFTransfoXLLMHeadModel", ]: for k in key_differences: if k in ["loss", "losses"]: tf_keys.discard(k) pt_keys.discard(k) elif model_class.__name__.startswith("TFGPT2"): # `TFGPT2` has `past_key_values` as a tensor while `GPT2` has it as a tuple. tf_keys.discard("past_key_values") pt_keys.discard("past_key_values") # create new outputs from the remaining fields new_tf_outputs = type(tf_outputs)(**{k: tf_outputs[k] for k in tf_keys}) new_pt_outputs = type(pt_outputs)(**{k: pt_outputs[k] for k in pt_keys}) return new_tf_outputs, new_pt_outputs def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None): """Check the outputs from PyTorch and TensorFlow models are close enough. Checks are done in a recursive way. Args: model_class: The class of the model that is currently testing. For example, `TFBertModel`, TFBertForMaskedLM`, `TFBertForSequenceClassification`, etc. Mainly used for providing more informative error messages. name (`str`): The name of the output. For example, `output.hidden_states`, `output.attentions`, etc. attributes (`Tuple[str]`): The names of the output's element if the output is a tuple/list with each element being a named field in the output. """ self.assertEqual(type(name), str) if attributes is not None: self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`") # Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`). if isinstance(tf_outputs, ModelOutput): self.assertTrue( isinstance(pt_outputs, ModelOutput), f"{name}: `pt_outputs` should an instance of `ModelOutput` when `tf_outputs` is", ) # Don't copy this block to model specific test file! # TODO: remove this method and this line after issues are fixed tf_outputs, pt_outputs = self._postprocessing_to_ignore_test_cases(tf_outputs, pt_outputs, model_class) tf_keys = [k for k, v in tf_outputs.items() if v is not None] pt_keys = [k for k, v in pt_outputs.items() if v is not None] self.assertEqual(tf_keys, pt_keys, f"{name}: Output keys differ between TF and PyTorch") # convert to the case of `tuple` # appending each key to the current (string) `names` attributes = tuple([f"{name}.{k}" for k in tf_keys]) self.check_pt_tf_outputs( tf_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes ) # Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.) elif type(tf_outputs) in [tuple, list]: self.assertEqual(type(tf_outputs), type(pt_outputs), f"{name}: Output types differ between TF and PyTorch") self.assertEqual(len(tf_outputs), len(pt_outputs), f"{name}: Output lengths differ between TF and PyTorch") if attributes is not None: # case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`) self.assertEqual( len(attributes), len(tf_outputs), f"{name}: The tuple `names` should have the same length as `tf_outputs`", ) else: # case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `names` attributes = tuple([f"{name}_{idx}" for idx in range(len(tf_outputs))]) for tf_output, pt_output, attr in zip(tf_outputs, pt_outputs, attributes): self.check_pt_tf_outputs(tf_output, pt_output, model_class, tol=tol, name=attr) elif isinstance(tf_outputs, tf.Tensor): self.assertTrue( isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `tf_outputs` is" ) tf_outputs = tf_outputs.numpy() pt_outputs = pt_outputs.detach().to("cpu").numpy() self.assertEqual( tf_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between TF and PyTorch" ) # deal with NumPy's scalars to make replacing nan values by 0 work. if np.isscalar(tf_outputs): tf_outputs = np.array([tf_outputs]) pt_outputs = np.array([pt_outputs]) tf_nans = np.isnan(tf_outputs) pt_nans = np.isnan(pt_outputs) pt_outputs[tf_nans] = 0 tf_outputs[tf_nans] = 0 pt_outputs[pt_nans] = 0 tf_outputs[pt_nans] = 0 max_diff = np.amax(np.abs(tf_outputs - pt_outputs)) self.assertLessEqual(max_diff, tol, f"{name}: Difference between torch and tf is {max_diff} (>= {tol}).") else: raise ValueError( "`tf_outputs` should be an instance of `tf.Tensor`, a `tuple`, or an instance of `tf.Tensor`. Got" f" {type(tf_outputs)} instead." ) def prepare_pt_inputs_from_tf_inputs(self, tf_inputs_dict): pt_inputs_dict = {} for name, key in tf_inputs_dict.items(): if type(key) == bool: pt_inputs_dict[name] = key elif name == "input_values": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) elif name == "pixel_values": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) elif name == "input_features": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) # other general float inputs elif tf_inputs_dict[name].dtype.is_floating: pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) else: pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long) return pt_inputs_dict def check_pt_tf_models(self, tf_model, pt_model, tf_inputs_dict): pt_inputs_dict = self.prepare_pt_inputs_from_tf_inputs(tf_inputs_dict) # send pytorch inputs to the correct device pt_inputs_dict = { k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs_dict.items() } # send pytorch model to the correct device pt_model.to(torch_device) # Check predictions on first output (logits/hidden-states) are close enough given low-level computational differences pt_model.eval() with torch.no_grad(): pt_outputs = pt_model(**pt_inputs_dict) tf_outputs = tf_model(tf_inputs_dict) # tf models returned loss is usually a tensor rather than a scalar. # (see `hf_compute_loss`: it uses `tf.keras.losses.Reduction.NONE`) # Change it here to a scalar to match PyTorch models' loss tf_loss = getattr(tf_outputs, "loss", None) if tf_loss is not None: tf_outputs.loss = tf.math.reduce_mean(tf_loss) self.check_pt_tf_outputs(tf_outputs, pt_outputs, type(tf_model)) @is_pt_tf_cross_test def test_pt_tf_model_equivalence(self): import transformers for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Output all for aggressive testing config.output_hidden_states = True config.output_attentions = self.has_attentions # Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency # of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`. # TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it. self._make_attention_mask_non_null(inputs_dict) pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beginning pt_model_class = getattr(transformers, pt_model_class_name) tf_model = model_class(config) pt_model = pt_model_class(config) tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class) tf_inputs_dict_with_labels = self._prepare_for_class( inputs_dict, model_class, # Not all models accept "labels" in the forward pass (yet :) ) return_labels=True if "labels" in inspect.signature(model_class.call).parameters.keys() else False, ) # For some models (e.g. base models), there is no label returned. # Set the input dict to `None` to avoid check outputs twice for the same input dicts. if set(tf_inputs_dict_with_labels.keys()).symmetric_difference(tf_inputs_dict.keys()): tf_inputs_dict_with_labels = None # Check we can load pt model in tf and vice-versa with model => model functions tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict) pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model) # Original test: check without `labels` self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict) # check with `labels` if tf_inputs_dict_with_labels: self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict_with_labels) # Check we can load pt model in tf and vice-versa with checkpoint => model functions with tempfile.TemporaryDirectory() as tmpdirname: pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin") torch.save(pt_model.state_dict(), pt_checkpoint_path) tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path) tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5") tf_model.save_weights(tf_checkpoint_path) pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path) # Original test: check without `labels` self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict) # check with `labels` if tf_inputs_dict_with_labels: self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict_with_labels) def test_compile_tf_model(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() max_input = getattr(self.model_tester, "max_position_embeddings", 512) optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy") for model_class in self.all_model_classes: if model_class.__name__ in ["TFSpeech2TextModel", "TFSpeech2TextForConditionalGeneration"]: inputs = { "decoder_input_ids": tf.keras.Input( batch_shape=(2, max_input), name="decoder_input_ids", dtype="int32", ), "input_features": tf.keras.Input( batch_shape=( 2, max_input, self.model_tester.input_feat_per_channel * self.model_tester.input_channels, ), name="input_features", dtype="float32", ), } elif model_class.__name__ in ["TFWhisperModel", "TFWhisperForConditionalGeneration"]: inputs = { "decoder_input_ids": tf.keras.Input( batch_shape=(2, max_input), name="decoder_input_ids", dtype="int32", ), "input_features": tf.keras.Input( batch_shape=( 2, self.model_tester.num_mel_bins, self.model_tester.seq_length, ), name="input_features", dtype="float32", ), } elif self.is_encoder_decoder: inputs = { "decoder_input_ids": tf.keras.Input( batch_shape=(2, max_input), name="decoder_input_ids", dtype="int32", ), "input_ids": tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32"), } # `pixel_values` implies that the input is an image elif model_class.main_input_name == "pixel_values": inputs = tf.keras.Input( batch_shape=( 3, self.model_tester.num_channels, self.model_tester.image_size, self.model_tester.image_size, ), name="pixel_values", dtype="float32", ) elif model_class.__name__ in ["TFCLIPModel", "TFGroupViTModel"]: inputs = { "input_ids": tf.keras.Input(batch_shape=(3, max_input), name="input_ids", dtype="int32"), "pixel_values": tf.keras.Input( batch_shape=( 3, self.model_tester.vision_model_tester.num_channels, self.model_tester.vision_model_tester.image_size, self.model_tester.vision_model_tester.image_size, ), name="pixel_values", dtype="float32", ), } elif model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs = tf.keras.Input(batch_shape=(4, 2, max_input), name="input_ids", dtype="int32") else: inputs = tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32") # Prepare our model model = model_class(config) model(self._prepare_for_class(inputs_dict, model_class)) # Model must be called before saving. # Let's load it from the disk to be sure we can use pretrained weights with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=False) model = model_class.from_pretrained(tmpdirname) outputs_dict = model(inputs) hidden_states = outputs_dict[0] # Add a dense layer on top to test integration with other keras modules outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states) # Compile extended model extended_model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric]) def test_keyword_and_dict_args(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) outputs_dict = model(inputs) inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) outputs_keywords = model(**inputs_keywords) output_dict = outputs_dict[0].numpy() output_keywords = outputs_keywords[0].numpy() self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", self.model_tester.seq_length) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length) decoder_key_length = getattr(self.model_tester, "key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) def check_decoder_attentions_output(outputs): out_len = len(outputs) self.assertEqual(min(out_len % 2, out_len % 5), 0) # differentiation due to newly added cross_attentions decoder_attentions = outputs.decoder_attentions self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) def check_encoder_attentions_output(outputs): attentions = [ t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions) ] self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True config.output_hidden_states = False model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) out_len = len(outputs) self.assertEqual(config.output_hidden_states, False) check_encoder_attentions_output(outputs) if self.is_encoder_decoder: model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(config.output_hidden_states, False) check_decoder_attentions_output(outputs) # Check that output attentions can also be changed via the config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(config.output_hidden_states, False) check_encoder_attentions_output(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True config.output_hidden_states = True model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs)) self.assertEqual(model.config.output_hidden_states, True) check_encoder_attentions_output(outputs) def test_headmasking(self): if not self.test_head_masking: return random.Random().seed(42) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() random.Random().seed() inputs_dict["output_attentions"] = True config.output_hidden_states = True configs_no_init = _config_zero_init(config) # To be sure we have no Nan for model_class in self.all_model_classes: model = model_class(config=configs_no_init) # Prepare head_mask def prepare_layer_head_mask(i, attention_heads, num_hidden_layers): if i == 0: return tf.concat( (tf.zeros(1, dtype=tf.float32), tf.ones(attention_heads - 1, dtype=tf.float32)), 0 ) elif i == num_hidden_layers - 1: return tf.concat( (tf.zeros(attention_heads - 1, dtype=tf.float32), tf.ones(1, dtype=tf.float32)), 0 ) else: return tf.ones(attention_heads, dtype=tf.float32) head_mask = tf.stack( [ prepare_layer_head_mask(i, config.num_attention_heads, config.num_hidden_layers) for i in range(config.num_hidden_layers) ], 0, ) inputs = self._prepare_for_class(inputs_dict, model_class).copy() inputs["head_mask"] = head_mask if model.config.is_encoder_decoder: signature = inspect.signature(model.call) arg_names = [*signature.parameters.keys()] if "decoder_head_mask" in arg_names: # necessary diferentiation because of T5 model inputs["decoder_head_mask"] = head_mask if "cross_attn_head_mask" in arg_names: inputs["cross_attn_head_mask"] = head_mask outputs = model(**inputs, return_dict=True) def check_attentions_validity(attentions): # Remove Nan for t in attentions: self.assertLess( (tf.math.reduce_sum(tf.cast(tf.math.is_nan(t), tf.float32))).numpy(), (tf.size(t) / 4).numpy() ) # Check we don't have more than 25% nans (arbitrary) attentions = [ tf.where(tf.math.is_nan(t), 0.0, t) for t in attentions ] # remove them (the test is less complete) self.assertAlmostEqual(tf.math.reduce_sum(attentions[0][..., 0, :, :]).numpy(), 0.0) self.assertNotEqual(tf.math.reduce_sum(attentions[0][..., -1, :, :]).numpy(), 0.0) if len(attentions) > 2: # encoder-decodere models have only 2 layers in each modules self.assertNotEqual(tf.math.reduce_sum(attentions[1][..., 0, :, :]).numpy(), 0.0) self.assertAlmostEqual(tf.math.reduce_sum(attentions[-1][..., -2, :, :]).numpy(), 0.0) self.assertNotEqual(tf.math.reduce_sum(attentions[-1][..., -1, :, :]).numpy(), 0.0) if model.config.is_encoder_decoder: check_attentions_validity(outputs.encoder_attentions) check_attentions_validity(outputs.decoder_attentions) if "cross_attn_head_mask" in arg_names: check_attentions_validity(outputs.cross_attentions) else: check_attentions_validity(outputs.attentions) def test_hidden_states_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_hidden_states_output(config, inputs_dict, model_class): model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) if model.config.is_encoder_decoder: encoder_hidden_states = outputs.encoder_hidden_states decoder_hidden_states = outputs.decoder_hidden_states self.assertEqual(config.output_attentions, False) self.assertEqual(len(encoder_hidden_states), expected_num_layers) self.assertListEqual( list(encoder_hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size], ) self.assertEqual(len(decoder_hidden_states), expected_num_layers) self.assertListEqual( list(decoder_hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size], ) else: hidden_states = outputs.hidden_states self.assertEqual(config.output_attentions, False) self.assertEqual(len(hidden_states), expected_num_layers) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size], ) for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(config, inputs_dict, model_class) del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(config, inputs_dict, model_class) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() text_in_text_out_models = ( get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING) + get_values(TF_MODEL_FOR_MASKED_LM_MAPPING) + get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING) ) speech_in_text_out_models = get_values(TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING) for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in text_in_text_out_models: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert isinstance(name, dict) for k, v in name.items(): assert isinstance(v, tf.Variable) elif model_class in speech_in_text_out_models: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert name is None else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None def test_determinism(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) first, second = ( model(self._prepare_for_class(inputs_dict, model_class), training=False)[0], model(self._prepare_for_class(inputs_dict, model_class), training=False)[0], ) out_1 = first.numpy() out_2 = second.numpy() out_1 = out_1[~np.isnan(out_1)] out_2 = out_2[~np.isnan(out_2)] max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs) dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple() def recursive_check(tuple_object, dict_object): if isinstance(tuple_object, (List, Tuple)): for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( all(tf.equal(tuple_object, dict_object)), msg=( "Tuple and dict output are not equal. Difference:" f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}" ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: model = model_class(config) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) if self.has_attentions: tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) # Not all models accept "labels" in the forward pass (yet :) ) if "labels" in inspect.signature(model.call).parameters.keys(): tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) if self.has_attentions: tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence( model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True} ) def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) inputs = copy.deepcopy(inputs_dict) if not self.is_encoder_decoder: input_ids = inputs["input_ids"] del inputs["input_ids"] else: encoder_input_ids = inputs["input_ids"] decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids) del inputs["input_ids"] inputs.pop("decoder_input_ids", None) if not self.is_encoder_decoder: inputs["inputs_embeds"] = model.get_input_embeddings()(input_ids) else: inputs["inputs_embeds"] = model.get_input_embeddings()(encoder_input_ids) inputs["decoder_inputs_embeds"] = model.get_input_embeddings()(decoder_input_ids) inputs = self._prepare_for_class(inputs, model_class) model(inputs) def test_numpy_arrays_inputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def prepare_numpy_arrays(inputs_dict): inputs_np_dict = {} for k, v in inputs_dict.items(): if tf.is_tensor(v): inputs_np_dict[k] = v.numpy() else: inputs_np_dict[k] = np.array(k) return inputs_np_dict for model_class in self.all_model_classes: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) inputs_np = prepare_numpy_arrays(inputs) output_for_dict_input = model(inputs_np) output_for_kw_input = model(**inputs_np) self.assert_outputs_same(output_for_dict_input, output_for_kw_input) def test_resize_token_embeddings(self): # TODO (joao): after the embeddings refactor is complete, rework this test so as to rely exclusively on # tf.keras.layers.Embedding if not self.test_resize_embeddings: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def _get_word_embedding_weight(model, embedding_layer): if isinstance(embedding_layer, tf.keras.layers.Embedding): # builds the embeddings layer model(model.dummy_inputs) return embedding_layer.embeddings else: return model._get_word_embedding_weight(embedding_layer) for model_class in self.all_model_classes: for size in [config.vocab_size - 10, config.vocab_size + 10, None]: # build the embeddings model = model_class(config=copy.deepcopy(config)) # `resize_token_embeddings` mutates `config` old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings()) old_bias = model.get_bias() old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings()) # reshape the embeddings model.resize_token_embeddings(size) new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings()) new_bias = model.get_bias() new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings()) # check that the resized embeddings size matches the desired size. assert_size = size if size is not None else config.vocab_size self.assertEqual(new_input_embeddings.shape[0], assert_size) # check that weights remain the same after resizing models_equal = True for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) if old_bias is not None and new_bias is not None: for old_weight, new_weight in zip(old_bias.values(), new_bias.values()): self.assertEqual(new_weight.shape[-1], assert_size) models_equal = True for p1, p2 in zip(tf.squeeze(old_weight), tf.squeeze(new_weight)): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) if old_output_embeddings is not None and new_output_embeddings is not None: self.assertEqual(new_output_embeddings.shape[0], assert_size) self.assertEqual(new_output_embeddings.shape[1], old_output_embeddings.shape[1]) models_equal = True for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) # TODO (Joao): this test is not slow, but it's tagged as such to keep track of failures on the scheduled CI runs, # while passing push CI. Fix the underlying issues and remove the tag. @slow def test_save_load_after_resize_token_embeddings(self): if not self.test_resize_embeddings: return config, original_inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # create a model with resized (expended) embeddings new_tokens_size = 10 old_total_size = config.vocab_size new_total_size = old_total_size + new_tokens_size model = model_class(config=copy.deepcopy(config)) # `resize_token_embeddings` mutates `config` model(model.dummy_inputs) # builds the embeddings layer model.resize_token_embeddings(new_total_size) # fetch the output for an input exclusively made of new members of the vocabulary inputs_dict = copy.deepcopy(original_inputs_dict) ids_feat_name = None if "input_ids" in inputs_dict: ids_feat_name = "input_ids" elif "decoder_input_ids" in inputs_dict: ids_feat_name = "decoder_input_ids" else: assert False, "No input ids feature found in the inputs dict" new_vocab_input_ids = ids_tensor(inputs_dict[ids_feat_name].shape, new_tokens_size) new_vocab_input_ids += old_total_size inputs_dict[ids_feat_name] = new_vocab_input_ids if "input_ids" in inputs_dict: inputs_dict["input_ids"] = new_vocab_input_ids if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"] = new_vocab_input_ids prepared_inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**prepared_inputs) # save and load the model with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=False) model = model_class.from_pretrained(tmpdirname) restored_model_outputs = model(**prepared_inputs) # check that the output for the restored model is the same self.assert_outputs_same(restored_model_outputs, outputs) @unittest.skipIf( not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0, reason="This test always passes on CPU.", ) def test_embeddings_out_of_bounds_raise_exception(self): # TF embeddings layers don't raise an exception when an index is out of bounds on GPU, so we manually raise it. # This test should only fail on GPU for models where we haven't added the safety check. if not self.test_resize_embeddings: return config, original_inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config=config) inputs_dict = copy.deepcopy(original_inputs_dict) if "input_ids" in inputs_dict: inputs_dict["input_ids"] = inputs_dict["input_ids"] * int(1e9) if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"] = inputs_dict["decoder_input_ids"] * int(1e9) prepared_inputs = self._prepare_for_class(inputs_dict, model_class) with self.assertRaises(tf.errors.InvalidArgumentError): model(**prepared_inputs) def test_lm_head_model_random_no_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict.get("input_ids", None) # iterate over all generative models for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_ids with self.assertRaises(ValueError): model.generate(do_sample=True, max_length=5) # num_return_sequences = 1 self._check_generated_ids(model.generate(input_ids, do_sample=True)) elif model_class.__name__ not in ["TFSpeech2TextForConditionalGeneration"]: # Models with non-text inputs won't work here; num_return_sequences = 1 self._check_generated_ids(model.generate(do_sample=True, max_length=5)) with self.assertRaises(ValueError): # generating multiple sequences when no beam search generation # is not allowed as it would always generate the same sequences model.generate(input_ids, do_sample=False, num_return_sequences=2) # num_return_sequences > 1, sample self._check_generated_ids(model.generate(input_ids, do_sample=True, num_return_sequences=2)) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_ids, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_ids.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) def test_lm_head_model_no_beam_search_generate_dict_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict.get("input_ids", None) if input_ids is None: input_ids = inputs_dict.get("input_features", None) # iterate over all generative models for model_class in self.all_generative_model_classes: model = model_class(config) output_greedy = model.generate( input_ids, do_sample=False, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) output_sample = model.generate( input_ids, do_sample=True, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) if model.config.is_encoder_decoder: self.assertIsInstance(output_greedy, TFGreedySearchEncoderDecoderOutput) self.assertIsInstance(output_sample, TFSampleEncoderDecoderOutput) else: self.assertIsInstance(output_greedy, TFGreedySearchDecoderOnlyOutput) self.assertIsInstance(output_sample, TFSampleDecoderOnlyOutput) def test_lm_head_model_random_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict.get("input_ids", None) for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_ids, num_return_sequences = 1 self._check_generated_ids(model.generate(input_ids, do_sample=True, num_beams=2)) else: # num_return_sequences = 1 self._check_generated_ids(model.generate(do_sample=True, max_length=5, num_beams=2)) with self.assertRaises(ValueError): # generating more sequences than having beams leads is not possible model.generate(input_ids, do_sample=False, num_return_sequences=3, num_beams=2) # num_return_sequences > 1, sample self._check_generated_ids( model.generate( input_ids, do_sample=True, num_beams=2, num_return_sequences=2, ) ) # num_return_sequences > 1, greedy self._check_generated_ids(model.generate(input_ids, do_sample=False, num_beams=2, num_return_sequences=2)) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_ids, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_ids.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) def test_lm_head_model_beam_search_generate_dict_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict.get("input_ids", None) if input_ids is None: input_ids = inputs_dict.get("input_features", None) # iterate over all generative models for model_class in self.all_generative_model_classes: model = model_class(config) output_beam_search = model.generate( input_ids, num_beams=2, do_sample=False, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) output_beam_sample = model.generate( input_ids, num_beams=2, do_sample=True, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) if model.config.is_encoder_decoder: self.assertIsInstance(output_beam_search, TFBeamSearchEncoderDecoderOutput) self.assertIsInstance(output_beam_sample, TFBeamSampleEncoderDecoderOutput) else: self.assertIsInstance(output_beam_search, TFBeamSearchDecoderOnlyOutput) self.assertIsInstance(output_beam_sample, TFBeamSampleDecoderOnlyOutput) def test_loss_computation(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) if not getattr(model, "hf_compute_loss", None) and not _return_type_has_loss(model): continue # The number of elements in the loss should be the same as the number of elements in the label prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) added_label_names = sorted(list(prepared_for_class.keys() - inputs_dict.keys()), reverse=True) if not added_label_names: continue # This test is only for models with easily-separable labels added_label = prepared_for_class[added_label_names[0]] expected_loss_size = added_label.shape.as_list()[:1] # Test that model correctly compute the loss with kwargs prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) possible_input_names = {"input_ids", "pixel_values", "input_features", "input_values"} input_name = possible_input_names.intersection(set(prepared_for_class)).pop() model_input = prepared_for_class.pop(input_name) loss = model(model_input, **prepared_for_class)[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]) # Test that model correctly compute the loss when we mask some positions prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) possible_input_names = {"input_ids", "pixel_values", "input_features", "input_values"} input_name = possible_input_names.intersection(set(prepared_for_class)).pop() model_input = prepared_for_class.pop(input_name) if "labels" in prepared_for_class: labels = prepared_for_class["labels"].numpy() if len(labels.shape) > 1 and labels.shape[1] != 1: labels[0] = -100 prepared_for_class["labels"] = tf.convert_to_tensor(labels) loss = model(model_input, **prepared_for_class)[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]) self.assertTrue(not np.any(np.isnan(loss.numpy()))) # Test that model correctly compute the loss with a dict prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) loss = model(prepared_for_class)[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]) # Test that model correctly compute the loss with a tuple prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) # Get keys that were added with the _prepare_for_class function label_keys = prepared_for_class.keys() - inputs_dict.keys() signature = inspect.signature(model.call).parameters signature_names = list(signature.keys()) # Create a dictionary holding the location of the tensors in the tuple tuple_index_mapping = {0: input_name} for label_key in label_keys: label_key_index = signature_names.index(label_key) tuple_index_mapping[label_key_index] = label_key sorted_tuple_index_mapping = sorted(tuple_index_mapping.items()) # Initialize a list with their default values, update the values and convert to a tuple list_input = [] for name in signature_names: if name != "kwargs": list_input.append(signature[name].default) for index, value in sorted_tuple_index_mapping: list_input[index] = prepared_for_class[value] tuple_input = tuple(list_input) # Send to model loss = model(tuple_input[:-1])[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]) def check_keras_fit_results(self, val_loss1, val_loss2, atol=1e-2, rtol=1e-3): self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol)) def test_keras_fit(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) if not getattr(model, "hf_compute_loss", False) and not _return_type_has_loss(model): continue # Test that model correctly compute the loss with kwargs prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) # Is there a better way to remove these decoder inputs? # We also remove "return_loss" as this is covered by the train_step when using fit() prepared_for_class = { key: val for key, val in prepared_for_class.items() if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids", "return_loss") } accuracy_classes = [ "ForPreTraining", "ForCausalLM", "ForMaskedLM", "ForQuestionAnswering", "ForMultipleChoice", "ForSequenceClassification", "ForTokenClassification", "ForNextSentencePrediction", "LMHeadModel", ] for accuracy_class in accuracy_classes: if model.__class__.__name__.endswith(accuracy_class): metrics = [tf.keras.metrics.SparseCategoricalAccuracy()] break else: metrics = [] if hasattr(self.model_tester, "batch_size"): sample_weight = tf.convert_to_tensor([0.5] * self.model_tester.batch_size, dtype=tf.float32) else: sample_weight = None model(model.dummy_inputs) # Build the model so we can get some constant weights model_weights = model.get_weights() # Run eagerly to save some expensive compilation times model.compile(optimizer=tf.keras.optimizers.SGD(0.0), run_eagerly=True, metrics=metrics) # Make sure the model fits without crashing regardless of where we pass the labels history1 = model.fit( prepared_for_class, validation_data=prepared_for_class, sample_weight=sample_weight, steps_per_epoch=1, validation_steps=1, shuffle=False, ) val_loss1 = history1.history["val_loss"][0] self.assertTrue(not isnan(val_loss1)) accuracy1 = {key: val[0] for key, val in history1.history.items() if key.endswith("accuracy")} possible_label_cols = { "labels", "label", "label_ids", "start_positions", "start_position", "end_positions", "end_position", "next_sentence_label", } label_names = possible_label_cols.intersection(set(prepared_for_class)) if len(label_names) == 0: # The next tests only make sense for models with separate inputs and labels, and do not make # sense for models that don't clearly distinguish between the two (e.g. CLIP) return labels = {key: val for key, val in prepared_for_class.items() if key in label_names} inputs_minus_labels = {key: val for key, val in prepared_for_class.items() if key not in label_names} self.assertGreater(len(inputs_minus_labels), 0) # We reinitialize the model here even though our learning rate was zero # because BatchNorm updates weights by means other than gradient descent. model.set_weights(model_weights) history2 = model.fit( inputs_minus_labels, labels, validation_data=(inputs_minus_labels, labels), sample_weight=sample_weight, steps_per_epoch=1, validation_steps=1, shuffle=False, ) val_loss2 = history2.history["val_loss"][0] self.assertTrue(not isnan(val_loss2)) accuracy2 = {key: val[0] for key, val in history2.history.items() if key.endswith("accuracy")} self.check_keras_fit_results(val_loss1, val_loss2) self.assertEqual(history1.history.keys(), history2.history.keys()) for key in history1.history.keys(): if not key.startswith("val_"): self.assertTrue("val_" + key in history1.history.keys(), "Outputs differ in train/test step!") if metrics: self.assertTrue(len(accuracy1) == len(accuracy2) > 0, "Missing metrics!") # Make sure fit works with tf.data.Dataset and results are consistent dataset = tf.data.Dataset.from_tensor_slices(prepared_for_class) if sample_weight is not None: # Add in the sample weight weighted_dataset = dataset.map(lambda x: (x, None, tf.convert_to_tensor(0.5, dtype=tf.float32))) else: weighted_dataset = dataset # Pass in all samples as a batch to match other `fit` calls weighted_dataset = weighted_dataset.batch(len(dataset)) dataset = dataset.batch(len(dataset)) # Reinitialize to fix batchnorm again model.set_weights(model_weights) # To match the other calls, don't pass sample weights in the validation data history3 = model.fit( weighted_dataset, validation_data=dataset, steps_per_epoch=1, validation_steps=1, shuffle=False, ) val_loss3 = history3.history["val_loss"][0] self.assertTrue(not isnan(val_loss3)) accuracy3 = {key: val[0] for key, val in history3.history.items() if key.endswith("accuracy")} self.check_keras_fit_results(val_loss1, val_loss3) self.assertEqual(history1.history.keys(), history3.history.keys()) if metrics: self.assertTrue(len(accuracy1) == len(accuracy3) > 0, "Missing metrics!") def test_int64_inputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: prepared_for_class = self._prepare_for_class( inputs_dict.copy(), model_class, return_labels=True if "labels" in inspect.signature(model_class.call).parameters.keys() else False, ) if not any( [tensor.dtype.is_integer for tensor in prepared_for_class.values() if isinstance(tensor, tf.Tensor)] ): return # No integer inputs means no need for this test prepared_for_class = { key: tf.cast(tensor, tf.int64) if isinstance(tensor, tf.Tensor) and tensor.dtype.is_integer else tensor for key, tensor in prepared_for_class.items() } model = model_class(config) model(**prepared_for_class) # No assertion, we're just checking this doesn't throw an error def test_generate_with_headmasking(self): attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"] config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_generative_model_classes: model = model_class(config) # We want to test only encoder-decoder models if not config.is_encoder_decoder: continue head_masking = { "head_mask": tf.zeros((config.encoder_layers, config.encoder_attention_heads)), "decoder_head_mask": tf.zeros((config.decoder_layers, config.decoder_attention_heads)), "cross_attn_head_mask": tf.zeros((config.decoder_layers, config.decoder_attention_heads)), } signature = inspect.signature(model.call) if set(head_masking.keys()) < set([*signature.parameters.keys()]): continue for attn_name, (name, mask) in zip(attention_names, head_masking.items()): out = model.generate( inputs_dict["input_ids"], num_beams=1, max_length=inputs_dict["input_ids"] + 5, output_attentions=True, return_dict_in_generate=True, **{name: mask}, ) # We check the state of decoder_attentions and cross_attentions just from the last step attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1] self.assertEqual(sum([tf.reduce_sum(w).numpy() for w in attn_weights]), 0.0) def test_load_with_mismatched_shapes(self): if not self.test_mismatched_shapes: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class not in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING): continue with self.subTest(msg=f"Testing {model_class}"): with tempfile.TemporaryDirectory() as tmp_dir: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) _ = model(**inputs) model.save_pretrained(tmp_dir) # Fails when we don't set ignore_mismatched_sizes=True with self.assertRaises(ValueError): new_model = TFAutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42) with self.assertRaises(ValueError): new_model_without_prefix = TFAutoModel.from_pretrained(tmp_dir, vocab_size=10) logger = logging.get_logger("transformers.modeling_tf_utils") with CaptureLogger(logger) as cl: new_model = TFAutoModelForSequenceClassification.from_pretrained( tmp_dir, num_labels=42, ignore_mismatched_sizes=True ) self.assertIn("the shapes did not match", cl.out) logits = new_model(**inputs).logits self.assertEqual(logits.shape[1], 42) with CaptureLogger(logger) as cl: new_model_without_prefix = TFAutoModel.from_pretrained( tmp_dir, vocab_size=10, ignore_mismatched_sizes=True ) self.assertIn("the shapes did not match", cl.out) # Although Tf models always have a prefix pointing to `MainLayer`, # we still add this "without prefix" test to keep a consistency between tf and pt tests. input_ids = ids_tensor((2, 8), 10) if self.is_encoder_decoder: new_model_without_prefix(input_ids, decoder_input_ids=input_ids) else: new_model_without_prefix(input_ids) def test_model_main_input_name(self): for model_class in self.all_model_classes: model_signature = inspect.signature(getattr(model_class, "call")) # The main input is the name of the argument after `self` observed_main_input_name = list(model_signature.parameters.keys())[1] self.assertEqual(model_class.main_input_name, observed_main_input_name) def test_dataset_conversion(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class, return_labels=False) tf_inputs_dict = { key: val for key, val in tf_inputs_dict.items() if "head_mask" not in key and isinstance(val, tf.Tensor) } tf_inputs_dict["extra_unwanted_column"] = list(tf_inputs_dict.values())[0] # Use a random other tensor input_dataset = Dataset.from_dict(tf_inputs_dict) tf_dataset = model.prepare_tf_dataset( input_dataset, batch_size=len(input_dataset), drop_remainder=False, shuffle=False ) test_batch = next(iter(tf_dataset)) if isinstance(test_batch, tf.Tensor): self.assertEqual(len(test_batch), len(input_dataset)) # Assert we didn't lose any data else: # Assert we discarded the unwanted extra column but kept everything else self.assertEqual(len(test_batch), len(input_dataset.features) - 1) self.assertNotIn("extra_unwanted_column", test_batch) for tensor in test_batch.values(): self.assertTrue(isinstance(tensor, tf.Tensor)) self.assertEqual(len(tensor), len(input_dataset)) # Assert we didn't lose any data model(test_batch, training=False) if "labels" in inspect.signature(model_class.call).parameters.keys(): tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class, return_labels=True) if "labels" not in tf_inputs_dict: return # This model isn't giving us labels after all, don't try training with it tf_inputs_dict = {key: val for key, val in tf_inputs_dict.items() if "head_mask" not in key} tf_inputs_dict["extra_unwanted_column"] = list(tf_inputs_dict.values())[0] # Use a random other tensor input_dataset = Dataset.from_dict(tf_inputs_dict) tf_dataset = model.prepare_tf_dataset( input_dataset, batch_size=len(input_dataset), drop_remainder=False, shuffle=False ) test_batch, test_batch_labels = next(iter(tf_dataset)) self.assertGreater(len(test_batch_labels), 0) # Assert the labels are present feature_columns = 1 if isinstance(test_batch, tf.Tensor) else len(test_batch) label_columns = 1 if isinstance(test_batch_labels, tf.Tensor) else len(test_batch_labels) # Assert we discarded the unwanted extra column but kept everything else self.assertEqual(feature_columns + label_columns, len(input_dataset.features) - 1) if isinstance(test_batch, dict): self.assertNotIn("extra_unwanted_column", test_batch) if isinstance(test_batch_labels, dict): self.assertNotIn("extra_unwanted_column", test_batch_labels) model.compile(optimizer="sgd", run_eagerly=True) model.train_on_batch(test_batch, test_batch_labels) def _test_xla_generate(self, num_beams, num_return_sequences, max_length, **generate_kwargs): def _generate_and_check_results(model, config, inputs_dict): if "input_ids" in inputs_dict: inputs = inputs_dict["input_ids"] # make sure there are no pad tokens in prompt, which may trigger unwanted behavior if config.pad_token_id is not None: if config.pad_token_id == 0: new_pad_token = config.pad_token_id + 1 else: new_pad_token = config.pad_token_id - 1 else: new_pad_token = None inputs = tf.where(inputs != config.pad_token_id, inputs, new_pad_token) elif "input_features" in inputs_dict: inputs = inputs_dict["input_features"] else: raise ValueError("No valid generate input found in inputs_dict") generated = model.generate(inputs, **generate_kwargs).numpy() generate_xla = tf.function(model.generate, jit_compile=True) generated_xla = generate_xla(inputs, **generate_kwargs).numpy() self.assertListEqual(generated.tolist(), generated_xla.tolist()) for model_class in self.all_generative_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.eos_token_id = None # Generate until max length config.max_length = max_length config.do_sample = False config.num_beams = num_beams config.num_return_sequences = num_return_sequences # fix config for models with additional sequence-length limiting settings for var_name in ["max_position_embeddings", "max_target_positions"]: if hasattr(config, var_name): try: setattr(config, var_name, max_length) except NotImplementedError: # xlnet will raise an exception when trying to set # max_position_embeddings. pass model = model_class(config) if model.supports_xla_generation: _generate_and_check_results(model, config, inputs_dict) else: with self.assertRaises(ValueError): _generate_and_check_results(model, config, inputs_dict) def test_xla_generate_fast(self): """ Basic quick test for generate-compatible classes that confirms that XLA-generated tokens are the same as their non XLA counterparts. Either the model supports XLA generation and passes the inner test, or it raises an appropriate exception """ num_beams = 1 num_return_sequences = 1 max_length = 10 self._test_xla_generate(num_beams, num_return_sequences, max_length) def test_xla_generate_contrastive(self): """ Similar to `test_xla_generate_fast`, but for contrastive search -- contrastive search directly manipulates the model cache and other outputs, and this test ensures that they are in a valid format that is also supported by XLA. Either the model supports XLA generation and passes the inner test, or it raises an appropriate exception """ num_beams = 1 num_return_sequences = 1 max_length = 10 self._test_xla_generate(num_beams, num_return_sequences, max_length, penalty_alpha=0.5, top_k=5) @slow def test_xla_generate_slow(self): """ Slow and challenging version of `test_xla_generate_fast` -- this test asks for several long sequences using beam search, with and without XLA. The two outputs should match, and a failure in this test indicates that the model may need further analysis if it is to be used for XLA generation. Either the model supports XLA generation and passes the inner test, or it raises an appropriate exception """ num_beams = 8 num_return_sequences = 2 max_length = 128 self._test_xla_generate(num_beams, num_return_sequences, max_length) def _generate_random_bad_tokens(self, num_bad_tokens, model): # special tokens cannot be bad tokens special_tokens = [] if model.config.bos_token_id is not None: special_tokens.append(model.config.bos_token_id) if model.config.pad_token_id is not None: special_tokens.append(model.config.pad_token_id) if model.config.eos_token_id is not None: special_tokens.append(model.config.eos_token_id) # create random bad tokens that are not special tokens bad_tokens = [] while len(bad_tokens) < num_bad_tokens: token = tf.squeeze(ids_tensor((1, 1), self.model_tester.vocab_size), 0).numpy()[0] if token not in special_tokens: bad_tokens.append(token) return bad_tokens def _check_generated_ids(self, output_ids): for token_id in output_ids[0].numpy().tolist(): self.assertGreaterEqual(token_id, 0) self.assertLess(token_id, self.model_tester.vocab_size) def _check_match_tokens(self, generated_ids, bad_words_ids): # for all bad word tokens for bad_word_ids in bad_words_ids: # for all slices in batch for generated_ids_slice in generated_ids: # for all word idx for i in range(len(bad_word_ids), len(generated_ids_slice)): # if tokens match if generated_ids_slice[i - len(bad_word_ids) : i] == bad_word_ids: return True return False def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None): """Creates a random int32 tensor of the shape within the vocab size.""" if rng is None: rng = random.Random() total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.randint(0, vocab_size - 1)) output = tf.constant(values, shape=shape, dtype=dtype if dtype is not None else tf.int32) return output def random_attention_mask(shape, rng=None, name=None, dtype=None): attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None, dtype=dtype) # make sure that at least one token is attended to for each batch attn_mask = tf.concat([attn_mask[:, :-1], tf.ones_like(attn_mask[:, -1:], dtype=dtype)], axis=-1) return attn_mask def floats_tensor(shape, scale=1.0, rng=None, name=None, dtype=None): """Creates a random float32 tensor""" if rng is None: rng = random.Random() total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.random() * scale) return tf.reshape(tf.constant(values, dtype=dtype if dtype is not None else tf.float32), shape=shape) @require_tf class UtilsFunctionsTest(unittest.TestCase): def test_cached_files_are_used_when_internet_is_down(self): # A mock response for an HTTP head request to emulate server down response_mock = mock.Mock() response_mock.status_code = 500 response_mock.headers = {} response_mock.raise_for_status.side_effect = HTTPError response_mock.json.return_value = {} # Download this model to make sure it's in the cache. _ = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # Under the mock environment we get a 500 error when trying to reach the model. with mock.patch("requests.request", return_value=response_mock) as mock_head: _ = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # This check we did call the fake head request mock_head.assert_called() def test_load_from_one_file(self): try: tmp_file = tempfile.mktemp() with open(tmp_file, "wb") as f: http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/tf_model.h5", f) config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") _ = TFBertModel.from_pretrained(tmp_file, config=config) finally: os.remove(tmp_file) def test_legacy_load_from_url(self): # This test is for deprecated behavior and can be removed in v5 config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") _ = TFBertModel.from_pretrained( "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/tf_model.h5", config=config ) # tests whether the unpack_inputs function behaves as expected def test_unpack_inputs(self): class DummyModel: def __init__(self): config_kwargs = {"output_attentions": False, "output_hidden_states": False, "return_dict": False} self.config = PretrainedConfig(**config_kwargs) self.main_input_name = "input_ids" @unpack_inputs def call( self, input_ids=None, past_key_values=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): return input_ids, past_key_values, output_attentions, output_hidden_states, return_dict @unpack_inputs def foo(self, pixel_values, output_attentions=None, output_hidden_states=None, return_dict=None): return pixel_values, output_attentions, output_hidden_states, return_dict dummy_model = DummyModel() input_ids = tf.constant([0, 1, 2, 3], dtype=tf.int64) past_key_values = tf.constant([4, 5, 6, 7], dtype=tf.int64) pixel_values = tf.constant([8, 9, 10, 11], dtype=tf.int64) # test case 1: Pass inputs as keyword arguments; Booleans are inherited from the config. output = dummy_model.call(input_ids=input_ids, past_key_values=past_key_values) tf.debugging.assert_equal(output[0], input_ids) tf.debugging.assert_equal(output[1], past_key_values) self.assertFalse(output[2]) self.assertFalse(output[3]) self.assertFalse(output[4]) # test case 2: Same as above, but with positional arguments. output = dummy_model.call(input_ids, past_key_values) tf.debugging.assert_equal(output[0], input_ids) tf.debugging.assert_equal(output[1], past_key_values) self.assertFalse(output[2]) self.assertFalse(output[3]) self.assertFalse(output[4]) # test case 3: We can also pack everything in the first input. output = dummy_model.call(input_ids={"input_ids": input_ids, "past_key_values": past_key_values}) tf.debugging.assert_equal(output[0], input_ids) tf.debugging.assert_equal(output[1], past_key_values) self.assertFalse(output[2]) self.assertFalse(output[3]) self.assertFalse(output[4]) # test case 4: Explicit boolean arguments should override the config. output = dummy_model.call( input_ids=input_ids, past_key_values=past_key_values, output_attentions=False, return_dict=True ) tf.debugging.assert_equal(output[0], input_ids) tf.debugging.assert_equal(output[1], past_key_values) self.assertFalse(output[2]) self.assertFalse(output[3]) self.assertTrue(output[4]) # test case 5: Unexpected arguments should raise an exception. with self.assertRaises(ValueError): output = dummy_model.call(input_ids=input_ids, past_key_values=past_key_values, foo="bar") # test case 6: the decorator is independent from `main_input_name` -- it treats the first argument of the # decorated function as its main input. output = dummy_model.foo(pixel_values=pixel_values) tf.debugging.assert_equal(output[0], pixel_values) self.assertFalse(output[1]) self.assertFalse(output[2]) self.assertFalse(output[3]) # Tests whether the stable softmax is stable on CPU, with and without XLA def test_xla_stable_softmax(self): large_penalty = -1e9 n_tokens = 10 batch_size = 8 def masked_softmax(x, boolean_mask): numerical_mask = (1.0 - tf.cast(boolean_mask, dtype=tf.float32)) * large_penalty masked_x = x + numerical_mask return stable_softmax(masked_x) xla_masked_softmax = tf.function(masked_softmax, jit_compile=True) xla_stable_softmax = tf.function(stable_softmax, jit_compile=True) x = tf.random.normal((batch_size, n_tokens)) # Same outcome regardless of the boolean mask here masked_tokens = random.randint(0, n_tokens) boolean_mask = tf.convert_to_tensor([[1] * (n_tokens - masked_tokens) + [0] * masked_tokens], dtype=tf.int32) # We can randomly mask a random numerical input OUTSIDE XLA numerical_mask = (1.0 - tf.cast(boolean_mask, dtype=tf.float32)) * large_penalty masked_x = x + numerical_mask xla_out = xla_stable_softmax(masked_x) out = stable_softmax(masked_x) assert tf.experimental.numpy.allclose(xla_out, out) # The stable softmax has the same output as the original softmax unstable_out = tf.nn.softmax(masked_x) assert tf.experimental.numpy.allclose(unstable_out, out) # We can randomly mask a random numerical input INSIDE XLA xla_out = xla_masked_softmax(x, boolean_mask) out = masked_softmax(x, boolean_mask) assert tf.experimental.numpy.allclose(xla_out, out) def test_checkpoint_sharding_from_hub(self): model = TFBertModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") # the model above is the same as the model below, just a sharded version. ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") for p1, p2 in zip(model.weights, ref_model.weights): assert np.allclose(p1.numpy(), p2.numpy()) @is_pt_tf_cross_test def test_checkpoint_sharding_local_from_pt(self): with tempfile.TemporaryDirectory() as tmp_dir: _ = Repository(local_dir=tmp_dir, clone_from="hf-internal-testing/tiny-random-bert-sharded") model = TFBertModel.from_pretrained(tmp_dir, from_pt=True) # the model above is the same as the model below, just a sharded pytorch version. ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") for p1, p2 in zip(model.weights, ref_model.weights): assert np.allclose(p1.numpy(), p2.numpy()) def test_shard_checkpoint(self): # This is the model we will use, total size 340,000 bytes. model = tf.keras.Sequential( [ tf.keras.layers.Dense(200, use_bias=False), # size 80,000 tf.keras.layers.Dense(200, use_bias=False), # size 160,000 tf.keras.layers.Dense(100, use_bias=False), # size 80,000 tf.keras.layers.Dense(50, use_bias=False), # size 20,000 ] ) inputs = tf.zeros((1, 100), dtype=tf.float32) model(inputs) weights = model.weights weights_dict = {w.name: w for w in weights} with self.subTest("No shard when max size is bigger than model size"): shards, index = tf_shard_checkpoint(weights) self.assertIsNone(index) self.assertDictEqual(shards, {TF2_WEIGHTS_NAME: weights}) with self.subTest("Test sharding, no weights bigger than max size"): shards, index = tf_shard_checkpoint(weights, max_shard_size="300kB") # Split is first two layers then last two. self.assertDictEqual( index, { "metadata": {"total_size": 340000}, "weight_map": { "dense/kernel:0": "tf_model-00001-of-00002.h5", "dense_1/kernel:0": "tf_model-00001-of-00002.h5", "dense_2/kernel:0": "tf_model-00002-of-00002.h5", "dense_3/kernel:0": "tf_model-00002-of-00002.h5", }, }, ) shard1 = [weights_dict["dense/kernel:0"], weights_dict["dense_1/kernel:0"]] shard2 = [weights_dict["dense_2/kernel:0"], weights_dict["dense_3/kernel:0"]] self.assertDictEqual(shards, {"tf_model-00001-of-00002.h5": shard1, "tf_model-00002-of-00002.h5": shard2}) with self.subTest("Test sharding with weights bigger than max size"): shards, index = tf_shard_checkpoint(weights, max_shard_size="100kB") # Split is first layer, second layer then last 2. self.assertDictEqual( index, { "metadata": {"total_size": 340000}, "weight_map": { "dense/kernel:0": "tf_model-00001-of-00003.h5", "dense_1/kernel:0": "tf_model-00002-of-00003.h5", "dense_2/kernel:0": "tf_model-00003-of-00003.h5", "dense_3/kernel:0": "tf_model-00003-of-00003.h5", }, }, ) shard1 = [weights_dict["dense/kernel:0"]] shard2 = [weights_dict["dense_1/kernel:0"]] shard3 = [weights_dict["dense_2/kernel:0"], weights_dict["dense_3/kernel:0"]] self.assertDictEqual( shards, { "tf_model-00001-of-00003.h5": shard1, "tf_model-00002-of-00003.h5": shard2, "tf_model-00003-of-00003.h5": shard3, }, ) @slow def test_special_layer_name_sharding(self): retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True) model = TFRagModel.from_pretrained("facebook/rag-token-nq", retriever=retriever) with tempfile.TemporaryDirectory() as tmp_dir: for max_size in ["150kB", "150kiB", "200kB", "200kiB"]: model.save_pretrained(tmp_dir, max_shard_size=max_size) ref_model = TFRagModel.from_pretrained(tmp_dir, retriever=retriever) for p1, p2 in zip(model.weights, ref_model.weights): assert np.allclose(p1.numpy(), p2.numpy()) def test_checkpoint_sharding_local(self): model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: # We use the same folder for various sizes to make sure a new save erases the old checkpoint. for max_size in ["150kB", "150kiB", "200kB", "200kiB"]: model.save_pretrained(tmp_dir, max_shard_size=max_size) # Get each shard file and its size shard_to_size = {} for shard in os.listdir(tmp_dir): if shard.endswith(".h5"): shard_file = os.path.join(tmp_dir, shard) shard_to_size[shard_file] = os.path.getsize(shard_file) index_file = os.path.join(tmp_dir, TF2_WEIGHTS_INDEX_NAME) # Check there is an index but no regular weight file self.assertTrue(os.path.isfile(index_file)) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, TF2_WEIGHTS_NAME))) # Check a file is bigger than max_size only when it has a single weight for shard_file, size in shard_to_size.items(): if max_size.endswith("kiB"): max_size_int = int(max_size[:-3]) * 2**10 else: max_size_int = int(max_size[:-2]) * 10**3 # Note: pickle adds some junk so the weight of the file can end up being slightly bigger than # the size asked for (since we count parameters) if size >= max_size_int + 50000: with h5py.File(shard_file, "r") as state_file: self.assertEqual(len(state_file), 1) # Check the index and the shard files found match with open(index_file, "r", encoding="utf-8") as f: index = json.loads(f.read()) all_shards = set(index["weight_map"].values()) shards_found = set(f for f in os.listdir(tmp_dir) if f.endswith(".h5")) self.assertSetEqual(all_shards, shards_found) # Finally, check the model can be reloaded new_model = TFBertModel.from_pretrained(tmp_dir) model(model.dummy_inputs) new_model(model.dummy_inputs) for p1, p2 in zip(model.weights, new_model.weights): self.assertTrue(np.allclose(p1.numpy(), p2.numpy())) def test_save_pretrained_signatures(self): model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # Short custom TF signature function. # `input_signature` is specific to BERT. @tf.function( input_signature=[ [ tf.TensorSpec([None, None], tf.int32, name="input_ids"), tf.TensorSpec([None, None], tf.int32, name="token_type_ids"), tf.TensorSpec([None, None], tf.int32, name="attention_mask"), ] ] ) def serving_fn(input): return model(input) # Using default signature (default behavior) overrides 'serving_default' with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, saved_model=True, signatures=None) model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1") self.assertTrue("serving_default" in list(model_loaded.signatures.keys())) # Providing custom signature function with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, saved_model=True, signatures={"custom_signature": serving_fn}) model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1") self.assertTrue("custom_signature" in list(model_loaded.signatures.keys())) # Providing multiple custom signature function with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained( tmp_dir, saved_model=True, signatures={"custom_signature_1": serving_fn, "custom_signature_2": serving_fn}, ) model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1") self.assertTrue("custom_signature_1" in list(model_loaded.signatures.keys())) self.assertTrue("custom_signature_2" in list(model_loaded.signatures.keys())) @require_safetensors def test_safetensors_save_and_load(self): model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True) # No tf_model.h5 file, only a model.safetensors self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME))) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, TF2_WEIGHTS_NAME))) new_model = TFBertModel.from_pretrained(tmp_dir) # Check models are equal for p1, p2 in zip(model.weights, new_model.weights): self.assertTrue(np.allclose(p1.numpy(), p2.numpy())) @is_pt_tf_cross_test def test_safetensors_save_and_load_pt_to_tf(self): model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") pt_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: pt_model.save_pretrained(tmp_dir, safe_serialization=True) # Check we have a model.safetensors file self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME))) new_model = TFBertModel.from_pretrained(tmp_dir) # Check models are equal for p1, p2 in zip(model.weights, new_model.weights): self.assertTrue(np.allclose(p1.numpy(), p2.numpy())) @require_safetensors def test_safetensors_load_from_hub(self): tf_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # Can load from the TF-formatted checkpoint safetensors_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors-tf") # Check models are equal for p1, p2 in zip(safetensors_model.weights, tf_model.weights): self.assertTrue(np.allclose(p1.numpy(), p2.numpy())) # Can load from the PyTorch-formatted checkpoint safetensors_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors") # Check models are equal for p1, p2 in zip(safetensors_model.weights, tf_model.weights): self.assertTrue(np.allclose(p1.numpy(), p2.numpy())) @require_tf @is_staging_test class TFModelPushToHubTester(unittest.TestCase): @classmethod def setUpClass(cls): cls._token = TOKEN set_access_token(TOKEN) HfFolder.save_token(TOKEN) @classmethod def tearDownClass(cls): try: delete_repo(token=cls._token, repo_id="test-model-tf") except HTTPError: pass try: delete_repo(token=cls._token, repo_id="valid_org/test-model-tf-org") except HTTPError: pass def test_push_to_hub(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = TFBertModel(config) # Make sure model is properly initialized _ = model(model.dummy_inputs) logging.set_verbosity_info() logger = logging.get_logger("transformers.utils.hub") with CaptureLogger(logger) as cl: model.push_to_hub("test-model-tf", use_auth_token=self._token) logging.set_verbosity_warning() # Check the model card was created and uploaded. self.assertIn("Uploading README.md to __DUMMY_TRANSFORMERS_USER__/test-model-tf", cl.out) new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf") models_equal = True for p1, p2 in zip(model.weights, new_model.weights): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) # Reset repo delete_repo(token=self._token, repo_id="test-model-tf") # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, repo_id="test-model-tf", push_to_hub=True, use_auth_token=self._token) new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf") models_equal = True for p1, p2 in zip(model.weights, new_model.weights): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) def test_push_to_hub_in_organization(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = TFBertModel(config) # Make sure model is properly initialized _ = model(model.dummy_inputs) model.push_to_hub("valid_org/test-model-tf-org", use_auth_token=self._token) new_model = TFBertModel.from_pretrained("valid_org/test-model-tf-org") models_equal = True for p1, p2 in zip(model.weights, new_model.weights): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) # Reset repo delete_repo(token=self._token, repo_id="valid_org/test-model-tf-org") # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained( tmp_dir, push_to_hub=True, use_auth_token=self._token, repo_id="valid_org/test-model-tf-org" ) new_model = TFBertModel.from_pretrained("valid_org/test-model-tf-org") models_equal = True for p1, p2 in zip(model.weights, new_model.weights): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal)

# coding=utf-8 # Copyright 2019 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import inspect import json import os import random import tempfile import unittest import unittest.mock as mock from dataclasses import fields from importlib import import_module from math import isnan from typing import List, Tuple, get_type_hints from datasets import Dataset from huggingface_hub import HfFolder, Repository, delete_repo, set_access_token from huggingface_hub.file_download import http_get from requests.exceptions import HTTPError from transformers import is_tf_available, is_torch_available from transformers.configuration_utils import PretrainedConfig from transformers.models.auto import get_values from transformers.testing_utils import ( # noqa: F401 TOKEN, USER, CaptureLogger, CaptureStdout, _tf_gpu_memory_limit, is_pt_tf_cross_test, is_staging_test, require_safetensors, require_tf, require_tf2onnx, slow, tooslow, torch_device, ) from transformers.utils import SAFE_WEIGHTS_NAME, TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging from transformers.utils.generic import ModelOutput logger = logging.get_logger(__name__) if is_tf_available(): import h5py import numpy as np import tensorflow as tf from transformers import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, BertConfig, PushToHubCallback, RagRetriever, TFAutoModel, TFAutoModelForSequenceClassification, TFBertForMaskedLM, TFBertModel, TFRagModel, TFSharedEmbeddings, ) from transformers.generation import ( TFBeamSampleDecoderOnlyOutput, TFBeamSampleEncoderDecoderOutput, TFBeamSearchDecoderOnlyOutput, TFBeamSearchEncoderDecoderOutput, TFGreedySearchDecoderOnlyOutput, TFGreedySearchEncoderDecoderOutput, TFSampleDecoderOnlyOutput, TFSampleEncoderDecoderOutput, ) from transformers.modeling_tf_utils import tf_shard_checkpoint, unpack_inputs from transformers.tf_utils import stable_softmax if _tf_gpu_memory_limit is not None: gpus = tf.config.list_physical_devices("GPU") for gpu in gpus: # Restrict TensorFlow to only allocate x GB of memory on the GPUs try: tf.config.set_logical_device_configuration( gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)] ) logical_gpus = tf.config.list_logical_devices("GPU") print("Logical GPUs", logical_gpus) except RuntimeError as e: # Virtual devices must be set before GPUs have been initialized print(e) if is_torch_available(): import torch from transformers import BertModel def _config_zero_init(config): configs_no_init = copy.deepcopy(config) for key in configs_no_init.__dict__.keys(): if "_range" in key or "_std" in key: setattr(configs_no_init, key, 0.0) return configs_no_init def _return_type_has_loss(model): return_type = get_type_hints(model.call) if "return" not in return_type: return False return_type = return_type["return"] if hasattr(return_type, "__args__"): # Awkward check for union because UnionType only turns up in 3.10 for type_annotation in return_type.__args__: if inspect.isclass(type_annotation) and issubclass(type_annotation, ModelOutput): field_names = [field.name for field in fields(type_annotation)] if "loss" in field_names: return True return False elif isinstance(return_type, tuple): return False elif isinstance(return_type, ModelOutput): class_fields = fields(return_type) return "loss" in class_fields return False @require_tf class TFModelTesterMixin: model_tester = None all_model_classes = () all_generative_model_classes = () test_mismatched_shapes = True test_resize_embeddings = True test_head_masking = True is_encoder_decoder = False has_attentions = True def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict: inputs_dict = copy.deepcopy(inputs_dict) if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict = { k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)) if isinstance(v, tf.Tensor) and v.ndim > 0 else v for k, v in inputs_dict.items() } if return_labels: if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32) elif model_class in [ *get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING), *get_values(TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING), ]: inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) elif model_class in [ *get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING), *get_values(TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING), ]: inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) elif model_class in get_values(TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING): inputs_dict["next_sentence_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) elif model_class in [ *get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING), *get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING), *get_values(TF_MODEL_FOR_MASKED_LM_MAPPING), *get_values(TF_MODEL_FOR_PRETRAINING_MAPPING), *get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING), *get_values(TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING), ] and "labels" in dict(inspect.signature(model_class.call).parameters): inputs_dict["labels"] = tf.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32 ) elif model_class in get_values(TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING): num_patches = self.model_tester.image_size // self.model_tester.patch_size inputs_dict["bool_masked_pos"] = tf.zeros( (self.model_tester.batch_size, num_patches**2), dtype=tf.int32 ) elif model_class in get_values(TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING): batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape inputs_dict["labels"] = tf.zeros((self.model_tester.batch_size, height, width), dtype=tf.int32) elif model_class.__name__.endswith("ForCTC"): # When we have enough CTC models for an AutoClass, we should use their mapping instead of name checks inputs_dict["labels"] = tf.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32 ) return inputs_dict def test_initialization(self): pass def test_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=False) model = model_class.from_pretrained(tmpdirname) after_outputs = model(self._prepare_for_class(inputs_dict, model_class)) self.assert_outputs_same(after_outputs, outputs) def test_save_load_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) model_config = model.get_config() # make sure that returned config is jsonifiable, which is required by keras json.dumps(model_config) new_model = model_class.from_config(model.get_config()) # make sure it also accepts a normal config _ = model_class.from_config(model.config) _ = new_model(self._prepare_for_class(inputs_dict, model_class)) # Build model new_model.set_weights(model.get_weights()) after_outputs = new_model(self._prepare_for_class(inputs_dict, model_class)) self.assert_outputs_same(after_outputs, outputs) @slow def test_saved_model_creation(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = False config.output_attentions = False if hasattr(config, "use_cache"): config.use_cache = False model_class = self.all_model_classes[0] class_inputs_dict = self._prepare_for_class(inputs_dict, model_class) model = model_class(config) model(class_inputs_dict) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=True) saved_model_dir = os.path.join(tmpdirname, "saved_model", "1") self.assertTrue(os.path.exists(saved_model_dir)) def test_prepare_serving_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = self.has_attentions for model_class in self.all_model_classes: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(inputs) serving_outputs = model.serving_output(outputs) for k, v in serving_outputs.items(): # Check that we have one of three possible outputs: None, tuple of tensors or a tensor if isinstance(v, tuple): self.assertTrue(all(isinstance(elem, tf.Tensor) for elem in v)) elif v is not None: self.assertIsInstance(v, tf.Tensor) else: self.assertIsNone(v) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.call) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] if model.config.is_encoder_decoder: expected_arg_names = [ "input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend(["decoder_position_ids"] if "decoder_position_ids" in arg_names else []) expected_arg_names.extend( ["head_mask", "decoder_head_mask"] if "head_mask" and "decoder_head_mask" in arg_names else [] ) expected_arg_names.extend( ["cross_attn_head_mask", "encoder_outputs"] if "cross_attn_head_mask" in arg_names else ["encoder_outputs"] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) else: expected_arg_names = ["input_ids"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_onnx_compliancy(self): if not self.test_onnx: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() INTERNAL_OPS = [ "Assert", "AssignVariableOp", "EmptyTensorList", "ReadVariableOp", "ResourceGather", "TruncatedNormal", "VarHandleOp", "VarIsInitializedOp", ] onnx_ops = [] with open(os.path.join(".", "utils", "tf_ops", "onnx.json")) as f: onnx_opsets = json.load(f)["opsets"] for i in range(1, self.onnx_min_opset + 1): onnx_ops.extend(onnx_opsets[str(i)]) for model_class in self.all_model_classes: model_op_names = set() with tf.Graph().as_default() as g: model = model_class(config) model(model.dummy_inputs) for op in g.get_operations(): model_op_names.add(op.node_def.op) model_op_names = sorted(model_op_names) incompatible_ops = [] for op in model_op_names: if op not in onnx_ops and op not in INTERNAL_OPS: incompatible_ops.append(op) self.assertEqual(len(incompatible_ops), 0, incompatible_ops) @require_tf2onnx @slow def test_onnx_runtime_optimize(self): if not self.test_onnx: return import onnxruntime import tf2onnx config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model(model.dummy_inputs) onnx_model_proto, _ = tf2onnx.convert.from_keras(model, opset=self.onnx_min_opset) onnxruntime.InferenceSession(onnx_model_proto.SerializeToString()) def test_keras_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() tf_main_layer_classes = set( module_member for model_class in self.all_model_classes for module in (import_module(model_class.__module__),) for module_member_name in dir(module) if module_member_name.endswith("MainLayer") # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`. and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")] for module_member in (getattr(module, module_member_name),) if isinstance(module_member, type) and tf.keras.layers.Layer in module_member.__bases__ and getattr(module_member, "_keras_serializable", False) ) for main_layer_class in tf_main_layer_classes: # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter if "T5" in main_layer_class.__name__: # Take the same values than in TFT5ModelTester for this shared layer shared = TFSharedEmbeddings(99, 32, name="shared") config.use_cache = inputs_dict.pop("use_cache", None) main_layer = main_layer_class(config, embed_tokens=shared) else: main_layer = main_layer_class(config) symbolic_inputs = { name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items() } model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs)) outputs = model(inputs_dict) with tempfile.TemporaryDirectory() as tmpdirname: filepath = os.path.join(tmpdirname, "keras_model.h5") model.save(filepath) if "T5" in main_layer_class.__name__: model = tf.keras.models.load_model( filepath, custom_objects={ main_layer_class.__name__: main_layer_class, "TFSharedEmbeddings": TFSharedEmbeddings, }, ) else: model = tf.keras.models.load_model( filepath, custom_objects={main_layer_class.__name__: main_layer_class} ) assert isinstance(model, tf.keras.Model) after_outputs = model(inputs_dict) self.assert_outputs_same(after_outputs, outputs) def assert_outputs_same(self, after_outputs, outputs): # Make sure we don't have nans if isinstance(after_outputs, tf.Tensor): out_1 = after_outputs.numpy() elif isinstance(after_outputs, dict): out_1 = after_outputs[list(after_outputs.keys())[0]].numpy() else: out_1 = after_outputs[0].numpy() out_2 = outputs[0].numpy() self.assertEqual(out_1.shape, out_2.shape) out_1 = out_1[~np.isnan(out_1)] out_2 = out_2[~np.isnan(out_2)] max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) # Don't copy this method to model specific test file! # TODO: remove this method once the issues are all fixed! def _make_attention_mask_non_null(self, inputs_dict): """Make sure no sequence has all zeros as attention mask""" for k in ["attention_mask", "encoder_attention_mask", "decoder_attention_mask"]: if k in inputs_dict: attention_mask = inputs_dict[k] # Make sure no all 0s attention masks - to avoid failure at this moment. # Put `1` at the beginning of sequences to make it still work when combining causal attention masks. # TODO: remove this line once a fix regarding large negative values for attention mask is done. attention_mask = tf.concat( [tf.ones_like(attention_mask[:, :1], dtype=attention_mask.dtype), attention_mask[:, 1:]], axis=-1 ) # Here we make the first sequence with all 0s as attention mask. # Currently, this will fail for `TFWav2Vec2Model`. This is caused by the different large negative # values, like `1e-4`, `1e-9`, `1e-30` and `-inf` for attention mask across models/frameworks. # TODO: enable this block once the large negative values thing is cleaned up. # (see https://github.com/huggingface/transformers/issues/14859) # attention_mask = tf.concat( # [ # tf.zeros_like(attention_mask[:1], dtype=tf.int32), # tf.cast(attention_mask[1:], dtype=tf.int32) # ], # axis=0 # ) inputs_dict[k] = attention_mask # Don't copy this method to model specific test file! # TODO: remove this method once the issues are all fixed! def _postprocessing_to_ignore_test_cases(self, tf_outputs, pt_outputs, model_class): """For temporarily ignoring some failed test cases (issues to be fixed)""" tf_keys = set([k for k, v in tf_outputs.items() if v is not None]) pt_keys = set([k for k, v in pt_outputs.items() if v is not None]) key_differences = tf_keys.symmetric_difference(pt_keys) if model_class.__name__ in [ "TFFlaubertWithLMHeadModel", "TFFunnelForPreTraining", "TFElectraForPreTraining", "TFXLMWithLMHeadModel", "TFTransfoXLLMHeadModel", ]: for k in key_differences: if k in ["loss", "losses"]: tf_keys.discard(k) pt_keys.discard(k) elif model_class.__name__.startswith("TFGPT2"): # `TFGPT2` has `past_key_values` as a tensor while `GPT2` has it as a tuple. tf_keys.discard("past_key_values") pt_keys.discard("past_key_values") # create new outputs from the remaining fields new_tf_outputs = type(tf_outputs)(**{k: tf_outputs[k] for k in tf_keys}) new_pt_outputs = type(pt_outputs)(**{k: pt_outputs[k] for k in pt_keys}) return new_tf_outputs, new_pt_outputs def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None): """Check the outputs from PyTorch and TensorFlow models are close enough. Checks are done in a recursive way. Args: model_class: The class of the model that is currently testing. For example, `TFBertModel`, TFBertForMaskedLM`, `TFBertForSequenceClassification`, etc. Mainly used for providing more informative error messages. name (`str`): The name of the output. For example, `output.hidden_states`, `output.attentions`, etc. attributes (`Tuple[str]`): The names of the output's element if the output is a tuple/list with each element being a named field in the output. """ self.assertEqual(type(name), str) if attributes is not None: self.assertEqual(type(attributes), tuple, f"{name}: The argument `attributes` should be a `tuple`") # Allow `ModelOutput` (e.g. `CLIPOutput` has `text_model_output` and `vision_model_output`). if isinstance(tf_outputs, ModelOutput): self.assertTrue( isinstance(pt_outputs, ModelOutput), f"{name}: `pt_outputs` should an instance of `ModelOutput` when `tf_outputs` is", ) # Don't copy this block to model specific test file! # TODO: remove this method and this line after issues are fixed tf_outputs, pt_outputs = self._postprocessing_to_ignore_test_cases(tf_outputs, pt_outputs, model_class) tf_keys = [k for k, v in tf_outputs.items() if v is not None] pt_keys = [k for k, v in pt_outputs.items() if v is not None] self.assertEqual(tf_keys, pt_keys, f"{name}: Output keys differ between TF and PyTorch") # convert to the case of `tuple` # appending each key to the current (string) `names` attributes = tuple([f"{name}.{k}" for k in tf_keys]) self.check_pt_tf_outputs( tf_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, tol=tol, name=name, attributes=attributes ) # Allow `list` (e.g. `TransfoXLModelOutput.mems` is a list of tensors.) elif type(tf_outputs) in [tuple, list]: self.assertEqual(type(tf_outputs), type(pt_outputs), f"{name}: Output types differ between TF and PyTorch") self.assertEqual(len(tf_outputs), len(pt_outputs), f"{name}: Output lengths differ between TF and PyTorch") if attributes is not None: # case 1: each output has assigned name (e.g. a tuple form of a `ModelOutput`) self.assertEqual( len(attributes), len(tf_outputs), f"{name}: The tuple `names` should have the same length as `tf_outputs`", ) else: # case 2: each output has no assigned name (e.g. hidden states of each layer) -> add an index to `names` attributes = tuple([f"{name}_{idx}" for idx in range(len(tf_outputs))]) for tf_output, pt_output, attr in zip(tf_outputs, pt_outputs, attributes): self.check_pt_tf_outputs(tf_output, pt_output, model_class, tol=tol, name=attr) elif isinstance(tf_outputs, tf.Tensor): self.assertTrue( isinstance(pt_outputs, torch.Tensor), f"{name}: `pt_outputs` should a tensor when `tf_outputs` is" ) tf_outputs = tf_outputs.numpy() pt_outputs = pt_outputs.detach().to("cpu").numpy() self.assertEqual( tf_outputs.shape, pt_outputs.shape, f"{name}: Output shapes differ between TF and PyTorch" ) # deal with NumPy's scalars to make replacing nan values by 0 work. if np.isscalar(tf_outputs): tf_outputs = np.array([tf_outputs]) pt_outputs = np.array([pt_outputs]) tf_nans = np.isnan(tf_outputs) pt_nans = np.isnan(pt_outputs) pt_outputs[tf_nans] = 0 tf_outputs[tf_nans] = 0 pt_outputs[pt_nans] = 0 tf_outputs[pt_nans] = 0 max_diff = np.amax(np.abs(tf_outputs - pt_outputs)) self.assertLessEqual(max_diff, tol, f"{name}: Difference between torch and tf is {max_diff} (>= {tol}).") else: raise ValueError( "`tf_outputs` should be an instance of `tf.Tensor`, a `tuple`, or an instance of `tf.Tensor`. Got" f" {type(tf_outputs)} instead." ) def prepare_pt_inputs_from_tf_inputs(self, tf_inputs_dict): pt_inputs_dict = {} for name, key in tf_inputs_dict.items(): if type(key) == bool: pt_inputs_dict[name] = key elif name == "input_values": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) elif name == "pixel_values": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) elif name == "input_features": pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) # other general float inputs elif tf_inputs_dict[name].dtype.is_floating: pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32) else: pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long) return pt_inputs_dict def check_pt_tf_models(self, tf_model, pt_model, tf_inputs_dict): pt_inputs_dict = self.prepare_pt_inputs_from_tf_inputs(tf_inputs_dict) # send pytorch inputs to the correct device pt_inputs_dict = { k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs_dict.items() } # send pytorch model to the correct device pt_model.to(torch_device) # Check predictions on first output (logits/hidden-states) are close enough given low-level computational differences pt_model.eval() with torch.no_grad(): pt_outputs = pt_model(**pt_inputs_dict) tf_outputs = tf_model(tf_inputs_dict) # tf models returned loss is usually a tensor rather than a scalar. # (see `hf_compute_loss`: it uses `tf.keras.losses.Reduction.NONE`) # Change it here to a scalar to match PyTorch models' loss tf_loss = getattr(tf_outputs, "loss", None) if tf_loss is not None: tf_outputs.loss = tf.math.reduce_mean(tf_loss) self.check_pt_tf_outputs(tf_outputs, pt_outputs, type(tf_model)) @is_pt_tf_cross_test def test_pt_tf_model_equivalence(self): import transformers for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Output all for aggressive testing config.output_hidden_states = True config.output_attentions = self.has_attentions # Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency # of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`. # TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it. self._make_attention_mask_non_null(inputs_dict) pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beginning pt_model_class = getattr(transformers, pt_model_class_name) tf_model = model_class(config) pt_model = pt_model_class(config) tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class) tf_inputs_dict_with_labels = self._prepare_for_class( inputs_dict, model_class, # Not all models accept "labels" in the forward pass (yet :) ) return_labels=True if "labels" in inspect.signature(model_class.call).parameters.keys() else False, ) # For some models (e.g. base models), there is no label returned. # Set the input dict to `None` to avoid check outputs twice for the same input dicts. if set(tf_inputs_dict_with_labels.keys()).symmetric_difference(tf_inputs_dict.keys()): tf_inputs_dict_with_labels = None # Check we can load pt model in tf and vice-versa with model => model functions tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict) pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model) # Original test: check without `labels` self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict) # check with `labels` if tf_inputs_dict_with_labels: self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict_with_labels) # Check we can load pt model in tf and vice-versa with checkpoint => model functions with tempfile.TemporaryDirectory() as tmpdirname: pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin") torch.save(pt_model.state_dict(), pt_checkpoint_path) tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path) tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5") tf_model.save_weights(tf_checkpoint_path) pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path) # Original test: check without `labels` self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict) # check with `labels` if tf_inputs_dict_with_labels: self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict_with_labels) def test_compile_tf_model(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() max_input = getattr(self.model_tester, "max_position_embeddings", 512) optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy") for model_class in self.all_model_classes: if model_class.__name__ in ["TFSpeech2TextModel", "TFSpeech2TextForConditionalGeneration"]: inputs = { "decoder_input_ids": tf.keras.Input( batch_shape=(2, max_input), name="decoder_input_ids", dtype="int32", ), "input_features": tf.keras.Input( batch_shape=( 2, max_input, self.model_tester.input_feat_per_channel * self.model_tester.input_channels, ), name="input_features", dtype="float32", ), } elif model_class.__name__ in ["TFWhisperModel", "TFWhisperForConditionalGeneration"]: inputs = { "decoder_input_ids": tf.keras.Input( batch_shape=(2, max_input), name="decoder_input_ids", dtype="int32", ), "input_features": tf.keras.Input( batch_shape=( 2, self.model_tester.num_mel_bins, self.model_tester.seq_length, ), name="input_features", dtype="float32", ), } elif self.is_encoder_decoder: inputs = { "decoder_input_ids": tf.keras.Input( batch_shape=(2, max_input), name="decoder_input_ids", dtype="int32", ), "input_ids": tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32"), } # `pixel_values` implies that the input is an image elif model_class.main_input_name == "pixel_values": inputs = tf.keras.Input( batch_shape=( 3, self.model_tester.num_channels, self.model_tester.image_size, self.model_tester.image_size, ), name="pixel_values", dtype="float32", ) elif model_class.__name__ in ["TFCLIPModel", "TFGroupViTModel"]: inputs = { "input_ids": tf.keras.Input(batch_shape=(3, max_input), name="input_ids", dtype="int32"), "pixel_values": tf.keras.Input( batch_shape=( 3, self.model_tester.vision_model_tester.num_channels, self.model_tester.vision_model_tester.image_size, self.model_tester.vision_model_tester.image_size, ), name="pixel_values", dtype="float32", ), } elif model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs = tf.keras.Input(batch_shape=(4, 2, max_input), name="input_ids", dtype="int32") else: inputs = tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32") # Prepare our model model = model_class(config) model(self._prepare_for_class(inputs_dict, model_class)) # Model must be called before saving. # Let's load it from the disk to be sure we can use pretrained weights with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=False) model = model_class.from_pretrained(tmpdirname) outputs_dict = model(inputs) hidden_states = outputs_dict[0] # Add a dense layer on top to test integration with other keras modules outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states) # Compile extended model extended_model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric]) def test_keyword_and_dict_args(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) outputs_dict = model(inputs) inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) outputs_keywords = model(**inputs_keywords) output_dict = outputs_dict[0].numpy() output_keywords = outputs_keywords[0].numpy() self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", self.model_tester.seq_length) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length) decoder_key_length = getattr(self.model_tester, "key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) def check_decoder_attentions_output(outputs): out_len = len(outputs) self.assertEqual(min(out_len % 2, out_len % 5), 0) # differentiation due to newly added cross_attentions decoder_attentions = outputs.decoder_attentions self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) def check_encoder_attentions_output(outputs): attentions = [ t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions) ] self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True config.output_hidden_states = False model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) out_len = len(outputs) self.assertEqual(config.output_hidden_states, False) check_encoder_attentions_output(outputs) if self.is_encoder_decoder: model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(config.output_hidden_states, False) check_decoder_attentions_output(outputs) # Check that output attentions can also be changed via the config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(config.output_hidden_states, False) check_encoder_attentions_output(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True config.output_hidden_states = True model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs)) self.assertEqual(model.config.output_hidden_states, True) check_encoder_attentions_output(outputs) def test_headmasking(self): if not self.test_head_masking: return random.Random().seed(42) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() random.Random().seed() inputs_dict["output_attentions"] = True config.output_hidden_states = True configs_no_init = _config_zero_init(config) # To be sure we have no Nan for model_class in self.all_model_classes: model = model_class(config=configs_no_init) # Prepare head_mask def prepare_layer_head_mask(i, attention_heads, num_hidden_layers): if i == 0: return tf.concat( (tf.zeros(1, dtype=tf.float32), tf.ones(attention_heads - 1, dtype=tf.float32)), 0 ) elif i == num_hidden_layers - 1: return tf.concat( (tf.zeros(attention_heads - 1, dtype=tf.float32), tf.ones(1, dtype=tf.float32)), 0 ) else: return tf.ones(attention_heads, dtype=tf.float32) head_mask = tf.stack( [ prepare_layer_head_mask(i, config.num_attention_heads, config.num_hidden_layers) for i in range(config.num_hidden_layers) ], 0, ) inputs = self._prepare_for_class(inputs_dict, model_class).copy() inputs["head_mask"] = head_mask if model.config.is_encoder_decoder: signature = inspect.signature(model.call) arg_names = [*signature.parameters.keys()] if "decoder_head_mask" in arg_names: # necessary diferentiation because of T5 model inputs["decoder_head_mask"] = head_mask if "cross_attn_head_mask" in arg_names: inputs["cross_attn_head_mask"] = head_mask outputs = model(**inputs, return_dict=True) def check_attentions_validity(attentions): # Remove Nan for t in attentions: self.assertLess( (tf.math.reduce_sum(tf.cast(tf.math.is_nan(t), tf.float32))).numpy(), (tf.size(t) / 4).numpy() ) # Check we don't have more than 25% nans (arbitrary) attentions = [ tf.where(tf.math.is_nan(t), 0.0, t) for t in attentions ] # remove them (the test is less complete) self.assertAlmostEqual(tf.math.reduce_sum(attentions[0][..., 0, :, :]).numpy(), 0.0) self.assertNotEqual(tf.math.reduce_sum(attentions[0][..., -1, :, :]).numpy(), 0.0) if len(attentions) > 2: # encoder-decodere models have only 2 layers in each modules self.assertNotEqual(tf.math.reduce_sum(attentions[1][..., 0, :, :]).numpy(), 0.0) self.assertAlmostEqual(tf.math.reduce_sum(attentions[-1][..., -2, :, :]).numpy(), 0.0) self.assertNotEqual(tf.math.reduce_sum(attentions[-1][..., -1, :, :]).numpy(), 0.0) if model.config.is_encoder_decoder: check_attentions_validity(outputs.encoder_attentions) check_attentions_validity(outputs.decoder_attentions) if "cross_attn_head_mask" in arg_names: check_attentions_validity(outputs.cross_attentions) else: check_attentions_validity(outputs.attentions) def test_hidden_states_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_hidden_states_output(config, inputs_dict, model_class): model = model_class(config) outputs = model(self._prepare_for_class(inputs_dict, model_class)) expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) if model.config.is_encoder_decoder: encoder_hidden_states = outputs.encoder_hidden_states decoder_hidden_states = outputs.decoder_hidden_states self.assertEqual(config.output_attentions, False) self.assertEqual(len(encoder_hidden_states), expected_num_layers) self.assertListEqual( list(encoder_hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size], ) self.assertEqual(len(decoder_hidden_states), expected_num_layers) self.assertListEqual( list(decoder_hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size], ) else: hidden_states = outputs.hidden_states self.assertEqual(config.output_attentions, False) self.assertEqual(len(hidden_states), expected_num_layers) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size], ) for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(config, inputs_dict, model_class) del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(config, inputs_dict, model_class) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() text_in_text_out_models = ( get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING) + get_values(TF_MODEL_FOR_MASKED_LM_MAPPING) + get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING) ) speech_in_text_out_models = get_values(TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING) for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in text_in_text_out_models: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert isinstance(name, dict) for k, v in name.items(): assert isinstance(v, tf.Variable) elif model_class in speech_in_text_out_models: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert name is None else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None def test_determinism(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) first, second = ( model(self._prepare_for_class(inputs_dict, model_class), training=False)[0], model(self._prepare_for_class(inputs_dict, model_class), training=False)[0], ) out_1 = first.numpy() out_2 = second.numpy() out_1 = out_1[~np.isnan(out_1)] out_2 = out_2[~np.isnan(out_2)] max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs) dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple() def recursive_check(tuple_object, dict_object): if isinstance(tuple_object, (List, Tuple)): for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( all(tf.equal(tuple_object, dict_object)), msg=( "Tuple and dict output are not equal. Difference:" f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}" ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: model = model_class(config) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) if self.has_attentions: tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) # Not all models accept "labels" in the forward pass (yet :) ) if "labels" in inspect.signature(model.call).parameters.keys(): tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) if self.has_attentions: tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence( model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True} ) def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) inputs = copy.deepcopy(inputs_dict) if not self.is_encoder_decoder: input_ids = inputs["input_ids"] del inputs["input_ids"] else: encoder_input_ids = inputs["input_ids"] decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids) del inputs["input_ids"] inputs.pop("decoder_input_ids", None) if not self.is_encoder_decoder: inputs["inputs_embeds"] = model.get_input_embeddings()(input_ids) else: inputs["inputs_embeds"] = model.get_input_embeddings()(encoder_input_ids) inputs["decoder_inputs_embeds"] = model.get_input_embeddings()(decoder_input_ids) inputs = self._prepare_for_class(inputs, model_class) model(inputs) def test_numpy_arrays_inputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def prepare_numpy_arrays(inputs_dict): inputs_np_dict = {} for k, v in inputs_dict.items(): if tf.is_tensor(v): inputs_np_dict[k] = v.numpy() else: inputs_np_dict[k] = np.array(k) return inputs_np_dict for model_class in self.all_model_classes: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) inputs_np = prepare_numpy_arrays(inputs) output_for_dict_input = model(inputs_np) output_for_kw_input = model(**inputs_np) self.assert_outputs_same(output_for_dict_input, output_for_kw_input) def test_resize_token_embeddings(self): # TODO (joao): after the embeddings refactor is complete, rework this test so as to rely exclusively on # tf.keras.layers.Embedding if not self.test_resize_embeddings: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def _get_word_embedding_weight(model, embedding_layer): if isinstance(embedding_layer, tf.keras.layers.Embedding): # builds the embeddings layer model(model.dummy_inputs) return embedding_layer.embeddings else: return model._get_word_embedding_weight(embedding_layer) for model_class in self.all_model_classes: for size in [config.vocab_size - 10, config.vocab_size + 10, None]: # build the embeddings model = model_class(config=copy.deepcopy(config)) # `resize_token_embeddings` mutates `config` old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings()) old_bias = model.get_bias() old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings()) # reshape the embeddings model.resize_token_embeddings(size) new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings()) new_bias = model.get_bias() new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings()) # check that the resized embeddings size matches the desired size. assert_size = size if size is not None else config.vocab_size self.assertEqual(new_input_embeddings.shape[0], assert_size) # check that weights remain the same after resizing models_equal = True for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) if old_bias is not None and new_bias is not None: for old_weight, new_weight in zip(old_bias.values(), new_bias.values()): self.assertEqual(new_weight.shape[-1], assert_size) models_equal = True for p1, p2 in zip(tf.squeeze(old_weight), tf.squeeze(new_weight)): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) if old_output_embeddings is not None and new_output_embeddings is not None: self.assertEqual(new_output_embeddings.shape[0], assert_size) self.assertEqual(new_output_embeddings.shape[1], old_output_embeddings.shape[1]) models_equal = True for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) # TODO (Joao): this test is not slow, but it's tagged as such to keep track of failures on the scheduled CI runs, # while passing push CI. Fix the underlying issues and remove the tag. @slow def test_save_load_after_resize_token_embeddings(self): if not self.test_resize_embeddings: return config, original_inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # create a model with resized (expended) embeddings new_tokens_size = 10 old_total_size = config.vocab_size new_total_size = old_total_size + new_tokens_size model = model_class(config=copy.deepcopy(config)) # `resize_token_embeddings` mutates `config` model(model.dummy_inputs) # builds the embeddings layer model.resize_token_embeddings(new_total_size) # fetch the output for an input exclusively made of new members of the vocabulary inputs_dict = copy.deepcopy(original_inputs_dict) ids_feat_name = None if "input_ids" in inputs_dict: ids_feat_name = "input_ids" elif "decoder_input_ids" in inputs_dict: ids_feat_name = "decoder_input_ids" else: assert False, "No input ids feature found in the inputs dict" new_vocab_input_ids = ids_tensor(inputs_dict[ids_feat_name].shape, new_tokens_size) new_vocab_input_ids += old_total_size inputs_dict[ids_feat_name] = new_vocab_input_ids if "input_ids" in inputs_dict: inputs_dict["input_ids"] = new_vocab_input_ids if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"] = new_vocab_input_ids prepared_inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**prepared_inputs) # save and load the model with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=False) model = model_class.from_pretrained(tmpdirname) restored_model_outputs = model(**prepared_inputs) # check that the output for the restored model is the same self.assert_outputs_same(restored_model_outputs, outputs) @unittest.skipIf( not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0, reason="This test always passes on CPU.", ) def test_embeddings_out_of_bounds_raise_exception(self): # TF embeddings layers don't raise an exception when an index is out of bounds on GPU, so we manually raise it. # This test should only fail on GPU for models where we haven't added the safety check. if not self.test_resize_embeddings: return config, original_inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config=config) inputs_dict = copy.deepcopy(original_inputs_dict) if "input_ids" in inputs_dict: inputs_dict["input_ids"] = inputs_dict["input_ids"] * int(1e9) if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"] = inputs_dict["decoder_input_ids"] * int(1e9) prepared_inputs = self._prepare_for_class(inputs_dict, model_class) with self.assertRaises(tf.errors.InvalidArgumentError): model(**prepared_inputs) def test_lm_head_model_random_no_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict.get("input_ids", None) # iterate over all generative models for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_ids with self.assertRaises(ValueError): model.generate(do_sample=True, max_length=5) # num_return_sequences = 1 self._check_generated_ids(model.generate(input_ids, do_sample=True)) elif model_class.__name__ not in ["TFSpeech2TextForConditionalGeneration"]: # Models with non-text inputs won't work here; num_return_sequences = 1 self._check_generated_ids(model.generate(do_sample=True, max_length=5)) with self.assertRaises(ValueError): # generating multiple sequences when no beam search generation # is not allowed as it would always generate the same sequences model.generate(input_ids, do_sample=False, num_return_sequences=2) # num_return_sequences > 1, sample self._check_generated_ids(model.generate(input_ids, do_sample=True, num_return_sequences=2)) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_ids, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_ids.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) def test_lm_head_model_no_beam_search_generate_dict_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict.get("input_ids", None) if input_ids is None: input_ids = inputs_dict.get("input_features", None) # iterate over all generative models for model_class in self.all_generative_model_classes: model = model_class(config) output_greedy = model.generate( input_ids, do_sample=False, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) output_sample = model.generate( input_ids, do_sample=True, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) if model.config.is_encoder_decoder: self.assertIsInstance(output_greedy, TFGreedySearchEncoderDecoderOutput) self.assertIsInstance(output_sample, TFSampleEncoderDecoderOutput) else: self.assertIsInstance(output_greedy, TFGreedySearchDecoderOnlyOutput) self.assertIsInstance(output_sample, TFSampleDecoderOnlyOutput) def test_lm_head_model_random_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict.get("input_ids", None) for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_ids, num_return_sequences = 1 self._check_generated_ids(model.generate(input_ids, do_sample=True, num_beams=2)) else: # num_return_sequences = 1 self._check_generated_ids(model.generate(do_sample=True, max_length=5, num_beams=2)) with self.assertRaises(ValueError): # generating more sequences than having beams leads is not possible model.generate(input_ids, do_sample=False, num_return_sequences=3, num_beams=2) # num_return_sequences > 1, sample self._check_generated_ids( model.generate( input_ids, do_sample=True, num_beams=2, num_return_sequences=2, ) ) # num_return_sequences > 1, greedy self._check_generated_ids(model.generate(input_ids, do_sample=False, num_beams=2, num_return_sequences=2)) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_ids, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_ids.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) def test_lm_head_model_beam_search_generate_dict_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict.get("input_ids", None) if input_ids is None: input_ids = inputs_dict.get("input_features", None) # iterate over all generative models for model_class in self.all_generative_model_classes: model = model_class(config) output_beam_search = model.generate( input_ids, num_beams=2, do_sample=False, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) output_beam_sample = model.generate( input_ids, num_beams=2, do_sample=True, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) if model.config.is_encoder_decoder: self.assertIsInstance(output_beam_search, TFBeamSearchEncoderDecoderOutput) self.assertIsInstance(output_beam_sample, TFBeamSampleEncoderDecoderOutput) else: self.assertIsInstance(output_beam_search, TFBeamSearchDecoderOnlyOutput) self.assertIsInstance(output_beam_sample, TFBeamSampleDecoderOnlyOutput) def test_loss_computation(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) if not getattr(model, "hf_compute_loss", None) and not _return_type_has_loss(model): continue # The number of elements in the loss should be the same as the number of elements in the label prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) added_label_names = sorted(list(prepared_for_class.keys() - inputs_dict.keys()), reverse=True) if not added_label_names: continue # This test is only for models with easily-separable labels added_label = prepared_for_class[added_label_names[0]] expected_loss_size = added_label.shape.as_list()[:1] # Test that model correctly compute the loss with kwargs prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) possible_input_names = {"input_ids", "pixel_values", "input_features", "input_values"} input_name = possible_input_names.intersection(set(prepared_for_class)).pop() model_input = prepared_for_class.pop(input_name) loss = model(model_input, **prepared_for_class)[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]) # Test that model correctly compute the loss when we mask some positions prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) possible_input_names = {"input_ids", "pixel_values", "input_features", "input_values"} input_name = possible_input_names.intersection(set(prepared_for_class)).pop() model_input = prepared_for_class.pop(input_name) if "labels" in prepared_for_class: labels = prepared_for_class["labels"].numpy() if len(labels.shape) > 1 and labels.shape[1] != 1: labels[0] = -100 prepared_for_class["labels"] = tf.convert_to_tensor(labels) loss = model(model_input, **prepared_for_class)[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]) self.assertTrue(not np.any(np.isnan(loss.numpy()))) # Test that model correctly compute the loss with a dict prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) loss = model(prepared_for_class)[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]) # Test that model correctly compute the loss with a tuple prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) # Get keys that were added with the _prepare_for_class function label_keys = prepared_for_class.keys() - inputs_dict.keys() signature = inspect.signature(model.call).parameters signature_names = list(signature.keys()) # Create a dictionary holding the location of the tensors in the tuple tuple_index_mapping = {0: input_name} for label_key in label_keys: label_key_index = signature_names.index(label_key) tuple_index_mapping[label_key_index] = label_key sorted_tuple_index_mapping = sorted(tuple_index_mapping.items()) # Initialize a list with their default values, update the values and convert to a tuple list_input = [] for name in signature_names: if name != "kwargs": list_input.append(signature[name].default) for index, value in sorted_tuple_index_mapping: list_input[index] = prepared_for_class[value] tuple_input = tuple(list_input) # Send to model loss = model(tuple_input[:-1])[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]) def check_keras_fit_results(self, val_loss1, val_loss2, atol=1e-2, rtol=1e-3): self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol)) def test_keras_fit(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) if not getattr(model, "hf_compute_loss", False) and not _return_type_has_loss(model): continue # Test that model correctly compute the loss with kwargs prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) # Is there a better way to remove these decoder inputs? # We also remove "return_loss" as this is covered by the train_step when using fit() prepared_for_class = { key: val for key, val in prepared_for_class.items() if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids", "return_loss") } accuracy_classes = [ "ForPreTraining", "ForCausalLM", "ForMaskedLM", "ForQuestionAnswering", "ForMultipleChoice", "ForSequenceClassification", "ForTokenClassification", "ForNextSentencePrediction", "LMHeadModel", ] for accuracy_class in accuracy_classes: if model.__class__.__name__.endswith(accuracy_class): metrics = [tf.keras.metrics.SparseCategoricalAccuracy()] break else: metrics = [] if hasattr(self.model_tester, "batch_size"): sample_weight = tf.convert_to_tensor([0.5] * self.model_tester.batch_size, dtype=tf.float32) else: sample_weight = None model(model.dummy_inputs) # Build the model so we can get some constant weights model_weights = model.get_weights() # Run eagerly to save some expensive compilation times model.compile(optimizer=tf.keras.optimizers.SGD(0.0), run_eagerly=True, metrics=metrics) # Make sure the model fits without crashing regardless of where we pass the labels history1 = model.fit( prepared_for_class, validation_data=prepared_for_class, sample_weight=sample_weight, steps_per_epoch=1, validation_steps=1, shuffle=False, ) val_loss1 = history1.history["val_loss"][0] self.assertTrue(not isnan(val_loss1)) accuracy1 = {key: val[0] for key, val in history1.history.items() if key.endswith("accuracy")} possible_label_cols = { "labels", "label", "label_ids", "start_positions", "start_position", "end_positions", "end_position", "next_sentence_label", } label_names = possible_label_cols.intersection(set(prepared_for_class)) if len(label_names) == 0: # The next tests only make sense for models with separate inputs and labels, and do not make # sense for models that don't clearly distinguish between the two (e.g. CLIP) return labels = {key: val for key, val in prepared_for_class.items() if key in label_names} inputs_minus_labels = {key: val for key, val in prepared_for_class.items() if key not in label_names} self.assertGreater(len(inputs_minus_labels), 0) # We reinitialize the model here even though our learning rate was zero # because BatchNorm updates weights by means other than gradient descent. model.set_weights(model_weights) history2 = model.fit( inputs_minus_labels, labels, validation_data=(inputs_minus_labels, labels), sample_weight=sample_weight, steps_per_epoch=1, validation_steps=1, shuffle=False, ) val_loss2 = history2.history["val_loss"][0] self.assertTrue(not isnan(val_loss2)) accuracy2 = {key: val[0] for key, val in history2.history.items() if key.endswith("accuracy")} self.check_keras_fit_results(val_loss1, val_loss2) self.assertEqual(history1.history.keys(), history2.history.keys()) for key in history1.history.keys(): if not key.startswith("val_"): self.assertTrue("val_" + key in history1.history.keys(), "Outputs differ in train/test step!") if metrics: self.assertTrue(len(accuracy1) == len(accuracy2) > 0, "Missing metrics!") # Make sure fit works with tf.data.Dataset and results are consistent dataset = tf.data.Dataset.from_tensor_slices(prepared_for_class) if sample_weight is not None: # Add in the sample weight weighted_dataset = dataset.map(lambda x: (x, None, tf.convert_to_tensor(0.5, dtype=tf.float32))) else: weighted_dataset = dataset # Pass in all samples as a batch to match other `fit` calls weighted_dataset = weighted_dataset.batch(len(dataset)) dataset = dataset.batch(len(dataset)) # Reinitialize to fix batchnorm again model.set_weights(model_weights) # To match the other calls, don't pass sample weights in the validation data history3 = model.fit( weighted_dataset, validation_data=dataset, steps_per_epoch=1, validation_steps=1, shuffle=False, ) val_loss3 = history3.history["val_loss"][0] self.assertTrue(not isnan(val_loss3)) accuracy3 = {key: val[0] for key, val in history3.history.items() if key.endswith("accuracy")} self.check_keras_fit_results(val_loss1, val_loss3) self.assertEqual(history1.history.keys(), history3.history.keys()) if metrics: self.assertTrue(len(accuracy1) == len(accuracy3) > 0, "Missing metrics!") def test_int64_inputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: prepared_for_class = self._prepare_for_class( inputs_dict.copy(), model_class, return_labels=True if "labels" in inspect.signature(model_class.call).parameters.keys() else False, ) if not any( [tensor.dtype.is_integer for tensor in prepared_for_class.values() if isinstance(tensor, tf.Tensor)] ): return # No integer inputs means no need for this test prepared_for_class = { key: tf.cast(tensor, tf.int64) if isinstance(tensor, tf.Tensor) and tensor.dtype.is_integer else tensor for key, tensor in prepared_for_class.items() } model = model_class(config) model(**prepared_for_class) # No assertion, we're just checking this doesn't throw an error def test_generate_with_headmasking(self): attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"] config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_generative_model_classes: model = model_class(config) # We want to test only encoder-decoder models if not config.is_encoder_decoder: continue head_masking = { "head_mask": tf.zeros((config.encoder_layers, config.encoder_attention_heads)), "decoder_head_mask": tf.zeros((config.decoder_layers, config.decoder_attention_heads)), "cross_attn_head_mask": tf.zeros((config.decoder_layers, config.decoder_attention_heads)), } signature = inspect.signature(model.call) if set(head_masking.keys()) < set([*signature.parameters.keys()]): continue for attn_name, (name, mask) in zip(attention_names, head_masking.items()): out = model.generate( inputs_dict["input_ids"], num_beams=1, max_length=inputs_dict["input_ids"] + 5, output_attentions=True, return_dict_in_generate=True, **{name: mask}, ) # We check the state of decoder_attentions and cross_attentions just from the last step attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1] self.assertEqual(sum([tf.reduce_sum(w).numpy() for w in attn_weights]), 0.0) def test_load_with_mismatched_shapes(self): if not self.test_mismatched_shapes: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if model_class not in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING): continue with self.subTest(msg=f"Testing {model_class}"): with tempfile.TemporaryDirectory() as tmp_dir: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) _ = model(**inputs) model.save_pretrained(tmp_dir) # Fails when we don't set ignore_mismatched_sizes=True with self.assertRaises(ValueError): new_model = TFAutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42) with self.assertRaises(ValueError): new_model_without_prefix = TFAutoModel.from_pretrained(tmp_dir, vocab_size=10) logger = logging.get_logger("transformers.modeling_tf_utils") with CaptureLogger(logger) as cl: new_model = TFAutoModelForSequenceClassification.from_pretrained( tmp_dir, num_labels=42, ignore_mismatched_sizes=True ) self.assertIn("the shapes did not match", cl.out) logits = new_model(**inputs).logits self.assertEqual(logits.shape[1], 42) with CaptureLogger(logger) as cl: new_model_without_prefix = TFAutoModel.from_pretrained( tmp_dir, vocab_size=10, ignore_mismatched_sizes=True ) self.assertIn("the shapes did not match", cl.out) # Although Tf models always have a prefix pointing to `MainLayer`, # we still add this "without prefix" test to keep a consistency between tf and pt tests. input_ids = ids_tensor((2, 8), 10) if self.is_encoder_decoder: new_model_without_prefix(input_ids, decoder_input_ids=input_ids) else: new_model_without_prefix(input_ids) def test_model_main_input_name(self): for model_class in self.all_model_classes: model_signature = inspect.signature(getattr(model_class, "call")) # The main input is the name of the argument after `self` observed_main_input_name = list(model_signature.parameters.keys())[1] self.assertEqual(model_class.main_input_name, observed_main_input_name) def test_dataset_conversion(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class, return_labels=False) tf_inputs_dict = { key: val for key, val in tf_inputs_dict.items() if "head_mask" not in key and isinstance(val, tf.Tensor) } tf_inputs_dict["extra_unwanted_column"] = list(tf_inputs_dict.values())[0] # Use a random other tensor input_dataset = Dataset.from_dict(tf_inputs_dict) tf_dataset = model.prepare_tf_dataset( input_dataset, batch_size=len(input_dataset), drop_remainder=False, shuffle=False ) test_batch = next(iter(tf_dataset)) if isinstance(test_batch, tf.Tensor): self.assertEqual(len(test_batch), len(input_dataset)) # Assert we didn't lose any data else: # Assert we discarded the unwanted extra column but kept everything else self.assertEqual(len(test_batch), len(input_dataset.features) - 1) self.assertNotIn("extra_unwanted_column", test_batch) for tensor in test_batch.values(): self.assertTrue(isinstance(tensor, tf.Tensor)) self.assertEqual(len(tensor), len(input_dataset)) # Assert we didn't lose any data model(test_batch, training=False) if "labels" in inspect.signature(model_class.call).parameters.keys(): tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class, return_labels=True) if "labels" not in tf_inputs_dict: return # This model isn't giving us labels after all, don't try training with it tf_inputs_dict = {key: val for key, val in tf_inputs_dict.items() if "head_mask" not in key} tf_inputs_dict["extra_unwanted_column"] = list(tf_inputs_dict.values())[0] # Use a random other tensor input_dataset = Dataset.from_dict(tf_inputs_dict) tf_dataset = model.prepare_tf_dataset( input_dataset, batch_size=len(input_dataset), drop_remainder=False, shuffle=False ) test_batch, test_batch_labels = next(iter(tf_dataset)) self.assertGreater(len(test_batch_labels), 0) # Assert the labels are present feature_columns = 1 if isinstance(test_batch, tf.Tensor) else len(test_batch) label_columns = 1 if isinstance(test_batch_labels, tf.Tensor) else len(test_batch_labels) # Assert we discarded the unwanted extra column but kept everything else self.assertEqual(feature_columns + label_columns, len(input_dataset.features) - 1) if isinstance(test_batch, dict): self.assertNotIn("extra_unwanted_column", test_batch) if isinstance(test_batch_labels, dict): self.assertNotIn("extra_unwanted_column", test_batch_labels) model.compile(optimizer="sgd", run_eagerly=True) model.train_on_batch(test_batch, test_batch_labels) def _test_xla_generate(self, num_beams, num_return_sequences, max_length, **generate_kwargs): def _generate_and_check_results(model, config, inputs_dict): if "input_ids" in inputs_dict: inputs = inputs_dict["input_ids"] # make sure there are no pad tokens in prompt, which may trigger unwanted behavior if config.pad_token_id is not None: if config.pad_token_id == 0: new_pad_token = config.pad_token_id + 1 else: new_pad_token = config.pad_token_id - 1 else: new_pad_token = None inputs = tf.where(inputs != config.pad_token_id, inputs, new_pad_token) elif "input_features" in inputs_dict: inputs = inputs_dict["input_features"] else: raise ValueError("No valid generate input found in inputs_dict") generated = model.generate(inputs, **generate_kwargs).numpy() generate_xla = tf.function(model.generate, jit_compile=True) generated_xla = generate_xla(inputs, **generate_kwargs).numpy() self.assertListEqual(generated.tolist(), generated_xla.tolist()) for model_class in self.all_generative_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.eos_token_id = None # Generate until max length config.max_length = max_length config.do_sample = False config.num_beams = num_beams config.num_return_sequences = num_return_sequences # fix config for models with additional sequence-length limiting settings for var_name in ["max_position_embeddings", "max_target_positions"]: if hasattr(config, var_name): try: setattr(config, var_name, max_length) except NotImplementedError: # xlnet will raise an exception when trying to set # max_position_embeddings. pass model = model_class(config) if model.supports_xla_generation: _generate_and_check_results(model, config, inputs_dict) else: with self.assertRaises(ValueError): _generate_and_check_results(model, config, inputs_dict) def test_xla_generate_fast(self): """ Basic quick test for generate-compatible classes that confirms that XLA-generated tokens are the same as their non XLA counterparts. Either the model supports XLA generation and passes the inner test, or it raises an appropriate exception """ num_beams = 1 num_return_sequences = 1 max_length = 10 self._test_xla_generate(num_beams, num_return_sequences, max_length) def test_xla_generate_contrastive(self): """ Similar to `test_xla_generate_fast`, but for contrastive search -- contrastive search directly manipulates the model cache and other outputs, and this test ensures that they are in a valid format that is also supported by XLA. Either the model supports XLA generation and passes the inner test, or it raises an appropriate exception """ num_beams = 1 num_return_sequences = 1 max_length = 10 self._test_xla_generate(num_beams, num_return_sequences, max_length, penalty_alpha=0.5, top_k=5) @slow def test_xla_generate_slow(self): """ Slow and challenging version of `test_xla_generate_fast` -- this test asks for several long sequences using beam search, with and without XLA. The two outputs should match, and a failure in this test indicates that the model may need further analysis if it is to be used for XLA generation. Either the model supports XLA generation and passes the inner test, or it raises an appropriate exception """ num_beams = 8 num_return_sequences = 2 max_length = 128 self._test_xla_generate(num_beams, num_return_sequences, max_length) def _generate_random_bad_tokens(self, num_bad_tokens, model): # special tokens cannot be bad tokens special_tokens = [] if model.config.bos_token_id is not None: special_tokens.append(model.config.bos_token_id) if model.config.pad_token_id is not None: special_tokens.append(model.config.pad_token_id) if model.config.eos_token_id is not None: special_tokens.append(model.config.eos_token_id) # create random bad tokens that are not special tokens bad_tokens = [] while len(bad_tokens) < num_bad_tokens: token = tf.squeeze(ids_tensor((1, 1), self.model_tester.vocab_size), 0).numpy()[0] if token not in special_tokens: bad_tokens.append(token) return bad_tokens def _check_generated_ids(self, output_ids): for token_id in output_ids[0].numpy().tolist(): self.assertGreaterEqual(token_id, 0) self.assertLess(token_id, self.model_tester.vocab_size) def _check_match_tokens(self, generated_ids, bad_words_ids): # for all bad word tokens for bad_word_ids in bad_words_ids: # for all slices in batch for generated_ids_slice in generated_ids: # for all word idx for i in range(len(bad_word_ids), len(generated_ids_slice)): # if tokens match if generated_ids_slice[i - len(bad_word_ids) : i] == bad_word_ids: return True return False def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None): """Creates a random int32 tensor of the shape within the vocab size.""" if rng is None: rng = random.Random() total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.randint(0, vocab_size - 1)) output = tf.constant(values, shape=shape, dtype=dtype if dtype is not None else tf.int32) return output def random_attention_mask(shape, rng=None, name=None, dtype=None): attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None, dtype=dtype) # make sure that at least one token is attended to for each batch attn_mask = tf.concat([attn_mask[:, :-1], tf.ones_like(attn_mask[:, -1:], dtype=dtype)], axis=-1) return attn_mask def floats_tensor(shape, scale=1.0, rng=None, name=None, dtype=None): """Creates a random float32 tensor""" if rng is None: rng = random.Random() total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.random() * scale) return tf.reshape(tf.constant(values, dtype=dtype if dtype is not None else tf.float32), shape=shape) @require_tf class UtilsFunctionsTest(unittest.TestCase): def test_cached_files_are_used_when_internet_is_down(self): # A mock response for an HTTP head request to emulate server down response_mock = mock.Mock() response_mock.status_code = 500 response_mock.headers = {} response_mock.raise_for_status.side_effect = HTTPError response_mock.json.return_value = {} # Download this model to make sure it's in the cache. _ = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # Under the mock environment we get a 500 error when trying to reach the model. with mock.patch("requests.request", return_value=response_mock) as mock_head: _ = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # This check we did call the fake head request mock_head.assert_called() def test_load_from_one_file(self): try: tmp_file = tempfile.mktemp() with open(tmp_file, "wb") as f: http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/tf_model.h5", f) config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") _ = TFBertModel.from_pretrained(tmp_file, config=config) finally: os.remove(tmp_file) def test_legacy_load_from_url(self): # This test is for deprecated behavior and can be removed in v5 config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert") _ = TFBertModel.from_pretrained( "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/tf_model.h5", config=config ) # tests whether the unpack_inputs function behaves as expected def test_unpack_inputs(self): class DummyModel: def __init__(self): config_kwargs = {"output_attentions": False, "output_hidden_states": False, "return_dict": False} self.config = PretrainedConfig(**config_kwargs) self.main_input_name = "input_ids" @unpack_inputs def call( self, input_ids=None, past_key_values=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): return input_ids, past_key_values, output_attentions, output_hidden_states, return_dict @unpack_inputs def foo(self, pixel_values, output_attentions=None, output_hidden_states=None, return_dict=None): return pixel_values, output_attentions, output_hidden_states, return_dict dummy_model = DummyModel() input_ids = tf.constant([0, 1, 2, 3], dtype=tf.int64) past_key_values = tf.constant([4, 5, 6, 7], dtype=tf.int64) pixel_values = tf.constant([8, 9, 10, 11], dtype=tf.int64) # test case 1: Pass inputs as keyword arguments; Booleans are inherited from the config. output = dummy_model.call(input_ids=input_ids, past_key_values=past_key_values) tf.debugging.assert_equal(output[0], input_ids) tf.debugging.assert_equal(output[1], past_key_values) self.assertFalse(output[2]) self.assertFalse(output[3]) self.assertFalse(output[4]) # test case 2: Same as above, but with positional arguments. output = dummy_model.call(input_ids, past_key_values) tf.debugging.assert_equal(output[0], input_ids) tf.debugging.assert_equal(output[1], past_key_values) self.assertFalse(output[2]) self.assertFalse(output[3]) self.assertFalse(output[4]) # test case 3: We can also pack everything in the first input. output = dummy_model.call(input_ids={"input_ids": input_ids, "past_key_values": past_key_values}) tf.debugging.assert_equal(output[0], input_ids) tf.debugging.assert_equal(output[1], past_key_values) self.assertFalse(output[2]) self.assertFalse(output[3]) self.assertFalse(output[4]) # test case 4: Explicit boolean arguments should override the config. output = dummy_model.call( input_ids=input_ids, past_key_values=past_key_values, output_attentions=False, return_dict=True ) tf.debugging.assert_equal(output[0], input_ids) tf.debugging.assert_equal(output[1], past_key_values) self.assertFalse(output[2]) self.assertFalse(output[3]) self.assertTrue(output[4]) # test case 5: Unexpected arguments should raise an exception. with self.assertRaises(ValueError): output = dummy_model.call(input_ids=input_ids, past_key_values=past_key_values, foo="bar") # test case 6: the decorator is independent from `main_input_name` -- it treats the first argument of the # decorated function as its main input. output = dummy_model.foo(pixel_values=pixel_values) tf.debugging.assert_equal(output[0], pixel_values) self.assertFalse(output[1]) self.assertFalse(output[2]) self.assertFalse(output[3]) # Tests whether the stable softmax is stable on CPU, with and without XLA def test_xla_stable_softmax(self): large_penalty = -1e9 n_tokens = 10 batch_size = 8 def masked_softmax(x, boolean_mask): numerical_mask = (1.0 - tf.cast(boolean_mask, dtype=tf.float32)) * large_penalty masked_x = x + numerical_mask return stable_softmax(masked_x) xla_masked_softmax = tf.function(masked_softmax, jit_compile=True) xla_stable_softmax = tf.function(stable_softmax, jit_compile=True) x = tf.random.normal((batch_size, n_tokens)) # Same outcome regardless of the boolean mask here masked_tokens = random.randint(0, n_tokens) boolean_mask = tf.convert_to_tensor([[1] * (n_tokens - masked_tokens) + [0] * masked_tokens], dtype=tf.int32) # We can randomly mask a random numerical input OUTSIDE XLA numerical_mask = (1.0 - tf.cast(boolean_mask, dtype=tf.float32)) * large_penalty masked_x = x + numerical_mask xla_out = xla_stable_softmax(masked_x) out = stable_softmax(masked_x) assert tf.experimental.numpy.allclose(xla_out, out) # The stable softmax has the same output as the original softmax unstable_out = tf.nn.softmax(masked_x) assert tf.experimental.numpy.allclose(unstable_out, out) # We can randomly mask a random numerical input INSIDE XLA xla_out = xla_masked_softmax(x, boolean_mask) out = masked_softmax(x, boolean_mask) assert tf.experimental.numpy.allclose(xla_out, out) def test_checkpoint_sharding_from_hub(self): model = TFBertModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") # the model above is the same as the model below, just a sharded version. ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") for p1, p2 in zip(model.weights, ref_model.weights): assert np.allclose(p1.numpy(), p2.numpy()) @is_pt_tf_cross_test def test_checkpoint_sharding_local_from_pt(self): with tempfile.TemporaryDirectory() as tmp_dir: _ = Repository(local_dir=tmp_dir, clone_from="hf-internal-testing/tiny-random-bert-sharded") model = TFBertModel.from_pretrained(tmp_dir, from_pt=True) # the model above is the same as the model below, just a sharded pytorch version. ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") for p1, p2 in zip(model.weights, ref_model.weights): assert np.allclose(p1.numpy(), p2.numpy()) def test_shard_checkpoint(self): # This is the model we will use, total size 340,000 bytes. model = tf.keras.Sequential( [ tf.keras.layers.Dense(200, use_bias=False), # size 80,000 tf.keras.layers.Dense(200, use_bias=False), # size 160,000 tf.keras.layers.Dense(100, use_bias=False), # size 80,000 tf.keras.layers.Dense(50, use_bias=False), # size 20,000 ] ) inputs = tf.zeros((1, 100), dtype=tf.float32) model(inputs) weights = model.weights weights_dict = {w.name: w for w in weights} with self.subTest("No shard when max size is bigger than model size"): shards, index = tf_shard_checkpoint(weights) self.assertIsNone(index) self.assertDictEqual(shards, {TF2_WEIGHTS_NAME: weights}) with self.subTest("Test sharding, no weights bigger than max size"): shards, index = tf_shard_checkpoint(weights, max_shard_size="300kB") # Split is first two layers then last two. self.assertDictEqual( index, { "metadata": {"total_size": 340000}, "weight_map": { "dense/kernel:0": "tf_model-00001-of-00002.h5", "dense_1/kernel:0": "tf_model-00001-of-00002.h5", "dense_2/kernel:0": "tf_model-00002-of-00002.h5", "dense_3/kernel:0": "tf_model-00002-of-00002.h5", }, }, ) shard1 = [weights_dict["dense/kernel:0"], weights_dict["dense_1/kernel:0"]] shard2 = [weights_dict["dense_2/kernel:0"], weights_dict["dense_3/kernel:0"]] self.assertDictEqual(shards, {"tf_model-00001-of-00002.h5": shard1, "tf_model-00002-of-00002.h5": shard2}) with self.subTest("Test sharding with weights bigger than max size"): shards, index = tf_shard_checkpoint(weights, max_shard_size="100kB") # Split is first layer, second layer then last 2. self.assertDictEqual( index, { "metadata": {"total_size": 340000}, "weight_map": { "dense/kernel:0": "tf_model-00001-of-00003.h5", "dense_1/kernel:0": "tf_model-00002-of-00003.h5", "dense_2/kernel:0": "tf_model-00003-of-00003.h5", "dense_3/kernel:0": "tf_model-00003-of-00003.h5", }, }, ) shard1 = [weights_dict["dense/kernel:0"]] shard2 = [weights_dict["dense_1/kernel:0"]] shard3 = [weights_dict["dense_2/kernel:0"], weights_dict["dense_3/kernel:0"]] self.assertDictEqual( shards, { "tf_model-00001-of-00003.h5": shard1, "tf_model-00002-of-00003.h5": shard2, "tf_model-00003-of-00003.h5": shard3, }, ) @slow def test_special_layer_name_sharding(self): retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True) model = TFRagModel.from_pretrained("facebook/rag-token-nq", retriever=retriever) with tempfile.TemporaryDirectory() as tmp_dir: for max_size in ["150kB", "150kiB", "200kB", "200kiB"]: model.save_pretrained(tmp_dir, max_shard_size=max_size) ref_model = TFRagModel.from_pretrained(tmp_dir, retriever=retriever) for p1, p2 in zip(model.weights, ref_model.weights): assert np.allclose(p1.numpy(), p2.numpy()) def test_checkpoint_sharding_local(self): model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: # We use the same folder for various sizes to make sure a new save erases the old checkpoint. for max_size in ["150kB", "150kiB", "200kB", "200kiB"]: model.save_pretrained(tmp_dir, max_shard_size=max_size) # Get each shard file and its size shard_to_size = {} for shard in os.listdir(tmp_dir): if shard.endswith(".h5"): shard_file = os.path.join(tmp_dir, shard) shard_to_size[shard_file] = os.path.getsize(shard_file) index_file = os.path.join(tmp_dir, TF2_WEIGHTS_INDEX_NAME) # Check there is an index but no regular weight file self.assertTrue(os.path.isfile(index_file)) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, TF2_WEIGHTS_NAME))) # Check a file is bigger than max_size only when it has a single weight for shard_file, size in shard_to_size.items(): if max_size.endswith("kiB"): max_size_int = int(max_size[:-3]) * 2**10 else: max_size_int = int(max_size[:-2]) * 10**3 # Note: pickle adds some junk so the weight of the file can end up being slightly bigger than # the size asked for (since we count parameters) if size >= max_size_int + 50000: with h5py.File(shard_file, "r") as state_file: self.assertEqual(len(state_file), 1) # Check the index and the shard files found match with open(index_file, "r", encoding="utf-8") as f: index = json.loads(f.read()) all_shards = set(index["weight_map"].values()) shards_found = set(f for f in os.listdir(tmp_dir) if f.endswith(".h5")) self.assertSetEqual(all_shards, shards_found) # Finally, check the model can be reloaded new_model = TFBertModel.from_pretrained(tmp_dir) model(model.dummy_inputs) new_model(model.dummy_inputs) for p1, p2 in zip(model.weights, new_model.weights): self.assertTrue(np.allclose(p1.numpy(), p2.numpy())) def test_save_pretrained_signatures(self): model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # Short custom TF signature function. # `input_signature` is specific to BERT. @tf.function( input_signature=[ [ tf.TensorSpec([None, None], tf.int32, name="input_ids"), tf.TensorSpec([None, None], tf.int32, name="token_type_ids"), tf.TensorSpec([None, None], tf.int32, name="attention_mask"), ] ] ) def serving_fn(input): return model(input) # Using default signature (default behavior) overrides 'serving_default' with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, saved_model=True, signatures=None) model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1") self.assertTrue("serving_default" in list(model_loaded.signatures.keys())) # Providing custom signature function with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, saved_model=True, signatures={"custom_signature": serving_fn}) model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1") self.assertTrue("custom_signature" in list(model_loaded.signatures.keys())) # Providing multiple custom signature function with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained( tmp_dir, saved_model=True, signatures={"custom_signature_1": serving_fn, "custom_signature_2": serving_fn}, ) model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1") self.assertTrue("custom_signature_1" in list(model_loaded.signatures.keys())) self.assertTrue("custom_signature_2" in list(model_loaded.signatures.keys())) @require_safetensors def test_safetensors_save_and_load(self): model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, safe_serialization=True) # No tf_model.h5 file, only a model.safetensors self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME))) self.assertFalse(os.path.isfile(os.path.join(tmp_dir, TF2_WEIGHTS_NAME))) new_model = TFBertModel.from_pretrained(tmp_dir) # Check models are equal for p1, p2 in zip(model.weights, new_model.weights): self.assertTrue(np.allclose(p1.numpy(), p2.numpy())) @is_pt_tf_cross_test def test_safetensors_save_and_load_pt_to_tf(self): model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") pt_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert") with tempfile.TemporaryDirectory() as tmp_dir: pt_model.save_pretrained(tmp_dir, safe_serialization=True) # Check we have a model.safetensors file self.assertTrue(os.path.isfile(os.path.join(tmp_dir, SAFE_WEIGHTS_NAME))) new_model = TFBertModel.from_pretrained(tmp_dir) # Check models are equal for p1, p2 in zip(model.weights, new_model.weights): self.assertTrue(np.allclose(p1.numpy(), p2.numpy())) @require_safetensors def test_safetensors_load_from_hub(self): tf_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert") # Can load from the TF-formatted checkpoint safetensors_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors-tf") # Check models are equal for p1, p2 in zip(safetensors_model.weights, tf_model.weights): self.assertTrue(np.allclose(p1.numpy(), p2.numpy())) # Can load from the PyTorch-formatted checkpoint safetensors_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-safetensors") # Check models are equal for p1, p2 in zip(safetensors_model.weights, tf_model.weights): self.assertTrue(np.allclose(p1.numpy(), p2.numpy())) @require_tf @is_staging_test class TFModelPushToHubTester(unittest.TestCase): @classmethod def setUpClass(cls): cls._token = TOKEN set_access_token(TOKEN) HfFolder.save_token(TOKEN) @classmethod def tearDownClass(cls): try: delete_repo(token=cls._token, repo_id="test-model-tf") except HTTPError: pass try: delete_repo(token=cls._token, repo_id="test-model-tf-callback") except HTTPError: pass try: delete_repo(token=cls._token, repo_id="valid_org/test-model-tf-org") except HTTPError: pass def test_push_to_hub(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = TFBertModel(config) # Make sure model is properly initialized _ = model(model.dummy_inputs) logging.set_verbosity_info() logger = logging.get_logger("transformers.utils.hub") with CaptureLogger(logger) as cl: model.push_to_hub("test-model-tf", use_auth_token=self._token) logging.set_verbosity_warning() # Check the model card was created and uploaded. self.assertIn("Uploading the following files to __DUMMY_TRANSFORMERS_USER__/test-model-tf", cl.out) new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf") models_equal = True for p1, p2 in zip(model.weights, new_model.weights): if not tf.math.reduce_all(p1 == p2): models_equal = False break self.assertTrue(models_equal) # Reset repo delete_repo(token=self._token, repo_id="test-model-tf") # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir, repo_id="test-model-tf", push_to_hub=True, use_auth_token=self._token) new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf") models_equal = True for p1, p2 in zip(model.weights, new_model.weights): if not tf.math.reduce_all(p1 == p2): models_equal = False break self.assertTrue(models_equal) def test_push_to_hub_callback(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = TFBertForMaskedLM(config) model.compile() with tempfile.TemporaryDirectory() as tmp_dir: push_to_hub_callback = PushToHubCallback( output_dir=tmp_dir, hub_model_id="test-model-tf-callback", hub_token=self._token, ) model.fit(model.dummy_inputs, model.dummy_inputs, epochs=1, callbacks=[push_to_hub_callback]) new_model = TFBertForMaskedLM.from_pretrained(f"{USER}/test-model-tf-callback") models_equal = True for p1, p2 in zip(model.weights, new_model.weights): if not tf.math.reduce_all(p1 == p2): models_equal = False break self.assertTrue(models_equal) def test_push_to_hub_in_organization(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) model = TFBertModel(config) # Make sure model is properly initialized _ = model(model.dummy_inputs) model.push_to_hub("valid_org/test-model-tf-org", use_auth_token=self._token) new_model = TFBertModel.from_pretrained("valid_org/test-model-tf-org") models_equal = True for p1, p2 in zip(model.weights, new_model.weights): if not tf.math.reduce_all(p1 == p2): models_equal = False break self.assertTrue(models_equal) # Reset repo delete_repo(token=self._token, repo_id="valid_org/test-model-tf-org") # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained( tmp_dir, push_to_hub=True, use_auth_token=self._token, repo_id="valid_org/test-model-tf-org" ) new_model = TFBertModel.from_pretrained("valid_org/test-model-tf-org") models_equal = True for p1, p2 in zip(model.weights, new_model.weights): if not tf.math.reduce_all(p1 == p2): models_equal = False break self.assertTrue(models_equal)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/pytorch/test_accelerate_examples.py

# coding=utf-8 # Copyright 2018 HuggingFace Inc.. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import logging import os import shutil import sys import tempfile from unittest import mock import torch from accelerate.utils import write_basic_config from transformers.testing_utils import TestCasePlus, get_gpu_count, is_flaky, run_command, slow, torch_device from transformers.utils import is_apex_available logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger() def get_setup_file(): parser = argparse.ArgumentParser() parser.add_argument("-f") args = parser.parse_args() return args.f def get_results(output_dir): results = {} path = os.path.join(output_dir, "all_results.json") if os.path.exists(path): with open(path, "r") as f: results = json.load(f) else: raise ValueError(f"can't find {path}") return results def is_cuda_and_apex_available(): is_using_cuda = torch.cuda.is_available() and torch_device == "cuda" return is_using_cuda and is_apex_available() stream_handler = logging.StreamHandler(sys.stdout) logger.addHandler(stream_handler) class ExamplesTestsNoTrainer(TestCasePlus): @classmethod def setUpClass(cls): # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU cls.tmpdir = tempfile.mkdtemp() cls.configPath = os.path.join(cls.tmpdir, "default_config.yml") write_basic_config(save_location=cls.configPath) cls._launch_args = ["accelerate", "launch", "--config_file", cls.configPath] @classmethod def tearDownClass(cls): shutil.rmtree(cls.tmpdir) @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_glue_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py --model_name_or_path distilbert-base-uncased --output_dir {tmp_dir} --train_file ./tests/fixtures/tests_samples/MRPC/train.csv --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --learning_rate=1e-4 --seed=42 --checkpointing_steps epoch --with_tracking """.split() if is_cuda_and_apex_available(): testargs.append("--fp16") run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_accuracy"], 0.75) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "glue_no_trainer"))) @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_clm_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py --model_name_or_path distilgpt2 --train_file ./tests/fixtures/sample_text.txt --validation_file ./tests/fixtures/sample_text.txt --block_size 128 --per_device_train_batch_size 5 --per_device_eval_batch_size 5 --num_train_epochs 2 --output_dir {tmp_dir} --checkpointing_steps epoch --with_tracking """.split() if torch.cuda.device_count() > 1: # Skipping because there are not enough batches to train the model + would need a drop_last to work. return run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertLess(result["perplexity"], 100) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "clm_no_trainer"))) @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_mlm_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py --model_name_or_path distilroberta-base --train_file ./tests/fixtures/sample_text.txt --validation_file ./tests/fixtures/sample_text.txt --output_dir {tmp_dir} --num_train_epochs=1 --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertLess(result["perplexity"], 42) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "mlm_no_trainer"))) @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_ner_no_trainer(self): # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu epochs = 7 if get_gpu_count() > 1 else 2 tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py --model_name_or_path bert-base-uncased --train_file tests/fixtures/tests_samples/conll/sample.json --validation_file tests/fixtures/tests_samples/conll/sample.json --output_dir {tmp_dir} --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=2 --num_train_epochs={epochs} --seed 7 --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_accuracy"], 0.75) self.assertLess(result["train_loss"], 0.5) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "ner_no_trainer"))) @is_flaky() @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_squad_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py --model_name_or_path bert-base-uncased --version_2_with_negative --train_file tests/fixtures/tests_samples/SQUAD/sample.json --validation_file tests/fixtures/tests_samples/SQUAD/sample.json --output_dir {tmp_dir} --max_train_steps=10 --num_warmup_steps=2 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics. self.assertGreaterEqual(result["eval_f1"], 28) self.assertGreaterEqual(result["eval_exact"], 28) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "qa_no_trainer"))) @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_swag_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py --model_name_or_path bert-base-uncased --train_file tests/fixtures/tests_samples/swag/sample.json --validation_file tests/fixtures/tests_samples/swag/sample.json --output_dir {tmp_dir} --max_train_steps=20 --num_warmup_steps=2 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --with_tracking """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_accuracy"], 0.8) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "swag_no_trainer"))) @slow @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_summarization_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py --model_name_or_path t5-small --train_file tests/fixtures/tests_samples/xsum/sample.json --validation_file tests/fixtures/tests_samples/xsum/sample.json --output_dir {tmp_dir} --max_train_steps=50 --num_warmup_steps=8 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_rouge1"], 10) self.assertGreaterEqual(result["eval_rouge2"], 2) self.assertGreaterEqual(result["eval_rougeL"], 7) self.assertGreaterEqual(result["eval_rougeLsum"], 7) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "summarization_no_trainer"))) @slow @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_translation_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py --model_name_or_path sshleifer/student_marian_en_ro_6_1 --source_lang en --target_lang ro --train_file tests/fixtures/tests_samples/wmt16/sample.json --validation_file tests/fixtures/tests_samples/wmt16/sample.json --output_dir {tmp_dir} --max_train_steps=50 --num_warmup_steps=8 --learning_rate=3e-3 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --source_lang en_XX --target_lang ro_RO --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_bleu"], 30) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "translation_no_trainer"))) @slow def test_run_semantic_segmentation_no_trainer(self): stream_handler = logging.StreamHandler(sys.stdout) logger.addHandler(stream_handler) tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py --dataset_name huggingface/semantic-segmentation-test-sample --output_dir {tmp_dir} --max_train_steps=10 --num_warmup_steps=2 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --checkpointing_steps epoch """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_overall_accuracy"], 0.10) @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_image_classification_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py --model_name_or_path google/vit-base-patch16-224-in21k --dataset_name hf-internal-testing/cats_vs_dogs_sample --learning_rate 1e-4 --per_device_train_batch_size 2 --per_device_eval_batch_size 1 --max_train_steps 2 --train_val_split 0.1 --seed 42 --output_dir {tmp_dir} --with_tracking --checkpointing_steps 1 """.split() if is_cuda_and_apex_available(): testargs.append("--fp16") run_command(self._launch_args + testargs) result = get_results(tmp_dir) # The base model scores a 25% self.assertGreaterEqual(result["eval_accuracy"], 0.6) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "step_1"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "image_classification_no_trainer")))

# coding=utf-8 # Copyright 2018 HuggingFace Inc.. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import logging import os import shutil import sys import tempfile from unittest import mock import torch from accelerate.utils import write_basic_config from transformers.testing_utils import TestCasePlus, get_gpu_count, is_flaky, run_command, slow, torch_device from transformers.utils import is_apex_available logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger() def get_setup_file(): parser = argparse.ArgumentParser() parser.add_argument("-f") args = parser.parse_args() return args.f def get_results(output_dir): results = {} path = os.path.join(output_dir, "all_results.json") if os.path.exists(path): with open(path, "r") as f: results = json.load(f) else: raise ValueError(f"can't find {path}") return results def is_cuda_and_apex_available(): is_using_cuda = torch.cuda.is_available() and torch_device == "cuda" return is_using_cuda and is_apex_available() stream_handler = logging.StreamHandler(sys.stdout) logger.addHandler(stream_handler) class ExamplesTestsNoTrainer(TestCasePlus): @classmethod def setUpClass(cls): # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU cls.tmpdir = tempfile.mkdtemp() cls.configPath = os.path.join(cls.tmpdir, "default_config.yml") write_basic_config(save_location=cls.configPath) cls._launch_args = ["accelerate", "launch", "--config_file", cls.configPath] @classmethod def tearDownClass(cls): shutil.rmtree(cls.tmpdir) @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_glue_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py --model_name_or_path distilbert-base-uncased --output_dir {tmp_dir} --train_file ./tests/fixtures/tests_samples/MRPC/train.csv --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --learning_rate=1e-4 --seed=42 --checkpointing_steps epoch --with_tracking """.split() if is_cuda_and_apex_available(): testargs.append("--fp16") run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_accuracy"], 0.75) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "glue_no_trainer"))) @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_clm_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py --model_name_or_path distilgpt2 --train_file ./tests/fixtures/sample_text.txt --validation_file ./tests/fixtures/sample_text.txt --block_size 128 --per_device_train_batch_size 5 --per_device_eval_batch_size 5 --num_train_epochs 2 --output_dir {tmp_dir} --checkpointing_steps epoch --with_tracking """.split() if torch.cuda.device_count() > 1: # Skipping because there are not enough batches to train the model + would need a drop_last to work. return run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertLess(result["perplexity"], 100) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "clm_no_trainer"))) @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_mlm_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py --model_name_or_path distilroberta-base --train_file ./tests/fixtures/sample_text.txt --validation_file ./tests/fixtures/sample_text.txt --output_dir {tmp_dir} --num_train_epochs=1 --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertLess(result["perplexity"], 42) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "mlm_no_trainer"))) @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_ner_no_trainer(self): # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu epochs = 7 if get_gpu_count() > 1 else 2 tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py --model_name_or_path bert-base-uncased --train_file tests/fixtures/tests_samples/conll/sample.json --validation_file tests/fixtures/tests_samples/conll/sample.json --output_dir {tmp_dir} --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=2 --num_train_epochs={epochs} --seed 7 --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_accuracy"], 0.75) self.assertLess(result["train_loss"], 0.5) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "ner_no_trainer"))) @is_flaky() @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_squad_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py --model_name_or_path bert-base-uncased --version_2_with_negative --train_file tests/fixtures/tests_samples/SQUAD/sample.json --validation_file tests/fixtures/tests_samples/SQUAD/sample.json --output_dir {tmp_dir} --max_train_steps=10 --num_warmup_steps=2 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics. self.assertGreaterEqual(result["eval_f1"], 28) self.assertGreaterEqual(result["eval_exact"], 28) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "qa_no_trainer"))) @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_swag_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py --model_name_or_path bert-base-uncased --train_file tests/fixtures/tests_samples/swag/sample.json --validation_file tests/fixtures/tests_samples/swag/sample.json --output_dir {tmp_dir} --max_train_steps=20 --num_warmup_steps=2 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --with_tracking """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_accuracy"], 0.8) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "swag_no_trainer"))) @slow @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_summarization_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py --model_name_or_path t5-small --train_file tests/fixtures/tests_samples/xsum/sample.json --validation_file tests/fixtures/tests_samples/xsum/sample.json --output_dir {tmp_dir} --max_train_steps=50 --num_warmup_steps=8 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_rouge1"], 10) self.assertGreaterEqual(result["eval_rouge2"], 2) self.assertGreaterEqual(result["eval_rougeL"], 7) self.assertGreaterEqual(result["eval_rougeLsum"], 7) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "summarization_no_trainer"))) @slow @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_translation_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py --model_name_or_path sshleifer/student_marian_en_ro_6_1 --source_lang en --target_lang ro --train_file tests/fixtures/tests_samples/wmt16/sample.json --validation_file tests/fixtures/tests_samples/wmt16/sample.json --output_dir {tmp_dir} --max_train_steps=50 --num_warmup_steps=8 --learning_rate=3e-3 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --source_lang en_XX --target_lang ro_RO --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_bleu"], 30) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "translation_no_trainer"))) @slow def test_run_semantic_segmentation_no_trainer(self): stream_handler = logging.StreamHandler(sys.stdout) logger.addHandler(stream_handler) tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py --dataset_name huggingface/semantic-segmentation-test-sample --output_dir {tmp_dir} --max_train_steps=10 --num_warmup_steps=2 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --checkpointing_steps epoch """.split() run_command(self._launch_args + testargs) result = get_results(tmp_dir) self.assertGreaterEqual(result["eval_overall_accuracy"], 0.10) @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"}) def test_run_image_classification_no_trainer(self): tmp_dir = self.get_auto_remove_tmp_dir() testargs = f""" {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py --model_name_or_path google/vit-base-patch16-224-in21k --dataset_name hf-internal-testing/cats_vs_dogs_sample --learning_rate 1e-4 --per_device_train_batch_size 2 --per_device_eval_batch_size 1 --max_train_steps 2 --train_val_split 0.1 --seed 42 --output_dir {tmp_dir} --with_tracking --checkpointing_steps 1 """.split() if is_cuda_and_apex_available(): testargs.append("--fp16") run_command(self._launch_args + testargs) result = get_results(tmp_dir) # The base model scores a 25% self.assertGreaterEqual(result["eval_accuracy"], 0.6) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "step_1"))) self.assertTrue(os.path.exists(os.path.join(tmp_dir, "image_classification_no_trainer")))

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/deformable_detr/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_timm_available, is_vision_available _import_structure = { "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_deformable_detr"] = ["DeformableDetrFeatureExtractor"] try: if not is_timm_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_deformable_detr"] = [ "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "DeformableDetrForObjectDetection", "DeformableDetrModel", "DeformableDetrPreTrainedModel", ] if TYPE_CHECKING: from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor try: if not is_timm_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_deformable_detr import ( DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, DeformableDetrForObjectDetection, DeformableDetrModel, DeformableDetrPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_timm_available, is_vision_available _import_structure = { "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_deformable_detr"] = ["DeformableDetrFeatureExtractor"] try: if not is_timm_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_deformable_detr"] = [ "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "DeformableDetrForObjectDetection", "DeformableDetrModel", "DeformableDetrPreTrainedModel", ] if TYPE_CHECKING: from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor try: if not is_timm_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_deformable_detr import ( DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, DeformableDetrForObjectDetection, DeformableDetrModel, DeformableDetrPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/training_args.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import contextlib import json import math import os import warnings from dataclasses import asdict, dataclass, field, fields from datetime import timedelta from enum import Enum from pathlib import Path from typing import Any, Dict, List, Optional, Union from packaging import version from .debug_utils import DebugOption from .trainer_utils import ( EvaluationStrategy, FSDPOption, HubStrategy, IntervalStrategy, SchedulerType, ShardedDDPOption, ) from .utils import ( ExplicitEnum, cached_property, ccl_version, get_full_repo_name, is_accelerate_available, is_psutil_available, is_sagemaker_dp_enabled, is_sagemaker_mp_enabled, is_torch_available, is_torch_bf16_cpu_available, is_torch_bf16_gpu_available, is_torch_tf32_available, is_torch_tpu_available, logging, requires_backends, torch_required, ) if is_torch_available(): import torch import torch.distributed as dist if is_torch_tpu_available(check_device=False): import torch_xla.core.xla_model as xm if is_sagemaker_mp_enabled(): import smdistributed.modelparallel.torch as smp smp.init() logger = logging.get_logger(__name__) log_levels = logging.get_log_levels_dict().copy() trainer_log_levels = dict(**log_levels, passive=-1) def default_logdir() -> str: """ Same default as PyTorch """ import socket from datetime import datetime current_time = datetime.now().strftime("%b%d_%H-%M-%S") return os.path.join("runs", current_time + "_" + socket.gethostname()) def get_int_from_env(env_keys, default): """Returns the first positive env value found in the `env_keys` list or the default.""" for e in env_keys: val = int(os.environ.get(e, -1)) if val >= 0: return val return default def get_xla_device_type(device: "torch.device") -> Optional[str]: """ Returns the xla device type (CPU|GPU|TPU) or None if the device is a non-xla device. """ if is_torch_tpu_available(): return xm.xla_real_devices([device])[0].split(":")[0] return None class OptimizerNames(ExplicitEnum): """ Stores the acceptable string identifiers for optimizers. """ ADAMW_HF = "adamw_hf" ADAMW_TORCH = "adamw_torch" ADAMW_TORCH_XLA = "adamw_torch_xla" ADAMW_APEX_FUSED = "adamw_apex_fused" ADAFACTOR = "adafactor" ADAMW_BNB = "adamw_bnb_8bit" SGD = "sgd" ADAGRAD = "adagrad" @dataclass class TrainingArguments: """ TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop itself**. Using [`HfArgumentParser`] we can turn this class into [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the command line. Parameters: output_dir (`str`): The output directory where the model predictions and checkpoints will be written. overwrite_output_dir (`bool`, *optional*, defaults to `False`): If `True`, overwrite the content of the output directory. Use this to continue training if `output_dir` points to a checkpoint directory. do_train (`bool`, *optional*, defaults to `False`): Whether to run training or not. This argument is not directly used by [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. do_eval (`bool`, *optional*): Whether to run evaluation on the validation set or not. Will be set to `True` if `evaluation_strategy` is different from `"no"`. This argument is not directly used by [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. do_predict (`bool`, *optional*, defaults to `False`): Whether to run predictions on the test set or not. This argument is not directly used by [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. evaluation_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`): The evaluation strategy to adopt during training. Possible values are: - `"no"`: No evaluation is done during training. - `"steps"`: Evaluation is done (and logged) every `eval_steps`. - `"epoch"`: Evaluation is done at the end of each epoch. prediction_loss_only (`bool`, *optional*, defaults to `False`): When performing evaluation and generating predictions, only returns the loss. per_device_train_batch_size (`int`, *optional*, defaults to 8): The batch size per GPU/TPU core/CPU for training. per_device_eval_batch_size (`int`, *optional*, defaults to 8): The batch size per GPU/TPU core/CPU for evaluation. gradient_accumulation_steps (`int`, *optional*, defaults to 1): Number of updates steps to accumulate the gradients for, before performing a backward/update pass. <Tip warning={true}> When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging, evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training examples. </Tip> eval_accumulation_steps (`int`, *optional*): Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but requires more memory). eval_delay (`float`, *optional*): Number of epochs or steps to wait for before the first evaluation can be performed, depending on the evaluation_strategy. learning_rate (`float`, *optional*, defaults to 5e-5): The initial learning rate for [`AdamW`] optimizer. weight_decay (`float`, *optional*, defaults to 0): The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in [`AdamW`] optimizer. adam_beta1 (`float`, *optional*, defaults to 0.9): The beta1 hyperparameter for the [`AdamW`] optimizer. adam_beta2 (`float`, *optional*, defaults to 0.999): The beta2 hyperparameter for the [`AdamW`] optimizer. adam_epsilon (`float`, *optional*, defaults to 1e-8): The epsilon hyperparameter for the [`AdamW`] optimizer. max_grad_norm (`float`, *optional*, defaults to 1.0): Maximum gradient norm (for gradient clipping). num_train_epochs(`float`, *optional*, defaults to 3.0): Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training). max_steps (`int`, *optional*, defaults to -1): If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`. In case of using a finite iterable dataset the training may stop before reaching the set number of steps when all data is exhausted lr_scheduler_type (`str` or [`SchedulerType`], *optional*, defaults to `"linear"`): The scheduler type to use. See the documentation of [`SchedulerType`] for all possible values. warmup_ratio (`float`, *optional*, defaults to 0.0): Ratio of total training steps used for a linear warmup from 0 to `learning_rate`. warmup_steps (`int`, *optional*, defaults to 0): Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`. log_level (`str`, *optional*, defaults to `passive`): Logger log level to use on the main process. Possible choices are the log levels as strings: 'debug', 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and lets the application set the level. log_level_replica (`str`, *optional*, defaults to `passive`): Logger log level to use on replicas. Same choices as `log_level`" log_on_each_node (`bool`, *optional*, defaults to `True`): In multinode distributed training, whether to log using `log_level` once per node, or only on the main node. logging_dir (`str`, *optional*): [TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***. logging_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`): The logging strategy to adopt during training. Possible values are: - `"no"`: No logging is done during training. - `"epoch"`: Logging is done at the end of each epoch. - `"steps"`: Logging is done every `logging_steps`. logging_first_step (`bool`, *optional*, defaults to `False`): Whether to log and evaluate the first `global_step` or not. logging_steps (`int`, *optional*, defaults to 500): Number of update steps between two logs if `logging_strategy="steps"`. logging_nan_inf_filter (`bool`, *optional*, defaults to `True`): Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is `nan` or `inf` is filtered and the average loss of the current logging window is taken instead. <Tip> `logging_nan_inf_filter` only influences the logging of loss values, it does not change the behavior the gradient is computed or applied to the model. </Tip> save_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`): The checkpoint save strategy to adopt during training. Possible values are: - `"no"`: No save is done during training. - `"epoch"`: Save is done at the end of each epoch. - `"steps"`: Save is done every `save_steps`. save_steps (`int`, *optional*, defaults to 500): Number of updates steps before two checkpoint saves if `save_strategy="steps"`. save_total_limit (`int`, *optional*): If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. save_on_each_node (`bool`, *optional*, defaults to `False`): When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one. This should not be activated when the different nodes use the same storage as the files will be saved with the same names for each node. no_cuda (`bool`, *optional*, defaults to `False`): Whether to not use CUDA even when it is available or not. seed (`int`, *optional*, defaults to 42): Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the [`~Trainer.model_init`] function to instantiate the model if it has some randomly initialized parameters. data_seed (`int`, *optional*): Random seed to be used with data samplers. If not set, random generators for data sampling will use the same seed as `seed`. This can be used to ensure reproducibility of data sampling, independent of the model seed. jit_mode_eval (`bool`, *optional*, defaults to `False`): Whether or not to use PyTorch jit trace for inference. use_ipex (`bool`, *optional*, defaults to `False`): Use Intel extension for PyTorch when it is available. [IPEX installation](https://github.com/intel/intel-extension-for-pytorch). bf16 (`bool`, *optional*, defaults to `False`): Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher NVIDIA architecture or using CPU (no_cuda). This is an experimental API and it may change. fp16 (`bool`, *optional*, defaults to `False`): Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training. fp16_opt_level (`str`, *optional*, defaults to 'O1'): For `fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details on the [Apex documentation](https://nvidia.github.io/apex/amp). fp16_backend (`str`, *optional*, defaults to `"auto"`): This argument is deprecated. Use `half_precision_backend` instead. half_precision_backend (`str`, *optional*, defaults to `"auto"`): The backend to use for mixed precision training. Must be one of `"auto", "cuda_amp", "apex", "cpu_amp"`. `"auto"` will use CPU/CUDA AMP or APEX depending on the PyTorch version detected, while the other choices will force the requested backend. bf16_full_eval (`bool`, *optional*, defaults to `False`): Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values. This is an experimental API and it may change. fp16_full_eval (`bool`, *optional*, defaults to `False`): Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values. tf32 (`bool`, *optional*): Whether to enable the TF32 mode, available in Ampere and newer GPU architectures. The default value depends on PyTorch's version default of `torch.backends.cuda.matmul.allow_tf32`. For more details please refer to the [TF32](https://huggingface.co/docs/transformers/performance#tf32) documentation. This is an experimental API and it may change. local_rank (`int`, *optional*, defaults to -1): Rank of the process during distributed training. xpu_backend (`str`, *optional*): The backend to use for xpu distributed training. Must be one of `"mpi"` or `"ccl"` or `"gloo"`. tpu_num_cores (`int`, *optional*): When training on TPU, the number of TPU cores (automatically passed by launcher script). dataloader_drop_last (`bool`, *optional*, defaults to `False`): Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size) or not. eval_steps (`int`, *optional*): Number of update steps between two evaluations if `evaluation_strategy="steps"`. Will default to the same value as `logging_steps` if not set. dataloader_num_workers (`int`, *optional*, defaults to 0): Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process. past_index (`int`, *optional*, defaults to -1): Some models like [TransformerXL](../model_doc/transformerxl) or [XLNet](../model_doc/xlnet) can make use of the past hidden states for their predictions. If this argument is set to a positive int, the `Trainer` will use the corresponding output (usually index 2) as the past state and feed it to the model at the next training step under the keyword argument `mems`. run_name (`str`, *optional*): A descriptor for the run. Typically used for [wandb](https://www.wandb.com/) and [mlflow](https://www.mlflow.org/) logging. disable_tqdm (`bool`, *optional*): Whether or not to disable the tqdm progress bars and table of metrics produced by [`~notebook.NotebookTrainingTracker`] in Jupyter Notebooks. Will default to `True` if the logging level is set to warn or lower (default), `False` otherwise. remove_unused_columns (`bool`, *optional*, defaults to `True`): Whether or not to automatically remove the columns unused by the model forward method. (Note that this behavior is not implemented for [`TFTrainer`] yet.) label_names (`List[str]`, *optional*): The list of keys in your dictionary of inputs that correspond to the labels. Will eventually default to `["labels"]` except if the model used is one of the `XxxForQuestionAnswering` in which case it will default to `["start_positions", "end_positions"]`. load_best_model_at_end (`bool`, *optional*, defaults to `False`): Whether or not to load the best model found during training at the end of training. <Tip> When set to `True`, the parameters `save_strategy` needs to be the same as `evaluation_strategy`, and in the case it is "steps", `save_steps` must be a round multiple of `eval_steps`. </Tip> metric_for_best_model (`str`, *optional*): Use in conjunction with `load_best_model_at_end` to specify the metric to use to compare two different models. Must be the name of a metric returned by the evaluation with or without the prefix `"eval_"`. Will default to `"loss"` if unspecified and `load_best_model_at_end=True` (to use the evaluation loss). If you set this value, `greater_is_better` will default to `True`. Don't forget to set it to `False` if your metric is better when lower. greater_is_better (`bool`, *optional*): Use in conjunction with `load_best_model_at_end` and `metric_for_best_model` to specify if better models should have a greater metric or not. Will default to: - `True` if `metric_for_best_model` is set to a value that isn't `"loss"` or `"eval_loss"`. - `False` if `metric_for_best_model` is not set, or set to `"loss"` or `"eval_loss"`. ignore_data_skip (`bool`, *optional*, defaults to `False`): When resuming training, whether or not to skip the epochs and batches to get the data loading at the same stage as in the previous training. If set to `True`, the training will begin faster (as that skipping step can take a long time) but will not yield the same results as the interrupted training would have. sharded_ddp (`bool`, `str` or list of [`~trainer_utils.ShardedDDPOption`], *optional*, defaults to `False`): Use Sharded DDP training from [FairScale](https://github.com/facebookresearch/fairscale) (in distributed training only). This is an experimental feature. A list of options along the following: - `"simple"`: to use first instance of sharded DDP released by fairscale (`ShardedDDP`) similar to ZeRO-2. - `"zero_dp_2"`: to use the second instance of sharded DPP released by fairscale (`FullyShardedDDP`) in Zero-2 mode (with `reshard_after_forward=False`). - `"zero_dp_3"`: to use the second instance of sharded DPP released by fairscale (`FullyShardedDDP`) in Zero-3 mode (with `reshard_after_forward=True`). - `"offload"`: to add ZeRO-offload (only compatible with `"zero_dp_2"` and `"zero_dp_3"`). If a string is passed, it will be split on space. If a bool is passed, it will be converted to an empty list for `False` and `["simple"]` for `True`. fsdp (`bool`, `str` or list of [`~trainer_utils.FSDPOption`], *optional*, defaults to `False`): Use PyTorch Distributed Parallel Training (in distributed training only). A list of options along the following: - `"full_shard"`: Shard parameters, gradients and optimizer states. - `"shard_grad_op"`: Shard optimizer states and gradients. - `"offload"`: Offload parameters and gradients to CPUs (only compatible with `"full_shard"` and `"shard_grad_op"`). - `"auto_wrap"`: Automatically recursively wrap layers with FSDP using `default_auto_wrap_policy`. fsdp_min_num_params (`int`, *optional*, defaults to `0`): FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is passed). deepspeed (`str` or `dict`, *optional*): Use [Deepspeed](https://github.com/microsoft/deepspeed). This is an experimental feature and its API may evolve in the future. The value is either the location of DeepSpeed json config file (e.g., `ds_config.json`) or an already loaded json file as a `dict`" label_smoothing_factor (`float`, *optional*, defaults to 0.0): The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded labels are changed from 0s and 1s to `label_smoothing_factor/num_labels` and `1 - label_smoothing_factor + label_smoothing_factor/num_labels` respectively. debug (`str` or list of [`~debug_utils.DebugOption`], *optional*, defaults to `""`): Enable one or more debug features. This is an experimental feature. Possible options are: - `"underflow_overflow"`: detects overflow in model's input/outputs and reports the last frames that led to the event - `"tpu_metrics_debug"`: print debug metrics on TPU The options should be separated by whitespaces. optim (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_hf"`): The optimizer to use: adamw_hf, adamw_torch, adamw_apex_fused, or adafactor. adafactor (`bool`, *optional*, defaults to `False`): This argument is deprecated. Use `--optim adafactor` instead. group_by_length (`bool`, *optional*, defaults to `False`): Whether or not to group together samples of roughly the same length in the training dataset (to minimize padding applied and be more efficient). Only useful if applying dynamic padding. length_column_name (`str`, *optional*, defaults to `"length"`): Column name for precomputed lengths. If the column exists, grouping by length will use these values rather than computing them on train startup. Ignored unless `group_by_length` is `True` and the dataset is an instance of `Dataset`. report_to (`str` or `List[str]`, *optional*, defaults to `"all"`): The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`, `"comet_ml"`, `"mlflow"`, `"neptune"`, `"tensorboard"`,`"clearml"` and `"wandb"`. Use `"all"` to report to all integrations installed, `"none"` for no integrations. ddp_find_unused_parameters (`bool`, *optional*): When using distributed training, the value of the flag `find_unused_parameters` passed to `DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise. ddp_bucket_cap_mb (`int`, *optional*): When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`. dataloader_pin_memory (`bool`, *optional*, defaults to `True`): Whether you want to pin memory in data loaders or not. Will default to `True`. skip_memory_metrics (`bool`, *optional*, defaults to `True`): Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows down the training and evaluation speed. push_to_hub (`bool`, *optional*, defaults to `False`): Whether or not to push the model to the Hub every time the model is saved. If this is activated, `output_dir` will begin a git directory synced with the repo (determined by `hub_model_id`) and the content will be pushed each time a save is triggered (depending on your `save_strategy`). Calling [`~Trainer.save_model`] will also trigger a push. <Tip warning={true}> If `output_dir` exists, it needs to be a local clone of the repository to which the [`Trainer`] will be pushed. </Tip> resume_from_checkpoint (`str`, *optional*): The path to a folder with a valid checkpoint for your model. This argument is not directly used by [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. hub_model_id (`str`, *optional*): The name of the repository to keep in sync with the local *output_dir*. It can be a simple model ID in which case the model will be pushed in your namespace. Otherwise it should be the whole repository name, for instance `"user_name/model"`, which allows you to push to an organization you are a member of with `"organization_name/model"`. Will default to `user_name/output_dir_name` with *output_dir_name* being the name of `output_dir`. Will default to the name of `output_dir`. hub_strategy (`str` or [`~trainer_utils.HubStrategy`], *optional*, defaults to `"every_save"`): Defines the scope of what is pushed to the Hub and when. Possible values are: - `"end"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a draft of a model card when the [`~Trainer.save_model`] method is called. - `"every_save"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a draft of a model card each time there is a model save. The pushes are asynchronous to not block training, and in case the save are very frequent, a new push is only attempted if the previous one is finished. A last push is made with the final model at the end of training. - `"checkpoint"`: like `"every_save"` but the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to resume training easily with `trainer.train(resume_from_checkpoint="last-checkpoint")`. - `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the output folder (so you will get one checkpoint folder per folder in your final repository) hub_token (`str`, *optional*): The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with `huggingface-cli login`. hub_private_repo (`bool`, *optional*, defaults to `False`): If True, the Hub repo will be set to private. gradient_checkpointing (`bool`, *optional*, defaults to `False`): If True, use gradient checkpointing to save memory at the expense of slower backward pass. include_inputs_for_metrics (`bool`, *optional*, defaults to `False`): Whether or not the inputs will be passed to the `compute_metrics` function. This is intended for metrics that need inputs, predictions and references for scoring calculation in Metric class. auto_find_batch_size (`bool`, *optional*, defaults to `False`) Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding CUDA Out-of-Memory errors. Requires accelerate to be installed (`pip install accelerate`) full_determinism (`bool`, *optional*, defaults to `False`) If `True`, [`enable_full_determinism`] is called instead of [`set_seed`] to ensure reproducible results in distributed training torchdynamo (`str`, *optional*): The token that is used to set the backend compiler for TorchDynamo. Possible choices are ["eager", "nvfuser]. This is an experimental API and subject to change. ray_scope (`str`, *optional*, defaults to `"last"`): The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the [Ray documentation]( https://docs.ray.io/en/latest/tune/api_docs/analysis.html#ray.tune.ExperimentAnalysis.get_best_trial) for more options. ddp_timeout (`int`, *optional*, defaults to 1800): The timeout for `torch.distributed.init_process_group` calls, used to avoid GPU socket timeouts when performing slow operations in distributed runnings. Please refer the [PyTorch documentation] (https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for more information. use_mps_device (`bool`, *optional*, defaults to `False`): Whether to use Apple Silicon chip based `mps` device. """ framework = "pt" output_dir: str = field( metadata={"help": "The output directory where the model predictions and checkpoints will be written."}, ) overwrite_output_dir: bool = field( default=False, metadata={ "help": ( "Overwrite the content of the output directory. " "Use this to continue training if output_dir points to a checkpoint directory." ) }, ) do_train: bool = field(default=False, metadata={"help": "Whether to run training."}) do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."}) do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."}) evaluation_strategy: Union[IntervalStrategy, str] = field( default="no", metadata={"help": "The evaluation strategy to use."}, ) prediction_loss_only: bool = field( default=False, metadata={"help": "When performing evaluation and predictions, only returns the loss."}, ) per_device_train_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."} ) per_device_eval_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."} ) per_gpu_train_batch_size: Optional[int] = field( default=None, metadata={ "help": ( "Deprecated, the use of `--per_device_train_batch_size` is preferred. " "Batch size per GPU/TPU core/CPU for training." ) }, ) per_gpu_eval_batch_size: Optional[int] = field( default=None, metadata={ "help": ( "Deprecated, the use of `--per_device_eval_batch_size` is preferred. " "Batch size per GPU/TPU core/CPU for evaluation." ) }, ) gradient_accumulation_steps: int = field( default=1, metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."}, ) eval_accumulation_steps: Optional[int] = field( default=None, metadata={"help": "Number of predictions steps to accumulate before moving the tensors to the CPU."}, ) eval_delay: Optional[float] = field( default=0, metadata={ "help": ( "Number of epochs or steps to wait for before the first evaluation can be performed, depending on the" " evaluation_strategy." ) }, ) learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."}) weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."}) adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"}) adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"}) adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}) max_grad_norm: float = field(default=1.0, metadata={"help": "Max gradient norm."}) num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."}) max_steps: int = field( default=-1, metadata={"help": "If > 0: set total number of training steps to perform. Override num_train_epochs."}, ) lr_scheduler_type: Union[SchedulerType, str] = field( default="linear", metadata={"help": "The scheduler type to use."}, ) warmup_ratio: float = field( default=0.0, metadata={"help": "Linear warmup over warmup_ratio fraction of total steps."} ) warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."}) log_level: Optional[str] = field( default="passive", metadata={ "help": ( "Logger log level to use on the main node. Possible choices are the log levels as strings: 'debug'," " 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and" " lets the application set the level. Defaults to 'passive'." ), "choices": trainer_log_levels.keys(), }, ) log_level_replica: Optional[str] = field( default="passive", metadata={ "help": "Logger log level to use on replica nodes. Same choices and defaults as ``log_level``", "choices": trainer_log_levels.keys(), }, ) log_on_each_node: bool = field( default=True, metadata={ "help": ( "When doing a multinode distributed training, whether to log once per node or just once on the main" " node." ) }, ) logging_dir: Optional[str] = field(default=None, metadata={"help": "Tensorboard log dir."}) logging_strategy: Union[IntervalStrategy, str] = field( default="steps", metadata={"help": "The logging strategy to use."}, ) logging_first_step: bool = field(default=False, metadata={"help": "Log the first global_step"}) logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."}) logging_nan_inf_filter: bool = field(default=True, metadata={"help": "Filter nan and inf losses for logging."}) save_strategy: Union[IntervalStrategy, str] = field( default="steps", metadata={"help": "The checkpoint save strategy to use."}, ) save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."}) save_total_limit: Optional[int] = field( default=None, metadata={ "help": ( "Limit the total amount of checkpoints. " "Deletes the older checkpoints in the output_dir. Default is unlimited checkpoints" ) }, ) save_on_each_node: bool = field( default=False, metadata={ "help": ( "When doing multi-node distributed training, whether to save models and checkpoints on each node, or" " only on the main one" ) }, ) no_cuda: bool = field(default=False, metadata={"help": "Do not use CUDA even when it is available"}) use_mps_device: bool = field( default=False, metadata={"help": "Whether to use Apple Silicon chip based `mps` device."} ) seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."}) data_seed: Optional[int] = field(default=None, metadata={"help": "Random seed to be used with data samplers."}) jit_mode_eval: bool = field( default=False, metadata={"help": "Whether or not to use PyTorch jit trace for inference"} ) use_ipex: bool = field( default=False, metadata={ "help": ( "Use Intel extension for PyTorch when it is available, installation:" " 'https://github.com/intel/intel-extension-for-pytorch'" ) }, ) bf16: bool = field( default=False, metadata={ "help": ( "Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA" " architecture or using CPU (no_cuda). This is an experimental API and it may change." ) }, ) fp16: bool = field( default=False, metadata={"help": "Whether to use fp16 (mixed) precision instead of 32-bit"}, ) fp16_opt_level: str = field( default="O1", metadata={ "help": ( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. " "See details at https://nvidia.github.io/apex/amp.html" ) }, ) half_precision_backend: str = field( default="auto", metadata={ "help": "The backend to be used for half precision.", "choices": ["auto", "cuda_amp", "apex", "cpu_amp"], }, ) bf16_full_eval: bool = field( default=False, metadata={ "help": ( "Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may" " change." ) }, ) fp16_full_eval: bool = field( default=False, metadata={"help": "Whether to use full float16 evaluation instead of 32-bit"}, ) tf32: Optional[bool] = field( default=None, metadata={ "help": ( "Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental" " API and it may change." ) }, ) local_rank: int = field(default=-1, metadata={"help": "For distributed training: local_rank"}) xpu_backend: Optional[str] = field( default=None, metadata={ "help": "The backend to be used for distributed training on Intel XPU.", "choices": ["mpi", "ccl", "gloo"], }, ) tpu_num_cores: Optional[int] = field( default=None, metadata={"help": "TPU: Number of TPU cores (automatically passed by launcher script)"} ) tpu_metrics_debug: bool = field( default=False, metadata={ "help": ( "Deprecated, the use of `--debug tpu_metrics_debug` is preferred. TPU: Whether to print debug metrics" ) }, ) debug: str = field( default="", metadata={ "help": ( "Whether or not to enable debug mode. Current options: " "`underflow_overflow` (Detect underflow and overflow in activations and weights), " "`tpu_metrics_debug` (print debug metrics on TPU)." ) }, ) dataloader_drop_last: bool = field( default=False, metadata={"help": "Drop the last incomplete batch if it is not divisible by the batch size."} ) eval_steps: Optional[int] = field(default=None, metadata={"help": "Run an evaluation every X steps."}) dataloader_num_workers: int = field( default=0, metadata={ "help": ( "Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded" " in the main process." ) }, ) past_index: int = field( default=-1, metadata={"help": "If >=0, uses the corresponding part of the output as the past state for next step."}, ) run_name: Optional[str] = field( default=None, metadata={"help": "An optional descriptor for the run. Notably used for wandb logging."} ) disable_tqdm: Optional[bool] = field( default=None, metadata={"help": "Whether or not to disable the tqdm progress bars."} ) remove_unused_columns: Optional[bool] = field( default=True, metadata={"help": "Remove columns not required by the model when using an nlp.Dataset."} ) label_names: Optional[List[str]] = field( default=None, metadata={"help": "The list of keys in your dictionary of inputs that correspond to the labels."} ) load_best_model_at_end: Optional[bool] = field( default=False, metadata={"help": "Whether or not to load the best model found during training at the end of training."}, ) metric_for_best_model: Optional[str] = field( default=None, metadata={"help": "The metric to use to compare two different models."} ) greater_is_better: Optional[bool] = field( default=None, metadata={"help": "Whether the `metric_for_best_model` should be maximized or not."} ) ignore_data_skip: bool = field( default=False, metadata={ "help": ( "When resuming training, whether or not to skip the first epochs and batches to get to the same" " training data." ) }, ) sharded_ddp: str = field( default="", metadata={ "help": ( "Whether or not to use sharded DDP training (in distributed training only). The base option should be" " `simple`, `zero_dp_2` or `zero_dp_3` and you can add CPU-offload to `zero_dp_2` or `zero_dp_3` like" " this: zero_dp_2 offload` or `zero_dp_3 offload`. You can add auto-wrap to `zero_dp_2` or `zero_dp_3`" " with the same syntax: zero_dp_2 auto_wrap` or `zero_dp_3 auto_wrap`." ), }, ) fsdp: str = field( default="", metadata={ "help": ( "Whether or not to use PyTorch Fully Sharded Data Parallel (FSDP) training (in distributed training" " only). The base option should be `full_shard`, `shard_grad_op` or `no_shard` and you can add" " CPU-offload to `full_shard` or `shard_grad_op` like this: full_shard offload` or `shard_grad_op" " offload`. You can add auto-wrap to `full_shard` or `shard_grad_op` with the same syntax: full_shard" " auto_wrap` or `shard_grad_op auto_wrap`." ), }, ) fsdp_min_num_params: int = field( default=0, metadata={ "help": ( "FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is" " passed)." ) }, ) fsdp_transformer_layer_cls_to_wrap: Optional[str] = field( default=None, metadata={ "help": ( "Transformer layer class name (case-sensitive) to wrap ,e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... " "(useful only when `fsdp` flag is passed)." ) }, ) deepspeed: Optional[str] = field( default=None, metadata={ "help": ( "Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already" " loaded json file as a dict" ) }, ) label_smoothing_factor: float = field( default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."} ) optim: Union[OptimizerNames, str] = field( default="adamw_hf", metadata={"help": "The optimizer to use."}, ) adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."}) group_by_length: bool = field( default=False, metadata={"help": "Whether or not to group samples of roughly the same length together when batching."}, ) length_column_name: Optional[str] = field( default="length", metadata={"help": "Column name with precomputed lengths to use when grouping by length."}, ) report_to: Optional[List[str]] = field( default=None, metadata={"help": "The list of integrations to report the results and logs to."} ) ddp_find_unused_parameters: Optional[bool] = field( default=None, metadata={ "help": ( "When using distributed training, the value of the flag `find_unused_parameters` passed to " "`DistributedDataParallel`." ) }, ) ddp_bucket_cap_mb: Optional[int] = field( default=None, metadata={ "help": ( "When using distributed training, the value of the flag `bucket_cap_mb` passed to " "`DistributedDataParallel`." ) }, ) dataloader_pin_memory: bool = field( default=True, metadata={"help": "Whether or not to pin memory for DataLoader."} ) skip_memory_metrics: bool = field( default=True, metadata={"help": "Whether or not to skip adding of memory profiler reports to metrics."} ) use_legacy_prediction_loop: bool = field( default=False, metadata={"help": "Whether or not to use the legacy prediction_loop in the Trainer."} ) push_to_hub: bool = field( default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."} ) resume_from_checkpoint: Optional[str] = field( default=None, metadata={"help": "The path to a folder with a valid checkpoint for your model."}, ) hub_model_id: Optional[str] = field( default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."} ) hub_strategy: Union[HubStrategy, str] = field( default="every_save", metadata={"help": "The hub strategy to use when `--push_to_hub` is activated."}, ) hub_token: Optional[str] = field(default=None, metadata={"help": "The token to use to push to the Model Hub."}) hub_private_repo: bool = field(default=False, metadata={"help": "Whether the model repository is private or not."}) gradient_checkpointing: bool = field( default=False, metadata={ "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass." }, ) include_inputs_for_metrics: bool = field( default=False, metadata={"help": "Whether or not the inputs will be passed to the `compute_metrics` function."} ) # Deprecated arguments fp16_backend: str = field( default="auto", metadata={ "help": "Deprecated. Use half_precision_backend instead", "choices": ["auto", "cuda_amp", "apex", "cpu_amp"], }, ) push_to_hub_model_id: Optional[str] = field( default=None, metadata={"help": "The name of the repository to which push the `Trainer`."} ) push_to_hub_organization: Optional[str] = field( default=None, metadata={"help": "The name of the organization in with to which push the `Trainer`."} ) push_to_hub_token: Optional[str] = field( default=None, metadata={"help": "The token to use to push to the Model Hub."} ) _n_gpu: int = field(init=False, repr=False, default=-1) mp_parameters: str = field( default="", metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer"}, ) auto_find_batch_size: bool = field( default=False, metadata={ "help": ( "Whether to automatically decrease the batch size in half and rerun the training loop again each time" " a CUDA Out-of-Memory was reached" ) }, ) full_determinism: bool = field( default=False, metadata={ "help": ( "Whether to call enable_full_determinism instead of set_seed for reproducibility in distributed" " training" ) }, ) torchdynamo: Optional[str] = field( default=None, metadata={ "help": ( "Sets up the backend compiler for TorchDynamo. TorchDynamo is a Python level JIT compiler designed to" " make unmodified PyTorch programs faster. TorchDynamo dynamically modifies the Python bytecode right" " before its executed. It rewrites Python bytecode to extract sequences of PyTorch operations" " and lifts them up into Fx graph. We can then pass these Fx graphs to other backend compilers. There" " are two options - eager and nvfuser. Eager defaults to pytorch eager and is useful for debugging." " nvfuser path uses AOT Autograd and nvfuser compiler to optimize the models." ), "choices": ["eager", "nvfuser", "fx2trt", "fx2trt-fp16"], }, ) ray_scope: Optional[str] = field( default="last", metadata={ "help": ( 'The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray' " will then use the last checkpoint of all trials, compare those, and select the best one. However," " other options are also available. See the Ray documentation" " (https://docs.ray.io/en/latest/tune/api_docs/analysis.html" "#ray.tune.ExperimentAnalysis.get_best_trial)" " for more options." ) }, ) ddp_timeout: Optional[int] = field( default=1800, metadata={ "help": "Overrides the default timeout for distributed training (value should be given in seconds)." }, ) def __post_init__(self): # Handle --use_env option in torch.distributed.launch (local_rank not passed as an arg then). # This needs to happen before any call to self.device or self.n_gpu. env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != self.local_rank: self.local_rank = env_local_rank # expand paths, if not os.makedirs("~/bar") will make directory # in the current directory instead of the actual home #  see https://github.com/huggingface/transformers/issues/10628 if self.output_dir is not None: self.output_dir = os.path.expanduser(self.output_dir) if self.logging_dir is None and self.output_dir is not None: self.logging_dir = os.path.join(self.output_dir, default_logdir()) if self.logging_dir is not None: self.logging_dir = os.path.expanduser(self.logging_dir) if self.disable_tqdm is None: self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN if isinstance(self.evaluation_strategy, EvaluationStrategy): warnings.warn( "using `EvaluationStrategy` for `evaluation_strategy` is deprecated and will be removed in version 5" " of 🤗 Transformers. Use `IntervalStrategy` instead", FutureWarning, ) # Go back to the underlying string or we won't be able to instantiate `IntervalStrategy` on it. self.evaluation_strategy = self.evaluation_strategy.value self.evaluation_strategy = IntervalStrategy(self.evaluation_strategy) self.logging_strategy = IntervalStrategy(self.logging_strategy) self.save_strategy = IntervalStrategy(self.save_strategy) self.hub_strategy = HubStrategy(self.hub_strategy) self.lr_scheduler_type = SchedulerType(self.lr_scheduler_type) if self.do_eval is False and self.evaluation_strategy != IntervalStrategy.NO: self.do_eval = True # eval_steps has to be defined and non-zero, fallbacks to logging_steps if the latter is non-zero if self.evaluation_strategy == IntervalStrategy.STEPS and (self.eval_steps is None or self.eval_steps == 0): if self.logging_steps > 0: logger.info(f"using `logging_steps` to initialize `eval_steps` to {self.logging_steps}") self.eval_steps = self.logging_steps else: raise ValueError( f"evaluation strategy {self.evaluation_strategy} requires either non-zero --eval_steps or" " --logging_steps" ) # logging_steps must be non-zero for logging_strategy that is other than 'no' if self.logging_strategy == IntervalStrategy.STEPS and self.logging_steps == 0: raise ValueError(f"logging strategy {self.logging_strategy} requires non-zero --logging_steps") # Sanity checks for load_best_model_at_end: we require save and eval strategies to be compatible. if self.load_best_model_at_end: if self.evaluation_strategy != self.save_strategy: raise ValueError( "--load_best_model_at_end requires the save and eval strategy to match, but found\n- Evaluation " f"strategy: {self.evaluation_strategy}\n- Save strategy: {self.save_strategy}" ) if self.evaluation_strategy == IntervalStrategy.STEPS and self.save_steps % self.eval_steps != 0: raise ValueError( "--load_best_model_at_end requires the saving steps to be a round multiple of the evaluation " f"steps, but found {self.save_steps}, which is not a round multiple of {self.eval_steps}." ) if self.load_best_model_at_end and self.metric_for_best_model is None: self.metric_for_best_model = "loss" if self.greater_is_better is None and self.metric_for_best_model is not None: self.greater_is_better = self.metric_for_best_model not in ["loss", "eval_loss"] if self.run_name is None: self.run_name = self.output_dir if self.framework == "pt" and is_torch_available(): if self.fp16_backend and self.fp16_backend != "auto": warnings.warn( "`fp16_backend` is deprecated and will be removed in version 5 of 🤗 Transformers. Use" " `half_precision_backend` instead", FutureWarning, ) self.half_precision_backend = self.fp16_backend if self.bf16 or self.bf16_full_eval: if self.no_cuda and not is_torch_bf16_cpu_available(): # cpu raise ValueError("Your setup doesn't support bf16/cpu. You need torch>=1.10") elif not self.no_cuda and not is_torch_bf16_gpu_available(): # gpu raise ValueError( "Your setup doesn't support bf16/gpu. You need torch>=1.10, using Ampere GPU with cuda>=11.0" ) if self.fp16 and self.bf16: raise ValueError("At most one of fp16 and bf16 can be True, but not both") if self.fp16_full_eval and self.bf16_full_eval: raise ValueError("At most one of fp16 and bf16 can be True for full eval, but not both") if self.bf16: if self.half_precision_backend == "apex": raise ValueError( " `--half_precision_backend apex`: GPU bf16 is not supported by apex. Use" " `--half_precision_backend cuda_amp` instead" ) if not (self.sharded_ddp == "" or not self.sharded_ddp): raise ValueError("sharded_ddp is not supported with bf16") self.optim = OptimizerNames(self.optim) if self.adafactor: warnings.warn( "`--adafactor` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `--optim" " adafactor` instead", FutureWarning, ) self.optim = OptimizerNames.ADAFACTOR if ( self.framework == "pt" and is_torch_available() and (self.device.type != "cuda") and (get_xla_device_type(self.device) != "GPU") and (self.fp16 or self.fp16_full_eval) ): raise ValueError( "FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation" " (`--fp16_full_eval`) can only be used on CUDA devices." ) if ( self.framework == "pt" and is_torch_available() and (self.device.type != "cuda") and (get_xla_device_type(self.device) != "GPU") and (self.device.type != "cpu") and (self.bf16 or self.bf16_full_eval) ): raise ValueError( "BF16 Mixed precision training with AMP (`--bf16`) and BF16 half precision evaluation" " (`--bf16_full_eval`) can only be used on CUDA or CPU devices." ) if self.framework == "pt" and is_torch_available() and self.tf32 is not None: if self.tf32: if is_torch_tf32_available(): torch.backends.cuda.matmul.allow_tf32 = True else: raise ValueError("--tf32 requires Ampere or a newer GPU arch, cuda>=11 and torch>=1.7") else: if is_torch_tf32_available(): torch.backends.cuda.matmul.allow_tf32 = False # no need to assert on else if self.report_to is None: logger.info( "The default value for the training argument `--report_to` will change in v5 (from all installed " "integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as " "now. You should start updating your code and make this info disappear :-)." ) self.report_to = "all" if self.report_to == "all" or self.report_to == ["all"]: # Import at runtime to avoid a circular import. from .integrations import get_available_reporting_integrations self.report_to = get_available_reporting_integrations() elif self.report_to == "none" or self.report_to == ["none"]: self.report_to = [] elif not isinstance(self.report_to, list): self.report_to = [self.report_to] if self.warmup_ratio < 0 or self.warmup_ratio > 1: raise ValueError("warmup_ratio must lie in range [0,1]") elif self.warmup_ratio > 0 and self.warmup_steps > 0: logger.info( "Both warmup_ratio and warmup_steps given, warmup_steps will override any effect of warmup_ratio" " during training" ) if isinstance(self.sharded_ddp, bool): self.sharded_ddp = "simple" if self.sharded_ddp else "" if isinstance(self.sharded_ddp, str): self.sharded_ddp = [ShardedDDPOption(s) for s in self.sharded_ddp.split()] if self.sharded_ddp == [ShardedDDPOption.OFFLOAD]: raise ValueError( "`--sharded_ddp offload` can't work on its own. It needs to be added to `--sharded_ddp zero_dp_2` or " '`--sharded_ddp zero_dp_3`. For example, `--sharded_ddp "zero_dp_2 offload"`.' ) elif len(self.sharded_ddp) > 1 and ShardedDDPOption.SIMPLE in self.sharded_ddp: raise ValueError("`--sharded_ddp simple` is not compatible with any other option.") elif ShardedDDPOption.ZERO_DP_2 in self.sharded_ddp and ShardedDDPOption.ZERO_DP_3 in self.sharded_ddp: raise ValueError("`--sharded_ddp zero_dp_2` is not compatible with `--sharded_ddp zero_dp_3`.") if isinstance(self.fsdp, bool): self.fsdp = "full_shard" if self.fsdp else "" if isinstance(self.fsdp, str): self.fsdp = [FSDPOption(s) for s in self.fsdp.split()] if self.fsdp == [FSDPOption.OFFLOAD]: raise ValueError( "`--fsdp offload` can't work on its own. It needs to be added to `--fsdp full_shard` or " '`--fsdp shard_grad_op`. For example, `--fsdp "full_shard offload"`.' ) elif FSDPOption.FULL_SHARD in self.fsdp and FSDPOption.SHARD_GRAD_OP in self.fsdp: raise ValueError("`--fsdp full_shard` is not compatible with `--fsdp shard_grad_op`.") if len(self.fsdp) == 0 and self.fsdp_min_num_params > 0: warnings.warn("`--fsdp_min_num_params` is useful only when `--fsdp` is specified.") if len(self.fsdp) == 0 and self.fsdp_transformer_layer_cls_to_wrap is not None: warnings.warn("`--fsdp_transformer_layer_cls_to_wrap` is useful only when `--fsdp` is specified.") if len(self.fsdp) > 0 and self.fsdp_min_num_params > 0 and self.fsdp_transformer_layer_cls_to_wrap is not None: raise ValueError( "`--fsdp_min_num_params` and `--fsdp_transformer_layer_cls_to_wrap` are mutually exclusive." ) if self.tpu_metrics_debug: warnings.warn( "using `--tpu_metrics_debug` is deprecated and will be removed in version 5 of 🤗 Transformers. Use" " `--debug tpu_metrics_debug` instead", FutureWarning, ) self.debug += " tpu_metrics_debug" self.tpu_metrics_debug = False if isinstance(self.debug, str): self.debug = [DebugOption(s) for s in self.debug.split()] if self.deepspeed: # - must be run very last in arg parsing, since it will use a lot of these settings. # - must be run before the model is created. if not is_accelerate_available(): raise ValueError("--deepspeed requires Accelerate to be installed: `pip install accelerate`.") from transformers.deepspeed import HfTrainerDeepSpeedConfig # will be used later by the Trainer # note: leave self.deepspeed unmodified in case a user relies on it not to be modified) self.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.deepspeed) self.hf_deepspeed_config.trainer_config_process(self) if self.push_to_hub_token is not None: warnings.warn( "`--push_to_hub_token` is deprecated and will be removed in version 5 of 🤗 Transformers. Use " "`--hub_token` instead.", FutureWarning, ) self.hub_token = self.push_to_hub_token if self.push_to_hub_model_id is not None: self.hub_model_id = get_full_repo_name( self.push_to_hub_model_id, organization=self.push_to_hub_organization, token=self.hub_token ) if self.push_to_hub_organization is not None: warnings.warn( "`--push_to_hub_model_id` and `--push_to_hub_organization` are deprecated and will be removed in " "version 5 of 🤗 Transformers. Use `--hub_model_id` instead and pass the full repo name to this " f"argument (in this case {self.hub_model_id}).", FutureWarning, ) else: warnings.warn( "`--push_to_hub_model_id` is deprecated and will be removed in version 5 of 🤗 Transformers. Use " "`--hub_model_id` instead and pass the full repo name to this argument (in this case " f"{self.hub_model_id}).", FutureWarning, ) elif self.push_to_hub_organization is not None: self.hub_model_id = f"{self.push_to_hub_organization}/{Path(self.output_dir).name}" warnings.warn( "`--push_to_hub_organization` is deprecated and will be removed in version 5 of 🤗 Transformers. Use " "`--hub_model_id` instead and pass the full repo name to this argument (in this case " f"{self.hub_model_id}).", FutureWarning, ) def __str__(self): self_as_dict = asdict(self) # Remove deprecated arguments. That code should be removed once # those deprecated arguments are removed from TrainingArguments. (TODO: v5) del self_as_dict["per_gpu_train_batch_size"] del self_as_dict["per_gpu_eval_batch_size"] self_as_dict = {k: f"<{k.upper()}>" if k.endswith("_token") else v for k, v in self_as_dict.items()} attrs_as_str = [f"{k}={v},\n" for k, v in sorted(self_as_dict.items())] return f"{self.__class__.__name__}(\n{''.join(attrs_as_str)})" __repr__ = __str__ @property def train_batch_size(self) -> int: """ The actual batch size for training (may differ from `per_gpu_train_batch_size` in distributed training). """ if self.per_gpu_train_batch_size: logger.warning( "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future " "version. Using `--per_device_train_batch_size` is preferred." ) per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size train_batch_size = per_device_batch_size * max(1, self.n_gpu) return train_batch_size @property def eval_batch_size(self) -> int: """ The actual batch size for evaluation (may differ from `per_gpu_eval_batch_size` in distributed training). """ if self.per_gpu_eval_batch_size: logger.warning( "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future " "version. Using `--per_device_eval_batch_size` is preferred." ) per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size eval_batch_size = per_device_batch_size * max(1, self.n_gpu) return eval_batch_size @property def ddp_timeout_delta(self) -> timedelta: """ The actual timeout for torch.distributed.init_process_group since it expects a timedelta variable. """ return timedelta(seconds=self.ddp_timeout) @cached_property @torch_required def _setup_devices(self) -> "torch.device": logger.info("PyTorch: setting up devices") if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1: logger.warning( "torch.distributed process group is initialized, but local_rank == -1. " "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" ) if self.no_cuda: device = torch.device("cpu") self._n_gpu = 0 self.local_rank = get_int_from_env( ["LOCAL_RANK", "MPI_LOCALRANKID", "OMPI_COMM_WORLD_LOCAL_RANK", "MV2_COMM_WORLD_LOCAL_RANK"], self.local_rank, ) if self.local_rank != -1 and not torch.distributed.is_initialized(): # Initializes distributed backend for cpu if self.xpu_backend not in ("mpi", "ccl", "gloo"): raise ValueError( "CPU distributed training backend is not properly set. " "Please set '--xpu_backend' to either 'mpi' or 'ccl' or 'gloo'." ) if self.xpu_backend == "ccl": requires_backends(self, "oneccl_bind_pt") if ccl_version >= "1.12": import oneccl_bindings_for_pytorch # noqa: F401 else: import torch_ccl # noqa: F401 if int(os.environ.get("CCL_WORKER_COUNT", 0)) < 1: raise ValueError( "CPU distributed training backend is ccl. but CCL_WORKER_COUNT is not correctly set. " "Please use like 'export CCL_WORKER_COUNT = 1' to set." ) # Try to get launch configuration from environment variables set by MPI launcher - works for Intel MPI, OpenMPI and MVAPICH rank = get_int_from_env(["RANK", "PMI_RANK", "OMPI_COMM_WORLD_RANK", "MV2_COMM_WORLD_RANK"], 0) size = get_int_from_env(["WORLD_SIZE", "PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "MV2_COMM_WORLD_SIZE"], 1) local_size = get_int_from_env( ["MPI_LOCALNRANKS", "OMPI_COMM_WORLD_LOCAL_SIZE", "MV2_COMM_WORLD_LOCAL_SIZE"], 1 ) os.environ["RANK"] = str(rank) os.environ["WORLD_SIZE"] = str(size) os.environ["LOCAL_RANK"] = str(self.local_rank) if not os.environ.get("MASTER_PORT", None): os.environ["MASTER_PORT"] = "29500" if not os.environ.get("MASTER_ADDR", None): if local_size != size or self.xpu_backend != "mpi": raise ValueError( "Looks like distributed multinode run but MASTER_ADDR env not set, " "please try exporting rank 0's hostname as MASTER_ADDR" ) if ( torch.get_num_threads() == 1 and get_int_from_env(["OMP_NUM_THREADS", "MKL_NUM_THREADS"], 0) == 0 and is_psutil_available() ): import psutil num_cpu_threads_per_process = int(psutil.cpu_count(logical=False) / local_size) if num_cpu_threads_per_process == 0: num_cpu_threads_per_process = 1 torch.set_num_threads(num_cpu_threads_per_process) logger.info( f"num_cpu_threads_per_process unset, we set it at {num_cpu_threads_per_process} to improve oob" " performance." ) torch.distributed.init_process_group( backend=self.xpu_backend, rank=rank, world_size=size, timeout=self.ddp_timeout_delta ) elif is_torch_tpu_available(): device = xm.xla_device() self._n_gpu = 0 elif is_sagemaker_mp_enabled(): local_rank = smp.local_rank() device = torch.device("cuda", local_rank) self._n_gpu = 1 elif is_sagemaker_dp_enabled(): import smdistributed.dataparallel.torch.torch_smddp # noqa: F401 dist.init_process_group(backend="smddp", timeout=self.ddp_timeout_delta) self.local_rank = int(os.getenv("SMDATAPARALLEL_LOCAL_RANK")) device = torch.device("cuda", self.local_rank) self._n_gpu = 1 elif self.deepspeed: # deepspeed inits torch.distributed internally from .deepspeed import is_deepspeed_available if not is_deepspeed_available(): raise ImportError("--deepspeed requires deepspeed: `pip install deepspeed`.") import deepspeed deepspeed.init_distributed() # workaround for setups like notebooks where the launcher can't be used, # but deepspeed requires a dist env. # env LOCAL_RANK could be set manually by the user, or via init_distributed if mpi4py is installed self.local_rank = int(os.environ.get("LOCAL_RANK", "-1")) device = torch.device("cuda", self.local_rank) self._n_gpu = 1 elif self.local_rank == -1: if self.use_mps_device: if not torch.backends.mps.is_available(): if not torch.backends.mps.is_built(): raise AssertionError( "MPS not available because the current PyTorch install was not " "built with MPS enabled. Please install torch version >=1.12.0 on " "your Apple silicon Mac running macOS 12.3 or later with a native " "version (arm64) of Python" ) else: raise AssertionError( "MPS not available because the current MacOS version is not 12.3+ " "and/or you do not have an MPS-enabled device on this machine." ) else: if not version.parse(version.parse(torch.__version__).base_version) > version.parse("1.12.0"): warnings.warn( "We strongly recommend to install PyTorch >= 1.13 (nightly version at the time of writing)" " on your MacOS machine. It has major fixes related to model correctness and performance" " improvements for transformer based models. Please refer to" " https://github.com/pytorch/pytorch/issues/82707 for more details." ) device = torch.device("mps") self._n_gpu = 1 else: # if n_gpu is > 1 we'll use nn.DataParallel. # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0` # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will # trigger an error that a device index is missing. Index 0 takes into account the # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0` # will use the first GPU in that env, i.e. GPU#1 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at # the default value. self._n_gpu = torch.cuda.device_count() else: # Here, we'll use torch.distributed. # Initializes the distributed backend which will take care of synchronizing nodes/GPUs if not torch.distributed.is_initialized(): torch.distributed.init_process_group(backend="nccl", timeout=self.ddp_timeout_delta) device = torch.device("cuda", self.local_rank) self._n_gpu = 1 if device.type == "cuda": torch.cuda.set_device(device) return device @property @torch_required def device(self) -> "torch.device": """ The device used by this process. """ return self._setup_devices @property @torch_required def n_gpu(self): """ The number of GPUs used by this process. Note: This will only be greater than one when you have multiple GPUs available but are not using distributed training. For distributed training, it will always be 1. """ # Make sure `self._n_gpu` is properly setup. _ = self._setup_devices return self._n_gpu @property @torch_required def parallel_mode(self): """ The current mode used for parallelism if multiple GPUs/TPU cores are available. One of: - `ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU). - `ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses `torch.nn.DataParallel`). - `ParallelMode.DISTRIBUTED`: several GPUs, each having its own process (uses `torch.nn.DistributedDataParallel`). - `ParallelMode.TPU`: several TPU cores. """ if is_torch_tpu_available(): return ParallelMode.TPU elif is_sagemaker_mp_enabled(): return ParallelMode.SAGEMAKER_MODEL_PARALLEL elif is_sagemaker_dp_enabled(): return ParallelMode.SAGEMAKER_DATA_PARALLEL elif self.local_rank != -1: return ParallelMode.DISTRIBUTED elif self.n_gpu > 1: return ParallelMode.NOT_DISTRIBUTED else: return ParallelMode.NOT_PARALLEL @property @torch_required def world_size(self): """ The number of processes used in parallel. """ if is_torch_tpu_available(): return xm.xrt_world_size() elif is_sagemaker_mp_enabled(): return smp.dp_size() if not smp.state.cfg.prescaled_batch else smp.rdp_size() elif is_sagemaker_dp_enabled(): return dist.get_world_size() elif self.local_rank != -1: return torch.distributed.get_world_size() return 1 @property @torch_required def process_index(self): """ The index of the current process used. """ if is_torch_tpu_available(): return xm.get_ordinal() elif is_sagemaker_mp_enabled(): return smp.dp_rank() if not smp.state.cfg.prescaled_batch else smp.rdp_rank() elif is_sagemaker_dp_enabled(): return dist.get_rank() elif self.local_rank != -1: return torch.distributed.get_rank() return 0 @property @torch_required def local_process_index(self): """ The index of the local process used. """ if is_torch_tpu_available(): return xm.get_local_ordinal() elif is_sagemaker_mp_enabled(): return smp.local_rank() elif is_sagemaker_dp_enabled(): return dist.get_rank() elif self.local_rank != -1: return self.local_rank return 0 @property def should_log(self): """ Whether or not the current process should produce log. """ if self.log_on_each_node: return self.local_process_index == 0 else: if is_sagemaker_mp_enabled(): return smp.rank() == 0 else: return self.process_index == 0 @property def should_save(self): """ Whether or not the current process should write to disk, e.g., to save models and checkpoints. """ if self.save_on_each_node: return self.local_process_index == 0 else: if is_sagemaker_mp_enabled(): return smp.rank() == 0 else: return self.process_index == 0 def get_process_log_level(self): """ Returns the log level to be used depending on whether this process is the main process of node 0, main process of node non-0, or a non-main process. For the main process the log level defaults to `logging.INFO` unless overridden by `log_level` argument. For the replica processes the log level defaults to `logging.WARNING` unless overridden by `log_level_replica` argument. The choice between the main and replica process settings is made according to the return value of `should_log`. """ # convert to int log_level = trainer_log_levels[self.log_level] log_level_replica = trainer_log_levels[self.log_level_replica] log_level_main_node = logging.INFO if log_level == -1 else log_level log_level_replica_node = logging.WARNING if log_level_replica == -1 else log_level_replica return log_level_main_node if self.should_log else log_level_replica_node @property def place_model_on_device(self): """ Can be subclassed and overridden for some specific integrations. """ return not is_sagemaker_mp_enabled() @property def _no_sync_in_gradient_accumulation(self): """ Whether or not to use no_sync for the gradients when doing gradient accumulation. """ return not (self.deepspeed or is_sagemaker_dp_enabled() or is_sagemaker_mp_enabled()) @contextlib.contextmanager def main_process_first(self, local=True, desc="work"): """ A context manager for torch distributed environment where on needs to do something on the main process, while blocking replicas, and when it's finished releasing the replicas. One such use is for `datasets`'s `map` feature which to be efficient should be run once on the main process, which upon completion saves a cached version of results and which then automatically gets loaded by the replicas. Args: local (`bool`, *optional*, defaults to `True`): if `True` first means process of rank 0 of each node if `False` first means process of rank 0 of node rank 0 In multi-node environment with a shared filesystem you most likely will want to use `local=False` so that only the main process of the first node will do the processing. If however, the filesystem is not shared, then the main process of each node will need to do the processing, which is the default behavior. desc (`str`, *optional*, defaults to `"work"`): a work description to be used in debug logs """ if is_torch_available() and self.world_size > 1: main_process_desc = "main process" if local: is_main_process = self.local_process_index == 0 main_process_desc = "main local process" elif is_sagemaker_mp_enabled(): is_main_process = smp.rank() == 0 else: is_main_process = self.process_index == 0 try: if not is_main_process: # tell all replicas to wait logger.debug(f"{self.process_index}: waiting for the {main_process_desc} to perform {desc}") if is_torch_tpu_available(): xm.rendezvous(desc) elif is_sagemaker_dp_enabled(): dist.barrier() else: torch.distributed.barrier() yield finally: if is_main_process: # the wait is over logger.debug(f"{self.process_index}: {main_process_desc} completed {desc}, releasing all replicas") if is_torch_tpu_available(): xm.rendezvous(desc) elif is_sagemaker_dp_enabled(): dist.barrier() else: torch.distributed.barrier() else: yield def get_warmup_steps(self, num_training_steps: int): """ Get number of steps used for a linear warmup. """ warmup_steps = ( self.warmup_steps if self.warmup_steps > 0 else math.ceil(num_training_steps * self.warmup_ratio) ) return warmup_steps def to_dict(self): """ Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates the token values by removing their value. """ # filter out fields that are defined as field(init=False) d = dict((field.name, getattr(self, field.name)) for field in fields(self) if field.init) for k, v in d.items(): if isinstance(v, Enum): d[k] = v.value if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum): d[k] = [x.value for x in v] if k.endswith("_token"): d[k] = f"<{k.upper()}>" return d def to_json_string(self): """ Serializes this instance to a JSON string. """ return json.dumps(self.to_dict(), indent=2) def to_sanitized_dict(self) -> Dict[str, Any]: """ Sanitized serialization to use with TensorBoard’s hparams """ d = self.to_dict() d = {**d, **{"train_batch_size": self.train_batch_size, "eval_batch_size": self.eval_batch_size}} valid_types = [bool, int, float, str] if is_torch_available(): valid_types.append(torch.Tensor) return {k: v if type(v) in valid_types else str(v) for k, v in d.items()} class ParallelMode(Enum): NOT_PARALLEL = "not_parallel" NOT_DISTRIBUTED = "not_distributed" DISTRIBUTED = "distributed" SAGEMAKER_MODEL_PARALLEL = "sagemaker_model_parallel" SAGEMAKER_DATA_PARALLEL = "sagemaker_data_parallel" TPU = "tpu"

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import contextlib import json import math import os import warnings from dataclasses import asdict, dataclass, field, fields from datetime import timedelta from enum import Enum from pathlib import Path from typing import Any, Dict, List, Optional, Union from packaging import version from .debug_utils import DebugOption from .trainer_utils import ( EvaluationStrategy, FSDPOption, HubStrategy, IntervalStrategy, SchedulerType, ShardedDDPOption, ) from .utils import ( ExplicitEnum, cached_property, ccl_version, get_full_repo_name, is_accelerate_available, is_psutil_available, is_sagemaker_dp_enabled, is_sagemaker_mp_enabled, is_torch_available, is_torch_bf16_cpu_available, is_torch_bf16_gpu_available, is_torch_tf32_available, is_torch_tpu_available, logging, requires_backends, torch_required, ) if is_torch_available(): import torch import torch.distributed as dist if is_torch_tpu_available(check_device=False): import torch_xla.core.xla_model as xm if is_sagemaker_mp_enabled(): import smdistributed.modelparallel.torch as smp smp.init() logger = logging.get_logger(__name__) log_levels = logging.get_log_levels_dict().copy() trainer_log_levels = dict(**log_levels, passive=-1) def default_logdir() -> str: """ Same default as PyTorch """ import socket from datetime import datetime current_time = datetime.now().strftime("%b%d_%H-%M-%S") return os.path.join("runs", current_time + "_" + socket.gethostname()) def get_int_from_env(env_keys, default): """Returns the first positive env value found in the `env_keys` list or the default.""" for e in env_keys: val = int(os.environ.get(e, -1)) if val >= 0: return val return default def get_xla_device_type(device: "torch.device") -> Optional[str]: """ Returns the xla device type (CPU|GPU|TPU) or None if the device is a non-xla device. """ if is_torch_tpu_available(): return xm.xla_real_devices([device])[0].split(":")[0] return None class OptimizerNames(ExplicitEnum): """ Stores the acceptable string identifiers for optimizers. """ ADAMW_HF = "adamw_hf" ADAMW_TORCH = "adamw_torch" ADAMW_TORCH_XLA = "adamw_torch_xla" ADAMW_APEX_FUSED = "adamw_apex_fused" ADAFACTOR = "adafactor" ADAMW_BNB = "adamw_bnb_8bit" SGD = "sgd" ADAGRAD = "adagrad" @dataclass class TrainingArguments: """ TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop itself**. Using [`HfArgumentParser`] we can turn this class into [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the command line. Parameters: output_dir (`str`): The output directory where the model predictions and checkpoints will be written. overwrite_output_dir (`bool`, *optional*, defaults to `False`): If `True`, overwrite the content of the output directory. Use this to continue training if `output_dir` points to a checkpoint directory. do_train (`bool`, *optional*, defaults to `False`): Whether to run training or not. This argument is not directly used by [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. do_eval (`bool`, *optional*): Whether to run evaluation on the validation set or not. Will be set to `True` if `evaluation_strategy` is different from `"no"`. This argument is not directly used by [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. do_predict (`bool`, *optional*, defaults to `False`): Whether to run predictions on the test set or not. This argument is not directly used by [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. evaluation_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`): The evaluation strategy to adopt during training. Possible values are: - `"no"`: No evaluation is done during training. - `"steps"`: Evaluation is done (and logged) every `eval_steps`. - `"epoch"`: Evaluation is done at the end of each epoch. prediction_loss_only (`bool`, *optional*, defaults to `False`): When performing evaluation and generating predictions, only returns the loss. per_device_train_batch_size (`int`, *optional*, defaults to 8): The batch size per GPU/TPU core/CPU for training. per_device_eval_batch_size (`int`, *optional*, defaults to 8): The batch size per GPU/TPU core/CPU for evaluation. gradient_accumulation_steps (`int`, *optional*, defaults to 1): Number of updates steps to accumulate the gradients for, before performing a backward/update pass. <Tip warning={true}> When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging, evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training examples. </Tip> eval_accumulation_steps (`int`, *optional*): Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but requires more memory). eval_delay (`float`, *optional*): Number of epochs or steps to wait for before the first evaluation can be performed, depending on the evaluation_strategy. learning_rate (`float`, *optional*, defaults to 5e-5): The initial learning rate for [`AdamW`] optimizer. weight_decay (`float`, *optional*, defaults to 0): The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in [`AdamW`] optimizer. adam_beta1 (`float`, *optional*, defaults to 0.9): The beta1 hyperparameter for the [`AdamW`] optimizer. adam_beta2 (`float`, *optional*, defaults to 0.999): The beta2 hyperparameter for the [`AdamW`] optimizer. adam_epsilon (`float`, *optional*, defaults to 1e-8): The epsilon hyperparameter for the [`AdamW`] optimizer. max_grad_norm (`float`, *optional*, defaults to 1.0): Maximum gradient norm (for gradient clipping). num_train_epochs(`float`, *optional*, defaults to 3.0): Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training). max_steps (`int`, *optional*, defaults to -1): If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`. In case of using a finite iterable dataset the training may stop before reaching the set number of steps when all data is exhausted lr_scheduler_type (`str` or [`SchedulerType`], *optional*, defaults to `"linear"`): The scheduler type to use. See the documentation of [`SchedulerType`] for all possible values. warmup_ratio (`float`, *optional*, defaults to 0.0): Ratio of total training steps used for a linear warmup from 0 to `learning_rate`. warmup_steps (`int`, *optional*, defaults to 0): Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`. log_level (`str`, *optional*, defaults to `passive`): Logger log level to use on the main process. Possible choices are the log levels as strings: 'debug', 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and lets the application set the level. log_level_replica (`str`, *optional*, defaults to `passive`): Logger log level to use on replicas. Same choices as `log_level`" log_on_each_node (`bool`, *optional*, defaults to `True`): In multinode distributed training, whether to log using `log_level` once per node, or only on the main node. logging_dir (`str`, *optional*): [TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***. logging_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`): The logging strategy to adopt during training. Possible values are: - `"no"`: No logging is done during training. - `"epoch"`: Logging is done at the end of each epoch. - `"steps"`: Logging is done every `logging_steps`. logging_first_step (`bool`, *optional*, defaults to `False`): Whether to log and evaluate the first `global_step` or not. logging_steps (`int`, *optional*, defaults to 500): Number of update steps between two logs if `logging_strategy="steps"`. logging_nan_inf_filter (`bool`, *optional*, defaults to `True`): Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is `nan` or `inf` is filtered and the average loss of the current logging window is taken instead. <Tip> `logging_nan_inf_filter` only influences the logging of loss values, it does not change the behavior the gradient is computed or applied to the model. </Tip> save_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`): The checkpoint save strategy to adopt during training. Possible values are: - `"no"`: No save is done during training. - `"epoch"`: Save is done at the end of each epoch. - `"steps"`: Save is done every `save_steps`. save_steps (`int`, *optional*, defaults to 500): Number of updates steps before two checkpoint saves if `save_strategy="steps"`. save_total_limit (`int`, *optional*): If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. save_on_each_node (`bool`, *optional*, defaults to `False`): When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one. This should not be activated when the different nodes use the same storage as the files will be saved with the same names for each node. no_cuda (`bool`, *optional*, defaults to `False`): Whether to not use CUDA even when it is available or not. seed (`int`, *optional*, defaults to 42): Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the [`~Trainer.model_init`] function to instantiate the model if it has some randomly initialized parameters. data_seed (`int`, *optional*): Random seed to be used with data samplers. If not set, random generators for data sampling will use the same seed as `seed`. This can be used to ensure reproducibility of data sampling, independent of the model seed. jit_mode_eval (`bool`, *optional*, defaults to `False`): Whether or not to use PyTorch jit trace for inference. use_ipex (`bool`, *optional*, defaults to `False`): Use Intel extension for PyTorch when it is available. [IPEX installation](https://github.com/intel/intel-extension-for-pytorch). bf16 (`bool`, *optional*, defaults to `False`): Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher NVIDIA architecture or using CPU (no_cuda). This is an experimental API and it may change. fp16 (`bool`, *optional*, defaults to `False`): Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training. fp16_opt_level (`str`, *optional*, defaults to 'O1'): For `fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details on the [Apex documentation](https://nvidia.github.io/apex/amp). fp16_backend (`str`, *optional*, defaults to `"auto"`): This argument is deprecated. Use `half_precision_backend` instead. half_precision_backend (`str`, *optional*, defaults to `"auto"`): The backend to use for mixed precision training. Must be one of `"auto", "cuda_amp", "apex", "cpu_amp"`. `"auto"` will use CPU/CUDA AMP or APEX depending on the PyTorch version detected, while the other choices will force the requested backend. bf16_full_eval (`bool`, *optional*, defaults to `False`): Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values. This is an experimental API and it may change. fp16_full_eval (`bool`, *optional*, defaults to `False`): Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values. tf32 (`bool`, *optional*): Whether to enable the TF32 mode, available in Ampere and newer GPU architectures. The default value depends on PyTorch's version default of `torch.backends.cuda.matmul.allow_tf32`. For more details please refer to the [TF32](https://huggingface.co/docs/transformers/performance#tf32) documentation. This is an experimental API and it may change. local_rank (`int`, *optional*, defaults to -1): Rank of the process during distributed training. xpu_backend (`str`, *optional*): The backend to use for xpu distributed training. Must be one of `"mpi"` or `"ccl"` or `"gloo"`. tpu_num_cores (`int`, *optional*): When training on TPU, the number of TPU cores (automatically passed by launcher script). dataloader_drop_last (`bool`, *optional*, defaults to `False`): Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size) or not. eval_steps (`int`, *optional*): Number of update steps between two evaluations if `evaluation_strategy="steps"`. Will default to the same value as `logging_steps` if not set. dataloader_num_workers (`int`, *optional*, defaults to 0): Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process. past_index (`int`, *optional*, defaults to -1): Some models like [TransformerXL](../model_doc/transformerxl) or [XLNet](../model_doc/xlnet) can make use of the past hidden states for their predictions. If this argument is set to a positive int, the `Trainer` will use the corresponding output (usually index 2) as the past state and feed it to the model at the next training step under the keyword argument `mems`. run_name (`str`, *optional*): A descriptor for the run. Typically used for [wandb](https://www.wandb.com/) and [mlflow](https://www.mlflow.org/) logging. disable_tqdm (`bool`, *optional*): Whether or not to disable the tqdm progress bars and table of metrics produced by [`~notebook.NotebookTrainingTracker`] in Jupyter Notebooks. Will default to `True` if the logging level is set to warn or lower (default), `False` otherwise. remove_unused_columns (`bool`, *optional*, defaults to `True`): Whether or not to automatically remove the columns unused by the model forward method. (Note that this behavior is not implemented for [`TFTrainer`] yet.) label_names (`List[str]`, *optional*): The list of keys in your dictionary of inputs that correspond to the labels. Will eventually default to `["labels"]` except if the model used is one of the `XxxForQuestionAnswering` in which case it will default to `["start_positions", "end_positions"]`. load_best_model_at_end (`bool`, *optional*, defaults to `False`): Whether or not to load the best model found during training at the end of training. <Tip> When set to `True`, the parameters `save_strategy` needs to be the same as `evaluation_strategy`, and in the case it is "steps", `save_steps` must be a round multiple of `eval_steps`. </Tip> metric_for_best_model (`str`, *optional*): Use in conjunction with `load_best_model_at_end` to specify the metric to use to compare two different models. Must be the name of a metric returned by the evaluation with or without the prefix `"eval_"`. Will default to `"loss"` if unspecified and `load_best_model_at_end=True` (to use the evaluation loss). If you set this value, `greater_is_better` will default to `True`. Don't forget to set it to `False` if your metric is better when lower. greater_is_better (`bool`, *optional*): Use in conjunction with `load_best_model_at_end` and `metric_for_best_model` to specify if better models should have a greater metric or not. Will default to: - `True` if `metric_for_best_model` is set to a value that isn't `"loss"` or `"eval_loss"`. - `False` if `metric_for_best_model` is not set, or set to `"loss"` or `"eval_loss"`. ignore_data_skip (`bool`, *optional*, defaults to `False`): When resuming training, whether or not to skip the epochs and batches to get the data loading at the same stage as in the previous training. If set to `True`, the training will begin faster (as that skipping step can take a long time) but will not yield the same results as the interrupted training would have. sharded_ddp (`bool`, `str` or list of [`~trainer_utils.ShardedDDPOption`], *optional*, defaults to `False`): Use Sharded DDP training from [FairScale](https://github.com/facebookresearch/fairscale) (in distributed training only). This is an experimental feature. A list of options along the following: - `"simple"`: to use first instance of sharded DDP released by fairscale (`ShardedDDP`) similar to ZeRO-2. - `"zero_dp_2"`: to use the second instance of sharded DPP released by fairscale (`FullyShardedDDP`) in Zero-2 mode (with `reshard_after_forward=False`). - `"zero_dp_3"`: to use the second instance of sharded DPP released by fairscale (`FullyShardedDDP`) in Zero-3 mode (with `reshard_after_forward=True`). - `"offload"`: to add ZeRO-offload (only compatible with `"zero_dp_2"` and `"zero_dp_3"`). If a string is passed, it will be split on space. If a bool is passed, it will be converted to an empty list for `False` and `["simple"]` for `True`. fsdp (`bool`, `str` or list of [`~trainer_utils.FSDPOption`], *optional*, defaults to `False`): Use PyTorch Distributed Parallel Training (in distributed training only). A list of options along the following: - `"full_shard"`: Shard parameters, gradients and optimizer states. - `"shard_grad_op"`: Shard optimizer states and gradients. - `"offload"`: Offload parameters and gradients to CPUs (only compatible with `"full_shard"` and `"shard_grad_op"`). - `"auto_wrap"`: Automatically recursively wrap layers with FSDP using `default_auto_wrap_policy`. fsdp_min_num_params (`int`, *optional*, defaults to `0`): FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is passed). deepspeed (`str` or `dict`, *optional*): Use [Deepspeed](https://github.com/microsoft/deepspeed). This is an experimental feature and its API may evolve in the future. The value is either the location of DeepSpeed json config file (e.g., `ds_config.json`) or an already loaded json file as a `dict`" label_smoothing_factor (`float`, *optional*, defaults to 0.0): The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded labels are changed from 0s and 1s to `label_smoothing_factor/num_labels` and `1 - label_smoothing_factor + label_smoothing_factor/num_labels` respectively. debug (`str` or list of [`~debug_utils.DebugOption`], *optional*, defaults to `""`): Enable one or more debug features. This is an experimental feature. Possible options are: - `"underflow_overflow"`: detects overflow in model's input/outputs and reports the last frames that led to the event - `"tpu_metrics_debug"`: print debug metrics on TPU The options should be separated by whitespaces. optim (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_hf"`): The optimizer to use: adamw_hf, adamw_torch, adamw_apex_fused, or adafactor. adafactor (`bool`, *optional*, defaults to `False`): This argument is deprecated. Use `--optim adafactor` instead. group_by_length (`bool`, *optional*, defaults to `False`): Whether or not to group together samples of roughly the same length in the training dataset (to minimize padding applied and be more efficient). Only useful if applying dynamic padding. length_column_name (`str`, *optional*, defaults to `"length"`): Column name for precomputed lengths. If the column exists, grouping by length will use these values rather than computing them on train startup. Ignored unless `group_by_length` is `True` and the dataset is an instance of `Dataset`. report_to (`str` or `List[str]`, *optional*, defaults to `"all"`): The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`, `"comet_ml"`, `"mlflow"`, `"neptune"`, `"tensorboard"`,`"clearml"` and `"wandb"`. Use `"all"` to report to all integrations installed, `"none"` for no integrations. ddp_find_unused_parameters (`bool`, *optional*): When using distributed training, the value of the flag `find_unused_parameters` passed to `DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise. ddp_bucket_cap_mb (`int`, *optional*): When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`. dataloader_pin_memory (`bool`, *optional*, defaults to `True`): Whether you want to pin memory in data loaders or not. Will default to `True`. skip_memory_metrics (`bool`, *optional*, defaults to `True`): Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows down the training and evaluation speed. push_to_hub (`bool`, *optional*, defaults to `False`): Whether or not to push the model to the Hub every time the model is saved. If this is activated, `output_dir` will begin a git directory synced with the repo (determined by `hub_model_id`) and the content will be pushed each time a save is triggered (depending on your `save_strategy`). Calling [`~Trainer.save_model`] will also trigger a push. <Tip warning={true}> If `output_dir` exists, it needs to be a local clone of the repository to which the [`Trainer`] will be pushed. </Tip> resume_from_checkpoint (`str`, *optional*): The path to a folder with a valid checkpoint for your model. This argument is not directly used by [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details. hub_model_id (`str`, *optional*): The name of the repository to keep in sync with the local *output_dir*. It can be a simple model ID in which case the model will be pushed in your namespace. Otherwise it should be the whole repository name, for instance `"user_name/model"`, which allows you to push to an organization you are a member of with `"organization_name/model"`. Will default to `user_name/output_dir_name` with *output_dir_name* being the name of `output_dir`. Will default to the name of `output_dir`. hub_strategy (`str` or [`~trainer_utils.HubStrategy`], *optional*, defaults to `"every_save"`): Defines the scope of what is pushed to the Hub and when. Possible values are: - `"end"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a draft of a model card when the [`~Trainer.save_model`] method is called. - `"every_save"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a draft of a model card each time there is a model save. The pushes are asynchronous to not block training, and in case the save are very frequent, a new push is only attempted if the previous one is finished. A last push is made with the final model at the end of training. - `"checkpoint"`: like `"every_save"` but the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to resume training easily with `trainer.train(resume_from_checkpoint="last-checkpoint")`. - `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the output folder (so you will get one checkpoint folder per folder in your final repository) hub_token (`str`, *optional*): The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with `huggingface-cli login`. hub_private_repo (`bool`, *optional*, defaults to `False`): If True, the Hub repo will be set to private. gradient_checkpointing (`bool`, *optional*, defaults to `False`): If True, use gradient checkpointing to save memory at the expense of slower backward pass. include_inputs_for_metrics (`bool`, *optional*, defaults to `False`): Whether or not the inputs will be passed to the `compute_metrics` function. This is intended for metrics that need inputs, predictions and references for scoring calculation in Metric class. auto_find_batch_size (`bool`, *optional*, defaults to `False`) Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding CUDA Out-of-Memory errors. Requires accelerate to be installed (`pip install accelerate`) full_determinism (`bool`, *optional*, defaults to `False`) If `True`, [`enable_full_determinism`] is called instead of [`set_seed`] to ensure reproducible results in distributed training torchdynamo (`str`, *optional*): The token that is used to set the backend compiler for TorchDynamo. Possible choices are ["eager", "nvfuser]. This is an experimental API and subject to change. ray_scope (`str`, *optional*, defaults to `"last"`): The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the [Ray documentation]( https://docs.ray.io/en/latest/tune/api_docs/analysis.html#ray.tune.ExperimentAnalysis.get_best_trial) for more options. ddp_timeout (`int`, *optional*, defaults to 1800): The timeout for `torch.distributed.init_process_group` calls, used to avoid GPU socket timeouts when performing slow operations in distributed runnings. Please refer the [PyTorch documentation] (https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for more information. use_mps_device (`bool`, *optional*, defaults to `False`): Whether to use Apple Silicon chip based `mps` device. """ framework = "pt" output_dir: str = field( metadata={"help": "The output directory where the model predictions and checkpoints will be written."}, ) overwrite_output_dir: bool = field( default=False, metadata={ "help": ( "Overwrite the content of the output directory. " "Use this to continue training if output_dir points to a checkpoint directory." ) }, ) do_train: bool = field(default=False, metadata={"help": "Whether to run training."}) do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."}) do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."}) evaluation_strategy: Union[IntervalStrategy, str] = field( default="no", metadata={"help": "The evaluation strategy to use."}, ) prediction_loss_only: bool = field( default=False, metadata={"help": "When performing evaluation and predictions, only returns the loss."}, ) per_device_train_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."} ) per_device_eval_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."} ) per_gpu_train_batch_size: Optional[int] = field( default=None, metadata={ "help": ( "Deprecated, the use of `--per_device_train_batch_size` is preferred. " "Batch size per GPU/TPU core/CPU for training." ) }, ) per_gpu_eval_batch_size: Optional[int] = field( default=None, metadata={ "help": ( "Deprecated, the use of `--per_device_eval_batch_size` is preferred. " "Batch size per GPU/TPU core/CPU for evaluation." ) }, ) gradient_accumulation_steps: int = field( default=1, metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."}, ) eval_accumulation_steps: Optional[int] = field( default=None, metadata={"help": "Number of predictions steps to accumulate before moving the tensors to the CPU."}, ) eval_delay: Optional[float] = field( default=0, metadata={ "help": ( "Number of epochs or steps to wait for before the first evaluation can be performed, depending on the" " evaluation_strategy." ) }, ) learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."}) weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."}) adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"}) adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"}) adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}) max_grad_norm: float = field(default=1.0, metadata={"help": "Max gradient norm."}) num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."}) max_steps: int = field( default=-1, metadata={"help": "If > 0: set total number of training steps to perform. Override num_train_epochs."}, ) lr_scheduler_type: Union[SchedulerType, str] = field( default="linear", metadata={"help": "The scheduler type to use."}, ) warmup_ratio: float = field( default=0.0, metadata={"help": "Linear warmup over warmup_ratio fraction of total steps."} ) warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."}) log_level: Optional[str] = field( default="passive", metadata={ "help": ( "Logger log level to use on the main node. Possible choices are the log levels as strings: 'debug'," " 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and" " lets the application set the level. Defaults to 'passive'." ), "choices": trainer_log_levels.keys(), }, ) log_level_replica: Optional[str] = field( default="passive", metadata={ "help": "Logger log level to use on replica nodes. Same choices and defaults as ``log_level``", "choices": trainer_log_levels.keys(), }, ) log_on_each_node: bool = field( default=True, metadata={ "help": ( "When doing a multinode distributed training, whether to log once per node or just once on the main" " node." ) }, ) logging_dir: Optional[str] = field(default=None, metadata={"help": "Tensorboard log dir."}) logging_strategy: Union[IntervalStrategy, str] = field( default="steps", metadata={"help": "The logging strategy to use."}, ) logging_first_step: bool = field(default=False, metadata={"help": "Log the first global_step"}) logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."}) logging_nan_inf_filter: bool = field(default=True, metadata={"help": "Filter nan and inf losses for logging."}) save_strategy: Union[IntervalStrategy, str] = field( default="steps", metadata={"help": "The checkpoint save strategy to use."}, ) save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."}) save_total_limit: Optional[int] = field( default=None, metadata={ "help": ( "Limit the total amount of checkpoints. " "Deletes the older checkpoints in the output_dir. Default is unlimited checkpoints" ) }, ) save_on_each_node: bool = field( default=False, metadata={ "help": ( "When doing multi-node distributed training, whether to save models and checkpoints on each node, or" " only on the main one" ) }, ) no_cuda: bool = field(default=False, metadata={"help": "Do not use CUDA even when it is available"}) use_mps_device: bool = field( default=False, metadata={"help": "Whether to use Apple Silicon chip based `mps` device."} ) seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."}) data_seed: Optional[int] = field(default=None, metadata={"help": "Random seed to be used with data samplers."}) jit_mode_eval: bool = field( default=False, metadata={"help": "Whether or not to use PyTorch jit trace for inference"} ) use_ipex: bool = field( default=False, metadata={ "help": ( "Use Intel extension for PyTorch when it is available, installation:" " 'https://github.com/intel/intel-extension-for-pytorch'" ) }, ) bf16: bool = field( default=False, metadata={ "help": ( "Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA" " architecture or using CPU (no_cuda). This is an experimental API and it may change." ) }, ) fp16: bool = field( default=False, metadata={"help": "Whether to use fp16 (mixed) precision instead of 32-bit"}, ) fp16_opt_level: str = field( default="O1", metadata={ "help": ( "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. " "See details at https://nvidia.github.io/apex/amp.html" ) }, ) half_precision_backend: str = field( default="auto", metadata={ "help": "The backend to be used for half precision.", "choices": ["auto", "cuda_amp", "apex", "cpu_amp"], }, ) bf16_full_eval: bool = field( default=False, metadata={ "help": ( "Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may" " change." ) }, ) fp16_full_eval: bool = field( default=False, metadata={"help": "Whether to use full float16 evaluation instead of 32-bit"}, ) tf32: Optional[bool] = field( default=None, metadata={ "help": ( "Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental" " API and it may change." ) }, ) local_rank: int = field(default=-1, metadata={"help": "For distributed training: local_rank"}) xpu_backend: Optional[str] = field( default=None, metadata={ "help": "The backend to be used for distributed training on Intel XPU.", "choices": ["mpi", "ccl", "gloo"], }, ) tpu_num_cores: Optional[int] = field( default=None, metadata={"help": "TPU: Number of TPU cores (automatically passed by launcher script)"} ) tpu_metrics_debug: bool = field( default=False, metadata={ "help": ( "Deprecated, the use of `--debug tpu_metrics_debug` is preferred. TPU: Whether to print debug metrics" ) }, ) debug: str = field( default="", metadata={ "help": ( "Whether or not to enable debug mode. Current options: " "`underflow_overflow` (Detect underflow and overflow in activations and weights), " "`tpu_metrics_debug` (print debug metrics on TPU)." ) }, ) dataloader_drop_last: bool = field( default=False, metadata={"help": "Drop the last incomplete batch if it is not divisible by the batch size."} ) eval_steps: Optional[int] = field(default=None, metadata={"help": "Run an evaluation every X steps."}) dataloader_num_workers: int = field( default=0, metadata={ "help": ( "Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded" " in the main process." ) }, ) past_index: int = field( default=-1, metadata={"help": "If >=0, uses the corresponding part of the output as the past state for next step."}, ) run_name: Optional[str] = field( default=None, metadata={"help": "An optional descriptor for the run. Notably used for wandb logging."} ) disable_tqdm: Optional[bool] = field( default=None, metadata={"help": "Whether or not to disable the tqdm progress bars."} ) remove_unused_columns: Optional[bool] = field( default=True, metadata={"help": "Remove columns not required by the model when using an nlp.Dataset."} ) label_names: Optional[List[str]] = field( default=None, metadata={"help": "The list of keys in your dictionary of inputs that correspond to the labels."} ) load_best_model_at_end: Optional[bool] = field( default=False, metadata={"help": "Whether or not to load the best model found during training at the end of training."}, ) metric_for_best_model: Optional[str] = field( default=None, metadata={"help": "The metric to use to compare two different models."} ) greater_is_better: Optional[bool] = field( default=None, metadata={"help": "Whether the `metric_for_best_model` should be maximized or not."} ) ignore_data_skip: bool = field( default=False, metadata={ "help": ( "When resuming training, whether or not to skip the first epochs and batches to get to the same" " training data." ) }, ) sharded_ddp: str = field( default="", metadata={ "help": ( "Whether or not to use sharded DDP training (in distributed training only). The base option should be" " `simple`, `zero_dp_2` or `zero_dp_3` and you can add CPU-offload to `zero_dp_2` or `zero_dp_3` like" " this: zero_dp_2 offload` or `zero_dp_3 offload`. You can add auto-wrap to `zero_dp_2` or `zero_dp_3`" " with the same syntax: zero_dp_2 auto_wrap` or `zero_dp_3 auto_wrap`." ), }, ) fsdp: str = field( default="", metadata={ "help": ( "Whether or not to use PyTorch Fully Sharded Data Parallel (FSDP) training (in distributed training" " only). The base option should be `full_shard`, `shard_grad_op` or `no_shard` and you can add" " CPU-offload to `full_shard` or `shard_grad_op` like this: full_shard offload` or `shard_grad_op" " offload`. You can add auto-wrap to `full_shard` or `shard_grad_op` with the same syntax: full_shard" " auto_wrap` or `shard_grad_op auto_wrap`." ), }, ) fsdp_min_num_params: int = field( default=0, metadata={ "help": ( "FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is" " passed)." ) }, ) fsdp_transformer_layer_cls_to_wrap: Optional[str] = field( default=None, metadata={ "help": ( "Transformer layer class name (case-sensitive) to wrap ,e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... " "(useful only when `fsdp` flag is passed)." ) }, ) deepspeed: Optional[str] = field( default=None, metadata={ "help": ( "Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already" " loaded json file as a dict" ) }, ) label_smoothing_factor: float = field( default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."} ) optim: Union[OptimizerNames, str] = field( default="adamw_hf", metadata={"help": "The optimizer to use."}, ) adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."}) group_by_length: bool = field( default=False, metadata={"help": "Whether or not to group samples of roughly the same length together when batching."}, ) length_column_name: Optional[str] = field( default="length", metadata={"help": "Column name with precomputed lengths to use when grouping by length."}, ) report_to: Optional[List[str]] = field( default=None, metadata={"help": "The list of integrations to report the results and logs to."} ) ddp_find_unused_parameters: Optional[bool] = field( default=None, metadata={ "help": ( "When using distributed training, the value of the flag `find_unused_parameters` passed to " "`DistributedDataParallel`." ) }, ) ddp_bucket_cap_mb: Optional[int] = field( default=None, metadata={ "help": ( "When using distributed training, the value of the flag `bucket_cap_mb` passed to " "`DistributedDataParallel`." ) }, ) dataloader_pin_memory: bool = field( default=True, metadata={"help": "Whether or not to pin memory for DataLoader."} ) skip_memory_metrics: bool = field( default=True, metadata={"help": "Whether or not to skip adding of memory profiler reports to metrics."} ) use_legacy_prediction_loop: bool = field( default=False, metadata={"help": "Whether or not to use the legacy prediction_loop in the Trainer."} ) push_to_hub: bool = field( default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."} ) resume_from_checkpoint: Optional[str] = field( default=None, metadata={"help": "The path to a folder with a valid checkpoint for your model."}, ) hub_model_id: Optional[str] = field( default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."} ) hub_strategy: Union[HubStrategy, str] = field( default="every_save", metadata={"help": "The hub strategy to use when `--push_to_hub` is activated."}, ) hub_token: Optional[str] = field(default=None, metadata={"help": "The token to use to push to the Model Hub."}) hub_private_repo: bool = field(default=False, metadata={"help": "Whether the model repository is private or not."}) gradient_checkpointing: bool = field( default=False, metadata={ "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass." }, ) include_inputs_for_metrics: bool = field( default=False, metadata={"help": "Whether or not the inputs will be passed to the `compute_metrics` function."} ) # Deprecated arguments fp16_backend: str = field( default="auto", metadata={ "help": "Deprecated. Use half_precision_backend instead", "choices": ["auto", "cuda_amp", "apex", "cpu_amp"], }, ) push_to_hub_model_id: Optional[str] = field( default=None, metadata={"help": "The name of the repository to which push the `Trainer`."} ) push_to_hub_organization: Optional[str] = field( default=None, metadata={"help": "The name of the organization in with to which push the `Trainer`."} ) push_to_hub_token: Optional[str] = field( default=None, metadata={"help": "The token to use to push to the Model Hub."} ) _n_gpu: int = field(init=False, repr=False, default=-1) mp_parameters: str = field( default="", metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer"}, ) auto_find_batch_size: bool = field( default=False, metadata={ "help": ( "Whether to automatically decrease the batch size in half and rerun the training loop again each time" " a CUDA Out-of-Memory was reached" ) }, ) full_determinism: bool = field( default=False, metadata={ "help": ( "Whether to call enable_full_determinism instead of set_seed for reproducibility in distributed" " training" ) }, ) torchdynamo: Optional[str] = field( default=None, metadata={ "help": ( "Sets up the backend compiler for TorchDynamo. TorchDynamo is a Python level JIT compiler designed to" " make unmodified PyTorch programs faster. TorchDynamo dynamically modifies the Python bytecode right" " before its executed. It rewrites Python bytecode to extract sequences of PyTorch operations" " and lifts them up into Fx graph. We can then pass these Fx graphs to other backend compilers. There" " are two options - eager and nvfuser. Eager defaults to pytorch eager and is useful for debugging." " nvfuser path uses AOT Autograd and nvfuser compiler to optimize the models." ), "choices": ["eager", "nvfuser", "fx2trt", "fx2trt-fp16"], }, ) ray_scope: Optional[str] = field( default="last", metadata={ "help": ( 'The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray' " will then use the last checkpoint of all trials, compare those, and select the best one. However," " other options are also available. See the Ray documentation" " (https://docs.ray.io/en/latest/tune/api_docs/analysis.html" "#ray.tune.ExperimentAnalysis.get_best_trial)" " for more options." ) }, ) ddp_timeout: Optional[int] = field( default=1800, metadata={ "help": "Overrides the default timeout for distributed training (value should be given in seconds)." }, ) def __post_init__(self): # Handle --use_env option in torch.distributed.launch (local_rank not passed as an arg then). # This needs to happen before any call to self.device or self.n_gpu. env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != self.local_rank: self.local_rank = env_local_rank # expand paths, if not os.makedirs("~/bar") will make directory # in the current directory instead of the actual home #  see https://github.com/huggingface/transformers/issues/10628 if self.output_dir is not None: self.output_dir = os.path.expanduser(self.output_dir) if self.logging_dir is None and self.output_dir is not None: self.logging_dir = os.path.join(self.output_dir, default_logdir()) if self.logging_dir is not None: self.logging_dir = os.path.expanduser(self.logging_dir) if self.disable_tqdm is None: self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN if isinstance(self.evaluation_strategy, EvaluationStrategy): warnings.warn( "using `EvaluationStrategy` for `evaluation_strategy` is deprecated and will be removed in version 5" " of 🤗 Transformers. Use `IntervalStrategy` instead", FutureWarning, ) # Go back to the underlying string or we won't be able to instantiate `IntervalStrategy` on it. self.evaluation_strategy = self.evaluation_strategy.value self.evaluation_strategy = IntervalStrategy(self.evaluation_strategy) self.logging_strategy = IntervalStrategy(self.logging_strategy) self.save_strategy = IntervalStrategy(self.save_strategy) self.hub_strategy = HubStrategy(self.hub_strategy) self.lr_scheduler_type = SchedulerType(self.lr_scheduler_type) if self.do_eval is False and self.evaluation_strategy != IntervalStrategy.NO: self.do_eval = True # eval_steps has to be defined and non-zero, fallbacks to logging_steps if the latter is non-zero if self.evaluation_strategy == IntervalStrategy.STEPS and (self.eval_steps is None or self.eval_steps == 0): if self.logging_steps > 0: logger.info(f"using `logging_steps` to initialize `eval_steps` to {self.logging_steps}") self.eval_steps = self.logging_steps else: raise ValueError( f"evaluation strategy {self.evaluation_strategy} requires either non-zero --eval_steps or" " --logging_steps" ) # logging_steps must be non-zero for logging_strategy that is other than 'no' if self.logging_strategy == IntervalStrategy.STEPS and self.logging_steps == 0: raise ValueError(f"logging strategy {self.logging_strategy} requires non-zero --logging_steps") # Sanity checks for load_best_model_at_end: we require save and eval strategies to be compatible. if self.load_best_model_at_end: if self.evaluation_strategy != self.save_strategy: raise ValueError( "--load_best_model_at_end requires the save and eval strategy to match, but found\n- Evaluation " f"strategy: {self.evaluation_strategy}\n- Save strategy: {self.save_strategy}" ) if self.evaluation_strategy == IntervalStrategy.STEPS and self.save_steps % self.eval_steps != 0: raise ValueError( "--load_best_model_at_end requires the saving steps to be a round multiple of the evaluation " f"steps, but found {self.save_steps}, which is not a round multiple of {self.eval_steps}." ) if self.load_best_model_at_end and self.metric_for_best_model is None: self.metric_for_best_model = "loss" if self.greater_is_better is None and self.metric_for_best_model is not None: self.greater_is_better = self.metric_for_best_model not in ["loss", "eval_loss"] if self.run_name is None: self.run_name = self.output_dir if self.framework == "pt" and is_torch_available(): if self.fp16_backend and self.fp16_backend != "auto": warnings.warn( "`fp16_backend` is deprecated and will be removed in version 5 of 🤗 Transformers. Use" " `half_precision_backend` instead", FutureWarning, ) self.half_precision_backend = self.fp16_backend if self.bf16 or self.bf16_full_eval: if self.no_cuda and not is_torch_bf16_cpu_available(): # cpu raise ValueError("Your setup doesn't support bf16/cpu. You need torch>=1.10") elif not self.no_cuda and not is_torch_bf16_gpu_available(): # gpu raise ValueError( "Your setup doesn't support bf16/gpu. You need torch>=1.10, using Ampere GPU with cuda>=11.0" ) if self.fp16 and self.bf16: raise ValueError("At most one of fp16 and bf16 can be True, but not both") if self.fp16_full_eval and self.bf16_full_eval: raise ValueError("At most one of fp16 and bf16 can be True for full eval, but not both") if self.bf16: if self.half_precision_backend == "apex": raise ValueError( " `--half_precision_backend apex`: GPU bf16 is not supported by apex. Use" " `--half_precision_backend cuda_amp` instead" ) if not (self.sharded_ddp == "" or not self.sharded_ddp): raise ValueError("sharded_ddp is not supported with bf16") self.optim = OptimizerNames(self.optim) if self.adafactor: warnings.warn( "`--adafactor` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `--optim" " adafactor` instead", FutureWarning, ) self.optim = OptimizerNames.ADAFACTOR if ( self.framework == "pt" and is_torch_available() and (self.device.type != "cuda") and (get_xla_device_type(self.device) != "GPU") and (self.fp16 or self.fp16_full_eval) ): raise ValueError( "FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation" " (`--fp16_full_eval`) can only be used on CUDA devices." ) if ( self.framework == "pt" and is_torch_available() and (self.device.type != "cuda") and (get_xla_device_type(self.device) != "GPU") and (self.device.type != "cpu") and (self.bf16 or self.bf16_full_eval) ): raise ValueError( "BF16 Mixed precision training with AMP (`--bf16`) and BF16 half precision evaluation" " (`--bf16_full_eval`) can only be used on CUDA or CPU devices." ) if self.framework == "pt" and is_torch_available() and self.tf32 is not None: if self.tf32: if is_torch_tf32_available(): torch.backends.cuda.matmul.allow_tf32 = True else: raise ValueError("--tf32 requires Ampere or a newer GPU arch, cuda>=11 and torch>=1.7") else: if is_torch_tf32_available(): torch.backends.cuda.matmul.allow_tf32 = False # no need to assert on else if self.report_to is None: logger.info( "The default value for the training argument `--report_to` will change in v5 (from all installed " "integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as " "now. You should start updating your code and make this info disappear :-)." ) self.report_to = "all" if self.report_to == "all" or self.report_to == ["all"]: # Import at runtime to avoid a circular import. from .integrations import get_available_reporting_integrations self.report_to = get_available_reporting_integrations() elif self.report_to == "none" or self.report_to == ["none"]: self.report_to = [] elif not isinstance(self.report_to, list): self.report_to = [self.report_to] if self.warmup_ratio < 0 or self.warmup_ratio > 1: raise ValueError("warmup_ratio must lie in range [0,1]") elif self.warmup_ratio > 0 and self.warmup_steps > 0: logger.info( "Both warmup_ratio and warmup_steps given, warmup_steps will override any effect of warmup_ratio" " during training" ) if isinstance(self.sharded_ddp, bool): self.sharded_ddp = "simple" if self.sharded_ddp else "" if isinstance(self.sharded_ddp, str): self.sharded_ddp = [ShardedDDPOption(s) for s in self.sharded_ddp.split()] if self.sharded_ddp == [ShardedDDPOption.OFFLOAD]: raise ValueError( "`--sharded_ddp offload` can't work on its own. It needs to be added to `--sharded_ddp zero_dp_2` or " '`--sharded_ddp zero_dp_3`. For example, `--sharded_ddp "zero_dp_2 offload"`.' ) elif len(self.sharded_ddp) > 1 and ShardedDDPOption.SIMPLE in self.sharded_ddp: raise ValueError("`--sharded_ddp simple` is not compatible with any other option.") elif ShardedDDPOption.ZERO_DP_2 in self.sharded_ddp and ShardedDDPOption.ZERO_DP_3 in self.sharded_ddp: raise ValueError("`--sharded_ddp zero_dp_2` is not compatible with `--sharded_ddp zero_dp_3`.") if isinstance(self.fsdp, bool): self.fsdp = "full_shard" if self.fsdp else "" if isinstance(self.fsdp, str): self.fsdp = [FSDPOption(s) for s in self.fsdp.split()] if self.fsdp == [FSDPOption.OFFLOAD]: raise ValueError( "`--fsdp offload` can't work on its own. It needs to be added to `--fsdp full_shard` or " '`--fsdp shard_grad_op`. For example, `--fsdp "full_shard offload"`.' ) elif FSDPOption.FULL_SHARD in self.fsdp and FSDPOption.SHARD_GRAD_OP in self.fsdp: raise ValueError("`--fsdp full_shard` is not compatible with `--fsdp shard_grad_op`.") if len(self.fsdp) == 0 and self.fsdp_min_num_params > 0: warnings.warn("`--fsdp_min_num_params` is useful only when `--fsdp` is specified.") if len(self.fsdp) == 0 and self.fsdp_transformer_layer_cls_to_wrap is not None: warnings.warn("`--fsdp_transformer_layer_cls_to_wrap` is useful only when `--fsdp` is specified.") if len(self.fsdp) > 0 and self.fsdp_min_num_params > 0 and self.fsdp_transformer_layer_cls_to_wrap is not None: raise ValueError( "`--fsdp_min_num_params` and `--fsdp_transformer_layer_cls_to_wrap` are mutually exclusive." ) if self.tpu_metrics_debug: warnings.warn( "using `--tpu_metrics_debug` is deprecated and will be removed in version 5 of 🤗 Transformers. Use" " `--debug tpu_metrics_debug` instead", FutureWarning, ) self.debug += " tpu_metrics_debug" self.tpu_metrics_debug = False if isinstance(self.debug, str): self.debug = [DebugOption(s) for s in self.debug.split()] if self.deepspeed: # - must be run very last in arg parsing, since it will use a lot of these settings. # - must be run before the model is created. if not is_accelerate_available(): raise ValueError("--deepspeed requires Accelerate to be installed: `pip install accelerate`.") from transformers.deepspeed import HfTrainerDeepSpeedConfig # will be used later by the Trainer # note: leave self.deepspeed unmodified in case a user relies on it not to be modified) self.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.deepspeed) self.hf_deepspeed_config.trainer_config_process(self) if self.push_to_hub_token is not None: warnings.warn( "`--push_to_hub_token` is deprecated and will be removed in version 5 of 🤗 Transformers. Use " "`--hub_token` instead.", FutureWarning, ) self.hub_token = self.push_to_hub_token if self.push_to_hub_model_id is not None: self.hub_model_id = get_full_repo_name( self.push_to_hub_model_id, organization=self.push_to_hub_organization, token=self.hub_token ) if self.push_to_hub_organization is not None: warnings.warn( "`--push_to_hub_model_id` and `--push_to_hub_organization` are deprecated and will be removed in " "version 5 of 🤗 Transformers. Use `--hub_model_id` instead and pass the full repo name to this " f"argument (in this case {self.hub_model_id}).", FutureWarning, ) else: warnings.warn( "`--push_to_hub_model_id` is deprecated and will be removed in version 5 of 🤗 Transformers. Use " "`--hub_model_id` instead and pass the full repo name to this argument (in this case " f"{self.hub_model_id}).", FutureWarning, ) elif self.push_to_hub_organization is not None: self.hub_model_id = f"{self.push_to_hub_organization}/{Path(self.output_dir).name}" warnings.warn( "`--push_to_hub_organization` is deprecated and will be removed in version 5 of 🤗 Transformers. Use " "`--hub_model_id` instead and pass the full repo name to this argument (in this case " f"{self.hub_model_id}).", FutureWarning, ) def __str__(self): self_as_dict = asdict(self) # Remove deprecated arguments. That code should be removed once # those deprecated arguments are removed from TrainingArguments. (TODO: v5) del self_as_dict["per_gpu_train_batch_size"] del self_as_dict["per_gpu_eval_batch_size"] self_as_dict = {k: f"<{k.upper()}>" if k.endswith("_token") else v for k, v in self_as_dict.items()} attrs_as_str = [f"{k}={v},\n" for k, v in sorted(self_as_dict.items())] return f"{self.__class__.__name__}(\n{''.join(attrs_as_str)})" __repr__ = __str__ @property def train_batch_size(self) -> int: """ The actual batch size for training (may differ from `per_gpu_train_batch_size` in distributed training). """ if self.per_gpu_train_batch_size: logger.warning( "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future " "version. Using `--per_device_train_batch_size` is preferred." ) per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size train_batch_size = per_device_batch_size * max(1, self.n_gpu) return train_batch_size @property def eval_batch_size(self) -> int: """ The actual batch size for evaluation (may differ from `per_gpu_eval_batch_size` in distributed training). """ if self.per_gpu_eval_batch_size: logger.warning( "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future " "version. Using `--per_device_eval_batch_size` is preferred." ) per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size eval_batch_size = per_device_batch_size * max(1, self.n_gpu) return eval_batch_size @property def ddp_timeout_delta(self) -> timedelta: """ The actual timeout for torch.distributed.init_process_group since it expects a timedelta variable. """ return timedelta(seconds=self.ddp_timeout) @cached_property @torch_required def _setup_devices(self) -> "torch.device": logger.info("PyTorch: setting up devices") if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1: logger.warning( "torch.distributed process group is initialized, but local_rank == -1. " "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" ) if self.no_cuda: device = torch.device("cpu") self._n_gpu = 0 self.local_rank = get_int_from_env( ["LOCAL_RANK", "MPI_LOCALRANKID", "OMPI_COMM_WORLD_LOCAL_RANK", "MV2_COMM_WORLD_LOCAL_RANK"], self.local_rank, ) if self.local_rank != -1 and not torch.distributed.is_initialized(): # Initializes distributed backend for cpu if self.xpu_backend not in ("mpi", "ccl", "gloo"): raise ValueError( "CPU distributed training backend is not properly set. " "Please set '--xpu_backend' to either 'mpi' or 'ccl' or 'gloo'." ) if self.xpu_backend == "ccl": requires_backends(self, "oneccl_bind_pt") if ccl_version >= "1.12": import oneccl_bindings_for_pytorch # noqa: F401 else: import torch_ccl # noqa: F401 if int(os.environ.get("CCL_WORKER_COUNT", 0)) < 1: raise ValueError( "CPU distributed training backend is ccl. but CCL_WORKER_COUNT is not correctly set. " "Please use like 'export CCL_WORKER_COUNT = 1' to set." ) # Try to get launch configuration from environment variables set by MPI launcher - works for Intel MPI, OpenMPI and MVAPICH rank = get_int_from_env(["RANK", "PMI_RANK", "OMPI_COMM_WORLD_RANK", "MV2_COMM_WORLD_RANK"], 0) size = get_int_from_env(["WORLD_SIZE", "PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "MV2_COMM_WORLD_SIZE"], 1) local_size = get_int_from_env( ["MPI_LOCALNRANKS", "OMPI_COMM_WORLD_LOCAL_SIZE", "MV2_COMM_WORLD_LOCAL_SIZE"], 1 ) os.environ["RANK"] = str(rank) os.environ["WORLD_SIZE"] = str(size) os.environ["LOCAL_RANK"] = str(self.local_rank) if not os.environ.get("MASTER_PORT", None): os.environ["MASTER_PORT"] = "29500" if not os.environ.get("MASTER_ADDR", None): if local_size != size or self.xpu_backend != "mpi": raise ValueError( "Looks like distributed multinode run but MASTER_ADDR env not set, " "please try exporting rank 0's hostname as MASTER_ADDR" ) if ( torch.get_num_threads() == 1 and get_int_from_env(["OMP_NUM_THREADS", "MKL_NUM_THREADS"], 0) == 0 and is_psutil_available() ): import psutil num_cpu_threads_per_process = int(psutil.cpu_count(logical=False) / local_size) if num_cpu_threads_per_process == 0: num_cpu_threads_per_process = 1 torch.set_num_threads(num_cpu_threads_per_process) logger.info( f"num_cpu_threads_per_process unset, we set it at {num_cpu_threads_per_process} to improve oob" " performance." ) torch.distributed.init_process_group( backend=self.xpu_backend, rank=rank, world_size=size, timeout=self.ddp_timeout_delta ) elif is_torch_tpu_available(): device = xm.xla_device() self._n_gpu = 0 elif is_sagemaker_mp_enabled(): local_rank = smp.local_rank() device = torch.device("cuda", local_rank) self._n_gpu = 1 elif is_sagemaker_dp_enabled(): import smdistributed.dataparallel.torch.torch_smddp # noqa: F401 dist.init_process_group(backend="smddp", timeout=self.ddp_timeout_delta) self.local_rank = int(os.getenv("SMDATAPARALLEL_LOCAL_RANK")) device = torch.device("cuda", self.local_rank) self._n_gpu = 1 elif self.deepspeed: # deepspeed inits torch.distributed internally from .deepspeed import is_deepspeed_available if not is_deepspeed_available(): raise ImportError("--deepspeed requires deepspeed: `pip install deepspeed`.") import deepspeed deepspeed.init_distributed() # workaround for setups like notebooks where the launcher can't be used, # but deepspeed requires a dist env. # env LOCAL_RANK could be set manually by the user, or via init_distributed if mpi4py is installed self.local_rank = int(os.environ.get("LOCAL_RANK", "-1")) device = torch.device("cuda", self.local_rank) self._n_gpu = 1 elif self.local_rank == -1: if self.use_mps_device: if not torch.backends.mps.is_available(): if not torch.backends.mps.is_built(): raise AssertionError( "MPS not available because the current PyTorch install was not " "built with MPS enabled. Please install torch version >=1.12.0 on " "your Apple silicon Mac running macOS 12.3 or later with a native " "version (arm64) of Python" ) else: raise AssertionError( "MPS not available because the current MacOS version is not 12.3+ " "and/or you do not have an MPS-enabled device on this machine." ) else: if not version.parse(version.parse(torch.__version__).base_version) > version.parse("1.12.0"): warnings.warn( "We strongly recommend to install PyTorch >= 1.13 (nightly version at the time of writing)" " on your MacOS machine. It has major fixes related to model correctness and performance" " improvements for transformer based models. Please refer to" " https://github.com/pytorch/pytorch/issues/82707 for more details." ) device = torch.device("mps") self._n_gpu = 1 else: # if n_gpu is > 1 we'll use nn.DataParallel. # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0` # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will # trigger an error that a device index is missing. Index 0 takes into account the # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0` # will use the first GPU in that env, i.e. GPU#1 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at # the default value. self._n_gpu = torch.cuda.device_count() else: # Here, we'll use torch.distributed. # Initializes the distributed backend which will take care of synchronizing nodes/GPUs if not torch.distributed.is_initialized(): torch.distributed.init_process_group(backend="nccl", timeout=self.ddp_timeout_delta) device = torch.device("cuda", self.local_rank) self._n_gpu = 1 if device.type == "cuda": torch.cuda.set_device(device) return device @property @torch_required def device(self) -> "torch.device": """ The device used by this process. """ return self._setup_devices @property @torch_required def n_gpu(self): """ The number of GPUs used by this process. Note: This will only be greater than one when you have multiple GPUs available but are not using distributed training. For distributed training, it will always be 1. """ # Make sure `self._n_gpu` is properly setup. _ = self._setup_devices return self._n_gpu @property @torch_required def parallel_mode(self): """ The current mode used for parallelism if multiple GPUs/TPU cores are available. One of: - `ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU). - `ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses `torch.nn.DataParallel`). - `ParallelMode.DISTRIBUTED`: several GPUs, each having its own process (uses `torch.nn.DistributedDataParallel`). - `ParallelMode.TPU`: several TPU cores. """ if is_torch_tpu_available(): return ParallelMode.TPU elif is_sagemaker_mp_enabled(): return ParallelMode.SAGEMAKER_MODEL_PARALLEL elif is_sagemaker_dp_enabled(): return ParallelMode.SAGEMAKER_DATA_PARALLEL elif self.local_rank != -1: return ParallelMode.DISTRIBUTED elif self.n_gpu > 1: return ParallelMode.NOT_DISTRIBUTED else: return ParallelMode.NOT_PARALLEL @property @torch_required def world_size(self): """ The number of processes used in parallel. """ if is_torch_tpu_available(): return xm.xrt_world_size() elif is_sagemaker_mp_enabled(): return smp.dp_size() if not smp.state.cfg.prescaled_batch else smp.rdp_size() elif is_sagemaker_dp_enabled(): return dist.get_world_size() elif self.local_rank != -1: return torch.distributed.get_world_size() return 1 @property @torch_required def process_index(self): """ The index of the current process used. """ if is_torch_tpu_available(): return xm.get_ordinal() elif is_sagemaker_mp_enabled(): return smp.dp_rank() if not smp.state.cfg.prescaled_batch else smp.rdp_rank() elif is_sagemaker_dp_enabled(): return dist.get_rank() elif self.local_rank != -1: return torch.distributed.get_rank() return 0 @property @torch_required def local_process_index(self): """ The index of the local process used. """ if is_torch_tpu_available(): return xm.get_local_ordinal() elif is_sagemaker_mp_enabled(): return smp.local_rank() elif is_sagemaker_dp_enabled(): return dist.get_rank() elif self.local_rank != -1: return self.local_rank return 0 @property def should_log(self): """ Whether or not the current process should produce log. """ if self.log_on_each_node: return self.local_process_index == 0 else: if is_sagemaker_mp_enabled(): return smp.rank() == 0 else: return self.process_index == 0 @property def should_save(self): """ Whether or not the current process should write to disk, e.g., to save models and checkpoints. """ if self.save_on_each_node: return self.local_process_index == 0 else: if is_sagemaker_mp_enabled(): return smp.rank() == 0 else: return self.process_index == 0 def get_process_log_level(self): """ Returns the log level to be used depending on whether this process is the main process of node 0, main process of node non-0, or a non-main process. For the main process the log level defaults to `logging.INFO` unless overridden by `log_level` argument. For the replica processes the log level defaults to `logging.WARNING` unless overridden by `log_level_replica` argument. The choice between the main and replica process settings is made according to the return value of `should_log`. """ # convert to int log_level = trainer_log_levels[self.log_level] log_level_replica = trainer_log_levels[self.log_level_replica] log_level_main_node = logging.INFO if log_level == -1 else log_level log_level_replica_node = logging.WARNING if log_level_replica == -1 else log_level_replica return log_level_main_node if self.should_log else log_level_replica_node @property def place_model_on_device(self): """ Can be subclassed and overridden for some specific integrations. """ return not is_sagemaker_mp_enabled() @property def _no_sync_in_gradient_accumulation(self): """ Whether or not to use no_sync for the gradients when doing gradient accumulation. """ return not (self.deepspeed or is_sagemaker_dp_enabled() or is_sagemaker_mp_enabled()) @contextlib.contextmanager def main_process_first(self, local=True, desc="work"): """ A context manager for torch distributed environment where on needs to do something on the main process, while blocking replicas, and when it's finished releasing the replicas. One such use is for `datasets`'s `map` feature which to be efficient should be run once on the main process, which upon completion saves a cached version of results and which then automatically gets loaded by the replicas. Args: local (`bool`, *optional*, defaults to `True`): if `True` first means process of rank 0 of each node if `False` first means process of rank 0 of node rank 0 In multi-node environment with a shared filesystem you most likely will want to use `local=False` so that only the main process of the first node will do the processing. If however, the filesystem is not shared, then the main process of each node will need to do the processing, which is the default behavior. desc (`str`, *optional*, defaults to `"work"`): a work description to be used in debug logs """ if is_torch_available() and self.world_size > 1: main_process_desc = "main process" if local: is_main_process = self.local_process_index == 0 main_process_desc = "main local process" elif is_sagemaker_mp_enabled(): is_main_process = smp.rank() == 0 else: is_main_process = self.process_index == 0 try: if not is_main_process: # tell all replicas to wait logger.debug(f"{self.process_index}: waiting for the {main_process_desc} to perform {desc}") if is_torch_tpu_available(): xm.rendezvous(desc) elif is_sagemaker_dp_enabled(): dist.barrier() else: torch.distributed.barrier() yield finally: if is_main_process: # the wait is over logger.debug(f"{self.process_index}: {main_process_desc} completed {desc}, releasing all replicas") if is_torch_tpu_available(): xm.rendezvous(desc) elif is_sagemaker_dp_enabled(): dist.barrier() else: torch.distributed.barrier() else: yield def get_warmup_steps(self, num_training_steps: int): """ Get number of steps used for a linear warmup. """ warmup_steps = ( self.warmup_steps if self.warmup_steps > 0 else math.ceil(num_training_steps * self.warmup_ratio) ) return warmup_steps def to_dict(self): """ Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates the token values by removing their value. """ # filter out fields that are defined as field(init=False) d = dict((field.name, getattr(self, field.name)) for field in fields(self) if field.init) for k, v in d.items(): if isinstance(v, Enum): d[k] = v.value if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum): d[k] = [x.value for x in v] if k.endswith("_token"): d[k] = f"<{k.upper()}>" return d def to_json_string(self): """ Serializes this instance to a JSON string. """ return json.dumps(self.to_dict(), indent=2) def to_sanitized_dict(self) -> Dict[str, Any]: """ Sanitized serialization to use with TensorBoard’s hparams """ d = self.to_dict() d = {**d, **{"train_batch_size": self.train_batch_size, "eval_batch_size": self.eval_batch_size}} valid_types = [bool, int, float, str] if is_torch_available(): valid_types.append(torch.Tensor) return {k: v if type(v) in valid_types else str(v) for k, v in d.items()} class ParallelMode(Enum): NOT_PARALLEL = "not_parallel" NOT_DISTRIBUTED = "not_distributed" DISTRIBUTED = "distributed" SAGEMAKER_MODEL_PARALLEL = "sagemaker_model_parallel" SAGEMAKER_DATA_PARALLEL = "sagemaker_data_parallel" TPU = "tpu"

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/research_projects/mlm_wwm/run_mlm_wwm.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a text file or a dataset. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=fill-mask """ # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. import json import logging import math import os import sys from dataclasses import dataclass, field from typing import Optional from datasets import Dataset, load_dataset import transformers from transformers import ( CONFIG_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForWholeWordMask, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint, is_main_process logger = logging.getLogger(__name__) MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_overrides: Optional[str] = field( default=None, metadata={ "help": ( "Override some existing default config settings when a model is trained from scratch. Example: " "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" ) }, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) def __post_init__(self): if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None): raise ValueError( "--config_overrides can't be used in combination with --config_name or --model_name_or_path" ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) train_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}, ) validation_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) max_seq_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated. Default to the max input length of the model." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) mlm_probability: float = field( default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} ) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to `max_seq_length`. " "If False, will pad the samples dynamically when batching to the maximum length in the batch." ) }, ) def __post_init__(self): if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." def add_chinese_references(dataset, ref_file): with open(ref_file, "r", encoding="utf-8") as f: refs = [json.loads(line) for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())] assert len(dataset) == len(refs) dataset_dict = {c: dataset[c] for c in dataset.column_names} dataset_dict["chinese_ref"] = refs return Dataset.from_dict(dataset_dict) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() logger.info("Training/evaluation parameters %s", training_args) # Set seed before initializing model. set_seed(training_args.seed) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name) if "validation" not in datasets.keys(): datasets["validation"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[:{data_args.validation_split_percentage}%]", ) datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[{data_args.validation_split_percentage}%:]", ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.train_file.split(".")[-1] if extension == "txt": extension = "text" datasets = load_dataset(extension, data_files=data_files) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config_kwargs = { "cache_dir": model_args.cache_dir, "revision": model_args.model_revision, "use_auth_token": True if model_args.use_auth_token else None, } if model_args.config_name: config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs) elif model_args.model_name_or_path: config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if model_args.config_overrides is not None: logger.info(f"Overriding config: {model_args.config_overrides}") config.update_from_string(model_args.config_overrides) logger.info(f"New config: {config}") tokenizer_kwargs = { "cache_dir": model_args.cache_dir, "use_fast": model_args.use_fast_tokenizer, "revision": model_args.model_revision, "use_auth_token": True if model_args.use_auth_token else None, } if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if model_args.model_name_or_path: model = AutoModelForMaskedLM.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) else: logger.info("Training new model from scratch") model = AutoModelForMaskedLM.from_config(config) model.resize_token_embeddings(len(tokenizer)) # Preprocessing the datasets. # First we tokenize all the texts. if training_args.do_train: column_names = datasets["train"].column_names else: column_names = datasets["validation"].column_names text_column_name = "text" if "text" in column_names else column_names[0] padding = "max_length" if data_args.pad_to_max_length else False def tokenize_function(examples): # Remove empty lines examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()] return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length) tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[text_column_name], load_from_cache_file=not data_args.overwrite_cache, ) # Add the chinese references if provided if data_args.train_ref_file is not None: tokenized_datasets["train"] = add_chinese_references(tokenized_datasets["train"], data_args.train_ref_file) if data_args.validation_ref_file is not None: tokenized_datasets["validation"] = add_chinese_references( tokenized_datasets["validation"], data_args.validation_ref_file ) # If we have ref files, need to avoid it removed by trainer has_ref = data_args.train_ref_file or data_args.validation_ref_file if has_ref: training_args.remove_unused_columns = False # Data collator # This one will take care of randomly masking the tokens. data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability) # Initialize our Trainer trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets["train"] if training_args.do_train else None, eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None, tokenizer=tokenizer, data_collator=data_collator, ) # Training if training_args.do_train: if last_checkpoint is not None: checkpoint = last_checkpoint elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path): checkpoint = model_args.model_name_or_path else: checkpoint = None train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() # Saves the tokenizer too for easy upload output_train_file = os.path.join(training_args.output_dir, "train_results.txt") if trainer.is_world_process_zero(): with open(output_train_file, "w") as writer: logger.info("***** Train results *****") for key, value in sorted(train_result.metrics.items()): logger.info(f" {key} = {value}") writer.write(f"{key} = {value}\n") # Need to save the state, since Trainer.save_model saves only the tokenizer with the model trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json")) # Evaluation results = {} if training_args.do_eval: logger.info("*** Evaluate ***") eval_output = trainer.evaluate() perplexity = math.exp(eval_output["eval_loss"]) results["perplexity"] = perplexity output_eval_file = os.path.join(training_args.output_dir, "eval_results_mlm_wwm.txt") if trainer.is_world_process_zero(): with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key, value in sorted(results.items()): logger.info(f" {key} = {value}") writer.write(f"{key} = {value}\n") return results def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()

# coding=utf-8 # Copyright 2020 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a text file or a dataset. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=fill-mask """ # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. import json import logging import math import os import sys from dataclasses import dataclass, field from typing import Optional from datasets import Dataset, load_dataset import transformers from transformers import ( CONFIG_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForWholeWordMask, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint, is_main_process logger = logging.getLogger(__name__) MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_overrides: Optional[str] = field( default=None, metadata={ "help": ( "Override some existing default config settings when a model is trained from scratch. Example: " "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" ) }, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) def __post_init__(self): if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None): raise ValueError( "--config_overrides can't be used in combination with --config_name or --model_name_or_path" ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) train_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}, ) validation_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) max_seq_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated. Default to the max input length of the model." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) mlm_probability: float = field( default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} ) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to `max_seq_length`. " "If False, will pad the samples dynamically when batching to the maximum length in the batch." ) }, ) def __post_init__(self): if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." def add_chinese_references(dataset, ref_file): with open(ref_file, "r", encoding="utf-8") as f: refs = [json.loads(line) for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())] assert len(dataset) == len(refs) dataset_dict = {c: dataset[c] for c in dataset.column_names} dataset_dict["chinese_ref"] = refs return Dataset.from_dict(dataset_dict) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() logger.info("Training/evaluation parameters %s", training_args) # Set seed before initializing model. set_seed(training_args.seed) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name) if "validation" not in datasets.keys(): datasets["validation"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[:{data_args.validation_split_percentage}%]", ) datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[{data_args.validation_split_percentage}%:]", ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.train_file.split(".")[-1] if extension == "txt": extension = "text" datasets = load_dataset(extension, data_files=data_files) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config_kwargs = { "cache_dir": model_args.cache_dir, "revision": model_args.model_revision, "use_auth_token": True if model_args.use_auth_token else None, } if model_args.config_name: config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs) elif model_args.model_name_or_path: config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if model_args.config_overrides is not None: logger.info(f"Overriding config: {model_args.config_overrides}") config.update_from_string(model_args.config_overrides) logger.info(f"New config: {config}") tokenizer_kwargs = { "cache_dir": model_args.cache_dir, "use_fast": model_args.use_fast_tokenizer, "revision": model_args.model_revision, "use_auth_token": True if model_args.use_auth_token else None, } if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if model_args.model_name_or_path: model = AutoModelForMaskedLM.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) else: logger.info("Training new model from scratch") model = AutoModelForMaskedLM.from_config(config) model.resize_token_embeddings(len(tokenizer)) # Preprocessing the datasets. # First we tokenize all the texts. if training_args.do_train: column_names = datasets["train"].column_names else: column_names = datasets["validation"].column_names text_column_name = "text" if "text" in column_names else column_names[0] padding = "max_length" if data_args.pad_to_max_length else False def tokenize_function(examples): # Remove empty lines examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()] return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length) tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[text_column_name], load_from_cache_file=not data_args.overwrite_cache, ) # Add the chinese references if provided if data_args.train_ref_file is not None: tokenized_datasets["train"] = add_chinese_references(tokenized_datasets["train"], data_args.train_ref_file) if data_args.validation_ref_file is not None: tokenized_datasets["validation"] = add_chinese_references( tokenized_datasets["validation"], data_args.validation_ref_file ) # If we have ref files, need to avoid it removed by trainer has_ref = data_args.train_ref_file or data_args.validation_ref_file if has_ref: training_args.remove_unused_columns = False # Data collator # This one will take care of randomly masking the tokens. data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability) # Initialize our Trainer trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets["train"] if training_args.do_train else None, eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None, tokenizer=tokenizer, data_collator=data_collator, ) # Training if training_args.do_train: if last_checkpoint is not None: checkpoint = last_checkpoint elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path): checkpoint = model_args.model_name_or_path else: checkpoint = None train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() # Saves the tokenizer too for easy upload output_train_file = os.path.join(training_args.output_dir, "train_results.txt") if trainer.is_world_process_zero(): with open(output_train_file, "w") as writer: logger.info("***** Train results *****") for key, value in sorted(train_result.metrics.items()): logger.info(f" {key} = {value}") writer.write(f"{key} = {value}\n") # Need to save the state, since Trainer.save_model saves only the tokenizer with the model trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json")) # Evaluation results = {} if training_args.do_eval: logger.info("*** Evaluate ***") eval_output = trainer.evaluate() perplexity = math.exp(eval_output["eval_loss"]) results["perplexity"] = perplexity output_eval_file = os.path.join(training_args.output_dir, "eval_results_mlm_wwm.txt") if trainer.is_world_process_zero(): with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key, value in sorted(results.items()): logger.info(f" {key} = {value}") writer.write(f"{key} = {value}\n") return results def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/clipseg/modeling_clipseg.py

# coding=utf-8 # Copyright 2022 The OpenAI Team Authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch CLIPSeg model.""" import copy import math from dataclasses import dataclass from typing import Any, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from ...modeling_utils import PreTrainedModel from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_clipseg import CLIPSegConfig, CLIPSegTextConfig, CLIPSegVisionConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "CIDAS/clipseg-rd64-refined" CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST = [ "CIDAS/clipseg-rd64-refined", # See all CLIPSeg models at https://huggingface.co/models?filter=clipseg ] # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) # contrastive loss function, adapted from # https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIPSeg.html def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device)) # Copied from transformers.models.clip.modeling_clip.clip_loss with clip->clipseg def clipseg_loss(similarity: torch.Tensor) -> torch.Tensor: caption_loss = contrastive_loss(similarity) image_loss = contrastive_loss(similarity.t()) return (caption_loss + image_loss) / 2.0 @dataclass # Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->CLIPSeg class CLIPSegOutput(ModelOutput): """ Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): Contrastive loss for image-text similarity. logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text similarity scores. logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image similarity scores. text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegTextModel`]. image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegVisionModel`]. text_model_output(`BaseModelOutputWithPooling`): The output of the [`CLIPSegTextModel`]. vision_model_output(`BaseModelOutputWithPooling`): The output of the [`CLIPSegVisionModel`]. """ loss: Optional[torch.FloatTensor] = None logits_per_image: torch.FloatTensor = None logits_per_text: torch.FloatTensor = None text_embeds: torch.FloatTensor = None image_embeds: torch.FloatTensor = None text_model_output: BaseModelOutputWithPooling = None vision_model_output: BaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) @dataclass class CLIPSegDecoderOutput(ModelOutput): """ Args: logits (`torch.FloatTensor` of shape `(batch_size, height, width)`): Classification scores for each pixel. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class CLIPSegImageSegmentationOutput(ModelOutput): """ Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): Contrastive loss for image-text similarity. ... vision_model_output (`BaseModelOutputWithPooling`): The output of the [`CLIPSegVisionModel`]. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None conditional_embeddings: torch.FloatTensor = None pooled_output: torch.FloatTensor = None vision_model_output: BaseModelOutputWithPooling = None decoder_output: CLIPSegDecoderOutput = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["vision_model_output", "decoder_output"] else getattr(self, k).to_tuple() for k in self.keys() ) class CLIPSegVisionEmbeddings(nn.Module): # Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings.__init__ def __init__(self, config: CLIPSegVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.image_size = config.image_size self.patch_size = config.patch_size self.class_embedding = nn.Parameter(torch.randn(self.embed_dim)) self.patch_embedding = nn.Conv2d( in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False ) self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches + 1 self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1))) def interpolate_position_embeddings(self, new_size): if len(new_size) != 2: raise ValueError("new_size should consist of 2 values") num_patches_one_direction = int(self.num_patches**0.5) # we interpolate the position embeddings in 2D a = self.position_embedding.weight[1:].T.view( 1, self.config.hidden_size, num_patches_one_direction, num_patches_one_direction ) b = ( nn.functional.interpolate(a, new_size, mode="bicubic", align_corners=False) .squeeze(0) .view(self.config.hidden_size, new_size[0] * new_size[1]) .T ) result = torch.cat([self.position_embedding.weight[:1], b]) return result def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: batch_size = pixel_values.shape[0] patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid] patch_embeds = patch_embeds.flatten(2).transpose(1, 2) class_embeds = self.class_embedding.expand(batch_size, 1, -1) embeddings = torch.cat([class_embeds, patch_embeds], dim=1) if embeddings.shape[1] != self.num_positions: new_shape = int(math.sqrt(embeddings.shape[1] - 1)) embeddings = embeddings + self.interpolate_position_embeddings((new_shape, new_shape)) embeddings = embeddings.to(embeddings.dtype) else: embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings # Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->CLIPSeg class CLIPSegTextEmbeddings(nn.Module): def __init__(self, config: CLIPSegTextConfig): super().__init__() embed_dim = config.hidden_size self.token_embedding = nn.Embedding(config.vocab_size, embed_dim) self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) def forward( self, input_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.Tensor: seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if inputs_embeds is None: inputs_embeds = self.token_embedding(input_ids) position_embeddings = self.position_embedding(position_ids) embeddings = inputs_embeds + position_embeddings return embeddings # Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->CLIPSeg class CLIPSegAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config): super().__init__() self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.dropout = config.attention_dropout self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, causal_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" bsz, tgt_len, embed_dim = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scale key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) # apply the causal_attention_mask first if causal_attention_mask is not None: if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" f" {causal_attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if output_attentions: # this operation is a bit akward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(bsz, tgt_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped # Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->CLIPSeg class CLIPSegMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.activation_fn = ACT2FN[config.hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states # Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->CLIPSeg class CLIPSegEncoderLayer(nn.Module): def __init__(self, config: CLIPSegConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = CLIPSegAttention(config) self.layer_norm1 = nn.LayerNorm(self.embed_dim) self.mlp = CLIPSegMLP(config) self.layer_norm2 = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, causal_attention_mask: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. `(config.encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class CLIPSegPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CLIPSegConfig base_model_prefix = "clip" supports_gradient_checkpointing = True _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """Initialize the weights""" factor = self.config.initializer_factor if isinstance(module, CLIPSegTextEmbeddings): module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02) module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02) elif isinstance(module, CLIPSegVisionEmbeddings): factor = self.config.initializer_factor nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor) nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor) nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor) elif isinstance(module, CLIPSegAttention): factor = self.config.initializer_factor in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor out_proj_std = (module.embed_dim**-0.5) * factor nn.init.normal_(module.q_proj.weight, std=in_proj_std) nn.init.normal_(module.k_proj.weight, std=in_proj_std) nn.init.normal_(module.v_proj.weight, std=in_proj_std) nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, CLIPSegMLP): factor = self.config.initializer_factor in_proj_std = ( (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor ) fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) elif isinstance(module, CLIPSegModel): nn.init.normal_( module.text_projection.weight, std=module.text_embed_dim**-0.5 * self.config.initializer_factor, ) nn.init.normal_( module.visual_projection.weight, std=module.vision_embed_dim**-0.5 * self.config.initializer_factor, ) if isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, CLIPSegEncoder): module.gradient_checkpointing = value CLIPSEG_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CLIPSegConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ CLIPSEG_TEXT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ CLIPSEG_VISION_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ CLIPSEG_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details. return_loss (`bool`, *optional*): Whether or not to return the contrastive loss. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->CLIPSeg class CLIPSegEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`CLIPSegEncoderLayer`]. Args: config: CLIPSegConfig """ def __init__(self, config: CLIPSegConfig): super().__init__() self.config = config self.layers = nn.ModuleList([CLIPSegEncoderLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, inputs_embeds, attention_mask: Optional[torch.Tensor] = None, causal_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Causal mask for the text model. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None hidden_states = inputs_embeds for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(encoder_layer), hidden_states, attention_mask, causal_attention_mask, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, causal_attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class CLIPSegTextTransformer(nn.Module): # Copied from transformers.models.clip.modeling_clip.CLIPTextTransformer.__init__ with CLIP->CLIPSeg def __init__(self, config: CLIPSegTextConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = CLIPSegTextEmbeddings(config) self.encoder = CLIPSegEncoder(config) self.final_layer_norm = nn.LayerNorm(embed_dim) @add_start_docstrings_to_model_forward(CLIPSEG_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegTextConfig) # Copied from transformers.models.clip.modeling_clip.CLIPTextTransformer.forward with clip->clipseg, CLIP->CLIPSeg def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is None: raise ValueError("You have to specify either input_ids") input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) bsz, seq_len = input_shape # CLIPSeg's text model uses causal mask, prepare it here. # https://github.com/openai/CLIPSeg/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clipseg/model.py#L324 causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to( hidden_states.device ) # expand attention_mask if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask(attention_mask, hidden_states.dtype) encoder_outputs = self.encoder( inputs_embeds=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] last_hidden_state = self.final_layer_norm(last_hidden_state) # text_embeds.shape = [batch_size, sequence_length, transformer.width] # take features from the eot embedding (eot_token is the highest number in each sequence) # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14 pooled_output = last_hidden_state[ torch.arange(last_hidden_state.shape[0], device=input_ids.device), input_ids.to(torch.int).argmax(dim=-1) ] if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) def _build_causal_attention_mask(self, bsz, seq_len, dtype): # lazily create causal attention mask, with full attention between the vision tokens # pytorch uses additive attention mask; fill with -inf mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype) mask.fill_(torch.tensor(torch.finfo(dtype).min)) mask.triu_(1) # zero out the lower diagonal mask = mask.unsqueeze(1) # expand mask return mask class CLIPSegTextModel(CLIPSegPreTrainedModel): config_class = CLIPSegTextConfig _no_split_modules = ["CLIPSegEncoderLayer"] def __init__(self, config: CLIPSegTextConfig): super().__init__(config) self.text_model = CLIPSegTextTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.text_model.embeddings.token_embedding def set_input_embeddings(self, value): self.text_model.embeddings.token_embedding = value @add_start_docstrings_to_model_forward(CLIPSEG_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegTextConfig) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: Examples: ```python >>> from transformers import CLIPTokenizer, CLIPSegTextModel >>> tokenizer = CLIPTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined") >>> model = CLIPSegTextModel.from_pretrained("CIDAS/clipseg-rd64-refined") >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled (EOS token) states ```""" return self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class CLIPSegVisionTransformer(nn.Module): # Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.__init__ with CLIP->CLIPSeg def __init__(self, config: CLIPSegVisionConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = CLIPSegVisionEmbeddings(config) self.pre_layrnorm = nn.LayerNorm(embed_dim) self.encoder = CLIPSegEncoder(config) self.post_layernorm = nn.LayerNorm(embed_dim) @add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegVisionConfig) # Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") hidden_states = self.embeddings(pixel_values) hidden_states = self.pre_layrnorm(hidden_states) encoder_outputs = self.encoder( inputs_embeds=hidden_states, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] pooled_output = last_hidden_state[:, 0, :] pooled_output = self.post_layernorm(pooled_output) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class CLIPSegVisionModel(CLIPSegPreTrainedModel): config_class = CLIPSegVisionConfig main_input_name = "pixel_values" def __init__(self, config: CLIPSegVisionConfig): super().__init__(config) self.vision_model = CLIPSegVisionTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.vision_model.embeddings.patch_embedding @add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegVisionConfig) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import CLIPSegProcessor, CLIPSegVisionModel >>> processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") >>> model = CLIPSegVisionModel.from_pretrained("CIDAS/clipseg-rd64-refined") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled CLS states ```""" return self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) @add_start_docstrings(CLIPSEG_START_DOCSTRING) class CLIPSegModel(CLIPSegPreTrainedModel): config_class = CLIPSegConfig def __init__(self, config: CLIPSegConfig): super().__init__(config) if not isinstance(config.text_config, CLIPSegTextConfig): raise ValueError( "config.text_config is expected to be of type CLIPSegTextConfig but is of type" f" {type(config.text_config)}." ) if not isinstance(config.vision_config, CLIPSegVisionConfig): raise ValueError( "config.vision_config is expected to be of type CLIPSegVisionConfig but is of type" f" {type(config.vision_config)}." ) text_config = config.text_config vision_config = config.vision_config self.projection_dim = config.projection_dim self.text_embed_dim = text_config.hidden_size self.vision_embed_dim = vision_config.hidden_size self.text_model = CLIPSegTextTransformer(text_config) self.vision_model = CLIPSegVisionTransformer(vision_config) self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False) self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False) self.logit_scale = nn.Parameter(torch.ones([]) * self.config.logit_scale_init_value) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CLIPSEG_TEXT_INPUTS_DOCSTRING) def get_text_features( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegTextModel`]. Examples: ```python >>> from transformers import CLIPTokenizer, CLIPSegModel >>> tokenizer = CLIPTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined") >>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined") >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") >>> text_features = model.get_text_features(**inputs) ```""" # Use CLIPSEG model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = text_outputs[1] text_features = self.text_projection(pooled_output) return text_features @add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING) def get_image_features( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegVisionModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import CLIPSegProcessor, CLIPSegModel >>> processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") >>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> image_features = model.get_image_features(**inputs) ```""" # Use CLIPSEG model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = vision_outputs[1] # pooled_output image_features = self.visual_projection(pooled_output) return image_features @add_start_docstrings_to_model_forward(CLIPSEG_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=CLIPSegOutput, config_class=CLIPSegConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, return_loss: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CLIPSegOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import CLIPSegProcessor, CLIPSegModel >>> processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") >>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True ... ) >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ```""" # Use CLIPSEG model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) # normalized features image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) # cosine similarity as logits logit_scale = self.logit_scale.exp() logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale logits_per_image = logits_per_text.t() loss = None if return_loss: loss = clipseg_loss(logits_per_text) if not return_dict: output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return ((loss,) + output) if loss is not None else output return CLIPSegOutput( loss=loss, logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) class CLIPSegDecoderLayer(nn.Module): """ CLIPSeg decoder layer, which is identical to `CLIPSegEncoderLayer`, except that normalization is applied after self-attention/MLP, rather than before. """ # Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer.__init__ with CLIP->CLIPSeg def __init__(self, config: CLIPSegConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = CLIPSegAttention(config) self.layer_norm1 = nn.LayerNorm(self.embed_dim) self.mlp = CLIPSegMLP(config) self.layer_norm2 = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, causal_attention_mask: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. `(config.encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions, ) hidden_states = residual + hidden_states hidden_states = self.layer_norm1(hidden_states) residual = hidden_states hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states hidden_states = self.layer_norm2(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class CLIPSegDecoder(CLIPSegPreTrainedModel): def __init__(self, config: CLIPSegConfig): super().__init__(config) self.conditional_layer = config.conditional_layer self.film_mul = nn.Linear(config.projection_dim, config.reduce_dim) self.film_add = nn.Linear(config.projection_dim, config.reduce_dim) if config.use_complex_transposed_convolution: transposed_kernels = (config.vision_config.patch_size // 4, config.vision_config.patch_size // 4) self.transposed_convolution = nn.Sequential( nn.Conv2d(config.reduce_dim, config.reduce_dim, kernel_size=3, padding=1), nn.ReLU(), nn.ConvTranspose2d( config.reduce_dim, config.reduce_dim // 2, kernel_size=transposed_kernels[0], stride=transposed_kernels[0], ), nn.ReLU(), nn.ConvTranspose2d( config.reduce_dim // 2, 1, kernel_size=transposed_kernels[1], stride=transposed_kernels[1] ), ) else: self.transposed_convolution = nn.ConvTranspose2d( config.reduce_dim, 1, config.vision_config.patch_size, stride=config.vision_config.patch_size ) depth = len(config.extract_layers) self.reduces = nn.ModuleList( [nn.Linear(config.vision_config.hidden_size, config.reduce_dim) for _ in range(depth)] ) decoder_config = copy.deepcopy(config.vision_config) decoder_config.hidden_size = config.reduce_dim decoder_config.num_attention_heads = config.decoder_num_attention_heads decoder_config.intermediate_size = config.decoder_intermediate_size decoder_config.hidden_act = "relu" self.layers = nn.ModuleList([CLIPSegDecoderLayer(decoder_config) for _ in range(len(config.extract_layers))]) def forward( self, hidden_states: Tuple[torch.Tensor], conditional_embeddings: torch.Tensor, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = True, ): all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None activations = hidden_states[::-1] output = None for i, (activation, layer, reduce) in enumerate(zip(activations, self.layers, self.reduces)): if output is not None: output = reduce(activation) + output else: output = reduce(activation) if i == self.conditional_layer: output = self.film_mul(conditional_embeddings) * output.permute(1, 0, 2) + self.film_add( conditional_embeddings ) output = output.permute(1, 0, 2) layer_outputs = layer( output, attention_mask=None, causal_attention_mask=None, output_attentions=output_attentions ) output = layer_outputs[0] if output_hidden_states: all_hidden_states += (output,) if output_attentions: all_attentions += (layer_outputs[1],) output = output[:, 1:, :].permute(0, 2, 1) # remove cls token and reshape to [batch_size, reduce_dim, seq_len] size = int(math.sqrt(output.shape[2])) batch_size = conditional_embeddings.shape[0] output = output.view(batch_size, output.shape[1], size, size) logits = self.transposed_convolution(output).squeeze() if not return_dict: return tuple(v for v in [logits, all_hidden_states, all_attentions] if v is not None) return CLIPSegDecoderOutput( logits=logits, hidden_states=all_hidden_states, attentions=all_attentions, ) @add_start_docstrings( """ CLIPSeg model with a Transformer-based decoder on top for zero-shot and one-shot image segmentation. """, CLIPSEG_START_DOCSTRING, ) class CLIPSegForImageSegmentation(CLIPSegPreTrainedModel): config_class = CLIPSegConfig def __init__(self, config: CLIPSegConfig): super().__init__(config) self.config = config self.clip = CLIPSegModel(config) self.extract_layers = config.extract_layers self.decoder = CLIPSegDecoder(config) # Initialize weights and apply final processing self.post_init() def get_conditional_embeddings( self, batch_size: int = None, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, conditional_pixel_values: Optional[torch.Tensor] = None, ): if input_ids is not None: # compute conditional embeddings from texts if len(input_ids) != batch_size: raise ValueError("Make sure to pass as many prompt texts as there are query images") with torch.no_grad(): conditional_embeddings = self.clip.get_text_features( input_ids, attention_mask=attention_mask, position_ids=position_ids ) elif conditional_pixel_values is not None: # compute conditional embeddings from images if len(conditional_pixel_values) != batch_size: raise ValueError("Make sure to pass as many prompt images as there are query images") with torch.no_grad(): conditional_embeddings = self.clip.get_image_features(conditional_pixel_values) else: raise ValueError( "Invalid conditional, should be either provided as `input_ids` or `conditional_pixel_values`" ) return conditional_embeddings @add_start_docstrings_to_model_forward(CLIPSEG_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=CLIPSegImageSegmentationOutput, config_class=CLIPSegTextConfig) def forward( self, input_ids: Optional[torch.FloatTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, conditional_pixel_values: Optional[torch.FloatTensor] = None, conditional_embeddings: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CLIPSegOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation >>> from PIL import Image >>> import requests >>> processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") >>> model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> texts = ["a cat", "a remote", "a blanket"] >>> inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> print(logits.shape) torch.Size([3, 352, 352]) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict # step 1: forward the query images through the frozen CLIP vision encoder with torch.no_grad(): vision_outputs = self.clip.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) pooled_output = self.clip.visual_projection(vision_outputs[1]) hidden_states = vision_outputs.hidden_states if return_dict else vision_outputs[2] # we add +1 here as the hidden states also include the initial embeddings activations = [hidden_states[i + 1] for i in self.extract_layers] # update vision_outputs if return_dict: vision_outputs = BaseModelOutputWithPooling( last_hidden_state=vision_outputs.last_hidden_state, pooler_output=vision_outputs.pooler_output, hidden_states=vision_outputs.hidden_states if output_hidden_states else None, attentions=vision_outputs.attentions, ) else: vision_outputs = ( vision_outputs[:2] + vision_outputs[3:] if not output_hidden_states else vision_outputs ) # step 2: compute conditional embeddings, either from text, images or an own provided embedding if conditional_embeddings is None: conditional_embeddings = self.get_conditional_embeddings( batch_size=pixel_values.shape[0], input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, conditional_pixel_values=conditional_pixel_values, ) else: if conditional_embeddings.shape[0] != pixel_values.shape[0]: raise ValueError( "Make sure to pass as many conditional embeddings as there are query images in the batch" ) if conditional_embeddings.shape[1] != self.config.projection_dim: raise ValueError( "Make sure that the feature dimension of the conditional embeddings matches" " `config.projection_dim`." ) # step 3: forward both the pooled output and the activations through the lightweight decoder to predict masks decoder_outputs = self.decoder( activations, conditional_embeddings, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) logits = decoder_outputs.logits if return_dict else decoder_outputs[0] loss = None if labels is not None: loss_fn = nn.BCEWithLogitsLoss() loss = loss_fn(logits, labels) if not return_dict: output = (logits, conditional_embeddings, pooled_output, vision_outputs, decoder_outputs) return ((loss,) + output) if loss is not None else output return CLIPSegImageSegmentationOutput( loss=loss, logits=logits, conditional_embeddings=conditional_embeddings, pooled_output=pooled_output, vision_model_output=vision_outputs, decoder_output=decoder_outputs, )

# coding=utf-8 # Copyright 2022 The OpenAI Team Authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch CLIPSeg model.""" import copy import math from dataclasses import dataclass from typing import Any, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from ...modeling_utils import PreTrainedModel from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_clipseg import CLIPSegConfig, CLIPSegTextConfig, CLIPSegVisionConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "CIDAS/clipseg-rd64-refined" CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST = [ "CIDAS/clipseg-rd64-refined", # See all CLIPSeg models at https://huggingface.co/models?filter=clipseg ] # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) # contrastive loss function, adapted from # https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIPSeg.html def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device)) # Copied from transformers.models.clip.modeling_clip.clip_loss with clip->clipseg def clipseg_loss(similarity: torch.Tensor) -> torch.Tensor: caption_loss = contrastive_loss(similarity) image_loss = contrastive_loss(similarity.t()) return (caption_loss + image_loss) / 2.0 @dataclass # Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->CLIPSeg class CLIPSegOutput(ModelOutput): """ Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): Contrastive loss for image-text similarity. logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text similarity scores. logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image similarity scores. text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegTextModel`]. image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegVisionModel`]. text_model_output(`BaseModelOutputWithPooling`): The output of the [`CLIPSegTextModel`]. vision_model_output(`BaseModelOutputWithPooling`): The output of the [`CLIPSegVisionModel`]. """ loss: Optional[torch.FloatTensor] = None logits_per_image: torch.FloatTensor = None logits_per_text: torch.FloatTensor = None text_embeds: torch.FloatTensor = None image_embeds: torch.FloatTensor = None text_model_output: BaseModelOutputWithPooling = None vision_model_output: BaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) @dataclass class CLIPSegDecoderOutput(ModelOutput): """ Args: logits (`torch.FloatTensor` of shape `(batch_size, height, width)`): Classification scores for each pixel. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class CLIPSegImageSegmentationOutput(ModelOutput): """ Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): Contrastive loss for image-text similarity. ... vision_model_output (`BaseModelOutputWithPooling`): The output of the [`CLIPSegVisionModel`]. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None conditional_embeddings: torch.FloatTensor = None pooled_output: torch.FloatTensor = None vision_model_output: BaseModelOutputWithPooling = None decoder_output: CLIPSegDecoderOutput = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["vision_model_output", "decoder_output"] else getattr(self, k).to_tuple() for k in self.keys() ) class CLIPSegVisionEmbeddings(nn.Module): # Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings.__init__ def __init__(self, config: CLIPSegVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.image_size = config.image_size self.patch_size = config.patch_size self.class_embedding = nn.Parameter(torch.randn(self.embed_dim)) self.patch_embedding = nn.Conv2d( in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False ) self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches + 1 self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1))) def interpolate_position_embeddings(self, new_size): if len(new_size) != 2: raise ValueError("new_size should consist of 2 values") num_patches_one_direction = int(self.num_patches**0.5) # we interpolate the position embeddings in 2D a = self.position_embedding.weight[1:].T.view( 1, self.config.hidden_size, num_patches_one_direction, num_patches_one_direction ) b = ( nn.functional.interpolate(a, new_size, mode="bicubic", align_corners=False) .squeeze(0) .view(self.config.hidden_size, new_size[0] * new_size[1]) .T ) result = torch.cat([self.position_embedding.weight[:1], b]) return result def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: batch_size = pixel_values.shape[0] patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid] patch_embeds = patch_embeds.flatten(2).transpose(1, 2) class_embeds = self.class_embedding.expand(batch_size, 1, -1) embeddings = torch.cat([class_embeds, patch_embeds], dim=1) if embeddings.shape[1] != self.num_positions: new_shape = int(math.sqrt(embeddings.shape[1] - 1)) embeddings = embeddings + self.interpolate_position_embeddings((new_shape, new_shape)) embeddings = embeddings.to(embeddings.dtype) else: embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings # Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->CLIPSeg class CLIPSegTextEmbeddings(nn.Module): def __init__(self, config: CLIPSegTextConfig): super().__init__() embed_dim = config.hidden_size self.token_embedding = nn.Embedding(config.vocab_size, embed_dim) self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) def forward( self, input_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.Tensor: seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if inputs_embeds is None: inputs_embeds = self.token_embedding(input_ids) position_embeddings = self.position_embedding(position_ids) embeddings = inputs_embeds + position_embeddings return embeddings # Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->CLIPSeg class CLIPSegAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config): super().__init__() self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.dropout = config.attention_dropout self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, causal_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" bsz, tgt_len, embed_dim = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scale key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) # apply the causal_attention_mask first if causal_attention_mask is not None: if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" f" {causal_attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if output_attentions: # this operation is a bit akward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(bsz, tgt_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped # Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->CLIPSeg class CLIPSegMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.activation_fn = ACT2FN[config.hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states # Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->CLIPSeg class CLIPSegEncoderLayer(nn.Module): def __init__(self, config: CLIPSegConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = CLIPSegAttention(config) self.layer_norm1 = nn.LayerNorm(self.embed_dim) self.mlp = CLIPSegMLP(config) self.layer_norm2 = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, causal_attention_mask: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. `(config.encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class CLIPSegPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CLIPSegConfig base_model_prefix = "clip" supports_gradient_checkpointing = True _keys_to_ignore_on_load_missing = [r"position_ids"] def _init_weights(self, module): """Initialize the weights""" factor = self.config.initializer_factor if isinstance(module, CLIPSegTextEmbeddings): module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02) module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02) elif isinstance(module, CLIPSegVisionEmbeddings): factor = self.config.initializer_factor nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor) nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor) nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor) elif isinstance(module, CLIPSegAttention): factor = self.config.initializer_factor in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor out_proj_std = (module.embed_dim**-0.5) * factor nn.init.normal_(module.q_proj.weight, std=in_proj_std) nn.init.normal_(module.k_proj.weight, std=in_proj_std) nn.init.normal_(module.v_proj.weight, std=in_proj_std) nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, CLIPSegMLP): factor = self.config.initializer_factor in_proj_std = ( (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor ) fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) elif isinstance(module, CLIPSegModel): nn.init.normal_( module.text_projection.weight, std=module.text_embed_dim**-0.5 * self.config.initializer_factor, ) nn.init.normal_( module.visual_projection.weight, std=module.vision_embed_dim**-0.5 * self.config.initializer_factor, ) if isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, CLIPSegEncoder): module.gradient_checkpointing = value CLIPSEG_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CLIPSegConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ CLIPSEG_TEXT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ CLIPSEG_VISION_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ CLIPSEG_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details. return_loss (`bool`, *optional*): Whether or not to return the contrastive loss. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->CLIPSeg class CLIPSegEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`CLIPSegEncoderLayer`]. Args: config: CLIPSegConfig """ def __init__(self, config: CLIPSegConfig): super().__init__() self.config = config self.layers = nn.ModuleList([CLIPSegEncoderLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, inputs_embeds, attention_mask: Optional[torch.Tensor] = None, causal_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Causal mask for the text model. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None hidden_states = inputs_embeds for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(encoder_layer), hidden_states, attention_mask, causal_attention_mask, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, causal_attention_mask, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class CLIPSegTextTransformer(nn.Module): # Copied from transformers.models.clip.modeling_clip.CLIPTextTransformer.__init__ with CLIP->CLIPSeg def __init__(self, config: CLIPSegTextConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = CLIPSegTextEmbeddings(config) self.encoder = CLIPSegEncoder(config) self.final_layer_norm = nn.LayerNorm(embed_dim) @add_start_docstrings_to_model_forward(CLIPSEG_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegTextConfig) # Copied from transformers.models.clip.modeling_clip.CLIPTextTransformer.forward with clip->clipseg, CLIP->CLIPSeg def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is None: raise ValueError("You have to specify either input_ids") input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) bsz, seq_len = input_shape # CLIPSeg's text model uses causal mask, prepare it here. # https://github.com/openai/CLIPSeg/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clipseg/model.py#L324 causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to( hidden_states.device ) # expand attention_mask if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _expand_mask(attention_mask, hidden_states.dtype) encoder_outputs = self.encoder( inputs_embeds=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] last_hidden_state = self.final_layer_norm(last_hidden_state) # text_embeds.shape = [batch_size, sequence_length, transformer.width] # take features from the eot embedding (eot_token is the highest number in each sequence) # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14 pooled_output = last_hidden_state[ torch.arange(last_hidden_state.shape[0], device=input_ids.device), input_ids.to(torch.int).argmax(dim=-1) ] if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) def _build_causal_attention_mask(self, bsz, seq_len, dtype): # lazily create causal attention mask, with full attention between the vision tokens # pytorch uses additive attention mask; fill with -inf mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype) mask.fill_(torch.tensor(torch.finfo(dtype).min)) mask.triu_(1) # zero out the lower diagonal mask = mask.unsqueeze(1) # expand mask return mask class CLIPSegTextModel(CLIPSegPreTrainedModel): config_class = CLIPSegTextConfig _no_split_modules = ["CLIPSegEncoderLayer"] def __init__(self, config: CLIPSegTextConfig): super().__init__(config) self.text_model = CLIPSegTextTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.text_model.embeddings.token_embedding def set_input_embeddings(self, value): self.text_model.embeddings.token_embedding = value @add_start_docstrings_to_model_forward(CLIPSEG_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegTextConfig) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: Examples: ```python >>> from transformers import CLIPTokenizer, CLIPSegTextModel >>> tokenizer = CLIPTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined") >>> model = CLIPSegTextModel.from_pretrained("CIDAS/clipseg-rd64-refined") >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled (EOS token) states ```""" return self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class CLIPSegVisionTransformer(nn.Module): # Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.__init__ with CLIP->CLIPSeg def __init__(self, config: CLIPSegVisionConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = CLIPSegVisionEmbeddings(config) self.pre_layrnorm = nn.LayerNorm(embed_dim) self.encoder = CLIPSegEncoder(config) self.post_layernorm = nn.LayerNorm(embed_dim) @add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegVisionConfig) # Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") hidden_states = self.embeddings(pixel_values) hidden_states = self.pre_layrnorm(hidden_states) encoder_outputs = self.encoder( inputs_embeds=hidden_states, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] pooled_output = last_hidden_state[:, 0, :] pooled_output = self.post_layernorm(pooled_output) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class CLIPSegVisionModel(CLIPSegPreTrainedModel): config_class = CLIPSegVisionConfig main_input_name = "pixel_values" def __init__(self, config: CLIPSegVisionConfig): super().__init__(config) self.vision_model = CLIPSegVisionTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.vision_model.embeddings.patch_embedding @add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPSegVisionConfig) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import CLIPSegProcessor, CLIPSegVisionModel >>> processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") >>> model = CLIPSegVisionModel.from_pretrained("CIDAS/clipseg-rd64-refined") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled CLS states ```""" return self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) @add_start_docstrings(CLIPSEG_START_DOCSTRING) class CLIPSegModel(CLIPSegPreTrainedModel): config_class = CLIPSegConfig def __init__(self, config: CLIPSegConfig): super().__init__(config) if not isinstance(config.text_config, CLIPSegTextConfig): raise ValueError( "config.text_config is expected to be of type CLIPSegTextConfig but is of type" f" {type(config.text_config)}." ) if not isinstance(config.vision_config, CLIPSegVisionConfig): raise ValueError( "config.vision_config is expected to be of type CLIPSegVisionConfig but is of type" f" {type(config.vision_config)}." ) text_config = config.text_config vision_config = config.vision_config self.projection_dim = config.projection_dim self.text_embed_dim = text_config.hidden_size self.vision_embed_dim = vision_config.hidden_size self.text_model = CLIPSegTextTransformer(text_config) self.vision_model = CLIPSegVisionTransformer(vision_config) self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False) self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False) self.logit_scale = nn.Parameter(torch.ones([]) * self.config.logit_scale_init_value) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CLIPSEG_TEXT_INPUTS_DOCSTRING) def get_text_features( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegTextModel`]. Examples: ```python >>> from transformers import CLIPTokenizer, CLIPSegModel >>> tokenizer = CLIPTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined") >>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined") >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") >>> text_features = model.get_text_features(**inputs) ```""" # Use CLIPSEG model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = text_outputs[1] text_features = self.text_projection(pooled_output) return text_features @add_start_docstrings_to_model_forward(CLIPSEG_VISION_INPUTS_DOCSTRING) def get_image_features( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegVisionModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import CLIPSegProcessor, CLIPSegModel >>> processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") >>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> image_features = model.get_image_features(**inputs) ```""" # Use CLIPSEG model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = vision_outputs[1] # pooled_output image_features = self.visual_projection(pooled_output) return image_features @add_start_docstrings_to_model_forward(CLIPSEG_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=CLIPSegOutput, config_class=CLIPSegConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, return_loss: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CLIPSegOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import CLIPSegProcessor, CLIPSegModel >>> processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") >>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True ... ) >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ```""" # Use CLIPSEG model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) # normalized features image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) # cosine similarity as logits logit_scale = self.logit_scale.exp() logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale logits_per_image = logits_per_text.t() loss = None if return_loss: loss = clipseg_loss(logits_per_text) if not return_dict: output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return ((loss,) + output) if loss is not None else output return CLIPSegOutput( loss=loss, logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) class CLIPSegDecoderLayer(nn.Module): """ CLIPSeg decoder layer, which is identical to `CLIPSegEncoderLayer`, except that normalization is applied after self-attention/MLP, rather than before. """ # Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer.__init__ with CLIP->CLIPSeg def __init__(self, config: CLIPSegConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = CLIPSegAttention(config) self.layer_norm1 = nn.LayerNorm(self.embed_dim) self.mlp = CLIPSegMLP(config) self.layer_norm2 = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, causal_attention_mask: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. `(config.encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions, ) hidden_states = residual + hidden_states hidden_states = self.layer_norm1(hidden_states) residual = hidden_states hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states hidden_states = self.layer_norm2(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class CLIPSegDecoder(CLIPSegPreTrainedModel): def __init__(self, config: CLIPSegConfig): super().__init__(config) self.conditional_layer = config.conditional_layer self.film_mul = nn.Linear(config.projection_dim, config.reduce_dim) self.film_add = nn.Linear(config.projection_dim, config.reduce_dim) if config.use_complex_transposed_convolution: transposed_kernels = (config.vision_config.patch_size // 4, config.vision_config.patch_size // 4) self.transposed_convolution = nn.Sequential( nn.Conv2d(config.reduce_dim, config.reduce_dim, kernel_size=3, padding=1), nn.ReLU(), nn.ConvTranspose2d( config.reduce_dim, config.reduce_dim // 2, kernel_size=transposed_kernels[0], stride=transposed_kernels[0], ), nn.ReLU(), nn.ConvTranspose2d( config.reduce_dim // 2, 1, kernel_size=transposed_kernels[1], stride=transposed_kernels[1] ), ) else: self.transposed_convolution = nn.ConvTranspose2d( config.reduce_dim, 1, config.vision_config.patch_size, stride=config.vision_config.patch_size ) depth = len(config.extract_layers) self.reduces = nn.ModuleList( [nn.Linear(config.vision_config.hidden_size, config.reduce_dim) for _ in range(depth)] ) decoder_config = copy.deepcopy(config.vision_config) decoder_config.hidden_size = config.reduce_dim decoder_config.num_attention_heads = config.decoder_num_attention_heads decoder_config.intermediate_size = config.decoder_intermediate_size decoder_config.hidden_act = "relu" self.layers = nn.ModuleList([CLIPSegDecoderLayer(decoder_config) for _ in range(len(config.extract_layers))]) def forward( self, hidden_states: Tuple[torch.Tensor], conditional_embeddings: torch.Tensor, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = True, ): all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None activations = hidden_states[::-1] output = None for i, (activation, layer, reduce) in enumerate(zip(activations, self.layers, self.reduces)): if output is not None: output = reduce(activation) + output else: output = reduce(activation) if i == self.conditional_layer: output = self.film_mul(conditional_embeddings) * output.permute(1, 0, 2) + self.film_add( conditional_embeddings ) output = output.permute(1, 0, 2) layer_outputs = layer( output, attention_mask=None, causal_attention_mask=None, output_attentions=output_attentions ) output = layer_outputs[0] if output_hidden_states: all_hidden_states += (output,) if output_attentions: all_attentions += (layer_outputs[1],) output = output[:, 1:, :].permute(0, 2, 1) # remove cls token and reshape to [batch_size, reduce_dim, seq_len] size = int(math.sqrt(output.shape[2])) batch_size = conditional_embeddings.shape[0] output = output.view(batch_size, output.shape[1], size, size) logits = self.transposed_convolution(output).squeeze() if not return_dict: return tuple(v for v in [logits, all_hidden_states, all_attentions] if v is not None) return CLIPSegDecoderOutput( logits=logits, hidden_states=all_hidden_states, attentions=all_attentions, ) @add_start_docstrings( """ CLIPSeg model with a Transformer-based decoder on top for zero-shot and one-shot image segmentation. """, CLIPSEG_START_DOCSTRING, ) class CLIPSegForImageSegmentation(CLIPSegPreTrainedModel): config_class = CLIPSegConfig def __init__(self, config: CLIPSegConfig): super().__init__(config) self.config = config self.clip = CLIPSegModel(config) self.extract_layers = config.extract_layers self.decoder = CLIPSegDecoder(config) # Initialize weights and apply final processing self.post_init() def get_conditional_embeddings( self, batch_size: int = None, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, conditional_pixel_values: Optional[torch.Tensor] = None, ): if input_ids is not None: # compute conditional embeddings from texts if len(input_ids) != batch_size: raise ValueError("Make sure to pass as many prompt texts as there are query images") with torch.no_grad(): conditional_embeddings = self.clip.get_text_features( input_ids, attention_mask=attention_mask, position_ids=position_ids ) elif conditional_pixel_values is not None: # compute conditional embeddings from images if len(conditional_pixel_values) != batch_size: raise ValueError("Make sure to pass as many prompt images as there are query images") with torch.no_grad(): conditional_embeddings = self.clip.get_image_features(conditional_pixel_values) else: raise ValueError( "Invalid conditional, should be either provided as `input_ids` or `conditional_pixel_values`" ) return conditional_embeddings @add_start_docstrings_to_model_forward(CLIPSEG_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=CLIPSegImageSegmentationOutput, config_class=CLIPSegTextConfig) def forward( self, input_ids: Optional[torch.FloatTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, conditional_pixel_values: Optional[torch.FloatTensor] = None, conditional_embeddings: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CLIPSegOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation >>> from PIL import Image >>> import requests >>> processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") >>> model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> texts = ["a cat", "a remote", "a blanket"] >>> inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> print(logits.shape) torch.Size([3, 352, 352]) ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict # step 1: forward the query images through the frozen CLIP vision encoder with torch.no_grad(): vision_outputs = self.clip.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) pooled_output = self.clip.visual_projection(vision_outputs[1]) hidden_states = vision_outputs.hidden_states if return_dict else vision_outputs[2] # we add +1 here as the hidden states also include the initial embeddings activations = [hidden_states[i + 1] for i in self.extract_layers] # update vision_outputs if return_dict: vision_outputs = BaseModelOutputWithPooling( last_hidden_state=vision_outputs.last_hidden_state, pooler_output=vision_outputs.pooler_output, hidden_states=vision_outputs.hidden_states if output_hidden_states else None, attentions=vision_outputs.attentions, ) else: vision_outputs = ( vision_outputs[:2] + vision_outputs[3:] if not output_hidden_states else vision_outputs ) # step 2: compute conditional embeddings, either from text, images or an own provided embedding if conditional_embeddings is None: conditional_embeddings = self.get_conditional_embeddings( batch_size=pixel_values.shape[0], input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, conditional_pixel_values=conditional_pixel_values, ) else: if conditional_embeddings.shape[0] != pixel_values.shape[0]: raise ValueError( "Make sure to pass as many conditional embeddings as there are query images in the batch" ) if conditional_embeddings.shape[1] != self.config.projection_dim: raise ValueError( "Make sure that the feature dimension of the conditional embeddings matches" " `config.projection_dim`." ) # step 3: forward both the pooled output and the activations through the lightweight decoder to predict masks decoder_outputs = self.decoder( activations, conditional_embeddings, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) logits = decoder_outputs.logits if return_dict else decoder_outputs[0] loss = None if labels is not None: loss_fn = nn.BCEWithLogitsLoss() loss = loss_fn(logits, labels) if not return_dict: output = (logits, conditional_embeddings, pooled_output, vision_outputs, decoder_outputs) return ((loss,) + output) if loss is not None else output return CLIPSegImageSegmentationOutput( loss=loss, logits=logits, conditional_embeddings=conditional_embeddings, pooled_output=pooled_output, vision_model_output=vision_outputs, decoder_output=decoder_outputs, )

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/roberta/__init__.py

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/layoutlmv2/test_modeling_layoutlmv2.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch LayoutLMv2 model. """ import os import random import tempfile import unittest from transformers.testing_utils import require_detectron2, require_torch, require_torch_multi_gpu, slow, torch_device from transformers.utils import is_detectron2_available, is_torch_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( MODEL_MAPPING, LayoutLMv2Config, LayoutLMv2ForQuestionAnswering, LayoutLMv2ForSequenceClassification, LayoutLMv2ForTokenClassification, LayoutLMv2Model, ) from transformers.models.layoutlmv2.modeling_layoutlmv2 import LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST if is_detectron2_available(): from detectron2.structures.image_list import ImageList class LayoutLMv2ModelTester: def __init__( self, parent, batch_size=2, num_channels=3, image_size=4, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=36, num_hidden_layers=3, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, image_feature_pool_shape=[7, 7, 256], coordinate_size=6, shape_size=6, num_labels=3, num_choices=4, scope=None, range_bbox=1000, ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.image_feature_pool_shape = image_feature_pool_shape self.coordinate_size = coordinate_size self.shape_size = shape_size self.num_labels = num_labels self.num_choices = num_choices self.scope = scope self.range_bbox = range_bbox def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox) # Ensure that bbox is legal for i in range(bbox.shape[0]): for j in range(bbox.shape[1]): if bbox[i, j, 3] < bbox[i, j, 1]: t = bbox[i, j, 3] bbox[i, j, 3] = bbox[i, j, 1] bbox[i, j, 1] = t if bbox[i, j, 2] < bbox[i, j, 0]: t = bbox[i, j, 2] bbox[i, j, 2] = bbox[i, j, 0] bbox[i, j, 0] = t image = ImageList( torch.zeros(self.batch_size, self.num_channels, self.image_size, self.image_size, device=torch_device), self.image_size, ) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) config = LayoutLMv2Config( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, image_feature_pool_shape=self.image_feature_pool_shape, coordinate_size=self.coordinate_size, shape_size=self.shape_size, ) # use smaller resnet backbone to make tests faster config.detectron2_config_args["MODEL.RESNETS.DEPTH"] = 18 config.detectron2_config_args["MODEL.RESNETS.RES2_OUT_CHANNELS"] = 64 config.detectron2_config_args["MODEL.RESNETS.NUM_GROUPS"] = 1 return config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels def create_and_check_model( self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels ): model = LayoutLMv2Model(config=config) model.to(torch_device) model.eval() result = model(input_ids, bbox=bbox, image=image, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, bbox=bbox, image=image, token_type_ids=token_type_ids) result = model(input_ids, bbox=bbox, image=image) # LayoutLMv2 has a different expected sequence length, namely also visual tokens are added expected_seq_len = self.seq_length + self.image_feature_pool_shape[0] * self.image_feature_pool_shape[1] self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_sequence_classification( self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels ): config.num_labels = self.num_labels model = LayoutLMv2ForSequenceClassification(config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, image=image, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification( self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels ): config.num_labels = self.num_labels model = LayoutLMv2ForTokenClassification(config=config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, image=image, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_question_answering( self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels ): model = LayoutLMv2ForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, image=image, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "bbox": bbox, "image": image, "token_type_ids": token_type_ids, "attention_mask": input_mask, } return config, inputs_dict @require_torch @require_detectron2 class LayoutLMv2ModelTest(ModelTesterMixin, unittest.TestCase): test_pruning = False test_torchscript = True test_mismatched_shapes = False all_model_classes = ( ( LayoutLMv2Model, LayoutLMv2ForSequenceClassification, LayoutLMv2ForTokenClassification, LayoutLMv2ForQuestionAnswering, ) if is_torch_available() else () ) def setUp(self): self.model_tester = LayoutLMv2ModelTester(self) self.config_tester = ConfigTester(self, config_class=LayoutLMv2Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @require_torch_multi_gpu @unittest.skip( reason=( "LayoutLMV2 and its dependency `detectron2` have some layers using `add_module` which doesn't work well" " with `nn.DataParallel`" ) ) def test_multi_gpu_data_parallel_forward(self): pass def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_save_load_fast_init_from_base(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() base_class = MODEL_MAPPING[config.__class__] if isinstance(base_class, tuple): base_class = base_class[0] for model_class in self.all_model_classes: if model_class == base_class: continue # make a copy of model class to not break future tests # from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class class CopyClass(model_class): pass model_class_copy = CopyClass # make sure that all keys are expected for test model_class_copy._keys_to_ignore_on_load_missing = [] # make init deterministic, but make sure that # non-initialized weights throw errors nevertheless model_class_copy._init_weights = self._mock_init_weights model = base_class(config) state_dict = model.state_dict() # this will often delete a single weight of a multi-weight module # to test an edge case random_key_to_del = random.choice(list(state_dict.keys())) del state_dict[random_key_to_del] # check that certain keys didn't get saved with the model with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin")) model_fast_init = model_class_copy.from_pretrained(tmpdirname) model_slow_init = model_class_copy.from_pretrained(tmpdirname, _fast_init=False) for key in model_fast_init.state_dict().keys(): if key == "layoutlmv2.visual_segment_embedding": # we skip the visual segment embedding as it has a custom initialization scheme continue max_diff = (model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]).sum().item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True # LayoutLMv2 has a different expected sequence length expected_seq_len = ( self.model_tester.seq_length + self.model_tester.image_feature_pool_shape[0] * self.model_tester.image_feature_pool_shape[1] ) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, expected_seq_len, expected_seq_len], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, expected_seq_len, expected_seq_len], ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) # LayoutLMv2 has a different expected sequence length expected_seq_len = ( self.model_tester.seq_length + self.model_tester.image_feature_pool_shape[0] * self.model_tester.image_feature_pool_shape[1] ) self.assertListEqual( list(hidden_states[0].shape[-2:]), [expected_seq_len, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) @slow def test_model_from_pretrained(self): for model_name in LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = LayoutLMv2Model.from_pretrained(model_name) self.assertIsNotNone(model) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if "backbone" in name or "visual_segment_embedding" in name: continue if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def prepare_layoutlmv2_batch_inputs(): # Here we prepare a batch of 2 sequences to test a LayoutLMv2 forward pass on: # fmt: off input_ids = torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]) # noqa: E231 bbox = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]) # noqa: E231 image = ImageList(torch.randn((2,3,224,224)), image_sizes=[(224,224), (224,224)]) # noqa: E231 attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]) # noqa: E231 token_type_ids = torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]) # noqa: E231 # fmt: on return input_ids, bbox, image, attention_mask, token_type_ids @require_torch @require_detectron2 class LayoutLMv2ModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head(self): model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased").to(torch_device) ( input_ids, bbox, image, attention_mask, token_type_ids, ) = prepare_layoutlmv2_batch_inputs() # forward pass outputs = model( input_ids=input_ids.to(torch_device), bbox=bbox.to(torch_device), image=image.to(torch_device), attention_mask=attention_mask.to(torch_device), token_type_ids=token_type_ids.to(torch_device), ) # verify the sequence output expected_shape = torch.Size( ( 2, input_ids.shape[1] + model.config.image_feature_pool_shape[0] * model.config.image_feature_pool_shape[1], model.config.hidden_size, ) ) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[-0.1087, 0.0727, -0.3075], [0.0799, -0.0427, -0.0751], [-0.0367, 0.0480, -0.1358]], device=torch_device ) self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-3)) # verify the pooled output expected_shape = torch.Size((2, model.config.hidden_size)) self.assertEqual(outputs.pooler_output.shape, expected_shape)

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch LayoutLMv2 model. """ import os import random import tempfile import unittest from transformers.testing_utils import require_detectron2, require_torch, require_torch_multi_gpu, slow, torch_device from transformers.utils import is_detectron2_available, is_torch_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( MODEL_MAPPING, LayoutLMv2Config, LayoutLMv2ForQuestionAnswering, LayoutLMv2ForSequenceClassification, LayoutLMv2ForTokenClassification, LayoutLMv2Model, ) from transformers.models.layoutlmv2.modeling_layoutlmv2 import LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST if is_detectron2_available(): from detectron2.structures.image_list import ImageList class LayoutLMv2ModelTester: def __init__( self, parent, batch_size=2, num_channels=3, image_size=4, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=36, num_hidden_layers=3, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, image_feature_pool_shape=[7, 7, 256], coordinate_size=6, shape_size=6, num_labels=3, num_choices=4, scope=None, range_bbox=1000, ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.image_feature_pool_shape = image_feature_pool_shape self.coordinate_size = coordinate_size self.shape_size = shape_size self.num_labels = num_labels self.num_choices = num_choices self.scope = scope self.range_bbox = range_bbox def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox) # Ensure that bbox is legal for i in range(bbox.shape[0]): for j in range(bbox.shape[1]): if bbox[i, j, 3] < bbox[i, j, 1]: t = bbox[i, j, 3] bbox[i, j, 3] = bbox[i, j, 1] bbox[i, j, 1] = t if bbox[i, j, 2] < bbox[i, j, 0]: t = bbox[i, j, 2] bbox[i, j, 2] = bbox[i, j, 0] bbox[i, j, 0] = t image = ImageList( torch.zeros(self.batch_size, self.num_channels, self.image_size, self.image_size, device=torch_device), self.image_size, ) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) config = LayoutLMv2Config( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, image_feature_pool_shape=self.image_feature_pool_shape, coordinate_size=self.coordinate_size, shape_size=self.shape_size, ) # use smaller resnet backbone to make tests faster config.detectron2_config_args["MODEL.RESNETS.DEPTH"] = 18 config.detectron2_config_args["MODEL.RESNETS.RES2_OUT_CHANNELS"] = 64 config.detectron2_config_args["MODEL.RESNETS.NUM_GROUPS"] = 1 return config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels def create_and_check_model( self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels ): model = LayoutLMv2Model(config=config) model.to(torch_device) model.eval() result = model(input_ids, bbox=bbox, image=image, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, bbox=bbox, image=image, token_type_ids=token_type_ids) result = model(input_ids, bbox=bbox, image=image) # LayoutLMv2 has a different expected sequence length, namely also visual tokens are added expected_seq_len = self.seq_length + self.image_feature_pool_shape[0] * self.image_feature_pool_shape[1] self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_sequence_classification( self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels ): config.num_labels = self.num_labels model = LayoutLMv2ForSequenceClassification(config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, image=image, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification( self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels ): config.num_labels = self.num_labels model = LayoutLMv2ForTokenClassification(config=config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, image=image, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_question_answering( self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels ): model = LayoutLMv2ForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, image=image, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "bbox": bbox, "image": image, "token_type_ids": token_type_ids, "attention_mask": input_mask, } return config, inputs_dict @require_torch @require_detectron2 class LayoutLMv2ModelTest(ModelTesterMixin, unittest.TestCase): test_pruning = False test_torchscript = True test_mismatched_shapes = False all_model_classes = ( ( LayoutLMv2Model, LayoutLMv2ForSequenceClassification, LayoutLMv2ForTokenClassification, LayoutLMv2ForQuestionAnswering, ) if is_torch_available() else () ) def setUp(self): self.model_tester = LayoutLMv2ModelTester(self) self.config_tester = ConfigTester(self, config_class=LayoutLMv2Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @require_torch_multi_gpu @unittest.skip( reason=( "LayoutLMV2 and its dependency `detectron2` have some layers using `add_module` which doesn't work well" " with `nn.DataParallel`" ) ) def test_multi_gpu_data_parallel_forward(self): pass def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_save_load_fast_init_from_base(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() base_class = MODEL_MAPPING[config.__class__] if isinstance(base_class, tuple): base_class = base_class[0] for model_class in self.all_model_classes: if model_class == base_class: continue # make a copy of model class to not break future tests # from https://stackoverflow.com/questions/9541025/how-to-copy-a-python-class class CopyClass(model_class): pass model_class_copy = CopyClass # make sure that all keys are expected for test model_class_copy._keys_to_ignore_on_load_missing = [] # make init deterministic, but make sure that # non-initialized weights throw errors nevertheless model_class_copy._init_weights = self._mock_init_weights model = base_class(config) state_dict = model.state_dict() # this will often delete a single weight of a multi-weight module # to test an edge case random_key_to_del = random.choice(list(state_dict.keys())) del state_dict[random_key_to_del] # check that certain keys didn't get saved with the model with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin")) model_fast_init = model_class_copy.from_pretrained(tmpdirname) model_slow_init = model_class_copy.from_pretrained(tmpdirname, _fast_init=False) for key in model_fast_init.state_dict().keys(): if key == "layoutlmv2.visual_segment_embedding": # we skip the visual segment embedding as it has a custom initialization scheme continue max_diff = (model_slow_init.state_dict()[key] - model_fast_init.state_dict()[key]).sum().item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True # LayoutLMv2 has a different expected sequence length expected_seq_len = ( self.model_tester.seq_length + self.model_tester.image_feature_pool_shape[0] * self.model_tester.image_feature_pool_shape[1] ) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, expected_seq_len, expected_seq_len], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, expected_seq_len, expected_seq_len], ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) # LayoutLMv2 has a different expected sequence length expected_seq_len = ( self.model_tester.seq_length + self.model_tester.image_feature_pool_shape[0] * self.model_tester.image_feature_pool_shape[1] ) self.assertListEqual( list(hidden_states[0].shape[-2:]), [expected_seq_len, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) @slow def test_model_from_pretrained(self): for model_name in LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = LayoutLMv2Model.from_pretrained(model_name) self.assertIsNotNone(model) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if "backbone" in name or "visual_segment_embedding" in name: continue if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def prepare_layoutlmv2_batch_inputs(): # Here we prepare a batch of 2 sequences to test a LayoutLMv2 forward pass on: # fmt: off input_ids = torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]) # noqa: E231 bbox = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]) # noqa: E231 image = ImageList(torch.randn((2,3,224,224)), image_sizes=[(224,224), (224,224)]) # noqa: E231 attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]) # noqa: E231 token_type_ids = torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]) # noqa: E231 # fmt: on return input_ids, bbox, image, attention_mask, token_type_ids @require_torch @require_detectron2 class LayoutLMv2ModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head(self): model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased").to(torch_device) ( input_ids, bbox, image, attention_mask, token_type_ids, ) = prepare_layoutlmv2_batch_inputs() # forward pass outputs = model( input_ids=input_ids.to(torch_device), bbox=bbox.to(torch_device), image=image.to(torch_device), attention_mask=attention_mask.to(torch_device), token_type_ids=token_type_ids.to(torch_device), ) # verify the sequence output expected_shape = torch.Size( ( 2, input_ids.shape[1] + model.config.image_feature_pool_shape[0] * model.config.image_feature_pool_shape[1], model.config.hidden_size, ) ) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[-0.1087, 0.0727, -0.3075], [0.0799, -0.0427, -0.0751], [-0.0367, 0.0480, -0.1358]], device=torch_device ) self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-3)) # verify the pooled output expected_shape = torch.Size((2, model.config.hidden_size)) self.assertEqual(outputs.pooler_output.shape, expected_shape)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/mobilevit/modeling_mobilevit.py

# coding=utf-8 # Copyright 2022 Apple Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Original license: https://github.com/apple/ml-cvnets/blob/main/LICENSE """ PyTorch MobileViT model.""" import math from typing import Dict, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention, SemanticSegmenterOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_mobilevit import MobileViTConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "MobileViTConfig" _FEAT_EXTRACTOR_FOR_DOC = "MobileViTFeatureExtractor" # Base docstring _CHECKPOINT_FOR_DOC = "apple/mobilevit-small" _EXPECTED_OUTPUT_SHAPE = [1, 640, 8, 8] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "apple/mobilevit-small" _IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "apple/mobilevit-small", "apple/mobilevit-x-small", "apple/mobilevit-xx-small", "apple/deeplabv3-mobilevit-small", "apple/deeplabv3-mobilevit-x-small", "apple/deeplabv3-mobilevit-xx-small", # See all MobileViT models at https://huggingface.co/models?filter=mobilevit ] def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int: """ Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the original TensorFlow repo. It can be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py """ if min_value is None: min_value = divisor new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_value < 0.9 * value: new_value += divisor return int(new_value) class MobileViTConvLayer(nn.Module): def __init__( self, config: MobileViTConfig, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, groups: int = 1, bias: bool = False, dilation: int = 1, use_normalization: bool = True, use_activation: Union[bool, str] = True, ) -> None: super().__init__() padding = int((kernel_size - 1) / 2) * dilation if in_channels % groups != 0: raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.") if out_channels % groups != 0: raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.") self.convolution = nn.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode="zeros", ) if use_normalization: self.normalization = nn.BatchNorm2d( num_features=out_channels, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True, ) else: self.normalization = None if use_activation: if isinstance(use_activation, str): self.activation = ACT2FN[use_activation] elif isinstance(config.hidden_act, str): self.activation = ACT2FN[config.hidden_act] else: self.activation = config.hidden_act else: self.activation = None def forward(self, features: torch.Tensor) -> torch.Tensor: features = self.convolution(features) if self.normalization is not None: features = self.normalization(features) if self.activation is not None: features = self.activation(features) return features class MobileViTInvertedResidual(nn.Module): """ Inverted residual block (MobileNetv2): https://arxiv.org/abs/1801.04381 """ def __init__( self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, dilation: int = 1 ) -> None: super().__init__() expanded_channels = make_divisible(int(round(in_channels * config.expand_ratio)), 8) if stride not in [1, 2]: raise ValueError(f"Invalid stride {stride}.") self.use_residual = (stride == 1) and (in_channels == out_channels) self.expand_1x1 = MobileViTConvLayer( config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1 ) self.conv_3x3 = MobileViTConvLayer( config, in_channels=expanded_channels, out_channels=expanded_channels, kernel_size=3, stride=stride, groups=expanded_channels, dilation=dilation, ) self.reduce_1x1 = MobileViTConvLayer( config, in_channels=expanded_channels, out_channels=out_channels, kernel_size=1, use_activation=False, ) def forward(self, features: torch.Tensor) -> torch.Tensor: residual = features features = self.expand_1x1(features) features = self.conv_3x3(features) features = self.reduce_1x1(features) return residual + features if self.use_residual else features class MobileViTMobileNetLayer(nn.Module): def __init__( self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int = 1, num_stages: int = 1 ) -> None: super().__init__() self.layer = nn.ModuleList() for i in range(num_stages): layer = MobileViTInvertedResidual( config, in_channels=in_channels, out_channels=out_channels, stride=stride if i == 0 else 1, ) self.layer.append(layer) in_channels = out_channels def forward(self, features: torch.Tensor) -> torch.Tensor: for layer_module in self.layer: features = layer_module(features) return features class MobileViTSelfAttention(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int) -> None: super().__init__() if hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size {hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) return context_layer class MobileViTSelfOutput(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int) -> None: super().__init__() self.dense = nn.Linear(hidden_size, hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class MobileViTAttention(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int) -> None: super().__init__() self.attention = MobileViTSelfAttention(config, hidden_size) self.output = MobileViTSelfOutput(config, hidden_size) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: self_outputs = self.attention(hidden_states) attention_output = self.output(self_outputs) return attention_output class MobileViTIntermediate(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None: super().__init__() self.dense = nn.Linear(hidden_size, intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class MobileViTOutput(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None: super().__init__() self.dense = nn.Linear(intermediate_size, hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class MobileViTTransformerLayer(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None: super().__init__() self.attention = MobileViTAttention(config, hidden_size) self.intermediate = MobileViTIntermediate(config, hidden_size, intermediate_size) self.output = MobileViTOutput(config, hidden_size, intermediate_size) self.layernorm_before = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: attention_output = self.attention(self.layernorm_before(hidden_states)) hidden_states = attention_output + hidden_states layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) layer_output = self.output(layer_output, hidden_states) return layer_output class MobileViTTransformer(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int, num_stages: int) -> None: super().__init__() self.layer = nn.ModuleList() for _ in range(num_stages): transformer_layer = MobileViTTransformerLayer( config, hidden_size=hidden_size, intermediate_size=int(hidden_size * config.mlp_ratio), ) self.layer.append(transformer_layer) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: for layer_module in self.layer: hidden_states = layer_module(hidden_states) return hidden_states class MobileViTLayer(nn.Module): """ MobileViT block: https://arxiv.org/abs/2110.02178 """ def __init__( self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, hidden_size: int, num_stages: int, dilation: int = 1, ) -> None: super().__init__() self.patch_width = config.patch_size self.patch_height = config.patch_size if stride == 2: self.downsampling_layer = MobileViTInvertedResidual( config, in_channels=in_channels, out_channels=out_channels, stride=stride if dilation == 1 else 1, dilation=dilation // 2 if dilation > 1 else 1, ) in_channels = out_channels else: self.downsampling_layer = None self.conv_kxk = MobileViTConvLayer( config, in_channels=in_channels, out_channels=in_channels, kernel_size=config.conv_kernel_size, ) self.conv_1x1 = MobileViTConvLayer( config, in_channels=in_channels, out_channels=hidden_size, kernel_size=1, use_normalization=False, use_activation=False, ) self.transformer = MobileViTTransformer( config, hidden_size=hidden_size, num_stages=num_stages, ) self.layernorm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) self.conv_projection = MobileViTConvLayer( config, in_channels=hidden_size, out_channels=in_channels, kernel_size=1 ) self.fusion = MobileViTConvLayer( config, in_channels=2 * in_channels, out_channels=in_channels, kernel_size=config.conv_kernel_size ) def unfolding(self, features: torch.Tensor) -> Tuple[torch.Tensor, Dict]: patch_width, patch_height = self.patch_width, self.patch_height patch_area = int(patch_width * patch_height) batch_size, channels, orig_height, orig_width = features.shape new_height = int(math.ceil(orig_height / patch_height) * patch_height) new_width = int(math.ceil(orig_width / patch_width) * patch_width) interpolate = False if new_width != orig_width or new_height != orig_height: # Note: Padding can be done, but then it needs to be handled in attention function. features = nn.functional.interpolate( features, size=(new_height, new_width), mode="bilinear", align_corners=False ) interpolate = True # number of patches along width and height num_patch_width = new_width // patch_width num_patch_height = new_height // patch_height num_patches = num_patch_height * num_patch_width # convert from shape (batch_size, channels, orig_height, orig_width) # to the shape (batch_size * patch_area, num_patches, channels) patches = features.reshape( batch_size * channels * num_patch_height, patch_height, num_patch_width, patch_width ) patches = patches.transpose(1, 2) patches = patches.reshape(batch_size, channels, num_patches, patch_area) patches = patches.transpose(1, 3) patches = patches.reshape(batch_size * patch_area, num_patches, -1) info_dict = { "orig_size": (orig_height, orig_width), "batch_size": batch_size, "channels": channels, "interpolate": interpolate, "num_patches": num_patches, "num_patches_width": num_patch_width, "num_patches_height": num_patch_height, } return patches, info_dict def folding(self, patches: torch.Tensor, info_dict: Dict) -> torch.Tensor: patch_width, patch_height = self.patch_width, self.patch_height patch_area = int(patch_width * patch_height) batch_size = info_dict["batch_size"] channels = info_dict["channels"] num_patches = info_dict["num_patches"] num_patch_height = info_dict["num_patches_height"] num_patch_width = info_dict["num_patches_width"] # convert from shape (batch_size * patch_area, num_patches, channels) # back to shape (batch_size, channels, orig_height, orig_width) features = patches.contiguous().view(batch_size, patch_area, num_patches, -1) features = features.transpose(1, 3) features = features.reshape( batch_size * channels * num_patch_height, num_patch_width, patch_height, patch_width ) features = features.transpose(1, 2) features = features.reshape( batch_size, channels, num_patch_height * patch_height, num_patch_width * patch_width ) if info_dict["interpolate"]: features = nn.functional.interpolate( features, size=info_dict["orig_size"], mode="bilinear", align_corners=False ) return features def forward(self, features: torch.Tensor) -> torch.Tensor: # reduce spatial dimensions if needed if self.downsampling_layer: features = self.downsampling_layer(features) residual = features # local representation features = self.conv_kxk(features) features = self.conv_1x1(features) # convert feature map to patches patches, info_dict = self.unfolding(features) # learn global representations patches = self.transformer(patches) patches = self.layernorm(patches) # convert patches back to feature maps features = self.folding(patches, info_dict) features = self.conv_projection(features) features = self.fusion(torch.cat((residual, features), dim=1)) return features class MobileViTEncoder(nn.Module): def __init__(self, config: MobileViTConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList() self.gradient_checkpointing = False # segmentation architectures like DeepLab and PSPNet modify the strides # of the classification backbones dilate_layer_4 = dilate_layer_5 = False if config.output_stride == 8: dilate_layer_4 = True dilate_layer_5 = True elif config.output_stride == 16: dilate_layer_5 = True dilation = 1 layer_1 = MobileViTMobileNetLayer( config, in_channels=config.neck_hidden_sizes[0], out_channels=config.neck_hidden_sizes[1], stride=1, num_stages=1, ) self.layer.append(layer_1) layer_2 = MobileViTMobileNetLayer( config, in_channels=config.neck_hidden_sizes[1], out_channels=config.neck_hidden_sizes[2], stride=2, num_stages=3, ) self.layer.append(layer_2) layer_3 = MobileViTLayer( config, in_channels=config.neck_hidden_sizes[2], out_channels=config.neck_hidden_sizes[3], stride=2, hidden_size=config.hidden_sizes[0], num_stages=2, ) self.layer.append(layer_3) if dilate_layer_4: dilation *= 2 layer_4 = MobileViTLayer( config, in_channels=config.neck_hidden_sizes[3], out_channels=config.neck_hidden_sizes[4], stride=2, hidden_size=config.hidden_sizes[1], num_stages=4, dilation=dilation, ) self.layer.append(layer_4) if dilate_layer_5: dilation *= 2 layer_5 = MobileViTLayer( config, in_channels=config.neck_hidden_sizes[4], out_channels=config.neck_hidden_sizes[5], stride=2, hidden_size=config.hidden_sizes[2], num_stages=3, dilation=dilation, ) self.layer.append(layer_5) def forward( self, hidden_states: torch.Tensor, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutputWithNoAttention]: all_hidden_states = () if output_hidden_states else None for i, layer_module in enumerate(self.layer): if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, ) else: hidden_states = layer_module(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states] if v is not None) return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states) class MobileViTPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = MobileViTConfig base_model_prefix = "mobilevit" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, MobileViTEncoder): module.gradient_checkpointing = value MOBILEVIT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`MobileViTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ MOBILEVIT_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`MobileViTFeatureExtractor`]. See [`MobileViTFeatureExtractor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare MobileViT model outputting raw hidden-states without any specific head on top.", MOBILEVIT_START_DOCSTRING, ) class MobileViTModel(MobileViTPreTrainedModel): def __init__(self, config: MobileViTConfig, expand_output: bool = True): super().__init__(config) self.config = config self.expand_output = expand_output self.conv_stem = MobileViTConvLayer( config, in_channels=config.num_channels, out_channels=config.neck_hidden_sizes[0], kernel_size=3, stride=2, ) self.encoder = MobileViTEncoder(config) if self.expand_output: self.conv_1x1_exp = MobileViTConvLayer( config, in_channels=config.neck_hidden_sizes[5], out_channels=config.neck_hidden_sizes[6], kernel_size=1, ) # Initialize weights and apply final processing self.post_init() def _prune_heads(self, heads_to_prune): """Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer_index, heads in heads_to_prune.items(): mobilevit_layer = self.encoder.layer[layer_index] if isinstance(mobilevit_layer, MobileViTLayer): for transformer_layer in mobilevit_layer.transformer.layer: transformer_layer.attention.prune_heads(heads) @add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndNoAttention, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]: output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") embedding_output = self.conv_stem(pixel_values) encoder_outputs = self.encoder( embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if self.expand_output: last_hidden_state = self.conv_1x1_exp(encoder_outputs[0]) # global average pooling: (batch_size, channels, height, width) -> (batch_size, channels) pooled_output = torch.mean(last_hidden_state, dim=[-2, -1], keepdim=False) else: last_hidden_state = encoder_outputs[0] pooled_output = None if not return_dict: output = (last_hidden_state, pooled_output) if pooled_output is not None else (last_hidden_state,) return output + encoder_outputs[1:] return BaseModelOutputWithPoolingAndNoAttention( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, ) @add_start_docstrings( """ MobileViT model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. """, MOBILEVIT_START_DOCSTRING, ) class MobileViTForImageClassification(MobileViTPreTrainedModel): def __init__(self, config: MobileViTConfig) -> None: super().__init__(config) self.num_labels = config.num_labels self.mobilevit = MobileViTModel(config) # Classifier head self.dropout = nn.Dropout(config.classifier_dropout_prob, inplace=True) self.classifier = ( nn.Linear(config.neck_hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity() ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=ImageClassifierOutputWithNoAttention, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, labels: Optional[torch.Tensor] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, ImageClassifierOutputWithNoAttention]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilevit(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict) pooled_output = outputs.pooler_output if return_dict else outputs[1] logits = self.classifier(self.dropout(pooled_output)) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutputWithNoAttention( loss=loss, logits=logits, hidden_states=outputs.hidden_states, ) class MobileViTASPPPooling(nn.Module): def __init__(self, config: MobileViTConfig, in_channels: int, out_channels: int) -> None: super().__init__() self.global_pool = nn.AdaptiveAvgPool2d(output_size=1) self.conv_1x1 = MobileViTConvLayer( config, in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, use_normalization=True, use_activation="relu", ) def forward(self, features: torch.Tensor) -> torch.Tensor: spatial_size = features.shape[-2:] features = self.global_pool(features) features = self.conv_1x1(features) features = nn.functional.interpolate(features, size=spatial_size, mode="bilinear", align_corners=False) return features class MobileViTASPP(nn.Module): """ ASPP module defined in DeepLab papers: https://arxiv.org/abs/1606.00915, https://arxiv.org/abs/1706.05587 """ def __init__(self, config: MobileViTConfig) -> None: super().__init__() in_channels = config.neck_hidden_sizes[-2] out_channels = config.aspp_out_channels if len(config.atrous_rates) != 3: raise ValueError("Expected 3 values for atrous_rates") self.convs = nn.ModuleList() in_projection = MobileViTConvLayer( config, in_channels=in_channels, out_channels=out_channels, kernel_size=1, use_activation="relu", ) self.convs.append(in_projection) self.convs.extend( [ MobileViTConvLayer( config, in_channels=in_channels, out_channels=out_channels, kernel_size=3, dilation=rate, use_activation="relu", ) for rate in config.atrous_rates ] ) pool_layer = MobileViTASPPPooling(config, in_channels, out_channels) self.convs.append(pool_layer) self.project = MobileViTConvLayer( config, in_channels=5 * out_channels, out_channels=out_channels, kernel_size=1, use_activation="relu" ) self.dropout = nn.Dropout(p=config.aspp_dropout_prob) def forward(self, features: torch.Tensor) -> torch.Tensor: pyramid = [] for conv in self.convs: pyramid.append(conv(features)) pyramid = torch.cat(pyramid, dim=1) pooled_features = self.project(pyramid) pooled_features = self.dropout(pooled_features) return pooled_features class MobileViTDeepLabV3(nn.Module): """ DeepLabv3 architecture: https://arxiv.org/abs/1706.05587 """ def __init__(self, config: MobileViTConfig) -> None: super().__init__() self.aspp = MobileViTASPP(config) self.dropout = nn.Dropout2d(config.classifier_dropout_prob) self.classifier = MobileViTConvLayer( config, in_channels=config.aspp_out_channels, out_channels=config.num_labels, kernel_size=1, use_normalization=False, use_activation=False, bias=True, ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: features = self.aspp(hidden_states[-1]) features = self.dropout(features) features = self.classifier(features) return features @add_start_docstrings( """ MobileViT model with a semantic segmentation head on top, e.g. for Pascal VOC. """, MOBILEVIT_START_DOCSTRING, ) class MobileViTForSemanticSegmentation(MobileViTPreTrainedModel): def __init__(self, config: MobileViTConfig) -> None: super().__init__(config) self.num_labels = config.num_labels self.mobilevit = MobileViTModel(config, expand_output=False) self.segmentation_head = MobileViTDeepLabV3(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, SemanticSegmenterOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import MobileViTFeatureExtractor, MobileViTForSemanticSegmentation >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = MobileViTFeatureExtractor.from_pretrained("apple/deeplabv3-mobilevit-small") >>> model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small") >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> # logits are of shape (batch_size, num_labels, height, width) >>> logits = outputs.logits ```""" output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilevit( pixel_values, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1] logits = self.segmentation_head(encoder_hidden_states) loss = None if labels is not None: if self.config.num_labels == 1: raise ValueError("The number of labels should be greater than one") else: # upsample logits to the images' original size upsampled_logits = nn.functional.interpolate( logits, size=labels.shape[-2:], mode="bilinear", align_corners=False ) loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index) loss = loss_fct(upsampled_logits, labels) if not return_dict: if output_hidden_states: output = (logits,) + outputs[1:] else: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SemanticSegmenterOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=None, )

# coding=utf-8 # Copyright 2022 Apple Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Original license: https://github.com/apple/ml-cvnets/blob/main/LICENSE """ PyTorch MobileViT model.""" import math from typing import Dict, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention, SemanticSegmenterOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_mobilevit import MobileViTConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "MobileViTConfig" _FEAT_EXTRACTOR_FOR_DOC = "MobileViTFeatureExtractor" # Base docstring _CHECKPOINT_FOR_DOC = "apple/mobilevit-small" _EXPECTED_OUTPUT_SHAPE = [1, 640, 8, 8] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "apple/mobilevit-small" _IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "apple/mobilevit-small", "apple/mobilevit-x-small", "apple/mobilevit-xx-small", "apple/deeplabv3-mobilevit-small", "apple/deeplabv3-mobilevit-x-small", "apple/deeplabv3-mobilevit-xx-small", # See all MobileViT models at https://huggingface.co/models?filter=mobilevit ] def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int: """ Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the original TensorFlow repo. It can be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py """ if min_value is None: min_value = divisor new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_value < 0.9 * value: new_value += divisor return int(new_value) class MobileViTConvLayer(nn.Module): def __init__( self, config: MobileViTConfig, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, groups: int = 1, bias: bool = False, dilation: int = 1, use_normalization: bool = True, use_activation: Union[bool, str] = True, ) -> None: super().__init__() padding = int((kernel_size - 1) / 2) * dilation if in_channels % groups != 0: raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.") if out_channels % groups != 0: raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.") self.convolution = nn.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode="zeros", ) if use_normalization: self.normalization = nn.BatchNorm2d( num_features=out_channels, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True, ) else: self.normalization = None if use_activation: if isinstance(use_activation, str): self.activation = ACT2FN[use_activation] elif isinstance(config.hidden_act, str): self.activation = ACT2FN[config.hidden_act] else: self.activation = config.hidden_act else: self.activation = None def forward(self, features: torch.Tensor) -> torch.Tensor: features = self.convolution(features) if self.normalization is not None: features = self.normalization(features) if self.activation is not None: features = self.activation(features) return features class MobileViTInvertedResidual(nn.Module): """ Inverted residual block (MobileNetv2): https://arxiv.org/abs/1801.04381 """ def __init__( self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, dilation: int = 1 ) -> None: super().__init__() expanded_channels = make_divisible(int(round(in_channels * config.expand_ratio)), 8) if stride not in [1, 2]: raise ValueError(f"Invalid stride {stride}.") self.use_residual = (stride == 1) and (in_channels == out_channels) self.expand_1x1 = MobileViTConvLayer( config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1 ) self.conv_3x3 = MobileViTConvLayer( config, in_channels=expanded_channels, out_channels=expanded_channels, kernel_size=3, stride=stride, groups=expanded_channels, dilation=dilation, ) self.reduce_1x1 = MobileViTConvLayer( config, in_channels=expanded_channels, out_channels=out_channels, kernel_size=1, use_activation=False, ) def forward(self, features: torch.Tensor) -> torch.Tensor: residual = features features = self.expand_1x1(features) features = self.conv_3x3(features) features = self.reduce_1x1(features) return residual + features if self.use_residual else features class MobileViTMobileNetLayer(nn.Module): def __init__( self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int = 1, num_stages: int = 1 ) -> None: super().__init__() self.layer = nn.ModuleList() for i in range(num_stages): layer = MobileViTInvertedResidual( config, in_channels=in_channels, out_channels=out_channels, stride=stride if i == 0 else 1, ) self.layer.append(layer) in_channels = out_channels def forward(self, features: torch.Tensor) -> torch.Tensor: for layer_module in self.layer: features = layer_module(features) return features class MobileViTSelfAttention(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int) -> None: super().__init__() if hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size {hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) return context_layer class MobileViTSelfOutput(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int) -> None: super().__init__() self.dense = nn.Linear(hidden_size, hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class MobileViTAttention(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int) -> None: super().__init__() self.attention = MobileViTSelfAttention(config, hidden_size) self.output = MobileViTSelfOutput(config, hidden_size) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: self_outputs = self.attention(hidden_states) attention_output = self.output(self_outputs) return attention_output class MobileViTIntermediate(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None: super().__init__() self.dense = nn.Linear(hidden_size, intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class MobileViTOutput(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None: super().__init__() self.dense = nn.Linear(intermediate_size, hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class MobileViTTransformerLayer(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None: super().__init__() self.attention = MobileViTAttention(config, hidden_size) self.intermediate = MobileViTIntermediate(config, hidden_size, intermediate_size) self.output = MobileViTOutput(config, hidden_size, intermediate_size) self.layernorm_before = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: attention_output = self.attention(self.layernorm_before(hidden_states)) hidden_states = attention_output + hidden_states layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) layer_output = self.output(layer_output, hidden_states) return layer_output class MobileViTTransformer(nn.Module): def __init__(self, config: MobileViTConfig, hidden_size: int, num_stages: int) -> None: super().__init__() self.layer = nn.ModuleList() for _ in range(num_stages): transformer_layer = MobileViTTransformerLayer( config, hidden_size=hidden_size, intermediate_size=int(hidden_size * config.mlp_ratio), ) self.layer.append(transformer_layer) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: for layer_module in self.layer: hidden_states = layer_module(hidden_states) return hidden_states class MobileViTLayer(nn.Module): """ MobileViT block: https://arxiv.org/abs/2110.02178 """ def __init__( self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, hidden_size: int, num_stages: int, dilation: int = 1, ) -> None: super().__init__() self.patch_width = config.patch_size self.patch_height = config.patch_size if stride == 2: self.downsampling_layer = MobileViTInvertedResidual( config, in_channels=in_channels, out_channels=out_channels, stride=stride if dilation == 1 else 1, dilation=dilation // 2 if dilation > 1 else 1, ) in_channels = out_channels else: self.downsampling_layer = None self.conv_kxk = MobileViTConvLayer( config, in_channels=in_channels, out_channels=in_channels, kernel_size=config.conv_kernel_size, ) self.conv_1x1 = MobileViTConvLayer( config, in_channels=in_channels, out_channels=hidden_size, kernel_size=1, use_normalization=False, use_activation=False, ) self.transformer = MobileViTTransformer( config, hidden_size=hidden_size, num_stages=num_stages, ) self.layernorm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) self.conv_projection = MobileViTConvLayer( config, in_channels=hidden_size, out_channels=in_channels, kernel_size=1 ) self.fusion = MobileViTConvLayer( config, in_channels=2 * in_channels, out_channels=in_channels, kernel_size=config.conv_kernel_size ) def unfolding(self, features: torch.Tensor) -> Tuple[torch.Tensor, Dict]: patch_width, patch_height = self.patch_width, self.patch_height patch_area = int(patch_width * patch_height) batch_size, channels, orig_height, orig_width = features.shape new_height = int(math.ceil(orig_height / patch_height) * patch_height) new_width = int(math.ceil(orig_width / patch_width) * patch_width) interpolate = False if new_width != orig_width or new_height != orig_height: # Note: Padding can be done, but then it needs to be handled in attention function. features = nn.functional.interpolate( features, size=(new_height, new_width), mode="bilinear", align_corners=False ) interpolate = True # number of patches along width and height num_patch_width = new_width // patch_width num_patch_height = new_height // patch_height num_patches = num_patch_height * num_patch_width # convert from shape (batch_size, channels, orig_height, orig_width) # to the shape (batch_size * patch_area, num_patches, channels) patches = features.reshape( batch_size * channels * num_patch_height, patch_height, num_patch_width, patch_width ) patches = patches.transpose(1, 2) patches = patches.reshape(batch_size, channels, num_patches, patch_area) patches = patches.transpose(1, 3) patches = patches.reshape(batch_size * patch_area, num_patches, -1) info_dict = { "orig_size": (orig_height, orig_width), "batch_size": batch_size, "channels": channels, "interpolate": interpolate, "num_patches": num_patches, "num_patches_width": num_patch_width, "num_patches_height": num_patch_height, } return patches, info_dict def folding(self, patches: torch.Tensor, info_dict: Dict) -> torch.Tensor: patch_width, patch_height = self.patch_width, self.patch_height patch_area = int(patch_width * patch_height) batch_size = info_dict["batch_size"] channels = info_dict["channels"] num_patches = info_dict["num_patches"] num_patch_height = info_dict["num_patches_height"] num_patch_width = info_dict["num_patches_width"] # convert from shape (batch_size * patch_area, num_patches, channels) # back to shape (batch_size, channels, orig_height, orig_width) features = patches.contiguous().view(batch_size, patch_area, num_patches, -1) features = features.transpose(1, 3) features = features.reshape( batch_size * channels * num_patch_height, num_patch_width, patch_height, patch_width ) features = features.transpose(1, 2) features = features.reshape( batch_size, channels, num_patch_height * patch_height, num_patch_width * patch_width ) if info_dict["interpolate"]: features = nn.functional.interpolate( features, size=info_dict["orig_size"], mode="bilinear", align_corners=False ) return features def forward(self, features: torch.Tensor) -> torch.Tensor: # reduce spatial dimensions if needed if self.downsampling_layer: features = self.downsampling_layer(features) residual = features # local representation features = self.conv_kxk(features) features = self.conv_1x1(features) # convert feature map to patches patches, info_dict = self.unfolding(features) # learn global representations patches = self.transformer(patches) patches = self.layernorm(patches) # convert patches back to feature maps features = self.folding(patches, info_dict) features = self.conv_projection(features) features = self.fusion(torch.cat((residual, features), dim=1)) return features class MobileViTEncoder(nn.Module): def __init__(self, config: MobileViTConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList() self.gradient_checkpointing = False # segmentation architectures like DeepLab and PSPNet modify the strides # of the classification backbones dilate_layer_4 = dilate_layer_5 = False if config.output_stride == 8: dilate_layer_4 = True dilate_layer_5 = True elif config.output_stride == 16: dilate_layer_5 = True dilation = 1 layer_1 = MobileViTMobileNetLayer( config, in_channels=config.neck_hidden_sizes[0], out_channels=config.neck_hidden_sizes[1], stride=1, num_stages=1, ) self.layer.append(layer_1) layer_2 = MobileViTMobileNetLayer( config, in_channels=config.neck_hidden_sizes[1], out_channels=config.neck_hidden_sizes[2], stride=2, num_stages=3, ) self.layer.append(layer_2) layer_3 = MobileViTLayer( config, in_channels=config.neck_hidden_sizes[2], out_channels=config.neck_hidden_sizes[3], stride=2, hidden_size=config.hidden_sizes[0], num_stages=2, ) self.layer.append(layer_3) if dilate_layer_4: dilation *= 2 layer_4 = MobileViTLayer( config, in_channels=config.neck_hidden_sizes[3], out_channels=config.neck_hidden_sizes[4], stride=2, hidden_size=config.hidden_sizes[1], num_stages=4, dilation=dilation, ) self.layer.append(layer_4) if dilate_layer_5: dilation *= 2 layer_5 = MobileViTLayer( config, in_channels=config.neck_hidden_sizes[4], out_channels=config.neck_hidden_sizes[5], stride=2, hidden_size=config.hidden_sizes[2], num_stages=3, dilation=dilation, ) self.layer.append(layer_5) def forward( self, hidden_states: torch.Tensor, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutputWithNoAttention]: all_hidden_states = () if output_hidden_states else None for i, layer_module in enumerate(self.layer): if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, ) else: hidden_states = layer_module(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states] if v is not None) return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states) class MobileViTPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = MobileViTConfig base_model_prefix = "mobilevit" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, MobileViTEncoder): module.gradient_checkpointing = value MOBILEVIT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`MobileViTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ MOBILEVIT_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`MobileViTFeatureExtractor`]. See [`MobileViTFeatureExtractor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare MobileViT model outputting raw hidden-states without any specific head on top.", MOBILEVIT_START_DOCSTRING, ) class MobileViTModel(MobileViTPreTrainedModel): def __init__(self, config: MobileViTConfig, expand_output: bool = True): super().__init__(config) self.config = config self.expand_output = expand_output self.conv_stem = MobileViTConvLayer( config, in_channels=config.num_channels, out_channels=config.neck_hidden_sizes[0], kernel_size=3, stride=2, ) self.encoder = MobileViTEncoder(config) if self.expand_output: self.conv_1x1_exp = MobileViTConvLayer( config, in_channels=config.neck_hidden_sizes[5], out_channels=config.neck_hidden_sizes[6], kernel_size=1, ) # Initialize weights and apply final processing self.post_init() def _prune_heads(self, heads_to_prune): """Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer_index, heads in heads_to_prune.items(): mobilevit_layer = self.encoder.layer[layer_index] if isinstance(mobilevit_layer, MobileViTLayer): for transformer_layer in mobilevit_layer.transformer.layer: transformer_layer.attention.prune_heads(heads) @add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndNoAttention, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]: output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") embedding_output = self.conv_stem(pixel_values) encoder_outputs = self.encoder( embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if self.expand_output: last_hidden_state = self.conv_1x1_exp(encoder_outputs[0]) # global average pooling: (batch_size, channels, height, width) -> (batch_size, channels) pooled_output = torch.mean(last_hidden_state, dim=[-2, -1], keepdim=False) else: last_hidden_state = encoder_outputs[0] pooled_output = None if not return_dict: output = (last_hidden_state, pooled_output) if pooled_output is not None else (last_hidden_state,) return output + encoder_outputs[1:] return BaseModelOutputWithPoolingAndNoAttention( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, ) @add_start_docstrings( """ MobileViT model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. """, MOBILEVIT_START_DOCSTRING, ) class MobileViTForImageClassification(MobileViTPreTrainedModel): def __init__(self, config: MobileViTConfig) -> None: super().__init__(config) self.num_labels = config.num_labels self.mobilevit = MobileViTModel(config) # Classifier head self.dropout = nn.Dropout(config.classifier_dropout_prob, inplace=True) self.classifier = ( nn.Linear(config.neck_hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity() ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=ImageClassifierOutputWithNoAttention, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, labels: Optional[torch.Tensor] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, ImageClassifierOutputWithNoAttention]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilevit(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict) pooled_output = outputs.pooler_output if return_dict else outputs[1] logits = self.classifier(self.dropout(pooled_output)) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutputWithNoAttention( loss=loss, logits=logits, hidden_states=outputs.hidden_states, ) class MobileViTASPPPooling(nn.Module): def __init__(self, config: MobileViTConfig, in_channels: int, out_channels: int) -> None: super().__init__() self.global_pool = nn.AdaptiveAvgPool2d(output_size=1) self.conv_1x1 = MobileViTConvLayer( config, in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, use_normalization=True, use_activation="relu", ) def forward(self, features: torch.Tensor) -> torch.Tensor: spatial_size = features.shape[-2:] features = self.global_pool(features) features = self.conv_1x1(features) features = nn.functional.interpolate(features, size=spatial_size, mode="bilinear", align_corners=False) return features class MobileViTASPP(nn.Module): """ ASPP module defined in DeepLab papers: https://arxiv.org/abs/1606.00915, https://arxiv.org/abs/1706.05587 """ def __init__(self, config: MobileViTConfig) -> None: super().__init__() in_channels = config.neck_hidden_sizes[-2] out_channels = config.aspp_out_channels if len(config.atrous_rates) != 3: raise ValueError("Expected 3 values for atrous_rates") self.convs = nn.ModuleList() in_projection = MobileViTConvLayer( config, in_channels=in_channels, out_channels=out_channels, kernel_size=1, use_activation="relu", ) self.convs.append(in_projection) self.convs.extend( [ MobileViTConvLayer( config, in_channels=in_channels, out_channels=out_channels, kernel_size=3, dilation=rate, use_activation="relu", ) for rate in config.atrous_rates ] ) pool_layer = MobileViTASPPPooling(config, in_channels, out_channels) self.convs.append(pool_layer) self.project = MobileViTConvLayer( config, in_channels=5 * out_channels, out_channels=out_channels, kernel_size=1, use_activation="relu" ) self.dropout = nn.Dropout(p=config.aspp_dropout_prob) def forward(self, features: torch.Tensor) -> torch.Tensor: pyramid = [] for conv in self.convs: pyramid.append(conv(features)) pyramid = torch.cat(pyramid, dim=1) pooled_features = self.project(pyramid) pooled_features = self.dropout(pooled_features) return pooled_features class MobileViTDeepLabV3(nn.Module): """ DeepLabv3 architecture: https://arxiv.org/abs/1706.05587 """ def __init__(self, config: MobileViTConfig) -> None: super().__init__() self.aspp = MobileViTASPP(config) self.dropout = nn.Dropout2d(config.classifier_dropout_prob) self.classifier = MobileViTConvLayer( config, in_channels=config.aspp_out_channels, out_channels=config.num_labels, kernel_size=1, use_normalization=False, use_activation=False, bias=True, ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: features = self.aspp(hidden_states[-1]) features = self.dropout(features) features = self.classifier(features) return features @add_start_docstrings( """ MobileViT model with a semantic segmentation head on top, e.g. for Pascal VOC. """, MOBILEVIT_START_DOCSTRING, ) class MobileViTForSemanticSegmentation(MobileViTPreTrainedModel): def __init__(self, config: MobileViTConfig) -> None: super().__init__(config) self.num_labels = config.num_labels self.mobilevit = MobileViTModel(config, expand_output=False) self.segmentation_head = MobileViTDeepLabV3(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, SemanticSegmenterOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import MobileViTFeatureExtractor, MobileViTForSemanticSegmentation >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = MobileViTFeatureExtractor.from_pretrained("apple/deeplabv3-mobilevit-small") >>> model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small") >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> # logits are of shape (batch_size, num_labels, height, width) >>> logits = outputs.logits ```""" output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilevit( pixel_values, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1] logits = self.segmentation_head(encoder_hidden_states) loss = None if labels is not None: if self.config.num_labels == 1: raise ValueError("The number of labels should be greater than one") else: # upsample logits to the images' original size upsampled_logits = nn.functional.interpolate( logits, size=labels.shape[-2:], mode="bilinear", align_corners=False ) loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index) loss = loss_fct(upsampled_logits, labels) if not return_dict: if output_hidden_states: output = (logits,) + outputs[1:] else: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SemanticSegmenterOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=None, )

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/xlm_roberta_xl/convert_xlm_roberta_xl_original_pytorch_checkpoint_to_pytorch.py

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert RoBERTa checkpoint.""" import argparse import pathlib import fairseq import torch from fairseq.models.roberta import RobertaModel as FairseqRobertaModel from fairseq.modules import TransformerSentenceEncoderLayer from packaging import version from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification from transformers.models.bert.modeling_bert import ( BertIntermediate, BertLayer, BertOutput, BertSelfAttention, BertSelfOutput, ) from transformers.models.roberta.modeling_roberta import RobertaAttention from transformers.utils import logging if version.parse(fairseq.__version__) < version.parse("1.0.0a"): raise Exception("requires fairseq >= 1.0.0a") logging.set_verbosity_info() logger = logging.get_logger(__name__) SAMPLE_TEXT = "Hello world! cécé herlolip" def convert_xlm_roberta_xl_checkpoint_to_pytorch( roberta_checkpoint_path: str, pytorch_dump_folder_path: str, classification_head: bool ): """ Copy/paste/tweak roberta's weights to our BERT structure. """ roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path) roberta.eval() # disable dropout roberta_sent_encoder = roberta.model.encoder.sentence_encoder config = XLMRobertaConfig( vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings, hidden_size=roberta.cfg.model.encoder_embed_dim, num_hidden_layers=roberta.cfg.model.encoder_layers, num_attention_heads=roberta.cfg.model.encoder_attention_heads, intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim, max_position_embeddings=514, type_vocab_size=1, layer_norm_eps=1e-5, # PyTorch default used in fairseq ) if classification_head: config.num_labels = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0] print("Our RoBERTa config:", config) model = XLMRobertaXLForSequenceClassification(config) if classification_head else XLMRobertaXLForMaskedLM(config) model.eval() # Now let's copy all the weights. # Embeddings model.roberta.embeddings.word_embeddings.weight = roberta_sent_encoder.embed_tokens.weight model.roberta.embeddings.position_embeddings.weight = roberta_sent_encoder.embed_positions.weight model.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like( model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them. model.roberta.encoder.LayerNorm.weight = roberta_sent_encoder.layer_norm.weight model.roberta.encoder.LayerNorm.bias = roberta_sent_encoder.layer_norm.bias for i in range(config.num_hidden_layers): # Encoder: start of layer layer: BertLayer = model.roberta.encoder.layer[i] roberta_layer: TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i] attention: RobertaAttention = layer.attention attention.self_attn_layer_norm.weight = roberta_layer.self_attn_layer_norm.weight attention.self_attn_layer_norm.bias = roberta_layer.self_attn_layer_norm.bias # self attention self_attn: BertSelfAttention = layer.attention.self assert ( roberta_layer.self_attn.k_proj.weight.data.shape == roberta_layer.self_attn.q_proj.weight.data.shape == roberta_layer.self_attn.v_proj.weight.data.shape == torch.Size((config.hidden_size, config.hidden_size)) ) self_attn.query.weight.data = roberta_layer.self_attn.q_proj.weight self_attn.query.bias.data = roberta_layer.self_attn.q_proj.bias self_attn.key.weight.data = roberta_layer.self_attn.k_proj.weight self_attn.key.bias.data = roberta_layer.self_attn.k_proj.bias self_attn.value.weight.data = roberta_layer.self_attn.v_proj.weight self_attn.value.bias.data = roberta_layer.self_attn.v_proj.bias # self-attention output self_output: BertSelfOutput = layer.attention.output assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape self_output.dense.weight = roberta_layer.self_attn.out_proj.weight self_output.dense.bias = roberta_layer.self_attn.out_proj.bias # this one is final layer norm layer.LayerNorm.weight = roberta_layer.final_layer_norm.weight layer.LayerNorm.bias = roberta_layer.final_layer_norm.bias # intermediate intermediate: BertIntermediate = layer.intermediate assert intermediate.dense.weight.shape == roberta_layer.fc1.weight.shape intermediate.dense.weight = roberta_layer.fc1.weight intermediate.dense.bias = roberta_layer.fc1.bias # output bert_output: BertOutput = layer.output assert bert_output.dense.weight.shape == roberta_layer.fc2.weight.shape bert_output.dense.weight = roberta_layer.fc2.weight bert_output.dense.bias = roberta_layer.fc2.bias # end of layer if classification_head: model.classifier.dense.weight = roberta.model.classification_heads["mnli"].dense.weight model.classifier.dense.bias = roberta.model.classification_heads["mnli"].dense.bias model.classifier.out_proj.weight = roberta.model.classification_heads["mnli"].out_proj.weight model.classifier.out_proj.bias = roberta.model.classification_heads["mnli"].out_proj.bias else: # LM Head model.lm_head.dense.weight = roberta.model.encoder.lm_head.dense.weight model.lm_head.dense.bias = roberta.model.encoder.lm_head.dense.bias model.lm_head.layer_norm.weight = roberta.model.encoder.lm_head.layer_norm.weight model.lm_head.layer_norm.bias = roberta.model.encoder.lm_head.layer_norm.bias model.lm_head.decoder.weight = roberta.model.encoder.lm_head.weight model.lm_head.decoder.bias = roberta.model.encoder.lm_head.bias # Let's check that we get the same results. input_ids: torch.Tensor = roberta.encode(SAMPLE_TEXT).unsqueeze(0) # batch of size 1 our_output = model(input_ids)[0] if classification_head: their_output = roberta.model.classification_heads["mnli"](roberta.extract_features(input_ids)) else: their_output = roberta.model(input_ids)[0] print(our_output.shape, their_output.shape) max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item() print(f"max_absolute_diff = {max_absolute_diff}") # ~ 1e-7 success = torch.allclose(our_output, their_output, atol=1e-3) print("Do both models output the same tensors?", "🔥" if success else "💩") if not success: raise Exception("Something went wRoNg") pathlib.Path(pytorch_dump_folder_path).mkdir(parents=True, exist_ok=True) print(f"Saving model to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump." ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) parser.add_argument( "--classification_head", action="store_true", help="Whether to convert a final classification head." ) args = parser.parse_args() convert_xlm_roberta_xl_checkpoint_to_pytorch( args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head )

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert RoBERTa checkpoint.""" import argparse import pathlib import fairseq import torch from fairseq.models.roberta import RobertaModel as FairseqRobertaModel from fairseq.modules import TransformerSentenceEncoderLayer from packaging import version from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification from transformers.models.bert.modeling_bert import ( BertIntermediate, BertLayer, BertOutput, BertSelfAttention, BertSelfOutput, ) from transformers.models.roberta.modeling_roberta import RobertaAttention from transformers.utils import logging if version.parse(fairseq.__version__) < version.parse("1.0.0a"): raise Exception("requires fairseq >= 1.0.0a") logging.set_verbosity_info() logger = logging.get_logger(__name__) SAMPLE_TEXT = "Hello world! cécé herlolip" def convert_xlm_roberta_xl_checkpoint_to_pytorch( roberta_checkpoint_path: str, pytorch_dump_folder_path: str, classification_head: bool ): """ Copy/paste/tweak roberta's weights to our BERT structure. """ roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path) roberta.eval() # disable dropout roberta_sent_encoder = roberta.model.encoder.sentence_encoder config = XLMRobertaConfig( vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings, hidden_size=roberta.cfg.model.encoder_embed_dim, num_hidden_layers=roberta.cfg.model.encoder_layers, num_attention_heads=roberta.cfg.model.encoder_attention_heads, intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim, max_position_embeddings=514, type_vocab_size=1, layer_norm_eps=1e-5, # PyTorch default used in fairseq ) if classification_head: config.num_labels = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0] print("Our RoBERTa config:", config) model = XLMRobertaXLForSequenceClassification(config) if classification_head else XLMRobertaXLForMaskedLM(config) model.eval() # Now let's copy all the weights. # Embeddings model.roberta.embeddings.word_embeddings.weight = roberta_sent_encoder.embed_tokens.weight model.roberta.embeddings.position_embeddings.weight = roberta_sent_encoder.embed_positions.weight model.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like( model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them. model.roberta.encoder.LayerNorm.weight = roberta_sent_encoder.layer_norm.weight model.roberta.encoder.LayerNorm.bias = roberta_sent_encoder.layer_norm.bias for i in range(config.num_hidden_layers): # Encoder: start of layer layer: BertLayer = model.roberta.encoder.layer[i] roberta_layer: TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i] attention: RobertaAttention = layer.attention attention.self_attn_layer_norm.weight = roberta_layer.self_attn_layer_norm.weight attention.self_attn_layer_norm.bias = roberta_layer.self_attn_layer_norm.bias # self attention self_attn: BertSelfAttention = layer.attention.self assert ( roberta_layer.self_attn.k_proj.weight.data.shape == roberta_layer.self_attn.q_proj.weight.data.shape == roberta_layer.self_attn.v_proj.weight.data.shape == torch.Size((config.hidden_size, config.hidden_size)) ) self_attn.query.weight.data = roberta_layer.self_attn.q_proj.weight self_attn.query.bias.data = roberta_layer.self_attn.q_proj.bias self_attn.key.weight.data = roberta_layer.self_attn.k_proj.weight self_attn.key.bias.data = roberta_layer.self_attn.k_proj.bias self_attn.value.weight.data = roberta_layer.self_attn.v_proj.weight self_attn.value.bias.data = roberta_layer.self_attn.v_proj.bias # self-attention output self_output: BertSelfOutput = layer.attention.output assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape self_output.dense.weight = roberta_layer.self_attn.out_proj.weight self_output.dense.bias = roberta_layer.self_attn.out_proj.bias # this one is final layer norm layer.LayerNorm.weight = roberta_layer.final_layer_norm.weight layer.LayerNorm.bias = roberta_layer.final_layer_norm.bias # intermediate intermediate: BertIntermediate = layer.intermediate assert intermediate.dense.weight.shape == roberta_layer.fc1.weight.shape intermediate.dense.weight = roberta_layer.fc1.weight intermediate.dense.bias = roberta_layer.fc1.bias # output bert_output: BertOutput = layer.output assert bert_output.dense.weight.shape == roberta_layer.fc2.weight.shape bert_output.dense.weight = roberta_layer.fc2.weight bert_output.dense.bias = roberta_layer.fc2.bias # end of layer if classification_head: model.classifier.dense.weight = roberta.model.classification_heads["mnli"].dense.weight model.classifier.dense.bias = roberta.model.classification_heads["mnli"].dense.bias model.classifier.out_proj.weight = roberta.model.classification_heads["mnli"].out_proj.weight model.classifier.out_proj.bias = roberta.model.classification_heads["mnli"].out_proj.bias else: # LM Head model.lm_head.dense.weight = roberta.model.encoder.lm_head.dense.weight model.lm_head.dense.bias = roberta.model.encoder.lm_head.dense.bias model.lm_head.layer_norm.weight = roberta.model.encoder.lm_head.layer_norm.weight model.lm_head.layer_norm.bias = roberta.model.encoder.lm_head.layer_norm.bias model.lm_head.decoder.weight = roberta.model.encoder.lm_head.weight model.lm_head.decoder.bias = roberta.model.encoder.lm_head.bias # Let's check that we get the same results. input_ids: torch.Tensor = roberta.encode(SAMPLE_TEXT).unsqueeze(0) # batch of size 1 our_output = model(input_ids)[0] if classification_head: their_output = roberta.model.classification_heads["mnli"](roberta.extract_features(input_ids)) else: their_output = roberta.model(input_ids)[0] print(our_output.shape, their_output.shape) max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item() print(f"max_absolute_diff = {max_absolute_diff}") # ~ 1e-7 success = torch.allclose(our_output, their_output, atol=1e-3) print("Do both models output the same tensors?", "🔥" if success else "💩") if not success: raise Exception("Something went wRoNg") pathlib.Path(pytorch_dump_folder_path).mkdir(parents=True, exist_ok=True) print(f"Saving model to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump." ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) parser.add_argument( "--classification_head", action="store_true", help="Whether to convert a final classification head." ) args = parser.parse_args() convert_xlm_roberta_xl_checkpoint_to_pytorch( args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head )

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/pytorch/text-generation/run_generation.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/CTRL/Transformer-XL/XLNet) """ import argparse import logging import numpy as np import torch from transformers import ( CTRLLMHeadModel, CTRLTokenizer, GPT2LMHeadModel, GPT2Tokenizer, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, TransfoXLLMHeadModel, TransfoXLTokenizer, XLMTokenizer, XLMWithLMHeadModel, XLNetLMHeadModel, XLNetTokenizer, ) logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger = logging.getLogger(__name__) MAX_LENGTH = int(10000) # Hardcoded max length to avoid infinite loop MODEL_CLASSES = { "gpt2": (GPT2LMHeadModel, GPT2Tokenizer), "ctrl": (CTRLLMHeadModel, CTRLTokenizer), "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), "xlnet": (XLNetLMHeadModel, XLNetTokenizer), "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer), "xlm": (XLMWithLMHeadModel, XLMTokenizer), } # Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia # in https://github.com/rusiaaman/XLNet-gen#methodology # and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e PREFIX = """In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision and denounces one of the men as a horse thief. Although his father initially slaps him for making such an accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing. <eod> </s> <eos>""" def set_seed(args): np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) # # Functions to prepare models' input # def prepare_ctrl_input(args, _, tokenizer, prompt_text): if args.temperature > 0.7: logger.info("CTRL typically works better with lower temperatures (and lower top_k).") encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False) if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()): logger.info("WARNING! You are not starting your generation from a control code so you won't get good results") return prompt_text def prepare_xlm_input(args, model, tokenizer, prompt_text): # kwargs = {"language": None, "mask_token_id": None} # Set the language use_lang_emb = hasattr(model.config, "use_lang_emb") and model.config.use_lang_emb if hasattr(model.config, "lang2id") and use_lang_emb: available_languages = model.config.lang2id.keys() if args.xlm_language in available_languages: language = args.xlm_language else: language = None while language not in available_languages: language = input("Using XLM. Select language in " + str(list(available_languages)) + " >>> ") model.config.lang_id = model.config.lang2id[language] # kwargs["language"] = tokenizer.lang2id[language] # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers # XLM masked-language modeling (MLM) models need masked token # is_xlm_mlm = "mlm" in args.model_name_or_path # if is_xlm_mlm: # kwargs["mask_token_id"] = tokenizer.mask_token_id return prompt_text def prepare_xlnet_input(args, _, tokenizer, prompt_text): prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX prompt_text = prefix + prompt_text return prompt_text def prepare_transfoxl_input(args, _, tokenizer, prompt_text): prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX prompt_text = prefix + prompt_text return prompt_text PREPROCESSING_FUNCTIONS = { "ctrl": prepare_ctrl_input, "xlm": prepare_xlm_input, "xlnet": prepare_xlnet_input, "transfo-xl": prepare_transfoxl_input, } def adjust_length_to_model(length, max_sequence_length): if length < 0 and max_sequence_length > 0: length = max_sequence_length elif 0 < max_sequence_length < length: length = max_sequence_length # No generation bigger than model size elif length < 0: length = MAX_LENGTH # avoid infinite loop return length def main(): parser = argparse.ArgumentParser() parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument("--prompt", type=str, default="") parser.add_argument("--length", type=int, default=20) parser.add_argument("--stop_token", type=str, default=None, help="Token at which text generation is stopped") parser.add_argument( "--temperature", type=float, default=1.0, help="temperature of 1.0 has no effect, lower tend toward greedy sampling", ) parser.add_argument( "--repetition_penalty", type=float, default=1.0, help="primarily useful for CTRL model; in that case, use 1.2" ) parser.add_argument("--k", type=int, default=0) parser.add_argument("--p", type=float, default=0.9) parser.add_argument("--prefix", type=str, default="", help="Text added prior to input.") parser.add_argument("--padding_text", type=str, default="", help="Deprecated, the use of `--prefix` is preferred.") parser.add_argument("--xlm_language", type=str, default="", help="Optional language when used with the XLM model.") parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available") parser.add_argument("--num_return_sequences", type=int, default=1, help="The number of samples to generate.") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) args = parser.parse_args() args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() logger.warning(f"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fp16}") set_seed(args) # Initialize the model and tokenizer try: args.model_type = args.model_type.lower() model_class, tokenizer_class = MODEL_CLASSES[args.model_type] except KeyError: raise KeyError("the model {} you specified is not supported. You are welcome to add it and open a PR :)") tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path) model = model_class.from_pretrained(args.model_name_or_path) model.to(args.device) if args.fp16: model.half() args.length = adjust_length_to_model(args.length, max_sequence_length=model.config.max_position_embeddings) logger.info(args) prompt_text = args.prompt if args.prompt else input("Model prompt >>> ") # Different models need different input formatting and/or extra arguments requires_preprocessing = args.model_type in PREPROCESSING_FUNCTIONS.keys() if requires_preprocessing: prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type) preprocessed_prompt_text = prepare_input(args, model, tokenizer, prompt_text) if model.__class__.__name__ in ["TransfoXLLMHeadModel"]: tokenizer_kwargs = {"add_space_before_punct_symbol": True} else: tokenizer_kwargs = {} encoded_prompt = tokenizer.encode( preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", **tokenizer_kwargs ) else: prefix = args.prefix if args.prefix else args.padding_text encoded_prompt = tokenizer.encode(prefix + prompt_text, add_special_tokens=False, return_tensors="pt") encoded_prompt = encoded_prompt.to(args.device) if encoded_prompt.size()[-1] == 0: input_ids = None else: input_ids = encoded_prompt output_sequences = model.generate( input_ids=input_ids, max_length=args.length + len(encoded_prompt[0]), temperature=args.temperature, top_k=args.k, top_p=args.p, repetition_penalty=args.repetition_penalty, do_sample=True, num_return_sequences=args.num_return_sequences, ) # Remove the batch dimension when returning multiple sequences if len(output_sequences.shape) > 2: output_sequences.squeeze_() generated_sequences = [] for generated_sequence_idx, generated_sequence in enumerate(output_sequences): print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===") generated_sequence = generated_sequence.tolist() # Decode text text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True) # Remove all text after the stop token text = text[: text.find(args.stop_token) if args.stop_token else None] # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing total_sequence = ( prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :] ) generated_sequences.append(total_sequence) print(total_sequence) return generated_sequences if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/CTRL/Transformer-XL/XLNet) """ import argparse import logging import numpy as np import torch from transformers import ( CTRLLMHeadModel, CTRLTokenizer, GPT2LMHeadModel, GPT2Tokenizer, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, TransfoXLLMHeadModel, TransfoXLTokenizer, XLMTokenizer, XLMWithLMHeadModel, XLNetLMHeadModel, XLNetTokenizer, ) logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger = logging.getLogger(__name__) MAX_LENGTH = int(10000) # Hardcoded max length to avoid infinite loop MODEL_CLASSES = { "gpt2": (GPT2LMHeadModel, GPT2Tokenizer), "ctrl": (CTRLLMHeadModel, CTRLTokenizer), "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer), "xlnet": (XLNetLMHeadModel, XLNetTokenizer), "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer), "xlm": (XLMWithLMHeadModel, XLMTokenizer), } # Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia # in https://github.com/rusiaaman/XLNet-gen#methodology # and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e PREFIX = """In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision and denounces one of the men as a horse thief. Although his father initially slaps him for making such an accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing. <eod> </s> <eos>""" def set_seed(args): np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) # # Functions to prepare models' input # def prepare_ctrl_input(args, _, tokenizer, prompt_text): if args.temperature > 0.7: logger.info("CTRL typically works better with lower temperatures (and lower top_k).") encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False) if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()): logger.info("WARNING! You are not starting your generation from a control code so you won't get good results") return prompt_text def prepare_xlm_input(args, model, tokenizer, prompt_text): # kwargs = {"language": None, "mask_token_id": None} # Set the language use_lang_emb = hasattr(model.config, "use_lang_emb") and model.config.use_lang_emb if hasattr(model.config, "lang2id") and use_lang_emb: available_languages = model.config.lang2id.keys() if args.xlm_language in available_languages: language = args.xlm_language else: language = None while language not in available_languages: language = input("Using XLM. Select language in " + str(list(available_languages)) + " >>> ") model.config.lang_id = model.config.lang2id[language] # kwargs["language"] = tokenizer.lang2id[language] # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers # XLM masked-language modeling (MLM) models need masked token # is_xlm_mlm = "mlm" in args.model_name_or_path # if is_xlm_mlm: # kwargs["mask_token_id"] = tokenizer.mask_token_id return prompt_text def prepare_xlnet_input(args, _, tokenizer, prompt_text): prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX prompt_text = prefix + prompt_text return prompt_text def prepare_transfoxl_input(args, _, tokenizer, prompt_text): prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX prompt_text = prefix + prompt_text return prompt_text PREPROCESSING_FUNCTIONS = { "ctrl": prepare_ctrl_input, "xlm": prepare_xlm_input, "xlnet": prepare_xlnet_input, "transfo-xl": prepare_transfoxl_input, } def adjust_length_to_model(length, max_sequence_length): if length < 0 and max_sequence_length > 0: length = max_sequence_length elif 0 < max_sequence_length < length: length = max_sequence_length # No generation bigger than model size elif length < 0: length = MAX_LENGTH # avoid infinite loop return length def main(): parser = argparse.ArgumentParser() parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument("--prompt", type=str, default="") parser.add_argument("--length", type=int, default=20) parser.add_argument("--stop_token", type=str, default=None, help="Token at which text generation is stopped") parser.add_argument( "--temperature", type=float, default=1.0, help="temperature of 1.0 has no effect, lower tend toward greedy sampling", ) parser.add_argument( "--repetition_penalty", type=float, default=1.0, help="primarily useful for CTRL model; in that case, use 1.2" ) parser.add_argument("--k", type=int, default=0) parser.add_argument("--p", type=float, default=0.9) parser.add_argument("--prefix", type=str, default="", help="Text added prior to input.") parser.add_argument("--padding_text", type=str, default="", help="Deprecated, the use of `--prefix` is preferred.") parser.add_argument("--xlm_language", type=str, default="", help="Optional language when used with the XLM model.") parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available") parser.add_argument("--num_return_sequences", type=int, default=1, help="The number of samples to generate.") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) args = parser.parse_args() args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() logger.warning(f"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fp16}") set_seed(args) # Initialize the model and tokenizer try: args.model_type = args.model_type.lower() model_class, tokenizer_class = MODEL_CLASSES[args.model_type] except KeyError: raise KeyError("the model {} you specified is not supported. You are welcome to add it and open a PR :)") tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path) model = model_class.from_pretrained(args.model_name_or_path) model.to(args.device) if args.fp16: model.half() args.length = adjust_length_to_model(args.length, max_sequence_length=model.config.max_position_embeddings) logger.info(args) prompt_text = args.prompt if args.prompt else input("Model prompt >>> ") # Different models need different input formatting and/or extra arguments requires_preprocessing = args.model_type in PREPROCESSING_FUNCTIONS.keys() if requires_preprocessing: prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type) preprocessed_prompt_text = prepare_input(args, model, tokenizer, prompt_text) if model.__class__.__name__ in ["TransfoXLLMHeadModel"]: tokenizer_kwargs = {"add_space_before_punct_symbol": True} else: tokenizer_kwargs = {} encoded_prompt = tokenizer.encode( preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", **tokenizer_kwargs ) else: prefix = args.prefix if args.prefix else args.padding_text encoded_prompt = tokenizer.encode(prefix + prompt_text, add_special_tokens=False, return_tensors="pt") encoded_prompt = encoded_prompt.to(args.device) if encoded_prompt.size()[-1] == 0: input_ids = None else: input_ids = encoded_prompt output_sequences = model.generate( input_ids=input_ids, max_length=args.length + len(encoded_prompt[0]), temperature=args.temperature, top_k=args.k, top_p=args.p, repetition_penalty=args.repetition_penalty, do_sample=True, num_return_sequences=args.num_return_sequences, ) # Remove the batch dimension when returning multiple sequences if len(output_sequences.shape) > 2: output_sequences.squeeze_() generated_sequences = [] for generated_sequence_idx, generated_sequence in enumerate(output_sequences): print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===") generated_sequence = generated_sequence.tolist() # Decode text text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True) # Remove all text after the stop token text = text[: text.find(args.stop_token) if args.stop_token else None] # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing total_sequence = ( prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :] ) generated_sequences.append(total_sequence) print(total_sequence) return generated_sequences if __name__ == "__main__": main()

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/sagemaker/scripts/tensorflow/run_tf_dist.py

import argparse import logging import os import sys import time import tensorflow as tf from datasets import load_dataset from tqdm import tqdm from transformers import AutoTokenizer, TFAutoModelForSequenceClassification from transformers.utils import is_sagemaker_dp_enabled if os.environ.get("SDP_ENABLED") or is_sagemaker_dp_enabled(): SDP_ENABLED = True os.environ["SAGEMAKER_INSTANCE_TYPE"] = "p3dn.24xlarge" import smdistributed.dataparallel.tensorflow as sdp else: SDP_ENABLED = False def fit(model, loss, opt, train_dataset, epochs, train_batch_size, max_steps=None): pbar = tqdm(train_dataset) for i, batch in enumerate(pbar): with tf.GradientTape() as tape: inputs, targets = batch outputs = model(batch) loss_value = loss(targets, outputs.logits) if SDP_ENABLED: tape = sdp.DistributedGradientTape(tape, sparse_as_dense=True) grads = tape.gradient(loss_value, model.trainable_variables) opt.apply_gradients(zip(grads, model.trainable_variables)) pbar.set_description(f"Loss: {loss_value:.4f}") if SDP_ENABLED and i == 0: sdp.broadcast_variables(model.variables, root_rank=0) sdp.broadcast_variables(opt.variables(), root_rank=0) if max_steps and i >= max_steps: break train_results = {"loss": loss_value.numpy()} return train_results def get_datasets(tokenizer, train_batch_size, eval_batch_size): # Load dataset train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"]) # Preprocess train dataset train_dataset = train_dataset.map( lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True ) train_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"]) train_features = { x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length]) for x in ["input_ids", "attention_mask"] } tf_train_dataset = tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])) # Preprocess test dataset test_dataset = test_dataset.map( lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True ) test_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"]) test_features = { x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length]) for x in ["input_ids", "attention_mask"] } tf_test_dataset = tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])) if SDP_ENABLED: tf_train_dataset = tf_train_dataset.shard(sdp.size(), sdp.rank()) tf_test_dataset = tf_test_dataset.shard(sdp.size(), sdp.rank()) tf_train_dataset = tf_train_dataset.batch(train_batch_size, drop_remainder=True) tf_test_dataset = tf_test_dataset.batch(eval_batch_size, drop_remainder=True) return tf_train_dataset, tf_test_dataset if __name__ == "__main__": parser = argparse.ArgumentParser() # Hyperparameters sent by the client are passed as command-line arguments to the script. parser.add_argument("--epochs", type=int, default=3) parser.add_argument("--per_device_train_batch_size", type=int, default=16) parser.add_argument("--per_device_eval_batch_size", type=int, default=8) parser.add_argument("--model_name_or_path", type=str) parser.add_argument("--learning_rate", type=str, default=5e-5) parser.add_argument("--do_train", type=bool, default=True) parser.add_argument("--do_eval", type=bool, default=True) parser.add_argument("--output_dir", type=str) parser.add_argument("--max_steps", type=int, default=None) # Data, model, and output directories parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"]) parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"]) parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"]) args, _ = parser.parse_known_args() # Set up logging logger = logging.getLogger(__name__) logging.basicConfig( level=logging.getLevelName("INFO"), handlers=[logging.StreamHandler(sys.stdout)], format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", ) if SDP_ENABLED: sdp.init() gpus = tf.config.experimental.list_physical_devices("GPU") for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) if gpus: tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], "GPU") # Load model and tokenizer model = TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path) tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path) # get datasets tf_train_dataset, tf_test_dataset = get_datasets( tokenizer=tokenizer, train_batch_size=args.per_device_train_batch_size, eval_batch_size=args.per_device_eval_batch_size, ) # fine optimizer and loss optimizer = tf.keras.optimizers.Adam(learning_rate=args.learning_rate) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) metrics = [tf.keras.metrics.SparseCategoricalAccuracy()] model.compile(optimizer=optimizer, loss=loss, metrics=metrics) # Training if args.do_train: # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size) start_train_time = time.time() train_results = fit( model, loss, optimizer, tf_train_dataset, args.epochs, args.per_device_train_batch_size, max_steps=args.max_steps, ) end_train_time = time.time() - start_train_time logger.info("*** Train ***") logger.info(f"train_runtime = {end_train_time}") output_eval_file = os.path.join(args.output_dir, "train_results.txt") if not SDP_ENABLED or sdp.rank() == 0: with open(output_eval_file, "w") as writer: logger.info("***** Train results *****") logger.info(train_results) for key, value in train_results.items(): logger.info(f" {key} = {value}") writer.write(f"{key} = {value}\n") # Evaluation if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0): result = model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True) logger.info("*** Evaluate ***") output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") logger.info(result) for key, value in result.items(): logger.info(f" {key} = {value}") writer.write(f"{key} = {value}\n") # Save result if SDP_ENABLED: if sdp.rank() == 0: model.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) else: model.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir)

import argparse import logging import os import sys import time import tensorflow as tf from datasets import load_dataset from tqdm import tqdm from transformers import AutoTokenizer, TFAutoModelForSequenceClassification from transformers.utils import is_sagemaker_dp_enabled if os.environ.get("SDP_ENABLED") or is_sagemaker_dp_enabled(): SDP_ENABLED = True os.environ["SAGEMAKER_INSTANCE_TYPE"] = "p3dn.24xlarge" import smdistributed.dataparallel.tensorflow as sdp else: SDP_ENABLED = False def fit(model, loss, opt, train_dataset, epochs, train_batch_size, max_steps=None): pbar = tqdm(train_dataset) for i, batch in enumerate(pbar): with tf.GradientTape() as tape: inputs, targets = batch outputs = model(batch) loss_value = loss(targets, outputs.logits) if SDP_ENABLED: tape = sdp.DistributedGradientTape(tape, sparse_as_dense=True) grads = tape.gradient(loss_value, model.trainable_variables) opt.apply_gradients(zip(grads, model.trainable_variables)) pbar.set_description(f"Loss: {loss_value:.4f}") if SDP_ENABLED and i == 0: sdp.broadcast_variables(model.variables, root_rank=0) sdp.broadcast_variables(opt.variables(), root_rank=0) if max_steps and i >= max_steps: break train_results = {"loss": loss_value.numpy()} return train_results def get_datasets(tokenizer, train_batch_size, eval_batch_size): # Load dataset train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"]) # Preprocess train dataset train_dataset = train_dataset.map( lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True ) train_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"]) train_features = { x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length]) for x in ["input_ids", "attention_mask"] } tf_train_dataset = tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])) # Preprocess test dataset test_dataset = test_dataset.map( lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True ) test_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"]) test_features = { x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length]) for x in ["input_ids", "attention_mask"] } tf_test_dataset = tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])) if SDP_ENABLED: tf_train_dataset = tf_train_dataset.shard(sdp.size(), sdp.rank()) tf_test_dataset = tf_test_dataset.shard(sdp.size(), sdp.rank()) tf_train_dataset = tf_train_dataset.batch(train_batch_size, drop_remainder=True) tf_test_dataset = tf_test_dataset.batch(eval_batch_size, drop_remainder=True) return tf_train_dataset, tf_test_dataset if __name__ == "__main__": parser = argparse.ArgumentParser() # Hyperparameters sent by the client are passed as command-line arguments to the script. parser.add_argument("--epochs", type=int, default=3) parser.add_argument("--per_device_train_batch_size", type=int, default=16) parser.add_argument("--per_device_eval_batch_size", type=int, default=8) parser.add_argument("--model_name_or_path", type=str) parser.add_argument("--learning_rate", type=str, default=5e-5) parser.add_argument("--do_train", type=bool, default=True) parser.add_argument("--do_eval", type=bool, default=True) parser.add_argument("--output_dir", type=str) parser.add_argument("--max_steps", type=int, default=None) # Data, model, and output directories parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"]) parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"]) parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"]) args, _ = parser.parse_known_args() # Set up logging logger = logging.getLogger(__name__) logging.basicConfig( level=logging.getLevelName("INFO"), handlers=[logging.StreamHandler(sys.stdout)], format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", ) if SDP_ENABLED: sdp.init() gpus = tf.config.experimental.list_physical_devices("GPU") for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) if gpus: tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], "GPU") # Load model and tokenizer model = TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path) tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path) # get datasets tf_train_dataset, tf_test_dataset = get_datasets( tokenizer=tokenizer, train_batch_size=args.per_device_train_batch_size, eval_batch_size=args.per_device_eval_batch_size, ) # fine optimizer and loss optimizer = tf.keras.optimizers.Adam(learning_rate=args.learning_rate) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) metrics = [tf.keras.metrics.SparseCategoricalAccuracy()] model.compile(optimizer=optimizer, loss=loss, metrics=metrics) # Training if args.do_train: # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size) start_train_time = time.time() train_results = fit( model, loss, optimizer, tf_train_dataset, args.epochs, args.per_device_train_batch_size, max_steps=args.max_steps, ) end_train_time = time.time() - start_train_time logger.info("*** Train ***") logger.info(f"train_runtime = {end_train_time}") output_eval_file = os.path.join(args.output_dir, "train_results.txt") if not SDP_ENABLED or sdp.rank() == 0: with open(output_eval_file, "w") as writer: logger.info("***** Train results *****") logger.info(train_results) for key, value in train_results.items(): logger.info(f" {key} = {value}") writer.write(f"{key} = {value}\n") # Evaluation if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0): result = model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True) logger.info("*** Evaluate ***") output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") logger.info(result) for key, value in result.items(): logger.info(f" {key} = {value}") writer.write(f"{key} = {value}\n") # Save result if SDP_ENABLED: if sdp.rank() == 0: model.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) else: model.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/bort/test_modeling_bort.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device if is_torch_available(): import torch from transformers import AutoModel @require_torch @require_sentencepiece @require_tokenizers class BortIntegrationTest(unittest.TestCase): @slow def test_output_embeds_base_model(self): model = AutoModel.from_pretrained("amazon/bort") model.to(torch_device) input_ids = torch.tensor( [[0, 18077, 4082, 7804, 8606, 6195, 2457, 3321, 11, 10489, 16, 269, 2579, 328, 2]], device=torch_device, dtype=torch.long, ) # Schloß Nymphenburg in Munich is really nice! output = model(input_ids)["last_hidden_state"] expected_shape = torch.Size((1, 15, 1024)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.tensor( [[[-0.0349, 0.0436, -1.8654], [-0.6964, 0.0835, -1.7393], [-0.9819, 0.2956, -0.2868]]], device=torch_device, dtype=torch.float, ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device if is_torch_available(): import torch from transformers import AutoModel @require_torch @require_sentencepiece @require_tokenizers class BortIntegrationTest(unittest.TestCase): @slow def test_output_embeds_base_model(self): model = AutoModel.from_pretrained("amazon/bort") model.to(torch_device) input_ids = torch.tensor( [[0, 18077, 4082, 7804, 8606, 6195, 2457, 3321, 11, 10489, 16, 269, 2579, 328, 2]], device=torch_device, dtype=torch.long, ) # Schloß Nymphenburg in Munich is really nice! output = model(input_ids)["last_hidden_state"] expected_shape = torch.Size((1, 15, 1024)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.tensor( [[[-0.0349, 0.0436, -1.8654], [-0.6964, 0.0835, -1.7393], [-0.9819, 0.2956, -0.2868]]], device=torch_device, dtype=torch.float, ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/blenderbot/__init__.py

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/videomae/test_feature_extraction_videomae.py

# coding=utf-8 # Copyright 2022 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_video_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import VideoMAEFeatureExtractor class VideoMAEFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, num_frames=10, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], crop_size=None, ): size = size if size is not None else {"shortest_edge": 18} crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.num_frames = num_frames self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.crop_size = crop_size def prepare_feat_extract_dict(self): return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, "crop_size": self.crop_size, } @require_torch @require_vision class VideoMAEFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase): feature_extraction_class = VideoMAEFeatureExtractor if is_vision_available() else None def setUp(self): self.feature_extract_tester = VideoMAEFeatureExtractionTester(self) @property def feat_extract_dict(self): return self.feature_extract_tester.prepare_feat_extract_dict() def test_feat_extract_properties(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) self.assertTrue(hasattr(feature_extractor, "image_mean")) self.assertTrue(hasattr(feature_extractor, "image_std")) self.assertTrue(hasattr(feature_extractor, "do_normalize")) self.assertTrue(hasattr(feature_extractor, "do_resize")) self.assertTrue(hasattr(feature_extractor, "do_center_crop")) self.assertTrue(hasattr(feature_extractor, "size")) def test_batch_feature(self): pass def test_call_pil(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PIL videos video_inputs = prepare_video_inputs(self.feature_extract_tester, equal_resolution=False) for video in video_inputs: self.assertIsInstance(video, list) self.assertIsInstance(video[0], Image.Image) # Test not batched input encoded_videos = feature_extractor(video_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_videos.shape, ( 1, self.feature_extract_tester.num_frames, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) # Test batched encoded_videos = feature_extractor(video_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_videos.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_frames, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) def test_call_numpy(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random numpy tensors video_inputs = prepare_video_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True) for video in video_inputs: self.assertIsInstance(video, list) self.assertIsInstance(video[0], np.ndarray) # Test not batched input encoded_videos = feature_extractor(video_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_videos.shape, ( 1, self.feature_extract_tester.num_frames, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) # Test batched encoded_videos = feature_extractor(video_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_videos.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_frames, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) def test_call_pytorch(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PyTorch tensors video_inputs = prepare_video_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True) for video in video_inputs: self.assertIsInstance(video, list) self.assertIsInstance(video[0], torch.Tensor) # Test not batched input encoded_videos = feature_extractor(video_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_videos.shape, ( 1, self.feature_extract_tester.num_frames, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) # Test batched encoded_videos = feature_extractor(video_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_videos.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_frames, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), )

# coding=utf-8 # Copyright 2022 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_video_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import VideoMAEFeatureExtractor class VideoMAEFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, num_frames=10, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], crop_size=None, ): size = size if size is not None else {"shortest_edge": 18} crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.num_frames = num_frames self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.crop_size = crop_size def prepare_feat_extract_dict(self): return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, "crop_size": self.crop_size, } @require_torch @require_vision class VideoMAEFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase): feature_extraction_class = VideoMAEFeatureExtractor if is_vision_available() else None def setUp(self): self.feature_extract_tester = VideoMAEFeatureExtractionTester(self) @property def feat_extract_dict(self): return self.feature_extract_tester.prepare_feat_extract_dict() def test_feat_extract_properties(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) self.assertTrue(hasattr(feature_extractor, "image_mean")) self.assertTrue(hasattr(feature_extractor, "image_std")) self.assertTrue(hasattr(feature_extractor, "do_normalize")) self.assertTrue(hasattr(feature_extractor, "do_resize")) self.assertTrue(hasattr(feature_extractor, "do_center_crop")) self.assertTrue(hasattr(feature_extractor, "size")) def test_batch_feature(self): pass def test_call_pil(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PIL videos video_inputs = prepare_video_inputs(self.feature_extract_tester, equal_resolution=False) for video in video_inputs: self.assertIsInstance(video, list) self.assertIsInstance(video[0], Image.Image) # Test not batched input encoded_videos = feature_extractor(video_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_videos.shape, ( 1, self.feature_extract_tester.num_frames, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) # Test batched encoded_videos = feature_extractor(video_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_videos.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_frames, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) def test_call_numpy(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random numpy tensors video_inputs = prepare_video_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True) for video in video_inputs: self.assertIsInstance(video, list) self.assertIsInstance(video[0], np.ndarray) # Test not batched input encoded_videos = feature_extractor(video_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_videos.shape, ( 1, self.feature_extract_tester.num_frames, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) # Test batched encoded_videos = feature_extractor(video_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_videos.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_frames, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) def test_call_pytorch(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PyTorch tensors video_inputs = prepare_video_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True) for video in video_inputs: self.assertIsInstance(video, list) self.assertIsInstance(video[0], torch.Tensor) # Test not batched input encoded_videos = feature_extractor(video_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_videos.shape, ( 1, self.feature_extract_tester.num_frames, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), ) # Test batched encoded_videos = feature_extractor(video_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_videos.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_frames, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size["height"], self.feature_extract_tester.crop_size["width"], ), )

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/research_projects/bert-loses-patience/pabee/modeling_pabee_bert.py

# coding=utf-8 # Copyright 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and Microsoft Corporation. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch BERT model with Patience-based Early Exit. """ import logging import torch from torch import nn from torch.nn import CrossEntropyLoss, MSELoss from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward from transformers.models.bert.modeling_bert import ( BERT_INPUTS_DOCSTRING, BERT_START_DOCSTRING, BertEncoder, BertModel, BertPreTrainedModel, ) logger = logging.getLogger(__name__) class BertEncoderWithPabee(BertEncoder): def adaptive_forward(self, hidden_states, current_layer, attention_mask=None, head_mask=None): layer_outputs = self.layer[current_layer](hidden_states, attention_mask, head_mask[current_layer]) hidden_states = layer_outputs[0] return hidden_states @add_start_docstrings( "The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.", BERT_START_DOCSTRING, ) class BertModelWithPabee(BertModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in `Attention is all you need`_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as a decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration set to :obj:`True`; an :obj:`encoder_hidden_states` is expected as an input to the forward pass. .. _`Attention is all you need`: https://arxiv.org/abs/1706.03762 """ def __init__(self, config): super().__init__(config) self.encoder = BertEncoderWithPabee(config) self.init_weights() self.patience = 0 self.inference_instances_num = 0 self.inference_layers_num = 0 self.regression_threshold = 0 def set_regression_threshold(self, threshold): self.regression_threshold = threshold def set_patience(self, patience): self.patience = patience def reset_stats(self): self.inference_instances_num = 0 self.inference_layers_num = 0 def log_stats(self): avg_inf_layers = self.inference_layers_num / self.inference_instances_num message = ( f"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up =" f" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***" ) print(message) @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, output_dropout=None, output_layers=None, regression=False, ): r""" Return: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pre-training. This output is usually *not* a good summary of the semantic content of the input, you're often better with averaging or pooling the sequence of hidden-states for the whole input sequence. hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device) # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds ) encoder_outputs = embedding_output if self.training: res = [] for i in range(self.config.num_hidden_layers): encoder_outputs = self.encoder.adaptive_forward( encoder_outputs, current_layer=i, attention_mask=extended_attention_mask, head_mask=head_mask ) pooled_output = self.pooler(encoder_outputs) logits = output_layers[i](output_dropout(pooled_output)) res.append(logits) elif self.patience == 0: # Use all layers for inference encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, ) pooled_output = self.pooler(encoder_outputs[0]) res = [output_layers[self.config.num_hidden_layers - 1](pooled_output)] else: patient_counter = 0 patient_result = None calculated_layer_num = 0 for i in range(self.config.num_hidden_layers): calculated_layer_num += 1 encoder_outputs = self.encoder.adaptive_forward( encoder_outputs, current_layer=i, attention_mask=extended_attention_mask, head_mask=head_mask ) pooled_output = self.pooler(encoder_outputs) logits = output_layers[i](pooled_output) if regression: labels = logits.detach() if patient_result is not None: patient_labels = patient_result.detach() if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold: patient_counter += 1 else: patient_counter = 0 else: labels = logits.detach().argmax(dim=1) if patient_result is not None: patient_labels = patient_result.detach().argmax(dim=1) if (patient_result is not None) and torch.all(labels.eq(patient_labels)): patient_counter += 1 else: patient_counter = 0 patient_result = logits if patient_counter == self.patience: break res = [patient_result] self.inference_layers_num += calculated_layer_num self.inference_instances_num += 1 return res @add_start_docstrings( """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, BERT_START_DOCSTRING, ) class BertForSequenceClassificationWithPabee(BertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.bert = BertModelWithPabee(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifiers = nn.ModuleList( [nn.Linear(config.hidden_size, self.config.num_labels) for _ in range(config.num_hidden_layers)] ) self.init_weights() @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ..., config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss), If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided): Classification (or regression if config.num_labels==1) loss. logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: from transformers import BertTokenizer, BertForSequenceClassification from pabee import BertForSequenceClassificationWithPabee from torch import nn import torch tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertForSequenceClassificationWithPabee.from_pretrained('bert-base-uncased') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=labels) loss, logits = outputs[:2] """ logits = self.bert( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_dropout=self.dropout, output_layers=self.classifiers, regression=self.num_labels == 1, ) outputs = (logits[-1],) if labels is not None: total_loss = None total_weights = 0 for ix, logits_item in enumerate(logits): if self.num_labels == 1: # We are doing regression loss_fct = MSELoss() loss = loss_fct(logits_item.view(-1), labels.view(-1)) else: loss_fct = CrossEntropyLoss() loss = loss_fct(logits_item.view(-1, self.num_labels), labels.view(-1)) if total_loss is None: total_loss = loss else: total_loss += loss * (ix + 1) total_weights += ix + 1 outputs = (total_loss / total_weights,) + outputs return outputs

# coding=utf-8 # Copyright 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and Microsoft Corporation. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch BERT model with Patience-based Early Exit. """ import logging import torch from torch import nn from torch.nn import CrossEntropyLoss, MSELoss from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward from transformers.models.bert.modeling_bert import ( BERT_INPUTS_DOCSTRING, BERT_START_DOCSTRING, BertEncoder, BertModel, BertPreTrainedModel, ) logger = logging.getLogger(__name__) class BertEncoderWithPabee(BertEncoder): def adaptive_forward(self, hidden_states, current_layer, attention_mask=None, head_mask=None): layer_outputs = self.layer[current_layer](hidden_states, attention_mask, head_mask[current_layer]) hidden_states = layer_outputs[0] return hidden_states @add_start_docstrings( "The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.", BERT_START_DOCSTRING, ) class BertModelWithPabee(BertModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in `Attention is all you need`_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as a decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration set to :obj:`True`; an :obj:`encoder_hidden_states` is expected as an input to the forward pass. .. _`Attention is all you need`: https://arxiv.org/abs/1706.03762 """ def __init__(self, config): super().__init__(config) self.encoder = BertEncoderWithPabee(config) self.init_weights() self.patience = 0 self.inference_instances_num = 0 self.inference_layers_num = 0 self.regression_threshold = 0 def set_regression_threshold(self, threshold): self.regression_threshold = threshold def set_patience(self, patience): self.patience = patience def reset_stats(self): self.inference_instances_num = 0 self.inference_layers_num = 0 def log_stats(self): avg_inf_layers = self.inference_layers_num / self.inference_instances_num message = ( f"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up =" f" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***" ) print(message) @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, output_dropout=None, output_layers=None, regression=False, ): r""" Return: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pre-training. This output is usually *not* a good summary of the semantic content of the input, you're often better with averaging or pooling the sequence of hidden-states for the whole input sequence. hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device) # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds ) encoder_outputs = embedding_output if self.training: res = [] for i in range(self.config.num_hidden_layers): encoder_outputs = self.encoder.adaptive_forward( encoder_outputs, current_layer=i, attention_mask=extended_attention_mask, head_mask=head_mask ) pooled_output = self.pooler(encoder_outputs) logits = output_layers[i](output_dropout(pooled_output)) res.append(logits) elif self.patience == 0: # Use all layers for inference encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, ) pooled_output = self.pooler(encoder_outputs[0]) res = [output_layers[self.config.num_hidden_layers - 1](pooled_output)] else: patient_counter = 0 patient_result = None calculated_layer_num = 0 for i in range(self.config.num_hidden_layers): calculated_layer_num += 1 encoder_outputs = self.encoder.adaptive_forward( encoder_outputs, current_layer=i, attention_mask=extended_attention_mask, head_mask=head_mask ) pooled_output = self.pooler(encoder_outputs) logits = output_layers[i](pooled_output) if regression: labels = logits.detach() if patient_result is not None: patient_labels = patient_result.detach() if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold: patient_counter += 1 else: patient_counter = 0 else: labels = logits.detach().argmax(dim=1) if patient_result is not None: patient_labels = patient_result.detach().argmax(dim=1) if (patient_result is not None) and torch.all(labels.eq(patient_labels)): patient_counter += 1 else: patient_counter = 0 patient_result = logits if patient_counter == self.patience: break res = [patient_result] self.inference_layers_num += calculated_layer_num self.inference_instances_num += 1 return res @add_start_docstrings( """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, BERT_START_DOCSTRING, ) class BertForSequenceClassificationWithPabee(BertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.bert = BertModelWithPabee(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifiers = nn.ModuleList( [nn.Linear(config.hidden_size, self.config.num_labels) for _ in range(config.num_hidden_layers)] ) self.init_weights() @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ..., config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss), If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs: loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided): Classification (or regression if config.num_labels==1) loss. logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: from transformers import BertTokenizer, BertForSequenceClassification from pabee import BertForSequenceClassificationWithPabee from torch import nn import torch tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertForSequenceClassificationWithPabee.from_pretrained('bert-base-uncased') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=labels) loss, logits = outputs[:2] """ logits = self.bert( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_dropout=self.dropout, output_layers=self.classifiers, regression=self.num_labels == 1, ) outputs = (logits[-1],) if labels is not None: total_loss = None total_weights = 0 for ix, logits_item in enumerate(logits): if self.num_labels == 1: # We are doing regression loss_fct = MSELoss() loss = loss_fct(logits_item.view(-1), labels.view(-1)) else: loss_fct = CrossEntropyLoss() loss = loss_fct(logits_item.view(-1, self.num_labels), labels.view(-1)) if total_loss is None: total_loss = loss else: total_loss += loss * (ix + 1) total_weights += ix + 1 outputs = (total_loss / total_weights,) + outputs return outputs

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/whisper/configuration_whisper.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Whisper model configuration""" from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig, OnnxSeq2SeqConfigWithPast from ...utils import logging if TYPE_CHECKING: from ...feature_extraction_utils import FeatureExtractionMixin from ...tokenization_utils_base import PreTrainedTokenizerBase from ...utils import TensorType logger = logging.get_logger(__name__) WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP = { "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json", } # fmt: off NON_SPEECH_TOKENS = [ 1, 2, 6, 7, 8, 9, 10, 12, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 357, 366, 438, 532, 685, 705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377, 1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211, 4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786, 11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791, 17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409, 34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361 ] NON_SPEECH_TOKENS_MULTI = [ 1, 2, 6, 7, 8, 9, 10, 12, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 359, 503, 522, 542, 873, 893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627, 3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647, 7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793, 14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675, 22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865, 42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362 ] # fmt: on class WhisperConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`WhisperModel`]. It is used to instantiate a Whisper model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Whisper [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 51865): Vocabulary size of the Whisper model. Defines the number of different tokens that can be represented by the `decoder_input_ids` passed when calling [`WhisperModel`] num_mel_bins (`int`, *optional*, defaults to 80): Number of mel features used per input features. Should correspond to the value used in the `WhisperProcessor` class. encoder_layers (`int`, *optional*, defaults to 6): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 6): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 4): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 4): Number of attention heads for each attention layer in the Transformer decoder. encoder_ffn_dim (`int`, *optional*, defaults to 1536): Dimensionality of the "intermediate" (often named feed-forward) layer in encoder. decoder_ffn_dim (`int`, *optional*, defaults to 1536): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_start_token_id (`int`, *optional*, defaults to 50257): Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids` are provided to the `generate` function. It is used to guide the model`s generation process depending on the task. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). is_encoder_decoder (`bool`, *optional*, defaults to `True`): Whether the model is used as an encoder/decoder or not. activation_function (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. d_model (`int`, *optional*, defaults to 256): Dimensionality of the layers. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. scale_embedding (`bool`, *optional*, defaults to False): Scale embeddings by diving by sqrt(d_model). max_source_positions (`int`, *optional*, defaults to 1500): The maximum sequence length of log-mel filter-bank features that this model might ever be used with. max_target_positions (`int`, *optional*, defaults to 448): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). pad_token_id (`int`, *optional*, defaults to 50256): Padding token id. bos_token_id (`int`, *optional*, defaults to 50256): Begin of stream token id. eos_token_id (`int`, *optional*, defaults to 50257): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie input and output embeddings. suppress_tokens (`List[int]`, *optional*): A list containing the non-speech tokens that will be used by the logit processor in the `generate` function. NON_SPEECH_TOKENS and NON_SPEECH_TOKENS_MULTI each correspond to the `english-only` and the `multilingual` model. begin_suppress_tokens (`List[int]`, *optional*, defaults to `[220,50256]`): A list containing tokens that will be supressed at the beginning of the sampling process. Initialized as the token for `" "` (`blank_token_id`) and the `eos_token_id` Example: ```python >>> from transformers import WhisperConfig, WhisperModel >>> # Initializing a Whisper tiny style configuration >>> configuration = WhisperConfig() >>> # Initializing a model (with random weights) from the tiny style configuration >>> model = WhisperModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "whisper" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=51865, num_mel_bins=80, encoder_layers=6, encoder_attention_heads=4, decoder_layers=6, decoder_attention_heads=4, decoder_ffn_dim=1536, encoder_ffn_dim=1536, encoder_layerdrop=0.0, decoder_layerdrop=0.0, decoder_start_token_id=50257, use_cache=True, is_encoder_decoder=True, activation_function="gelu", d_model=256, dropout=0.0, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, scale_embedding=False, max_source_positions=1500, max_target_positions=448, pad_token_id=50256, bos_token_id=50257, eos_token_id=50256, tie_word_embeddings=True, suppress_tokens=None, begin_suppress_tokens=[220, 50256], **kwargs ): self.vocab_size = vocab_size self.num_mel_bins = num_mel_bins self.d_model = d_model self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.encoder_ffn_dim = encoder_ffn_dim self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.tie_word_embeddings = tie_word_embeddings self.max_source_positions = max_source_positions self.max_target_positions = max_target_positions super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, tie_word_embeddings=tie_word_embeddings, suppress_tokens=suppress_tokens, begin_suppress_tokens=begin_suppress_tokens, **kwargs, ) class WhisperOnnxConfig(OnnxSeq2SeqConfigWithPast): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: common_inputs = OrderedDict( [ ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}), ] ) if self.use_past: common_inputs["decoder_input_ids"] = {0: "batch"} else: common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"} if self.use_past: self.fill_with_past_key_values_(common_inputs, direction="inputs") return common_inputs def generate_dummy_inputs( self, preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional["TensorType"] = None, sampling_rate: int = 22050, time_duration: float = 5.0, frequency: int = 220, ) -> Mapping[str, Any]: dummy_inputs = OrderedDict() encoder_inputs = OnnxConfig.generate_dummy_inputs( self, preprocessor=preprocessor.feature_extractor, batch_size=batch_size, framework=framework, sampling_rate=sampling_rate, time_duration=time_duration, frequency=frequency, ) decoder_inputs = super().generate_dummy_inputs( preprocessor.tokenizer, batch_size, seq_length, is_pair, framework ) dummy_inputs["input_features"] = encoder_inputs.pop("input_features") dummy_inputs["decoder_input_ids"] = decoder_inputs.pop("decoder_input_ids") if "past_key_values" in decoder_inputs: dummy_inputs["past_key_values"] = decoder_inputs.pop("past_key_values") return dummy_inputs @property def atol_for_validation(self) -> float: return 1e-3

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Whisper model configuration""" from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig, OnnxSeq2SeqConfigWithPast from ...utils import logging if TYPE_CHECKING: from ...feature_extraction_utils import FeatureExtractionMixin from ...tokenization_utils_base import PreTrainedTokenizerBase from ...utils import TensorType logger = logging.get_logger(__name__) WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP = { "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json", } # fmt: off NON_SPEECH_TOKENS = [ 1, 2, 6, 7, 8, 9, 10, 12, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 357, 366, 438, 532, 685, 705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377, 1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211, 4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786, 11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791, 17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409, 34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361 ] NON_SPEECH_TOKENS_MULTI = [ 1, 2, 6, 7, 8, 9, 10, 12, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 359, 503, 522, 542, 873, 893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627, 3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647, 7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793, 14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675, 22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865, 42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362 ] # fmt: on class WhisperConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`WhisperModel`]. It is used to instantiate a Whisper model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Whisper [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 51865): Vocabulary size of the Whisper model. Defines the number of different tokens that can be represented by the `decoder_input_ids` passed when calling [`WhisperModel`] num_mel_bins (`int`, *optional*, defaults to 80): Number of mel features used per input features. Should correspond to the value used in the `WhisperProcessor` class. encoder_layers (`int`, *optional*, defaults to 6): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 6): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 4): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 4): Number of attention heads for each attention layer in the Transformer decoder. encoder_ffn_dim (`int`, *optional*, defaults to 1536): Dimensionality of the "intermediate" (often named feed-forward) layer in encoder. decoder_ffn_dim (`int`, *optional*, defaults to 1536): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_start_token_id (`int`, *optional*, defaults to 50257): Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids` are provided to the `generate` function. It is used to guide the model`s generation process depending on the task. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). is_encoder_decoder (`bool`, *optional*, defaults to `True`): Whether the model is used as an encoder/decoder or not. activation_function (`str`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. d_model (`int`, *optional*, defaults to 256): Dimensionality of the layers. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. scale_embedding (`bool`, *optional*, defaults to False): Scale embeddings by diving by sqrt(d_model). max_source_positions (`int`, *optional*, defaults to 1500): The maximum sequence length of log-mel filter-bank features that this model might ever be used with. max_target_positions (`int`, *optional*, defaults to 448): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). pad_token_id (`int`, *optional*, defaults to 50256): Padding token id. bos_token_id (`int`, *optional*, defaults to 50256): Begin of stream token id. eos_token_id (`int`, *optional*, defaults to 50257): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie input and output embeddings. suppress_tokens (`List[int]`, *optional*): A list containing the non-speech tokens that will be used by the logit processor in the `generate` function. NON_SPEECH_TOKENS and NON_SPEECH_TOKENS_MULTI each correspond to the `english-only` and the `multilingual` model. begin_suppress_tokens (`List[int]`, *optional*, defaults to `[220,50256]`): A list containing tokens that will be supressed at the beginning of the sampling process. Initialized as the token for `" "` (`blank_token_id`) and the `eos_token_id` Example: ```python >>> from transformers import WhisperConfig, WhisperModel >>> # Initializing a Whisper tiny style configuration >>> configuration = WhisperConfig() >>> # Initializing a model (with random weights) from the tiny style configuration >>> model = WhisperModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "whisper" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=51865, num_mel_bins=80, encoder_layers=6, encoder_attention_heads=4, decoder_layers=6, decoder_attention_heads=4, decoder_ffn_dim=1536, encoder_ffn_dim=1536, encoder_layerdrop=0.0, decoder_layerdrop=0.0, decoder_start_token_id=50257, use_cache=True, is_encoder_decoder=True, activation_function="gelu", d_model=256, dropout=0.0, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, scale_embedding=False, max_source_positions=1500, max_target_positions=448, pad_token_id=50256, bos_token_id=50257, eos_token_id=50256, tie_word_embeddings=True, suppress_tokens=None, begin_suppress_tokens=[220, 50256], **kwargs ): self.vocab_size = vocab_size self.num_mel_bins = num_mel_bins self.d_model = d_model self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.encoder_ffn_dim = encoder_ffn_dim self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.tie_word_embeddings = tie_word_embeddings self.max_source_positions = max_source_positions self.max_target_positions = max_target_positions super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, tie_word_embeddings=tie_word_embeddings, suppress_tokens=suppress_tokens, begin_suppress_tokens=begin_suppress_tokens, **kwargs, ) class WhisperOnnxConfig(OnnxSeq2SeqConfigWithPast): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: common_inputs = OrderedDict( [ ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}), ] ) if self.use_past: common_inputs["decoder_input_ids"] = {0: "batch"} else: common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"} if self.use_past: self.fill_with_past_key_values_(common_inputs, direction="inputs") return common_inputs def generate_dummy_inputs( self, preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional["TensorType"] = None, sampling_rate: int = 22050, time_duration: float = 5.0, frequency: int = 220, ) -> Mapping[str, Any]: dummy_inputs = OrderedDict() encoder_inputs = OnnxConfig.generate_dummy_inputs( self, preprocessor=preprocessor.feature_extractor, batch_size=batch_size, framework=framework, sampling_rate=sampling_rate, time_duration=time_duration, frequency=frequency, ) decoder_inputs = super().generate_dummy_inputs( preprocessor.tokenizer, batch_size, seq_length, is_pair, framework ) dummy_inputs["input_features"] = encoder_inputs.pop("input_features") dummy_inputs["decoder_input_ids"] = decoder_inputs.pop("decoder_input_ids") if "past_key_values" in decoder_inputs: dummy_inputs["past_key_values"] = decoder_inputs.pop("past_key_values") return dummy_inputs @property def atol_for_validation(self) -> float: return 1e-3

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/codegen/tokenization_codegen_fast.py

# coding=utf-8 # Copyright 2022 The Salesforce authors, The Open AI Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for OpenAI GPT.""" import json import re from typing import TYPE_CHECKING, List, Optional, Tuple, Union import numpy as np from ...utils import is_tf_available, is_torch_available, logging if TYPE_CHECKING: if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf from tokenizers import pre_tokenizers from ...tokenization_utils_base import BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from .tokenization_codegen import CodeGenTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json", }, "merges_file": { "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt", }, "tokenizer_file": { "Salesforce/codegen-350M-mono": ( "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json" ), }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "Salesforce/codegen-350M-mono": 2048, } class CodeGenTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" CodeGen tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ``` >>> from transformers import CodeGenTokenizerFast >>> tokenizer = CodeGenTokenizerFast.from_pretrained("Salesforce/codegen-350M-mono") >>> tokenizer("Hello world")['input_ids'] [15496, 995] >>> tokenizer(" Hello world")['input_ids'] [18435, 995] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since the model was not pretrained this way, it might yield a decrease in performance. <Tip> When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. </Tip> This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. unk_token (`str`, *optional*, defaults to `<|endoftext|>`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. bos_token (`str`, *optional*, defaults to `<|endoftext|>`): The beginning of sequence token. eos_token (`str`, *optional*, defaults to `<|endoftext|>`): The end of sequence token. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (CodeGen tokenizer detect beginning of words by the preceding space). trim_offsets (`bool`, *optional*, defaults to `True`): Whether or not the post-processing step should trim offsets to avoid including whitespaces. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] slow_tokenizer_class = CodeGenTokenizer def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, unk_token="<|endoftext|>", bos_token="<|endoftext|>", eos_token="<|endoftext|>", add_prefix_space=False, **kwargs ): super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, add_prefix_space=add_prefix_space, **kwargs, ) if kwargs.pop("add_bos_token", False): model_id = kwargs.pop("name_or_path", "") raise ValueError( "Currenty GPT2's fast tokenizer does NOT support adding a BOS token." "Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n" f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n" f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n" "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005." " so that the fast tokenizer works correctly." ) pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) pre_tok_state["add_prefix_space"] = add_prefix_space self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) self.add_prefix_space = add_prefix_space def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*args, **kwargs) def _encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._encode_plus(*args, **kwargs) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) def decode( self, token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, truncate_before_pattern: Optional[List[str]] = None, **kwargs ) -> str: """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to clean up the tokenization spaces. truncate_before_pattern (`List[str]`, *optional*, defaults to `None`): A list of regular expression strings that will be used to truncate the returned string. This can be used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning of a new line). An example pattern could be `["^#", re.escape("<|endoftext|>"), "^'''", "\n\n\n"]`. kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str`: The decoded sentence. """ decoded_text = super().decode( token_ids=token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs, ) if truncate_before_pattern is not None and len(truncate_before_pattern) > 0: decoded_text = self.truncate(decoded_text, truncate_before_pattern) return decoded_text def truncate(self, completion, truncate_before_pattern): def find_re(string, pattern, start_pos): m = pattern.search(string, start_pos) return m.start() if m else -1 terminals = [re.compile(pattern, re.MULTILINE) for pattern in truncate_before_pattern] prints = list(re.finditer("^print", completion, re.MULTILINE)) if len(prints) > 1: completion = completion[: prints[1].start()] defs = list(re.finditer("^def", completion, re.MULTILINE)) if len(defs) > 1: completion = completion[: defs[1].start()] start_pos = 0 terminals_pos = [ pos for pos in [find_re(completion, terminal, start_pos) for terminal in terminals] if pos != -1 ] if len(terminals_pos) > 0: return completion[: min(terminals_pos)] else: return completion

# coding=utf-8 # Copyright 2022 The Salesforce authors, The Open AI Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for OpenAI GPT.""" import json import re from typing import TYPE_CHECKING, List, Optional, Tuple, Union import numpy as np from ...utils import is_tf_available, is_torch_available, logging if TYPE_CHECKING: if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf from tokenizers import pre_tokenizers from ...tokenization_utils_base import BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from .tokenization_codegen import CodeGenTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json", }, "merges_file": { "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt", }, "tokenizer_file": { "Salesforce/codegen-350M-mono": ( "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json" ), }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "Salesforce/codegen-350M-mono": 2048, } class CodeGenTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" CodeGen tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ``` >>> from transformers import CodeGenTokenizerFast >>> tokenizer = CodeGenTokenizerFast.from_pretrained("Salesforce/codegen-350M-mono") >>> tokenizer("Hello world")['input_ids'] [15496, 995] >>> tokenizer(" Hello world")['input_ids'] [18435, 995] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since the model was not pretrained this way, it might yield a decrease in performance. <Tip> When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. </Tip> This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. unk_token (`str`, *optional*, defaults to `<|endoftext|>`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. bos_token (`str`, *optional*, defaults to `<|endoftext|>`): The beginning of sequence token. eos_token (`str`, *optional*, defaults to `<|endoftext|>`): The end of sequence token. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (CodeGen tokenizer detect beginning of words by the preceding space). trim_offsets (`bool`, *optional*, defaults to `True`): Whether or not the post-processing step should trim offsets to avoid including whitespaces. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] slow_tokenizer_class = CodeGenTokenizer def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, unk_token="<|endoftext|>", bos_token="<|endoftext|>", eos_token="<|endoftext|>", add_prefix_space=False, **kwargs ): super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, add_prefix_space=add_prefix_space, **kwargs, ) if kwargs.pop("add_bos_token", False): model_id = kwargs.pop("name_or_path", "") raise ValueError( "Currenty GPT2's fast tokenizer does NOT support adding a BOS token." "Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n" f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n" f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n" "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005." " so that the fast tokenizer works correctly." ) pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) pre_tok_state["add_prefix_space"] = add_prefix_space self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) self.add_prefix_space = add_prefix_space def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*args, **kwargs) def _encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._encode_plus(*args, **kwargs) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) def decode( self, token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, truncate_before_pattern: Optional[List[str]] = None, **kwargs ) -> str: """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to clean up the tokenization spaces. truncate_before_pattern (`List[str]`, *optional*, defaults to `None`): A list of regular expression strings that will be used to truncate the returned string. This can be used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning of a new line). An example pattern could be `["^#", re.escape("<|endoftext|>"), "^'''", "\n\n\n"]`. kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str`: The decoded sentence. """ decoded_text = super().decode( token_ids=token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs, ) if truncate_before_pattern is not None and len(truncate_before_pattern) > 0: decoded_text = self.truncate(decoded_text, truncate_before_pattern) return decoded_text def truncate(self, completion, truncate_before_pattern): def find_re(string, pattern, start_pos): m = pattern.search(string, start_pos) return m.start() if m else -1 terminals = [re.compile(pattern, re.MULTILINE) for pattern in truncate_before_pattern] prints = list(re.finditer("^print", completion, re.MULTILINE)) if len(prints) > 1: completion = completion[: prints[1].start()] defs = list(re.finditer("^def", completion, re.MULTILINE)) if len(defs) > 1: completion = completion[: defs[1].start()] start_pos = 0 terminals_pos = [ pos for pos in [find_re(completion, terminal, start_pos) for terminal in terminals] if pos != -1 ] if len(terminals_pos) > 0: return completion[: min(terminals_pos)] else: return completion

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/research_projects/codeparrot/scripts/preprocessing.py

import gzip import hashlib import json import multiprocessing import os import re import shutil import time from pathlib import Path import numpy as np from datasets import load_dataset from arguments import PreprocessingArguments from minhash_deduplication import deduplicate_dataset from transformers import AutoTokenizer, HfArgumentParser PATTERN = re.compile(r"\s+") def get_hash(example): """Get hash of content field.""" return {"hash": hashlib.md5(re.sub(PATTERN, "", example["content"]).encode("utf-8")).hexdigest()} def line_stats(example): """Calculates mean and max line length of file.""" line_lengths = [len(line) for line in example["content"].splitlines()] return {"line_mean": np.mean(line_lengths), "line_max": max(line_lengths)} def alpha_stats(example): """Calculates mean and max line length of file.""" alpha_frac = np.mean([c.isalnum() for c in example["content"]]) return {"alpha_frac": alpha_frac} def check_uniques(example, uniques): """Check if current hash is still in set of unique hashes and remove if true.""" if example["hash"] in uniques: uniques.remove(example["hash"]) return True else: return False def is_autogenerated(example, scan_width=5): """Check if file is autogenerated by looking for keywords in the first few lines of the file.""" keywords = ["auto-generated", "autogenerated", "automatically generated"] lines = example["content"].splitlines() for _, line in zip(range(scan_width), lines): for keyword in keywords: if keyword in line.lower(): return {"autogenerated": True} else: return {"autogenerated": False} def is_config_or_test(example, scan_width=5, coeff=0.05): """Check if file is a configuration file or a unit test by : 1- looking for keywords in the first few lines of the file. 2- counting number of occurence of the words 'config' and 'test' with respect to number of lines. """ keywords = ["unit tests", "test file", "configuration file"] lines = example["content"].splitlines() count_config = 0 count_test = 0 # first test for _, line in zip(range(scan_width), lines): for keyword in keywords: if keyword in line.lower(): return {"config_or_test": True} # second test nlines = example["content"].count("\n") threshold = int(coeff * nlines) for line in lines: count_config += line.lower().count("config") count_test += line.lower().count("test") if count_config > threshold or count_test > threshold: return {"config_or_test": True} return {"config_or_test": False} def has_no_keywords(example): """Check if a python file has none of the keywords for: funcion, class, for loop, while loop.""" keywords = ["def ", "class ", "for ", "while "] lines = example["content"].splitlines() for line in lines: for keyword in keywords: if keyword in line.lower(): return {"has_no_keywords": False} return {"has_no_keywords": True} def has_few_assignments(example, minimum=4): """Check if file uses symbol '=' less than `minimum` times.""" lines = example["content"].splitlines() counter = 0 for line in lines: counter += line.lower().count("=") if counter > minimum: return {"has_few_assignments": False} return {"has_few_assignments": True} def char_token_ratio(example): """Compute character/token ratio of the file with tokenizer.""" input_ids = tokenizer(example["content"], truncation=False)["input_ids"] ratio = len(example["content"]) / len(input_ids) return {"ratio": ratio} def preprocess(example): """Chain all preprocessing steps into one function to not fill cache.""" results = dict() results.update(get_hash(example)) results.update(line_stats(example)) results.update(alpha_stats(example)) results.update(char_token_ratio(example)) results.update(is_autogenerated(example)) results.update(is_config_or_test(example)) results.update(has_no_keywords(example)) results.update(has_few_assignments(example)) return results def filter(example, uniques, args): """Filter dataset with heuristics. Config, test and has_no_keywords files are removed with a given probability.""" if not check_uniques(example, uniques): return False elif example["autogenerated"]: return False elif example["line_max"] > args.line_max: return False elif example["line_mean"] > args.line_mean: return False elif example["alpha_frac"] < args.alpha_frac: return False elif example["ratio"] < args.min_token_ratio: return False elif example["config_or_test"] and np.random.rand() <= args.filter_proba: return False elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba: return False elif example["has_few_assignments"]: return False else: return True def compress_file(file_path): """Compress a file with g-zip.""" with open(file_path, "rb") as f_in: with gzip.open(str(file_path) + ".gz", "wb", compresslevel=6) as f_out: shutil.copyfileobj(f_in, f_out) os.unlink(file_path) # Settings parser = HfArgumentParser(PreprocessingArguments) args = parser.parse_args() if args.num_workers is None: args.num_workers = multiprocessing.cpu_count() tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_dir) # Load dataset t_start = time.time() ds = load_dataset(args.dataset_name, split="train") print(f"Time to load dataset: {time.time()-t_start:.2f}") # Run preprocessing t_start = time.time() ds = ds.map(preprocess, num_proc=args.num_workers) print(f"Time to preprocess dataset: {time.time()-t_start:.2f}") # Deduplicate hashes uniques = set(ds.unique("hash")) frac = len(uniques) / len(ds) print(f"Fraction of duplicates: {1-frac:.2%}") # Deduplicate data and apply heuristics t_start = time.time() ds_filter = ds.filter(filter, fn_kwargs={"uniques": uniques, "args": args}) print(f"Time to filter dataset: {time.time()-t_start:.2f}") print(f"Size of filtered dataset: {len(ds_filter)}") # Deduplicate with minhash and jaccard similarity if args.near_deduplication: t_start = time.time() ds_filter, duplicate_clusters = deduplicate_dataset(ds_filter, args.jaccard_threshold) print(f"Time to deduplicate dataset: {time.time()-t_start:.2f}") print(f"Size of deduplicate dataset: {len(ds_filter)}") # Save data in batches of samples_per_file output_dir = Path(args.output_dir) output_dir.mkdir(exist_ok=True) # save duplicate_clusters in the output_dir as artifacts # not sure it is the right place the save it if args.near_deduplication: with open(output_dir / "duplicate_clusters.json", "w") as f: json.dump(duplicate_clusters, f) data_dir = output_dir / "data" data_dir.mkdir(exist_ok=True) t_start = time.time() for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)): file_path = str(data_dir / f"file-{file_number+1:012}.json") end_index = min(len(ds_filter), index + args.samples_per_file) ds_filter.select(list(range(index, end_index))).to_json(file_path) compress_file(file_path) print(f"Time to save dataset: {time.time()-t_start:.2f}")

import gzip import hashlib import json import multiprocessing import os import re import shutil import time from pathlib import Path import numpy as np from datasets import load_dataset from arguments import PreprocessingArguments from minhash_deduplication import deduplicate_dataset from transformers import AutoTokenizer, HfArgumentParser PATTERN = re.compile(r"\s+") def get_hash(example): """Get hash of content field.""" return {"hash": hashlib.md5(re.sub(PATTERN, "", example["content"]).encode("utf-8")).hexdigest()} def line_stats(example): """Calculates mean and max line length of file.""" line_lengths = [len(line) for line in example["content"].splitlines()] return {"line_mean": np.mean(line_lengths), "line_max": max(line_lengths)} def alpha_stats(example): """Calculates mean and max line length of file.""" alpha_frac = np.mean([c.isalnum() for c in example["content"]]) return {"alpha_frac": alpha_frac} def check_uniques(example, uniques): """Check if current hash is still in set of unique hashes and remove if true.""" if example["hash"] in uniques: uniques.remove(example["hash"]) return True else: return False def is_autogenerated(example, scan_width=5): """Check if file is autogenerated by looking for keywords in the first few lines of the file.""" keywords = ["auto-generated", "autogenerated", "automatically generated"] lines = example["content"].splitlines() for _, line in zip(range(scan_width), lines): for keyword in keywords: if keyword in line.lower(): return {"autogenerated": True} else: return {"autogenerated": False} def is_config_or_test(example, scan_width=5, coeff=0.05): """Check if file is a configuration file or a unit test by : 1- looking for keywords in the first few lines of the file. 2- counting number of occurence of the words 'config' and 'test' with respect to number of lines. """ keywords = ["unit tests", "test file", "configuration file"] lines = example["content"].splitlines() count_config = 0 count_test = 0 # first test for _, line in zip(range(scan_width), lines): for keyword in keywords: if keyword in line.lower(): return {"config_or_test": True} # second test nlines = example["content"].count("\n") threshold = int(coeff * nlines) for line in lines: count_config += line.lower().count("config") count_test += line.lower().count("test") if count_config > threshold or count_test > threshold: return {"config_or_test": True} return {"config_or_test": False} def has_no_keywords(example): """Check if a python file has none of the keywords for: funcion, class, for loop, while loop.""" keywords = ["def ", "class ", "for ", "while "] lines = example["content"].splitlines() for line in lines: for keyword in keywords: if keyword in line.lower(): return {"has_no_keywords": False} return {"has_no_keywords": True} def has_few_assignments(example, minimum=4): """Check if file uses symbol '=' less than `minimum` times.""" lines = example["content"].splitlines() counter = 0 for line in lines: counter += line.lower().count("=") if counter > minimum: return {"has_few_assignments": False} return {"has_few_assignments": True} def char_token_ratio(example): """Compute character/token ratio of the file with tokenizer.""" input_ids = tokenizer(example["content"], truncation=False)["input_ids"] ratio = len(example["content"]) / len(input_ids) return {"ratio": ratio} def preprocess(example): """Chain all preprocessing steps into one function to not fill cache.""" results = dict() results.update(get_hash(example)) results.update(line_stats(example)) results.update(alpha_stats(example)) results.update(char_token_ratio(example)) results.update(is_autogenerated(example)) results.update(is_config_or_test(example)) results.update(has_no_keywords(example)) results.update(has_few_assignments(example)) return results def filter(example, uniques, args): """Filter dataset with heuristics. Config, test and has_no_keywords files are removed with a given probability.""" if not check_uniques(example, uniques): return False elif example["autogenerated"]: return False elif example["line_max"] > args.line_max: return False elif example["line_mean"] > args.line_mean: return False elif example["alpha_frac"] < args.alpha_frac: return False elif example["ratio"] < args.min_token_ratio: return False elif example["config_or_test"] and np.random.rand() <= args.filter_proba: return False elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba: return False elif example["has_few_assignments"]: return False else: return True def compress_file(file_path): """Compress a file with g-zip.""" with open(file_path, "rb") as f_in: with gzip.open(str(file_path) + ".gz", "wb", compresslevel=6) as f_out: shutil.copyfileobj(f_in, f_out) os.unlink(file_path) # Settings parser = HfArgumentParser(PreprocessingArguments) args = parser.parse_args() if args.num_workers is None: args.num_workers = multiprocessing.cpu_count() tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_dir) # Load dataset t_start = time.time() ds = load_dataset(args.dataset_name, split="train") print(f"Time to load dataset: {time.time()-t_start:.2f}") # Run preprocessing t_start = time.time() ds = ds.map(preprocess, num_proc=args.num_workers) print(f"Time to preprocess dataset: {time.time()-t_start:.2f}") # Deduplicate hashes uniques = set(ds.unique("hash")) frac = len(uniques) / len(ds) print(f"Fraction of duplicates: {1-frac:.2%}") # Deduplicate data and apply heuristics t_start = time.time() ds_filter = ds.filter(filter, fn_kwargs={"uniques": uniques, "args": args}) print(f"Time to filter dataset: {time.time()-t_start:.2f}") print(f"Size of filtered dataset: {len(ds_filter)}") # Deduplicate with minhash and jaccard similarity if args.near_deduplication: t_start = time.time() ds_filter, duplicate_clusters = deduplicate_dataset(ds_filter, args.jaccard_threshold) print(f"Time to deduplicate dataset: {time.time()-t_start:.2f}") print(f"Size of deduplicate dataset: {len(ds_filter)}") # Save data in batches of samples_per_file output_dir = Path(args.output_dir) output_dir.mkdir(exist_ok=True) # save duplicate_clusters in the output_dir as artifacts # not sure it is the right place the save it if args.near_deduplication: with open(output_dir / "duplicate_clusters.json", "w") as f: json.dump(duplicate_clusters, f) data_dir = output_dir / "data" data_dir.mkdir(exist_ok=True) t_start = time.time() for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)): file_path = str(data_dir / f"file-{file_number+1:012}.json") end_index = min(len(ds_filter), index + args.samples_per_file) ds_filter.select(list(range(index, end_index))).to_json(file_path) compress_file(file_path) print(f"Time to save dataset: {time.time()-t_start:.2f}")

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/generation/test_logits_process.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a clone of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch, torch_device from ..test_modeling_common import ids_tensor if is_torch_available(): import torch from torch import nn from transformers.generation import ( EncoderNoRepeatNGramLogitsProcessor, ExponentialDecayLengthPenalty, ForcedBOSTokenLogitsProcessor, ForcedEOSTokenLogitsProcessor, HammingDiversityLogitsProcessor, InfNanRemoveLogitsProcessor, LogitNormalization, LogitsProcessorList, MinLengthLogitsProcessor, NoBadWordsLogitsProcessor, NoRepeatNGramLogitsProcessor, PrefixConstrainedLogitsProcessor, RepetitionPenaltyLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TypicalLogitsWarper, ) @require_torch class LogitsProcessorTest(unittest.TestCase): def _get_uniform_logits(self, batch_size: int, length: int): scores = torch.ones((batch_size, length), device=torch_device, dtype=torch.float) / length return scores def test_min_length_dist_processor(self): vocab_size = 20 batch_size = 4 eos_token_id = 0 min_dist_processor = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id) # check that min length is applied at length 5 input_ids = ids_tensor((batch_size, 5), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores_before_min_length = min_dist_processor(input_ids, scores) self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist(), 4 * [-float("inf")]) # check that min length is not applied anymore at length 15 input_ids = ids_tensor((batch_size, 15), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores_before_min_length = min_dist_processor(input_ids, scores) self.assertFalse(torch.isinf(scores_before_min_length).any()) def test_temperature_dist_warper(self): input_ids = None length = 20 scores = self._get_uniform_logits(batch_size=2, length=length) # tweak scores to not be uniform anymore scores[1, 5] = (1 / length) + 0.1 # peak, 1st batch scores[1, 10] = (1 / length) - 0.4 # valley, 1st batch # compute softmax probs = nn.functional.softmax(scores, dim=-1) temp_dist_warper_sharper = TemperatureLogitsWarper(temperature=0.5) temp_dist_warper_smoother = TemperatureLogitsWarper(temperature=1.3) warped_prob_sharp = nn.functional.softmax(temp_dist_warper_sharper(input_ids, scores.clone()), dim=-1) warped_prob_smooth = nn.functional.softmax(temp_dist_warper_smoother(input_ids, scores.clone()), dim=-1) # uniform distribution stays uniform self.assertTrue(torch.allclose(probs[0, :], warped_prob_sharp[0, :], atol=1e-3)) self.assertTrue(torch.allclose(probs[0, :], warped_prob_smooth[0, :], atol=1e-3)) # sharp peaks get higher, valleys get lower self.assertLess(probs[1, :].max(), warped_prob_sharp[1, :].max()) self.assertGreater(probs[1, :].min(), warped_prob_sharp[1, :].min()) # smooth peaks get lower, valleys get higher self.assertGreater(probs[1, :].max(), warped_prob_smooth[1, :].max()) self.assertLess(probs[1, :].min(), warped_prob_smooth[1, :].min()) def test_repetition_penalty_dist_process(self): input_ids = torch.tensor([[0, 1], [5, 0]], device=torch_device, dtype=torch.long) vocab_size = 10 scores = self._get_uniform_logits(batch_size=2, length=vocab_size) # give values special values scores[0, 0] = -(1 / vocab_size) scores[1, 5] = 4 / vocab_size rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0) scores = rep_penalty_proc(input_ids, scores.clone()) # check that values were correctly changed self.assertAlmostEqual(scores[0, 0].item(), -(1 / vocab_size) * 2) self.assertAlmostEqual(scores[0, 1].item(), (1 / vocab_size) / 2) self.assertAlmostEqual(scores[1, 0].item(), (1 / vocab_size) / 2) self.assertAlmostEqual(scores[1, 5].item(), (4 / vocab_size) / 2) def test_top_k_dist_warper(self): input_ids = None vocab_size = 10 batch_size = 2 # create ramp distribution ramp_logits = ( torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1) ) ramp_logits[1:, : vocab_size // 2] = ramp_logits[1:, : vocab_size // 2] + vocab_size top_k_warp = TopKLogitsWarper(3) scores = top_k_warp(input_ids, ramp_logits) # check that correct tokens are filtered self.assertListEqual(torch.isinf(scores[0]).tolist(), 7 * [True] + 3 * [False]) self.assertListEqual(torch.isinf(scores[1]).tolist(), 2 * [True] + 3 * [False] + 5 * [True]) # check special cases length = 5 logits = self._get_uniform_logits(batch_size=batch_size, length=length) top_k_warp_safety_check = TopKLogitsWarper(top_k=1, filter_value=0.0, min_tokens_to_keep=3) scores = top_k_warp_safety_check(input_ids, logits) # uniform dist is not changed self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [0, 0]) ramp_logits = torch.arange(length, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1) scores = top_k_warp_safety_check(input_ids, ramp_logits) # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2]) def test_top_p_dist_warper(self): input_ids = None vocab_size = 10 batch_size = 2 # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper) dist = torch.log( torch.tensor([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float) ) top_p_warp = TopPLogitsWarper(0.8) filtered_dist = torch.exp(top_p_warp(input_ids, dist)) # dist should be filtered to keep min num values so that sum is >= top_p # exp (-inf) => 0 EXPECTED_FILTERED_DIST = torch.tensor( [[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float ) self.assertTrue(torch.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3)) # check edge cases with negative and extreme logits ramp_logits = torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat( batch_size, 1 ) - (vocab_size // 2) # make ramp_logits more extreme ramp_logits[1] = ramp_logits[1] * 100.0 # make sure at least 2 tokens are kept top_p_warp = TopPLogitsWarper(0.9, min_tokens_to_keep=2, filter_value=0.0) filtered_dist = top_p_warp(input_ids, ramp_logits) # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2. self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [3, 2]) def test_typical_dist_warper(self): input_ids = None vocab_size = 10 batch_size = 2 # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper) dist = torch.log( torch.tensor([[0.97, 0.01, 0.01, 0.01], [0.4, 0.2, 0.2, 0.2]], device=torch_device, dtype=torch.float) ) typical_warp = TypicalLogitsWarper(0.5) filtered_dist = torch.exp(typical_warp(input_ids, dist)) # dist should be filtered to keep min num values so that sum is >= 0.7 # exp (-inf) => 0 EXPECTED_FILTERED_DIST = torch.tensor( [[0.97, 0.0, 0.0, 0.0], [0.0, 0.2, 0.2, 0.2]], device=torch_device, dtype=torch.float ) self.assertTrue(torch.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3)) # check special cases length = 5 logits = self._get_uniform_logits(batch_size=batch_size, length=length) typical_warp_safety_check = TypicalLogitsWarper(mass=0.5, filter_value=0.0, min_tokens_to_keep=3) scores = typical_warp_safety_check(input_ids, logits) # uniform dist is not changed self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [0, 0]) # check edge cases with negative and extreme logits ramp_logits = torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat( batch_size, 1 ) - (vocab_size // 2) # make ramp_logits more extreme ramp_logits[1] = ramp_logits[1] * 100.0 # make sure at least 2 tokens are kept typical_warp = TypicalLogitsWarper(0.7, min_tokens_to_keep=2, filter_value=0.0) filtered_dist = typical_warp(input_ids, ramp_logits) # first batch should keep two tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2. self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2]) def test_no_repeat_ngram_dist_processor(self): vocab_size = 3 batch_size = 2 input_ids = torch.tensor([[1, 1, 2, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size, vocab_size) no_repeat_proc_2_gram = NoRepeatNGramLogitsProcessor(2) no_repeat_proc_3_gram = NoRepeatNGramLogitsProcessor(3) filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone()) filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone()) # 2-gram would forbid 2nd and 3rd token (1,2) at 1st batch and 1st token (0) at 2nd batch self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [True, False, False]]) # 3-gram would forbid no token at 1st batch and 1st token (0) at 2nd batch self.assertListEqual( torch.isinf(filtered_scores_3_gram).tolist(), [[False, False, False], [True, False, False]] ) def test_encoder_no_repeat_ngram_dist_processor(self): vocab_size = 3 num_beams = 2 batch_size = 1 encoder_input_ids = torch.tensor([1, 2, 1, 1], device=torch_device, dtype=torch.long) input_ids = torch.tensor([[1, 2, 1], [8, 0, 2]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size * num_beams, vocab_size) no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids) no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids) filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone()) filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone()) # 2-gram would forbid 1st and 2nd token at 1st beam and 1st token (0) at 2nd beam self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [False, True, False]]) # 3-gram would forbid 1st token at 1st beam and no token at 2nd beam self.assertListEqual( torch.isinf(filtered_scores_3_gram).tolist(), [[False, True, False], [False, False, False]] ) # Batched input vocab_size = 3 num_beams = 2 batch_size = 2 encoder_input_ids = torch.tensor([[1, 2, 1, 1], [0, 0, 2, 1]], device=torch_device, dtype=torch.long) input_ids = torch.tensor([[1, 2, 1], [1, 0, 2], [0, 0, 0], [0, 2, 2]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size * num_beams, vocab_size) no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids) no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids) filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone()) filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone()) # 2gram # Batch 1 # - Beam 1: tokens (1, 2) forbidden # - Beam 2: tokens (1) forbidden # Batch 2 # - Beam 1: tokens (0, 2) forbidden # - Beam 2: tokens (1) forbidden self.assertListEqual( torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [False, True, False], [True, False, True], [False, True, False]], ) # Batch 1 # - Beam 1: tokens (1) forbidden # - Beam 2: tokens () forbidden # Batch 2 # - Beam 1: tokens (2) forbidden # - Beam 2: tokens () forbidden self.assertListEqual( torch.isinf(filtered_scores_3_gram).tolist(), [[False, True, False], [False, False, False], [False, False, True], [False, False, False]], ) def test_no_bad_words_dist_processor(self): vocab_size = 5 batch_size = 2 eos_token_id = 4 input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long) bad_word_tokens = [[1], [4], [1, 0], [0, 1, 2], [1, 3, 1, 3]] scores = self._get_uniform_logits(batch_size, vocab_size) no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=bad_word_tokens, eos_token_id=eos_token_id) filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone()) # batch 1: 1st, 2nd, and 4th (0, 1, 3) token are forbidden # batch 2: 1st, 2nd, and 3rd (0, 1, 2) token are forbidden # Note that 5th element cannot be forbidden as it is EOS token self.assertListEqual( torch.isinf(filtered_scores).tolist(), [[True, True, False, True, False], [True, True, True, False, False]] ) # check edge case no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[4]], eos_token_id=eos_token_id) filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone()) self.assertTrue(torch.allclose(scores, filtered_scores, atol=1e-3)) def test_processor_list(self): batch_size = 4 sequence_length = 10 vocab_size = 15 eos_token_id = 0 # dummy input_ids and scores input_ids = ids_tensor((batch_size, sequence_length), vocab_size) input_ids_comp = input_ids.clone() scores = self._get_uniform_logits(batch_size, vocab_size) scores_comp = scores.clone() # instantiate all dist processors min_dist_proc = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id) temp_dist_warp = TemperatureLogitsWarper(temperature=0.5) rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0) top_k_warp = TopKLogitsWarper(3) top_p_warp = TopPLogitsWarper(0.8) no_repeat_proc = NoRepeatNGramLogitsProcessor(2) no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[1]], eos_token_id=eos_token_id) # no processor list scores = min_dist_proc(input_ids, scores) scores = temp_dist_warp(input_ids, scores) scores = rep_penalty_proc(input_ids, scores) scores = top_k_warp(input_ids, scores) scores = top_p_warp(input_ids, scores) scores = no_repeat_proc(input_ids, scores) scores = no_bad_words_dist_proc(input_ids, scores) # with processor list processor = LogitsProcessorList( [ min_dist_proc, temp_dist_warp, rep_penalty_proc, top_k_warp, top_p_warp, no_repeat_proc, no_bad_words_dist_proc, ] ) scores_comp = processor(input_ids, scores_comp) # scores should be equal self.assertTrue(torch.allclose(scores, scores_comp, atol=1e-3)) # input_ids should never be changed self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist()) def test_prefix_constrained_logits_processor(self): vocab_size = 5 batch_size = 2 input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size, vocab_size) def prefix_allowed_tokens_fn(batch_id, inputs_ids): return [[0, 1], [2, 3]][batch_id] prefix_constrained_logits_proc = PrefixConstrainedLogitsProcessor(prefix_allowed_tokens_fn, 1) filtered_scores = prefix_constrained_logits_proc(input_ids, scores.clone()) # batch 1: 1st, 2nd (0, 1) token are allowed # batch 2: 3rd, 4th (2, 3) token are allowed self.assertListEqual( torch.isinf(filtered_scores).tolist(), [[False, False, True, True, True], [True, True, False, False, True]] ) def test_hamming_diversity(self): vocab_size = 4 num_beams = 2 num_beam_groups = 2 scores = self._get_uniform_logits(num_beams, vocab_size) # batch_idx = 0 -> index batch_idx * num_beam_groups -> idx = 0 * 2 = 0 -> penalises tokens 1 # batch_idx = 1 -> index batch_idx * num_beam_groups -> idx = 1 * 2 = 2 -> penalises tokens 1 current_tokens = torch.tensor([0, 3, 1, 2], device=torch_device, dtype=torch.long) diversity_logits_processor = HammingDiversityLogitsProcessor( diversity_penalty=1.0, num_beams=num_beams, num_beam_groups=num_beam_groups ) processed_scores = diversity_logits_processor(None, scores, current_tokens, 1) self.assertTrue( torch.allclose( processed_scores[0], torch.tensor([-0.7500, 0.2500, 0.2500, 0.2500], device=torch_device), atol=1e-3 ) ) self.assertTrue( torch.allclose( processed_scores[1], torch.tensor([0.2500, -0.7500, 0.2500, 0.2500], device=torch_device), atol=1e-3 ) ) def test_forced_bos_token_logits_processor(self): vocab_size = 20 batch_size = 4 bos_token_id = 0 logits_processor = ForcedBOSTokenLogitsProcessor(bos_token_id=bos_token_id) # check that all scores are -inf except the bos_token_id score input_ids = ids_tensor((batch_size, 1), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertTrue(torch.isneginf(scores[:, bos_token_id + 1 :]).all()) self.assertListEqual(scores[:, bos_token_id].tolist(), 4 * [0]) # score for bos_token_id shold be zero # check that bos_token_id is not forced if current length is greater than 1 input_ids = ids_tensor((batch_size, 4), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertFalse(torch.isinf(scores).any()) def test_forced_eos_token_logits_processor(self): vocab_size = 20 batch_size = 4 eos_token_id = 0 max_length = 5 logits_processor = ForcedEOSTokenLogitsProcessor(max_length=max_length, eos_token_id=eos_token_id) # check that all scores are -inf except the eos_token_id when max_length-1 is reached input_ids = ids_tensor((batch_size, 4), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertTrue(torch.isneginf(scores[:, eos_token_id + 1 :]).all()) self.assertListEqual(scores[:, eos_token_id].tolist(), 4 * [0]) # score for eos_token_id should be zero # check that eos_token_id is not forced if max_length-1 is not reached input_ids = ids_tensor((batch_size, 3), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertFalse(torch.isinf(scores).any()) def test_remove_nan_inf_logits_processor(self): scores = torch.tensor( [[0.0, 0.7, 0.8, float("nan")], [0.1, float("inf"), 0.3, float("-inf")]], device=torch_device ) input_ids = ids_tensor((2, 4), vocab_size=20) logits_processor = InfNanRemoveLogitsProcessor() scores = logits_processor(input_ids, scores) self.assertTrue( torch.allclose( scores, torch.tensor( [[0.0, 0.7, 0.8, 0.0], [0.1, torch.finfo(scores.dtype).max, 0.3, float("-inf")]], device=torch_device, ), atol=1e-6, ) ) def test_exponential_decay_length_penalty(self): vocab_size = 20 batch_size = 4 eos_token_id = 0 penalty_start = 5 penalty_factor = 1.1 input_ids = ids_tensor((batch_size, 2), vocab_size=vocab_size) input_ids_seq_length = input_ids.shape[-1] length_decay_processor = ExponentialDecayLengthPenalty( exponential_decay_length_penalty=(penalty_start, penalty_factor), eos_token_id=eos_token_id, input_ids_seq_length=input_ids_seq_length, ) # check that penalty is not applied before start scores = self._get_uniform_logits(batch_size, vocab_size) scores_before_start = length_decay_processor(input_ids, scores) self.assertListEqual(scores_before_start[:, eos_token_id].tolist(), scores[:, eos_token_id].tolist()) # check that penalty is applied after start input_ids = ids_tensor((batch_size, 20), vocab_size=vocab_size) scores = self._get_uniform_logits(batch_size, vocab_size) scores_after_start = length_decay_processor(input_ids, scores) self.assertTrue( torch.gt( scores_after_start[penalty_start + 1 :, eos_token_id], scores[penalty_start + 1 :, eos_token_id] ).all() ) def test_normalization(self): input_ids = None scores = torch.tensor( [[-23.18, -29.96, -43.54, 47.77], [-33.58, -26.87, -32.96, 22.51]], device=torch_device, dtype=torch.float ) logit_normalization = LogitNormalization() normalized_scores = logit_normalization(input_ids, scores).exp() ones = torch.ones(scores.shape[0], device=torch_device, dtype=torch.float) self.assertTrue(normalized_scores.sum(dim=-1).allclose(ones)) self.assertTrue(normalized_scores.allclose(scores.softmax(dim=-1)))

# coding=utf-8 # Copyright 2020 The HuggingFace Team Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a clone of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch, torch_device from ..test_modeling_common import ids_tensor if is_torch_available(): import torch from torch import nn from transformers.generation import ( EncoderNoRepeatNGramLogitsProcessor, ExponentialDecayLengthPenalty, ForcedBOSTokenLogitsProcessor, ForcedEOSTokenLogitsProcessor, HammingDiversityLogitsProcessor, InfNanRemoveLogitsProcessor, LogitNormalization, LogitsProcessorList, MinLengthLogitsProcessor, NoBadWordsLogitsProcessor, NoRepeatNGramLogitsProcessor, PrefixConstrainedLogitsProcessor, RepetitionPenaltyLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TypicalLogitsWarper, ) @require_torch class LogitsProcessorTest(unittest.TestCase): def _get_uniform_logits(self, batch_size: int, length: int): scores = torch.ones((batch_size, length), device=torch_device, dtype=torch.float) / length return scores def test_min_length_dist_processor(self): vocab_size = 20 batch_size = 4 eos_token_id = 0 min_dist_processor = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id) # check that min length is applied at length 5 input_ids = ids_tensor((batch_size, 5), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores_before_min_length = min_dist_processor(input_ids, scores) self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist(), 4 * [-float("inf")]) # check that min length is not applied anymore at length 15 input_ids = ids_tensor((batch_size, 15), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores_before_min_length = min_dist_processor(input_ids, scores) self.assertFalse(torch.isinf(scores_before_min_length).any()) def test_temperature_dist_warper(self): input_ids = None length = 20 scores = self._get_uniform_logits(batch_size=2, length=length) # tweak scores to not be uniform anymore scores[1, 5] = (1 / length) + 0.1 # peak, 1st batch scores[1, 10] = (1 / length) - 0.4 # valley, 1st batch # compute softmax probs = nn.functional.softmax(scores, dim=-1) temp_dist_warper_sharper = TemperatureLogitsWarper(temperature=0.5) temp_dist_warper_smoother = TemperatureLogitsWarper(temperature=1.3) warped_prob_sharp = nn.functional.softmax(temp_dist_warper_sharper(input_ids, scores.clone()), dim=-1) warped_prob_smooth = nn.functional.softmax(temp_dist_warper_smoother(input_ids, scores.clone()), dim=-1) # uniform distribution stays uniform self.assertTrue(torch.allclose(probs[0, :], warped_prob_sharp[0, :], atol=1e-3)) self.assertTrue(torch.allclose(probs[0, :], warped_prob_smooth[0, :], atol=1e-3)) # sharp peaks get higher, valleys get lower self.assertLess(probs[1, :].max(), warped_prob_sharp[1, :].max()) self.assertGreater(probs[1, :].min(), warped_prob_sharp[1, :].min()) # smooth peaks get lower, valleys get higher self.assertGreater(probs[1, :].max(), warped_prob_smooth[1, :].max()) self.assertLess(probs[1, :].min(), warped_prob_smooth[1, :].min()) def test_repetition_penalty_dist_process(self): input_ids = torch.tensor([[0, 1], [5, 0]], device=torch_device, dtype=torch.long) vocab_size = 10 scores = self._get_uniform_logits(batch_size=2, length=vocab_size) # give values special values scores[0, 0] = -(1 / vocab_size) scores[1, 5] = 4 / vocab_size rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0) scores = rep_penalty_proc(input_ids, scores.clone()) # check that values were correctly changed self.assertAlmostEqual(scores[0, 0].item(), -(1 / vocab_size) * 2) self.assertAlmostEqual(scores[0, 1].item(), (1 / vocab_size) / 2) self.assertAlmostEqual(scores[1, 0].item(), (1 / vocab_size) / 2) self.assertAlmostEqual(scores[1, 5].item(), (4 / vocab_size) / 2) def test_top_k_dist_warper(self): input_ids = None vocab_size = 10 batch_size = 2 # create ramp distribution ramp_logits = ( torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1) ) ramp_logits[1:, : vocab_size // 2] = ramp_logits[1:, : vocab_size // 2] + vocab_size top_k_warp = TopKLogitsWarper(3) scores = top_k_warp(input_ids, ramp_logits) # check that correct tokens are filtered self.assertListEqual(torch.isinf(scores[0]).tolist(), 7 * [True] + 3 * [False]) self.assertListEqual(torch.isinf(scores[1]).tolist(), 2 * [True] + 3 * [False] + 5 * [True]) # check special cases length = 5 logits = self._get_uniform_logits(batch_size=batch_size, length=length) top_k_warp_safety_check = TopKLogitsWarper(top_k=1, filter_value=0.0, min_tokens_to_keep=3) scores = top_k_warp_safety_check(input_ids, logits) # uniform dist is not changed self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [0, 0]) ramp_logits = torch.arange(length, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1) scores = top_k_warp_safety_check(input_ids, ramp_logits) # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2]) def test_top_p_dist_warper(self): input_ids = None vocab_size = 10 batch_size = 2 # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper) dist = torch.log( torch.tensor([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float) ) top_p_warp = TopPLogitsWarper(0.8) filtered_dist = torch.exp(top_p_warp(input_ids, dist)) # dist should be filtered to keep min num values so that sum is >= top_p # exp (-inf) => 0 EXPECTED_FILTERED_DIST = torch.tensor( [[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float ) self.assertTrue(torch.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3)) # check edge cases with negative and extreme logits ramp_logits = torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat( batch_size, 1 ) - (vocab_size // 2) # make ramp_logits more extreme ramp_logits[1] = ramp_logits[1] * 100.0 # make sure at least 2 tokens are kept top_p_warp = TopPLogitsWarper(0.9, min_tokens_to_keep=2, filter_value=0.0) filtered_dist = top_p_warp(input_ids, ramp_logits) # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2. self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [3, 2]) def test_typical_dist_warper(self): input_ids = None vocab_size = 10 batch_size = 2 # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper) dist = torch.log( torch.tensor([[0.97, 0.01, 0.01, 0.01], [0.4, 0.2, 0.2, 0.2]], device=torch_device, dtype=torch.float) ) typical_warp = TypicalLogitsWarper(0.5) filtered_dist = torch.exp(typical_warp(input_ids, dist)) # dist should be filtered to keep min num values so that sum is >= 0.7 # exp (-inf) => 0 EXPECTED_FILTERED_DIST = torch.tensor( [[0.97, 0.0, 0.0, 0.0], [0.0, 0.2, 0.2, 0.2]], device=torch_device, dtype=torch.float ) self.assertTrue(torch.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3)) # check special cases length = 5 logits = self._get_uniform_logits(batch_size=batch_size, length=length) typical_warp_safety_check = TypicalLogitsWarper(mass=0.5, filter_value=0.0, min_tokens_to_keep=3) scores = typical_warp_safety_check(input_ids, logits) # uniform dist is not changed self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [0, 0]) # check edge cases with negative and extreme logits ramp_logits = torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat( batch_size, 1 ) - (vocab_size // 2) # make ramp_logits more extreme ramp_logits[1] = ramp_logits[1] * 100.0 # make sure at least 2 tokens are kept typical_warp = TypicalLogitsWarper(0.7, min_tokens_to_keep=2, filter_value=0.0) filtered_dist = typical_warp(input_ids, ramp_logits) # first batch should keep two tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2. self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2]) def test_no_repeat_ngram_dist_processor(self): vocab_size = 3 batch_size = 2 input_ids = torch.tensor([[1, 1, 2, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size, vocab_size) no_repeat_proc_2_gram = NoRepeatNGramLogitsProcessor(2) no_repeat_proc_3_gram = NoRepeatNGramLogitsProcessor(3) filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone()) filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone()) # 2-gram would forbid 2nd and 3rd token (1,2) at 1st batch and 1st token (0) at 2nd batch self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [True, False, False]]) # 3-gram would forbid no token at 1st batch and 1st token (0) at 2nd batch self.assertListEqual( torch.isinf(filtered_scores_3_gram).tolist(), [[False, False, False], [True, False, False]] ) def test_encoder_no_repeat_ngram_dist_processor(self): vocab_size = 3 num_beams = 2 batch_size = 1 encoder_input_ids = torch.tensor([1, 2, 1, 1], device=torch_device, dtype=torch.long) input_ids = torch.tensor([[1, 2, 1], [8, 0, 2]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size * num_beams, vocab_size) no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids) no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids) filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone()) filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone()) # 2-gram would forbid 1st and 2nd token at 1st beam and 1st token (0) at 2nd beam self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [False, True, False]]) # 3-gram would forbid 1st token at 1st beam and no token at 2nd beam self.assertListEqual( torch.isinf(filtered_scores_3_gram).tolist(), [[False, True, False], [False, False, False]] ) # Batched input vocab_size = 3 num_beams = 2 batch_size = 2 encoder_input_ids = torch.tensor([[1, 2, 1, 1], [0, 0, 2, 1]], device=torch_device, dtype=torch.long) input_ids = torch.tensor([[1, 2, 1], [1, 0, 2], [0, 0, 0], [0, 2, 2]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size * num_beams, vocab_size) no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids) no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids) filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone()) filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone()) # 2gram # Batch 1 # - Beam 1: tokens (1, 2) forbidden # - Beam 2: tokens (1) forbidden # Batch 2 # - Beam 1: tokens (0, 2) forbidden # - Beam 2: tokens (1) forbidden self.assertListEqual( torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [False, True, False], [True, False, True], [False, True, False]], ) # Batch 1 # - Beam 1: tokens (1) forbidden # - Beam 2: tokens () forbidden # Batch 2 # - Beam 1: tokens (2) forbidden # - Beam 2: tokens () forbidden self.assertListEqual( torch.isinf(filtered_scores_3_gram).tolist(), [[False, True, False], [False, False, False], [False, False, True], [False, False, False]], ) def test_no_bad_words_dist_processor(self): vocab_size = 5 batch_size = 2 eos_token_id = 4 input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long) bad_word_tokens = [[1], [4], [1, 0], [0, 1, 2], [1, 3, 1, 3]] scores = self._get_uniform_logits(batch_size, vocab_size) no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=bad_word_tokens, eos_token_id=eos_token_id) filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone()) # batch 1: 1st, 2nd, and 4th (0, 1, 3) token are forbidden # batch 2: 1st, 2nd, and 3rd (0, 1, 2) token are forbidden # Note that 5th element cannot be forbidden as it is EOS token self.assertListEqual( torch.isinf(filtered_scores).tolist(), [[True, True, False, True, False], [True, True, True, False, False]] ) # check edge case no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[4]], eos_token_id=eos_token_id) filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone()) self.assertTrue(torch.allclose(scores, filtered_scores, atol=1e-3)) def test_processor_list(self): batch_size = 4 sequence_length = 10 vocab_size = 15 eos_token_id = 0 # dummy input_ids and scores input_ids = ids_tensor((batch_size, sequence_length), vocab_size) input_ids_comp = input_ids.clone() scores = self._get_uniform_logits(batch_size, vocab_size) scores_comp = scores.clone() # instantiate all dist processors min_dist_proc = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id) temp_dist_warp = TemperatureLogitsWarper(temperature=0.5) rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0) top_k_warp = TopKLogitsWarper(3) top_p_warp = TopPLogitsWarper(0.8) no_repeat_proc = NoRepeatNGramLogitsProcessor(2) no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[1]], eos_token_id=eos_token_id) # no processor list scores = min_dist_proc(input_ids, scores) scores = temp_dist_warp(input_ids, scores) scores = rep_penalty_proc(input_ids, scores) scores = top_k_warp(input_ids, scores) scores = top_p_warp(input_ids, scores) scores = no_repeat_proc(input_ids, scores) scores = no_bad_words_dist_proc(input_ids, scores) # with processor list processor = LogitsProcessorList( [ min_dist_proc, temp_dist_warp, rep_penalty_proc, top_k_warp, top_p_warp, no_repeat_proc, no_bad_words_dist_proc, ] ) scores_comp = processor(input_ids, scores_comp) # scores should be equal self.assertTrue(torch.allclose(scores, scores_comp, atol=1e-3)) # input_ids should never be changed self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist()) def test_prefix_constrained_logits_processor(self): vocab_size = 5 batch_size = 2 input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long) scores = self._get_uniform_logits(batch_size, vocab_size) def prefix_allowed_tokens_fn(batch_id, inputs_ids): return [[0, 1], [2, 3]][batch_id] prefix_constrained_logits_proc = PrefixConstrainedLogitsProcessor(prefix_allowed_tokens_fn, 1) filtered_scores = prefix_constrained_logits_proc(input_ids, scores.clone()) # batch 1: 1st, 2nd (0, 1) token are allowed # batch 2: 3rd, 4th (2, 3) token are allowed self.assertListEqual( torch.isinf(filtered_scores).tolist(), [[False, False, True, True, True], [True, True, False, False, True]] ) def test_hamming_diversity(self): vocab_size = 4 num_beams = 2 num_beam_groups = 2 scores = self._get_uniform_logits(num_beams, vocab_size) # batch_idx = 0 -> index batch_idx * num_beam_groups -> idx = 0 * 2 = 0 -> penalises tokens 1 # batch_idx = 1 -> index batch_idx * num_beam_groups -> idx = 1 * 2 = 2 -> penalises tokens 1 current_tokens = torch.tensor([0, 3, 1, 2], device=torch_device, dtype=torch.long) diversity_logits_processor = HammingDiversityLogitsProcessor( diversity_penalty=1.0, num_beams=num_beams, num_beam_groups=num_beam_groups ) processed_scores = diversity_logits_processor(None, scores, current_tokens, 1) self.assertTrue( torch.allclose( processed_scores[0], torch.tensor([-0.7500, 0.2500, 0.2500, 0.2500], device=torch_device), atol=1e-3 ) ) self.assertTrue( torch.allclose( processed_scores[1], torch.tensor([0.2500, -0.7500, 0.2500, 0.2500], device=torch_device), atol=1e-3 ) ) def test_forced_bos_token_logits_processor(self): vocab_size = 20 batch_size = 4 bos_token_id = 0 logits_processor = ForcedBOSTokenLogitsProcessor(bos_token_id=bos_token_id) # check that all scores are -inf except the bos_token_id score input_ids = ids_tensor((batch_size, 1), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertTrue(torch.isneginf(scores[:, bos_token_id + 1 :]).all()) self.assertListEqual(scores[:, bos_token_id].tolist(), 4 * [0]) # score for bos_token_id shold be zero # check that bos_token_id is not forced if current length is greater than 1 input_ids = ids_tensor((batch_size, 4), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertFalse(torch.isinf(scores).any()) def test_forced_eos_token_logits_processor(self): vocab_size = 20 batch_size = 4 eos_token_id = 0 max_length = 5 logits_processor = ForcedEOSTokenLogitsProcessor(max_length=max_length, eos_token_id=eos_token_id) # check that all scores are -inf except the eos_token_id when max_length-1 is reached input_ids = ids_tensor((batch_size, 4), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertTrue(torch.isneginf(scores[:, eos_token_id + 1 :]).all()) self.assertListEqual(scores[:, eos_token_id].tolist(), 4 * [0]) # score for eos_token_id should be zero # check that eos_token_id is not forced if max_length-1 is not reached input_ids = ids_tensor((batch_size, 3), vocab_size=20) scores = self._get_uniform_logits(batch_size, vocab_size) scores = logits_processor(input_ids, scores) self.assertFalse(torch.isinf(scores).any()) def test_remove_nan_inf_logits_processor(self): scores = torch.tensor( [[0.0, 0.7, 0.8, float("nan")], [0.1, float("inf"), 0.3, float("-inf")]], device=torch_device ) input_ids = ids_tensor((2, 4), vocab_size=20) logits_processor = InfNanRemoveLogitsProcessor() scores = logits_processor(input_ids, scores) self.assertTrue( torch.allclose( scores, torch.tensor( [[0.0, 0.7, 0.8, 0.0], [0.1, torch.finfo(scores.dtype).max, 0.3, float("-inf")]], device=torch_device, ), atol=1e-6, ) ) def test_exponential_decay_length_penalty(self): vocab_size = 20 batch_size = 4 eos_token_id = 0 penalty_start = 5 penalty_factor = 1.1 input_ids = ids_tensor((batch_size, 2), vocab_size=vocab_size) input_ids_seq_length = input_ids.shape[-1] length_decay_processor = ExponentialDecayLengthPenalty( exponential_decay_length_penalty=(penalty_start, penalty_factor), eos_token_id=eos_token_id, input_ids_seq_length=input_ids_seq_length, ) # check that penalty is not applied before start scores = self._get_uniform_logits(batch_size, vocab_size) scores_before_start = length_decay_processor(input_ids, scores) self.assertListEqual(scores_before_start[:, eos_token_id].tolist(), scores[:, eos_token_id].tolist()) # check that penalty is applied after start input_ids = ids_tensor((batch_size, 20), vocab_size=vocab_size) scores = self._get_uniform_logits(batch_size, vocab_size) scores_after_start = length_decay_processor(input_ids, scores) self.assertTrue( torch.gt( scores_after_start[penalty_start + 1 :, eos_token_id], scores[penalty_start + 1 :, eos_token_id] ).all() ) def test_normalization(self): input_ids = None scores = torch.tensor( [[-23.18, -29.96, -43.54, 47.77], [-33.58, -26.87, -32.96, 22.51]], device=torch_device, dtype=torch.float ) logit_normalization = LogitNormalization() normalized_scores = logit_normalization(input_ids, scores).exp() ones = torch.ones(scores.shape[0], device=torch_device, dtype=torch.float) self.assertTrue(normalized_scores.sum(dim=-1).allclose(ones)) self.assertTrue(normalized_scores.allclose(scores.softmax(dim=-1)))

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/research_projects/distillation/utils.py

# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utils to train DistilBERT adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM) """ import json import logging import os import socket import git import numpy as np import torch logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger = logging.getLogger(__name__) def git_log(folder_path: str): """ Log commit info. """ repo = git.Repo(search_parent_directories=True) repo_infos = { "repo_id": str(repo), "repo_sha": str(repo.head.object.hexsha), "repo_branch": str(repo.active_branch), } with open(os.path.join(folder_path, "git_log.json"), "w") as f: json.dump(repo_infos, f, indent=4) def init_gpu_params(params): """ Handle single and multi-GPU / multi-node. """ if params.n_gpu <= 0: params.local_rank = 0 params.master_port = -1 params.is_master = True params.multi_gpu = False return assert torch.cuda.is_available() logger.info("Initializing GPUs") if params.n_gpu > 1: assert params.local_rank != -1 params.world_size = int(os.environ["WORLD_SIZE"]) params.n_gpu_per_node = int(os.environ["N_GPU_NODE"]) params.global_rank = int(os.environ["RANK"]) # number of nodes / node ID params.n_nodes = params.world_size // params.n_gpu_per_node params.node_id = params.global_rank // params.n_gpu_per_node params.multi_gpu = True assert params.n_nodes == int(os.environ["N_NODES"]) assert params.node_id == int(os.environ["NODE_RANK"]) # local job (single GPU) else: assert params.local_rank == -1 params.n_nodes = 1 params.node_id = 0 params.local_rank = 0 params.global_rank = 0 params.world_size = 1 params.n_gpu_per_node = 1 params.multi_gpu = False # sanity checks assert params.n_nodes >= 1 assert 0 <= params.node_id < params.n_nodes assert 0 <= params.local_rank <= params.global_rank < params.world_size assert params.world_size == params.n_nodes * params.n_gpu_per_node # define whether this is the master process / if we are in multi-node distributed mode params.is_master = params.node_id == 0 and params.local_rank == 0 params.multi_node = params.n_nodes > 1 # summary PREFIX = f"--- Global rank: {params.global_rank} - " logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes) logger.info(PREFIX + "Node ID : %i" % params.node_id) logger.info(PREFIX + "Local rank : %i" % params.local_rank) logger.info(PREFIX + "World size : %i" % params.world_size) logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node) logger.info(PREFIX + "Master : %s" % str(params.is_master)) logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node)) logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu)) logger.info(PREFIX + "Hostname : %s" % socket.gethostname()) # set GPU device torch.cuda.set_device(params.local_rank) # initialize multi-GPU if params.multi_gpu: logger.info("Initializing PyTorch distributed") torch.distributed.init_process_group( init_method="env://", backend="nccl", ) def set_seed(args): """ Set the random seed. """ np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed)

# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utils to train DistilBERT adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM) """ import json import logging import os import socket import git import numpy as np import torch logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger = logging.getLogger(__name__) def git_log(folder_path: str): """ Log commit info. """ repo = git.Repo(search_parent_directories=True) repo_infos = { "repo_id": str(repo), "repo_sha": str(repo.head.object.hexsha), "repo_branch": str(repo.active_branch), } with open(os.path.join(folder_path, "git_log.json"), "w") as f: json.dump(repo_infos, f, indent=4) def init_gpu_params(params): """ Handle single and multi-GPU / multi-node. """ if params.n_gpu <= 0: params.local_rank = 0 params.master_port = -1 params.is_master = True params.multi_gpu = False return assert torch.cuda.is_available() logger.info("Initializing GPUs") if params.n_gpu > 1: assert params.local_rank != -1 params.world_size = int(os.environ["WORLD_SIZE"]) params.n_gpu_per_node = int(os.environ["N_GPU_NODE"]) params.global_rank = int(os.environ["RANK"]) # number of nodes / node ID params.n_nodes = params.world_size // params.n_gpu_per_node params.node_id = params.global_rank // params.n_gpu_per_node params.multi_gpu = True assert params.n_nodes == int(os.environ["N_NODES"]) assert params.node_id == int(os.environ["NODE_RANK"]) # local job (single GPU) else: assert params.local_rank == -1 params.n_nodes = 1 params.node_id = 0 params.local_rank = 0 params.global_rank = 0 params.world_size = 1 params.n_gpu_per_node = 1 params.multi_gpu = False # sanity checks assert params.n_nodes >= 1 assert 0 <= params.node_id < params.n_nodes assert 0 <= params.local_rank <= params.global_rank < params.world_size assert params.world_size == params.n_nodes * params.n_gpu_per_node # define whether this is the master process / if we are in multi-node distributed mode params.is_master = params.node_id == 0 and params.local_rank == 0 params.multi_node = params.n_nodes > 1 # summary PREFIX = f"--- Global rank: {params.global_rank} - " logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes) logger.info(PREFIX + "Node ID : %i" % params.node_id) logger.info(PREFIX + "Local rank : %i" % params.local_rank) logger.info(PREFIX + "World size : %i" % params.world_size) logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node) logger.info(PREFIX + "Master : %s" % str(params.is_master)) logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node)) logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu)) logger.info(PREFIX + "Hostname : %s" % socket.gethostname()) # set GPU device torch.cuda.set_device(params.local_rank) # initialize multi-GPU if params.multi_gpu: logger.info("Initializing PyTorch distributed") torch.distributed.init_process_group( init_method="env://", backend="nccl", ) def set_seed(args): """ Set the random seed. """ np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/benchmark/__init__.py

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/visual_bert/__init__.py