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README.md

@@ -1,13 +1,600 @@
----
-title: OFA OCR
-emoji: 📉
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.9.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<!---
+Copyright 2022 The OFA-Sys Team. 
+All rights reserved.
+This source code is licensed under the Apache 2.0 license found in the LICENSE file in the root directory.
+-->
+
+<p align="center">
+    <br>
+    <img src="examples/OFA_logo_tp_path.svg" width="150" />
+    <br>
+<p>
+<br>
+
+<p align="center">
+        <a href="modelscope.md">ModelScope</a>&nbsp ｜ &nbsp<a href="checkpoints.md">Checkpoints</a>&nbsp ｜ &nbsp<a href="colab.md">Colab</a>&nbsp ｜ &nbsp<a href="https://huggingface.co/ofa-sys">Demo</a>&nbsp ｜ &nbsp<a href="http://arxiv.org/abs/2202.03052">Paper </a>&nbsp ｜ &nbspBlog
+</p>
+
+<p align="center">
+    <br>
+    <img src="examples/demo.gif" width="800" />
+    <br>
+<p>
+
+[colab]: <https://colab.research.google.com/assets/colab-badge.svg>
+
+OFA is a unified sequence-to-sequence pretrained model (support **English** and **Chinese**) that unifies modalities (i.e., cross-modality, vision, language) and tasks (**finetuning** and **prompt tuning** are supported): image captioning (1st at the [MSCOCO Leaderboard](https://competitions.codalab.org/competitions/3221#results)), VQA ([link](https://eval.ai/web/challenges/challenge-page/830/leaderboard/2278)), visual grounding, text-to-image generation, text classification, text generation, image classification, etc. We provide **step-by-step** instructions for pretraining and finetuning and corresponding checkpoints (check official ckpt \[[EN](checkpoints.md)|[CN](checkpoints_cn.md)\] or [huggingface ckpt](https://huggingface.co/OFA-Sys)).
+
+We sincerely welcome contributions to our project. Feel free to contact us or send us issues / PRs!
+<br></br>
+
+
+# Online Demos
+We provide online demo via Hugging Face Spaces for you to interact with our pretrained and finetuned models. Below are the links to the demos:
+* Image Captioning \[[ModelScope](https://modelscope.cn/#/models/damo/ofa_image-caption_coco_large_en/summary)  |  [Spaces](https://huggingface.co/spaces/OFA-Sys/OFA-Image_Caption)\]
+* Visual Grounding \[[ModelScope](https://modelscope.cn/#/models/damo/ofa_visual-grounding_refcoco_large_en/summary) | [Spaces](https://huggingface.co/spaces/OFA-Sys/OFA-Visual_Grounding)\]
+* Visual Question Answering \[[ModelScope](https://modelscope.cn/#/models/damo/ofa_visual-question-answering_pretrain_large_en/summary) | [Spaces](https://huggingface.co/spaces/OFA-Sys/OFA-Visual_Question_Answering)\]
+* Text-to-Image Generation \[[ModelScope](https://modelscope.cn/#/models/damo/ofa_text-to-image-synthesis_coco_large_en/summary) | [Spaces](https://huggingface.co/spaces/OFA-Sys/OFA-Text2Image_Generation)\]
+* Generic Interface \[[Spaces](https://huggingface.co/spaces/OFA-Sys/OFA-Generic_Interface)\]
+
+Also we provide Colab notebooks for you to better perceive the procedures. Click [here](colab.md) to check them out!
+<br></br>
+
+# Use in Huggingface Transformers
+We support the inference of OFA in Huggingface Transformers. Check the [README](transformers.md) and [Colab Notebook](https://colab.research.google.com/drive/1Ho81RBV8jysZ7e0FhsSCk_v938QeDuy3?usp=sharing) for more information. Codes are released in this branch https://github.com/OFA-Sys/OFA/tree/feature/add_transformers
+<br><br>
+
+
+# News
+* 2022.8.16: Released the **Chinese** version of OFA. **OFA-CN** needs only switching to `bpe_dir=../../utils/BERT_CN_dict` and `bpe=bert` and using our provided Chinese checkpoints in [checkpoints_cn.md](checkpoints_cn.md). Temporarily, we only provide base-size and large-size pretrained checkpoints and finetuned checkpoints on [MUGE Caption](https://tianchi.aliyun.com/muge) and the Chinese version of RefCOCO(-/+/g) (to release soon). 
+* 2022.8.5: Released support of **prompt tuning** for OFA. Check our paper [here](https://arxiv.org/abs/2208.02532)! Please see the [prompt_tuning.md](prompt_tuning.md) for further details.
+* 2022.7.7: Updated support of OFA on **huggingface transformers** (fixed bugs in forward, add sequence generator from Fairseq to ensure performance, etc.). Refer to the doc [transformers.md](transformers.md) and the branch `feature/add_transformers`. 
+* 2022.6.17: Released the pretrained checkpoint of **OFA-Huge**. To use it, set `--arch=ofa_huge` in the script.
+* 2022.5.15: OFA was accepted by **ICML 2022**
+* 2022.4.28: Add support of inference on **huggingface transformers**. For how to use it, please refer to the doc [transformers.md](transformers.md) and our [huggingface models](https://huggingface.co/OFA-Sys). 
+* 2022.4.16: Released lightweight pretrained models **OFA-Medium** (~93M params) and **OFA-Tiny** (~33M params) in [checkpoints.md](checkpoints.md). To use them, you just need to load the corresponding checkpoint and set `--arch=ofa_medium` or `--arch=ofa_tiny` in the scripts.
+
+<details>
+    <summary><b>More News</b></summary>
+    <p>
+        <ul>
+            <li>2022.3.23: Added [Encouraging Loss](https://arxiv.org/pdf/2110.06537.pdf) as a feature. See [README_EncouragingLoss.md](README_EncouragingLoss.md). Leveraging this feature, OFA-Large has achieved improved results in both VQA (**test-std acc: 80.67**) and Image Classification (**test acc: 85.6**) recently.</li>
+            <li>2022.3.21: Released codes for pretraining OFA.</li>
+            <li>2022.3.18: Released the finetuned <b>OFA-Base</b> (~180M parameters) checkpoints and running scripts for vision & language tasks, including: <b>Caption (146.4 CIDEr), VQA (78.07 on test-std), SNLI-VE (89.3 on dev), RefCOCO (90.67 on testA), RefCOCO+ (87.15 on testA) and RefCOCOg (82.31 on test-u)</b>.</li>
+            <li>2022.3.11: Released the finetuning & inference code/checkpoints for <b>Gigaword</b>.</li>
+            <li>2022.3.08: Released the pretrained checkpoint of <b>OFA-Base</b> in <a href="https://github.com/OFA-Sys/OFA/blob/main/checkpoints.md">checkpoints.md</a>. To use OFA-Base, you just need to load <code>ofa_base.pt</code> and change <code>--arch=ofa_large</code> to <code>--arch=ofa_base</code> in the training scripts.</li>
+            <li>2022.3.07: Released the finetuning & inference code/checkpoints for <b>Image Classification</b>, which achieves <b>85.0</b> accuracy on ImageNet-1K, slightly better than reported in OFA paper.</li>
+            <li>2022.3.04: Released the finetuning & inference code/checkpoints for <b>Text-to-Image Generation</b>.</li>
+            <li>2022.3.03: Released the finetuning & inference code/checkpoints for <b>SNLI-VE</b> and <b>GLUE</b>.</li>
+            <li>2022.2.22: Released the finetuning & inference code/checkpoints for <b>Visual Question Answering</b>, which can reproduce <b>the reported VQA accuracy in OFA paper (80.02 on test-std)</b>. Check our results on the <a href="https://eval.ai/web/challenges/challenge-page/830/leaderboard/2278">VQA Challenge</a>.</li>
+            <li>2022.2.15: Released finetuning & inference code/checkpoints for <b>Referring Expression Comprehension</b></li>
+            <li>2022.2.10: Released the inference code & finetuned checkpoint for <b>Image captioning</b>, which can reproduce <b>the results on COCO Karparthy test split (149.6 CIDEr)</b>. OFA also achieves No.1 on the COCO image captioning online leaderboard <a href='https://competitions.codalab.org/competitions/3221#results'>Link</a> (marked as M6-Team).</li>
+        </ul>
+    </p>
+</details>
+<br></br>
+
+
+# Model Card
+We list the parameters and pretrained checkpoints of OFAs below. For finetuned checkpoints, please refer to [checkpoints.md](checkpoints.md). 
+
+<table border="1" width="100%">
+    <tr align="center">
+        <th>Model</th><th>Ckpt</th><th>Params</th><th>Backbone</th><th>Hidden size</th><th>Intermediate size</th><th>Num. of heads</th><th>Enc layers</th><th>Dec layers</th>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Tiny</sub></td><td><a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_tiny.pt">Download</a></td><td>33M</td><td>ResNet50</td><td>256</td><td>1024</td><td>4</td><td>4</td><td>4</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Medium</sub></td><td><a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_medium.pt">Download</a></td><td>93M</td><td>ResNet101</td><td>512</td></td><td>2048</td><td>8</td><td>4</td><td>4</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Base</sub></td><td><a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_base.pt">Download</a></td><td>180M</td><td>ResNet101</td><td>768</td></td><td>3072</td><td>12</td><td>6</td><td>6</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Large</sub></td><td><a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_large.pt">Download</a></td><td>470M</td><td>ResNet152</td><td>1024</td></td><td>4096</td><td>16</td><td>12</td><td>12</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Huge</sub></td><td><a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_huge.pt">Download</a></td><td>930M</td><td>ResNet152</td><td>1280</td></td><td>5120</td><td>16</td><td>24</td><td>12</td>
+    </tr>
+</table>
+<br></br>
+
+# Results
+Below we demonstrate the results of OFAs on cross-modal understanding and generation. 
+
+<table border="1" width="100%">
+    <tr align="center">
+        <th>Task</th><th>Image Captioning</th><th>VQA</th><th>Visual Entailment</th><th colspan="3">Referring Expression Comprehension</th>
+    </tr>
+    <tr align="center">
+        <td>Dataset</td><td>COCO</td><td>VQA v2</td><td>SNLI-VE</td><td>RefCOCO</td><td>RefCOCO+</td><td>RefCOCOg</td>
+    </tr>
+    <tr align="center">
+        <td>Split</td><td>Karpathy test (CE/CIDEr)</td><td>test-dev/test-std</td><td>val/test</td><td>val/test-a/test-b</td><td>val/test-a/test-b</td><td>val-u/test-u</td>
+    </tr>
+    <tr align="center">
+        <td>Metric</td><td>CIDEr</td><td>Acc.</td><td>Acc.</td><td colspan="3">Acc.</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Tiny</sub></td><td>119.0 / 128.7</td><td>70.3 / 70.4</td><td>85.3 / 85.2</td><td>80.20 / 84.07 / 75.00</td><td>68.22 / 75.13 / 57.66</td><td>72.02 / 69.74</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Medium</sub></td><td>130.4 / 140.3</td><td>75.4 / 75.5</td><td>86.6 / 87.0</td><td>85.34 / 87.68 / 77.92</td><td>76.09 / 83.04 / 66.25</td><td>78.76 / 78.58</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Base</sub></td><td>138.2 / 146.7</td><td>78.0 / 78.1</td><td>89.3 / 89.2</td><td>88.48 / 90.67 / 83.30</td><td>81.39 / 87.15 / 74.29</td><td>82.29 / 82.31</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Large</sub></td><td>142.2 / 150.7</td><td>80.4 / 80.7</td><td>90.3 / 90.2</td><td>90.05 / 92.93 / 85.26</td><td>85.80 / 89.87 / 79.22</td><td>85.89 / 86.55</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Huge</sub></td><td>145.3 / 154.9</td><td>82.0 / 82.0</td><td>91.0 / 91.2</td><td>92.04 / 94.03 / 88.44</td><td>87.86 / 91.70 / 80.71</td><td>88.07 / 88.78</td>
+    </tr>
+</table>
+<br></br>
+
+# Requirements
+* python 3.7.4
+* pytorch 1.8.1
+* torchvision 0.9.1
+* JAVA 1.8 (for COCO evaluation)
+<br></br>
+
+# Installation
+```bash
+git clone https://github.com/OFA-Sys/OFA
+pip install -r requirements.txt
+```
+<br></br>
+
+# Datasets and Checkpoints
+See [datasets.md](datasets.md) and [checkpoints.md](checkpoints.md).
+<br></br>
+
+# Training & Inference
+Below we provide methods for training and inference on different tasks. We provide both pretrained OFA-Large and OFA-Base in [checkpoints.md](checkpoints.md). The scripts mentioned in this section are prepared for OFA-Large. For reproducing the downstreaming results of OFA-Base, we have also provided the corresponding finetuning and inference scripts for OFA-Base in the `run_scripts/` folder.
+
+We recommend that your workspace directory should be organized like this: 
+```
+OFA/
+├── checkpoints/
+│   ├── ofa_base.pt
+│   ├── ofa_large.pt
+│   ├── caption_large_best_clean.pt
+│   └── ...
+├── criterions/
+├── data/
+├── dataset/
+│   ├── caption_data/
+│   ├── gigaword_data/
+│   └── ...
+├── fairseq/
+├── models/
+├── run_scripts/
+├── tasks/
+├── train.py
+├── trainer.py
+└── utils/
+```
+
+
+## Image Processing
+To ensure the efficiency of processing data, we did not store images with small files, but instead we encode them to base64 strings.
+Transforming image files to base64 strings is simple. Run the following code:
+```python
+from PIL import Image
+from io import BytesIO
+import base64
+
+img = Image.open(file_name) # path to file
+img_buffer = BytesIO()
+img.save(img_buffer, format=img.format)
+byte_data = img_buffer.getvalue()
+base64_str = base64.b64encode(byte_data) # bytes
+base64_str = base64_str.decode("utf-8") # str
+```
+
+## Pretraining
+Below we provide methods for pretraining OFA.
+
+<details>
+    <summary><b>1. Prepare the Dataset</b></summary>
+    <p>
+        To pretrain OFA, you should first download the dataset we provide (<a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/pretrain_data/pretrain_data_examples.zip">pretrain_data_examples.zip</a>, a small subset of the original pretraining data). For your customed pretraining datasets, please prepare your training samples into the same format. <code>pretrain_data_examples.zip</code> contains 4 TSV files: <code>vision_language_examples.tsv</code>, <code>text_examples.tsv</code>, <code>image_examples.tsv</code> and <code>detection_examples.tsv</code>. Details of these files are as follows: 
+        <br />
+        <ul type="circle">
+            <li><b>vision_language_examples.tsv</b>:
+    Each line contains uniq-id, image (base64 string), caption, question, answer, ground-truth objects (objects appearing in the caption or question), dataset name (source of the data) and task type (caption, qa or visual gronunding). Prepared for the pretraining tasks of visual grounding, grounded captioning, image-text matching, image captioning and visual question answering. </li>
+            <li><b>text_examples.tsv</b>: Each line contains uniq-id and text. Prepared for the pretraining task of text infilling. </li> 
+            <li><b>image_examples.tsv</b>: Each line contains uniq-id, image (base64 string, should be resized to 256*256 resolution) and image-code (generate the sparse codes for the central part of image through VQ-GAN). Prepared for the pretraining task of image infilling. </li>
+            <li><b>detection_examples.tsv</b>: Each line contains uniq-id, image (base64 string) and bounding box annotations (contains the top-left and bottom-right coordinates of the bounding box, object_id and object_name, seperated by commas). Prepared for the pretraining task of detection. </li>
+        </ul>
+        In addition, the folder negative_sample in pretrain_data_examples.zip contains three files <code>all_captions.txt</code>, <code>object.txt</code> and <code>type2ans.json</code>. The data in these files are used as negative samples for the image-text matching (ITM) task.
+    </p>
+</details>
+<details>
+    <summary><b>2. Pretraining</b></summary>
+    <p>
+        By default, the pretraining script will attempt to restore the released pretrained checkpoints of OFA-Base or OFA-Large and perform continuous pretraining. Continuous pretraining is more recommended, which achieves much better results compared with pretraining from scratch. For continuous pretraining, please download the pretrained weights in advance (see <a href='checkpoints.md'>checkpoints.md</a>) and put them in the correct directory <code>OFA/checkpoints/</code>. If not, the pretraining will begin from scratch.
+    </p>
+<pre>
+cd run_scripts/pretraining
+bash pretrain_ofa_large.sh # Pretrain OFA-Large. For OFA-Base, use pretrain_ofa_base.sh
+</pre>
+    <p>
+        If the pretrained OFA checkpoint is restored successfully, you will see the following information in the log:
+    </p>
+<pre>
+INFO: Loaded checkpoint ../../checkpoints/ofa_large.pt
+</pre>
+</details>
+
+## Image Captioning
+We provide procedures to reproduce our results of image captioning on our paper below.
+<details>
+    <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
+    <p>
+        Download data (see <a href='datasets.md'>datasets.md</a>) and models (see <a href='checkpoints.md'>checkpoints.md</a>) and put them in the correct directory. The dataset zipfile <code>caption_data.zip</code> contains caption_stage1_train.tsv, caption_stage2_train.tsv, caption_val.tsv and caption_test.tsv. Each image corresponds to only 1 caption in <code>caption_stage1_train.tsv</code> and corresponds to multiple captions in other TSV files (about 5 captions per image). Each line of the dataset represents a caption sample with the following format. The information of uniq-id, image-id, caption, predicted object labels (taken from <a href='https://github.com/pzzhang/VinVL'>VinVL</a>, not used), image base64 string are separated by tabs.
+    </p>
+<pre>
+162365  12455   the sun sets over the trees beyond some docks.  sky&&water&&dock&&pole  /9j/4AAQSkZJ....UCP/2Q==
+</pre>
+</details>
+<details>
+    <summary><b>2. Finetuning</b></summary>
+    <p>
+        Following previous standard practice, we divide the finetuning process of image captioning into two stages. In stage 1, we finetune OFA with cross-entropy loss on 4 NVIDIA-V100 GPUs with 32GB memory (expected to obtain ~139.5 CIDEr on the validation set at this stage). In stage 2, we select the best checkpoint of stage 1 and train with CIDEr optimization on 8 NVIDIA-V100 GPUs. <b>Note that CIDEr optimization is very unstable and requires careful hyperparameter tuning. If you encounter training errors in the stage2 finetuning, you can increase the batch size or reduce the learning rate. If neither of these works, you can directly set </b><code>--freeze-resnet</code><b> to freeze the inner states of batch normalization.</b>
+    </p>
+<pre>
+cd run_scripts/caption
+nohup sh train_caption_stage1.sh > train_stage1.out &  # stage 1, train with cross-entropy loss
+nohup sh train_caption_stage2.sh > train_stage2.out &  # stage 2, load the best ckpt of stage1 and train with CIDEr optimization 
+</pre>
+</details>
+<details>
+    <summary><b>3. Inference</b></summary>
+    <p>
+        Run the following commands to get your results and evaluate your model.
+    </p>
+<pre>
+cd run_scripts/caption ; sh evaluate_caption.sh  # inference & evaluate
+</pre>
+</details>
+
+## Text-to-Image Generation 
+This part provides procedures for the finetuning and inference of text-to-image generation. See below.
+
+<details>
+    <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
+    <p>
+        Download data (see <a href="datasets.md">datasets.md</a>) and models (see <a href="checkpoints.md">checkpoints.md</a>) and put them in the correct directory. The dataset zipfile <code>coco_image_gen.zip</code> contains <code>coco_vqgan_train.tsv</code>, <code>coco_vqgan_dev.tsv</code> and <code>coco_vqgan_full_test.tsv</code>. Each line of the dataset represents a sample with the following format. The information of uniq-id, image-code (produced by <a href="https://github.com/CompVis/taming-transformers">vqgan</a>, a list of integers separated by single-whitespaces), lowercased caption are separated by tabs.
+    </p>
+<pre>
+1	6674 4336 4532 5334 3251 5461 3615 2469 ...4965 4190 1846	the people are posing for a group photo.
+</pre>
+    <p>
+        The checkpoint zipfile <code>image_gen_large_best.zip</code> contains <code>image_gen_large_best.pt</code>, <code>vqgan/last.ckpt</code>, <code>vqgan/model.yaml</code> and <code>clip/Vit-B-16.pt</code>. 
+    </p>
+</details>
+<details>
+    <summary><b>2. Shuffle the Training Data</b></summary>
+    <p>
+        (Optional, but achieves better result): If the disk storage is sufficient, we recommend to prepare the shuffled training data for each epoch in advance. 
+    </p>
+<pre>
+cd dataset/image_gen
+ln coco_vqgan_train.tsv coco_vqgan_train_1.tsv
+for idx in `seq 1 9`;do shuf coco_vqgan_train_${idx}.tsv > coco_vqgan_train_$[${idx}+1].tsv;done # each file is used for an epoch
+</pre>
+</details>
+<details>
+    <summary><b>3. Finetuning</b></summary>
+    <p>
+        Following previous practice, we divide the finetuning process of image generating into two stages. In stage 1, we finetune OFA with cross-entropy loss on 4 8-V100-32G-GPU servers (expected to obtain ~32.5+ CLIP Score on the validation set at this stage). In stage 2, we select the last checkpoint of stage 1 and train with CLIP Score optimization on 4 8-V100-32G-GPU servers (expected to obtain ~34.0+ CLIP Score on the validation set at this stage). During the validation, the generated image will be dumped into <code>_GEN_IMAGE_PATH_</code>. 
+    </p>
+<pre>
+# run on each worker after the distributed and data configs have been correctly set following the guide in train_image_gen_stage1_distributed.sh 
+cd run_scripts/image_gen
+nohup sh train_image_gen_stage1_distributed.sh # stage 1, train with cross-entropy loss
+nohup sh train_image_gen_stage2_distributed.sh # stage 2, load the last ckpt of stage1 and train with CLIP Score optimization 
+</pre>
+</details>
+<details>
+    <summary><b>4. Inference</b></summary>
+    <p>
+        Run the command below to generate your images. 
+    </p>
+<pre>
+cd run_scripts/image_gen ; sh evaluate_image_gen.sh  # inference & evaluate (FID, IS and CLIP Score)
+</pre>
+</details>
+
+## Visual Question Answering
+Here we provide the finetuning and inference codes to reproduce the VQAv2 result reported in our paper (**test-std 80.02**). We believe much improvement on accuracy can still be achieved based on this codebase :)
+<details>
+    <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
+    <p>
+        Download data (see <a href="datasets.md">datasets.md</a>) and models (see <a href="checkpoints.md">checkpoints.md</a>) and put them in the correct directory. The dataset zipfile <code>vqa_data.zip</code> is around 100G and the decompressed data costs around 135G disk storage, which contains the training, validation and testing samples together with other necessary data resources. (Since <code>vqa_data.zip</code> is large in size, we have also provided chunked parts of the dataset files for more convenient and stable downloading. Please refer to <a href="https://github.com/OFA-Sys/OFA/issues/68#issuecomment-1096837349">issue #68</a>.) Following common practice, VG-QA samples are also included in the training data. To adapt to the seq2seq paradigm of OFA, we transform original VQA training questions with multiple golden answers into multiple training samples. For the original VQA validation set, we keep around 10k samples for our validation and utilize the other samples for training. Each line of the dataset represents a VQA sample with the following format. The information of question-id, image-id, question, answer (with confidence), predicted object labels (taken from <a href="https://github.com/pzzhang/VinVL">VinVL</a>, slightly brings around +0.1 accuracy improvement), image base64 string are separated by tabs. 
+    </p>
+<pre>
+79459   79459   is this person wearing shorts?  0.6|!+no    house&&short&&...&&sky  /9j/4AAQS...tigZ/9k=
+</pre>
+    <p>
+        For fine-tuning on customed VQA-formulated tasks, please refer to issue <a href="https://github.com/OFA-Sys/OFA/issues/76">#76</a>, <a href="https://github.com/OFA-Sys/OFA/issues/105">#105</a> and <a href="https://github.com/OFA-Sys/OFA/issues/73">#73</a> for more information.
+    </p>
+</details>
+<details>
+    <summary><b>2. Shuffle the Training Data</b></summary>
+    <p>
+        (Optional, but achieves better finetuning accuracy): If the disk storage is sufficient, we recommend to prepare the shuffled training data for each epoch in advance. In our experiments, we use shuffling which brings around <b>+0.3</b> improvement on VQA accuracy.
+    </p>
+<pre>
+cd dataset/vqa_data
+ln vqa_train.tsv vqa_train_1.tsv
+for idx in `seq 1 9`;do shuf vqa_train_${idx}.tsv > vqa_train_$[${idx}+1].tsv;done # each file is used for an epoch
+</pre>
+</details>
+<details>
+    <summary><b>3. Finetuning</b></summary>
+    <p>
+        In our experiments, the VQA finetuning is performed on 4 8-A100-GPU servers (<i>with RDMA</i>). Here provides the finetuning script <code>train_vqa_distributed.sh</code>, which supports multi-server distributed training (as well as single-server training). Please refer to the comments in the beginning of the script and set the configs correctly according to your distribution environment. If you have shuffled the training data in the previous step, please correctly specify the training data path following the guide in the script comments. <b>The command should be run on each worker.</b> 
+    </p>
+<pre>
+# run on each worker after the distributed and data configs have been correctly set following the guide in train_vqa_distributed.sh 
+cd run_scripts/vqa
+bash train_vqa_distributed.sh 
+</pre>
+    <p>
+        In our experiments, the finetuning costs around 36 hours (for 12 epochs). After each epoch, an evaluation on validation set is performed. The best validation accuracy during finetuning will be around 80.8. The log is saved in <code>${log_dir}</code>.
+    </p>
+    <p>
+        <i>(Update on validation time-cost)</i> As will be mentioned in the <i>4. Inference</i> section, we prepare 2 types of inference: beam-search and all-candidate inference. By default, all-candidate inference is used for validation during fine-tuning, which achieves better accuracy but costs much time. Now we have added a new option in the training scripts called <code>--val-inference-type</code> to switch the validation inference type during fine-tuning. If you feel the validation takes too long, you can refer to <a href="https://github.com/OFA-Sys/OFA/pull/79">PR #79</a> to activate beam-search validation, which significantly takes much less time, with around 0.5-0.6 validation score degradation compared with all-candidate validation.
+    </p>
+</details>
+<details>
+    <summary><b>4. Inference</b></summary>
+    <p>
+        We provide 2 types of inference, <b>beam-search</b> (much faster but gets sub-optimal accuracy) and <b>all-candidate evaluation</b> (slower but best accuracy). <br></br>
+        For beam-search inference, use the script <code>evaluate_vqa_beam.sh</code>. Refer to the command below. The inference on test set costs around 16 GPU hours. After inference on test set, the result JSON file will be dumped in the <code>${result_path}</code> defined in the shell script. You can submit the result <code>test_predict.json</code> to <a href="https://eval.ai/web/challenges/challenge-page/830/overview">EvalAI</a>. Using our released finetuned checkpoint, beam-search inference will get 80.15 validation accuracy, 79.36 test-dev accuracy and 79.48 test-std accuracy (around 0.6 lower than all-candidate evaluation).
+    </p>
+<pre>
+cd run_scripts/vqa
+bash evaluate_vqa_beam.sh val # specify 'val' or 'test'
+</pre>
+    <p>
+        For all-candidate evaluation, we recommend to use the distributed script <code>evaluate_vqa_allcand_distributed.sh</code>. Please refer to the guide in the script to set the distributed configs before running. The result JSON file will be dumped in the <code>${result_path}</code> defined in the shell script of rank-0 server. All-candidate evaluation computes scores on all the candidate answers in the VQA dataset, which achieves <b>80.82</b> validation accuracy, <b>79.87</b> test-dev accuracy and <b>80.02</b> test-std accuracy, reproducing our reported results in the paper. However, the inference on test set costs around 1k GPU hours, which is much slower.
+    </p>
+<pre>
+# run on each worker after the distributed configs have been correctly set following the guide in evaluate_vqa_allcand_distributed.sh
+cd run_scripts/vqa
+bash evaluate_vqa_allcand_distributed.sh val # specify 'val' or 'test'
+</pre>
+</details>
+
+## Visual Grounding (Referring Expression Comprehension)
+Here provides procedures for you to prepare data, train, and evaluate your model on visual grounding. 
+<details>
+    <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
+    <p>
+        Download data (see <a href='datasets.md'>datasets.md</a>) and models (see <a href='checkpoints.md'>checkpoints.md</a>) and put them in the correct directory. We provide RefCOCO (split by UNC), RefCOCO+ (split by UNC) and RefCOCOg (split by UMD) datasets. See <a href='https://www.tensorflow.org/datasets/catalog/ref_coco'>RefCOCO</a> and <a href="https://github.com/lichengunc/refer">Refer</a> for more details. Note that in the original dataset, each region-coord (or bounding box) may corresponds to multiple descriptive texts. We split these texts into multiple samples so that the region-coord in each sample corresponds to only one text. Each line of the processed dataset represents a sample with the following format. The information of uniq-id, image-id, text, region-coord (separated by commas), image base64 string are separated by tabs.
+    </p>
+<pre>
+79_1    237367  A woman in a white blouse holding a glass of wine.  230.79,121.75,423.66,463.06 9j/4AAQ...1pAz/9k=
+</pre>
+</details>
+<details>
+    <summary><b>2. Finetuning</b></summary>
+    <p>
+        Unlike the original paper, we finetune OFA with a drop-path rate of 0.2, and found that training with this hyper-parameter achieves better results. We will update the reported results of the paper later.
+    </p>
+<pre>
+cd run_scripts/refcoco
+nohup sh train_refcoco.sh > train_refcoco.out &  # finetune for refcoco
+nohup sh train_refcocoplus.sh > train_refcocoplus.out &  # finetune for refcoco+
+nohup sh train_refcocog.sh > train_refcocog.out &  # finetune for refcocog
+</pre>
+</details>
+<details>
+    <summary><b>3. Inference</b></summary>
+    <p>
+        Run the following commands for the evaluation. 
+    </p>
+<pre>
+cd run_scripts/refcoco ; sh evaluate_refcoco.sh  # inference & evaluate for refcoco/refcoco+/refcocog
+</pre>
+</details>
+
+## Visual Entailment
+We provide steps for you to reproduce our results in visual entailment. See the details below.
+
+<details>
+    <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
+    <p>
+        Download data (see <a href="datasets.md">datasets.md</a>) and models (see <a href="checkpoints.md">checkpoints.md</a>) and put them in the correct directory. Each line of the processed dataset represents a sample with the following format. The information of uniq-id, image-id, image base64 string, hypothesis, caption (or text premise), label are separated by tabs.
+    </p>
+<pre>
+252244149.jpg#1r1n  252244149   /9j/4AAQ...MD/2Q==   a man in pink and gold is chewing on a wooden toothpick.   a man in pink is chewing a toothpick on the subway.   neutral 
+</pre>
+</details>
+<details>
+    <summary><b>2. Finetuning</b></summary>
+    <p>
+        In our experiments, the SNLI-VE finetuning is performed on 8 NVIDIA-V100 GPUs with 32GB memory. In this task, we experimented with only a few sets of hyperparameters. We believe that proper hyperparameter tuning can lead to further accuracy improvement.
+    </p>
+<pre>
+cd run_scripts/snli_ve
+nohup sh train_snli_ve.sh > train_snli_ve.out &  # finetune for snli_ve
+</pre>
+</details>
+<details>
+    <summary><b>3. Inference</b></summary>
+    <p>
+        Run the following command to obtain the results.
+    </p>
+<pre>
+cd run_scripts/snli_ve ; sh evaluate_snli_ve.sh dev  # specify 'dev' or 'test'
+</pre>
+</details>
+   
+## GLUE
+Here we provide steps for you to finetune and evaluate our model on language understanding tasks. We demonstrate our practice for the GLUE benchmark. 
+
+<details>
+    <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
+    <p>
+        Download data (see <a href="datasets.md">datasets.md</a>) and models (see <a href="checkpoints.md">checkpoints.md</a>) and put them in the correct directory. we provide 7 language understanding datasets from GLUE benchmark, including COLA, MNLI, MRPC, QNLI, QQP, RTE and SST2. More details about these datasets can be found in this <a href="https://openreview.net/pdf?id=rJ4km2R5t7">link</a>.
+    </p>
+</details>
+<details>
+    <summary><b>2. Finetuning</b></summary>
+    <p>
+        For each task, we have tried multiple sets of hyperparameters (including learning rate, batch size, training epochs). The results under different sets of hyperparameters can be found in <code>${log_dir}</code>.
+    </p>
+<pre>
+cd run_scripts/glue
+nohup sh train_cola.sh > train_cola.out &  # finetune for cola
+nohup sh train_mnli.sh > train_mnli.out &  # finetune for mnli
+nohup sh train_mrpc.sh > train_mrpc.out &  # finetune for mrpc
+nohup sh train_qnli.sh > train_qnli.out &  # finetune for qnli
+nohup sh train_qqp.sh > train_qqp.out &  # finetune for qqp
+nohup sh train_rte.sh > train_rte.out &  # finetune for rte
+nohup sh train_sst2.sh > train_sst2.out &  # finetune for sst2
+</pre>
+</details>
+
+## Image Classification on ImageNet-1K
+We provide the finetuning and inference codes which reproduce **85.0 ImageNet-1K accuracy**, slightly better than reported in our paper. 
+
+<details>
+    <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
+    <p>
+        Download data (see <a href="datasets.md">datasets.md</a>) and models (see <a href="checkpoints.md">checkpoints.md</a>) and put them in the correct directory. Our provided data is derived from the original <a href="http://image-net.org/">ImageNet-1K</a> (ILSVRC2012 train & validation) dataset and shares the same data split with it. To formulate the classification task into seq2seq paradigm, we use the <a href="https://github.com/HoldenCaulfieldRye/caffe/blob/master/data/ilsvrc12/synset_words.txt">synset words</a> provided by Caffe as the generation target for each image class. Each line of the processed dataset represents a sample with the following format. The information of image base64 string, classification label (1-indexed, conform to the order in <code>synset_words.txt</code>), synset words of the label are separated by tabs.
+    </p>
+<pre>
+_9j_4AAQS...fzX__Z  769 rugby ball
+</pre>
+</details>
+<details>
+    <summary><b>2. Shuffle the Training Data</b></summary>
+    <p>
+        (Optional, but achieves better finetuning accuracy): If the disk storage is sufficient, we recommend to prepare the shuffled training data for each epoch in advance. In our experiments, we use shuffling which brings around <b>+0.2</b> improvement on ImageNet-1K accuracy.
+    </p>
+<pre>
+cd dataset/imagenet_1k_data
+ln imagenet_1k_train.tsv imagenet_1k_train_1.tsv
+for idx in `seq 1 9`;do shuf imagenet_1k_train_${idx}.tsv > imagenet_1k_train_$[${idx}+1].tsv;done # each file is used for an epoch one by one
+</pre>
+</details>
+<details>
+    <summary><b>3. Finetuning</b></summary>
+    <p>
+        In our experiments, the ImageNet-1K finetuning is performed on 2 8-A100-GPU servers (<i>with RDMA</i>). Here provides the finetuning script <code>train_imagenet_distributed.sh</code>, which supports multi-server distributed training (as well as single-server training). Please refer to the comments in the beginning of the script and set the configs correctly according to your distribution environment. If you have shuffled the training data in the previous step, please correctly specify the training data path following the guide in the script comments. <b>The command should be run on each worker.</b> For quick evaluation during finetuning, by default we sample 20% of the original validation split and report accuracy on this subset after each epoch. The accuracy on the validation subset is generally ±0.1 relative to accuracy on the whole validation split.
+    </p>
+<pre>
+# run on each worker after the distributed and data configs have been correctly set following the guide in train_imagenet_distributed.sh
+cd run_scripts/image_classify
+bash train_imagenet_distributed.sh
+</pre>
+    <p>
+        In our experiments, the finetuning costs around 80 hours (for 32 epochs). The best accuracy on validation subset during finetuning will be around 85.0. The log is saved in <code>${log_dir}</code>.
+    </p>
+</details>
+<details>
+    <summary><b>4. Inference</b></summary>
+    <p>
+        To get the validation accuracy on the whole ImageNet-1K validation set, run the following command. The evaluation costs around 10 GPU hours. The accuracy will be reported in the stdout (expected to be around <b>85.0</b>).
+    </p>
+<pre>
+cd run_scripts/image_classify ; sh evaluate_imagenet.sh  # inference & evaluate for imagenet-1k
+</pre>
+</details> 
+
+## Gigaword
+We provide steps for you to reproduce our results in Gigaword. See the details below.
+
+<details>
+    <summary><b>1. Prepare the Dataset & Checkpoints</b></summary>
+    <p>
+        Download data (see <a href="datasets.md">datasets.md</a>) and models (see <a href="checkpoints.md">checkpoints.md</a>) and put them in the correct directory. The original dataset is taken from <a href="https://github.com/microsoft/unilm/">UniLM</a> and we organized the data into the tsv format. Each line of the processed dataset represents a sample with the following format. The information of source and target texts are separated by tabs.
+    </p>
+<pre>
+factory orders for manufactured goods rose #.# percent in september...  us september factory orders up #.# percent
+</pre>
+</details>
+<details>
+    <summary><b>2. Finetuning</b></summary>
+    <p>
+        Run the following command to train the model.
+    </p>
+<pre>
+cd run_scripts/gigaword
+nohup sh train_gigaword.sh > train_gigaword.out &  # finetune for gigaword
+</pre>
+</details>
+<details>
+    <summary><b>3. Inference</b></summary>
+    <p>
+        Run the following command to obtain the results (~36.43 rougeL).
+    </p>
+<pre>
+cd run_scripts/gigaword ; sh evaluate_gigaword.sh  # inference & evaluate for gigaword
+</pre>
+</details> 
+
+<br></br>
+
+# Gallery
+Below we provide examples of OFA in text-to-image generation and open-ended VQA. Also, we demonstrate its performance in unseen task (Grounded QA) as well as unseen domain (Visual Grounding on images from unseen domains). 
+
+## Text-to-Image Generation
+
+![case1](examples/case1.png)
+
+
+## Open-Ended VQA
+![open_vqa](examples/open_vqa.png)
+
+## Grounded QA (unseen task)
+![grounded_qa](examples/grounded_qa.png)
+
+## Visual Grounding (unseen domain)
+![vg](examples/viusal_grounding.png)
+<br></br>
+
+# Related Codebase
+* [Fairseq](https://github.com/pytorch/fairseq)
+* [taming-transformers](https://github.com/CompVis/taming-transformers)
+<br></br>
+
+
+# Getting Involved
+Feel free to submit Github issues or pull requests. Welcome to contribute to our project!
+
+To contact us, never hestitate to send an email to `zheluo.wp@alibaba-inc.com` or `junyang.ljy@alibaba-inc.com`!
+<br></br>
+
+
+# Citation
+Please cite our paper if you find it helpful :)
+
+```
+@article{wang2022ofa,
+  author    = {Peng Wang and
+               An Yang and
+               Rui Men and
+               Junyang Lin and
+               Shuai Bai and
+               Zhikang Li and
+               Jianxin Ma and
+               Chang Zhou and
+               Jingren Zhou and
+               Hongxia Yang},
+  title     = {OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence
+               Learning Framework},
+  journal   = {CoRR},
+  volume    = {abs/2202.03052},
+  year      = {2022}
+}
+```
+<br></br>

README_EncouragingLoss.md

@@ -0,0 +1,34 @@
+# Finetuning with Encouraging Loss (EL)
+Below we provide methods for finetuning with label smoothed encouraging loss proposed in [_Well-classified Examples are Underestimated in Classification with Deep Neural Networks_](https://arxiv.org/pdf/2110.06537.pdf) on different downstream tasks.
+The implementation is in [label_smoothed_encouraging_loss.py](criterions/label_smoothed_encouraging_loss.py).
+You can set the `--criterion` to `adjust_label_smoothed_encouraging_loss` to use it. This criterion has a hyper-parameter `--log-end`. 
+`--log-end < 1` results in a approximated and conservative version of the full encouraging loss. 
+A high log_end will more strongly weaken the gradient vanishing, enhance the modeling of the data, and increase the growth rate of the margin, but it will also bring a larger gradient norm, which will bring challenges to the existing optimization system.
+We recommend higher log_end for cases with higher performance, and 0.75 or 0.5 as your first try.
+## Image Captioning
+We provide procedures for image captioning with EL below. The preprocessing is identical to default setting.
+
+<details>
+    <summary><b>Finetuning</b></summary>
+    <p>
+        We propose two scripts for stage1. </b>
+    </p>
+<pre>
+cd run_scripts/caption
+nohup sh train_caption_stage1_el.sh > train_stage1_el.out &  # stage 1, train with encouraging loss, expected cider 1.403
+nohup sh train_caption_stage1_el_db.sh > train_stage1_el.out &  # stage 1, train with encouraging loss, and drop best examples, expected cider 1.404
+</pre>
+</details>
+
+## Referring Expression Comprehension 
+We provide procedures for image captioning with EL below. The preprocessing is identical to default setting.
+<details>
+    <summary><b>Finetuning</b></summary>
+<pre>
+cd run_scripts/refcoco
+nohup sh train_refcoco_el.sh > train_refcoco_el.out &  # finetune for refcoco
+nohup sh train_refcocoplus_el.sh > train_refcocoplus_el.out &  # finetune for refcoco+
+nohup sh train_refcocog_el.sh > train_refcocog_el.out &  # finetune for refcocog
+</pre>
+</details>
+Evaluation is also the same as the default setting.

app.py

@@ -0,0 +1,197 @@
+import os
+
+os.system('cd fairseq;'
+          'pip install --use-feature=in-tree-build ./; cd ..')
+os.system('ls -l')
+
+import torch
+import numpy as np
+from fairseq import utils, tasks
+from fairseq import checkpoint_utils
+from utils.eval_utils import eval_step
+from data.mm_data.ocr_dataset import ocr_resize
+from tasks.mm_tasks.ocr import OcrTask
+from PIL import Image, ImageDraw
+from torchvision import transforms
+from typing import List, Tuple
+import cv2
+from easyocrlite import ReaderLite
+import gradio as gr
+
+
+# Register refcoco task
+tasks.register_task('ocr', OcrTask)
+
+os.system('wget http://xc-models.oss-cn-zhangjiakou.aliyuncs.com/ofa/chinese/ocr/general/checkpoint_last.pt; '
+          'mkdir -p checkpoints; mv checkpoint_last.pt checkpoints/ocr.pt')
+
+# turn on cuda if GPU is available
+use_cuda = torch.cuda.is_available()
+# use fp16 only when GPU is available
+use_fp16 = False
+
+mean = [0.5, 0.5, 0.5]
+std = [0.5, 0.5, 0.5]
+
+Rect = Tuple[int, int, int, int]
+FourPoint = Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int], Tuple[int, int]]
+
+
+def four_point_transform(image: np.ndarray, rect: FourPoint) -> np.ndarray:
+    (tl, tr, br, bl) = rect
+
+    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
+    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
+    maxWidth = max(int(widthA), int(widthB))
+
+    # compute the height of the new image, which will be the
+    # maximum distance between the top-right and bottom-right
+    # y-coordinates or the top-left and bottom-left y-coordinates
+    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
+    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
+    maxHeight = max(int(heightA), int(heightB))
+
+    dst = np.array(
+        [[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]],
+        dtype="float32",
+    )
+
+    # compute the perspective transform matrix and then apply it
+    M = cv2.getPerspectiveTransform(rect, dst)
+    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
+
+    return warped
+
+
+def get_images(image_path: str, reader: ReaderLite, **kwargs):
+    results = reader.process(image_path, **kwargs)
+    return results
+
+
+def draw_boxes(image, bounds, color='red', width=2):
+    draw = ImageDraw.Draw(image)
+    for bound in bounds:
+        p0, p1, p2, p3 = bound
+        draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width)
+    return image
+
+
+def encode_text(task, text, length=None, append_bos=False, append_eos=False):
+    bos_item = torch.LongTensor([task.src_dict.bos()])
+    eos_item = torch.LongTensor([task.src_dict.eos()])
+    pad_idx = task.src_dict.pad()
+
+    s = task.tgt_dict.encode_line(
+        line=task.bpe.encode(text),
+        add_if_not_exist=False,
+        append_eos=False
+    ).long()
+    if length is not None:
+        s = s[:length]
+    if append_bos:
+        s = torch.cat([bos_item, s])
+    if append_eos:
+        s = torch.cat([s, eos_item])
+    return s
+
+
+def patch_resize_transform(patch_image_size=480, is_document=False):
+    _patch_resize_transform = transforms.Compose(
+        [
+            lambda image: ocr_resize(
+                image, patch_image_size, is_document=is_document
+            ),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std),
+        ]
+    )
+    
+    return _patch_resize_transform
+
+
+# Construct input for caption task
+def construct_sample(task, image: Image, patch_image_size=480):
+    bos_item = torch.LongTensor([task.src_dict.bos()])
+    eos_item = torch.LongTensor([task.src_dict.eos()])
+    pad_idx = task.src_dict.pad()
+
+    patch_image = patch_resize_transform(patch_image_size)(image).unsqueeze(0)
+    patch_mask = torch.tensor([True])
+    src_text = encode_text(task, "图片上的文字是什么?", append_bos=True, append_eos=True).unsqueeze(0)
+    src_length = torch.LongTensor([s.ne(pad_idx).long().sum() for s in src_text])
+    sample = {
+        "id":np.array(['42']),
+        "net_input": {
+            "src_tokens": src_text,
+            "src_lengths": src_length,
+            "patch_images": patch_image,
+            "patch_masks": patch_mask,
+        },
+        "target": None
+    }
+    return sample
+
+
+# Function to turn FP32 to FP16
+def apply_half(t):
+    if t.dtype is torch.float32:
+        return t.to(dtype=torch.half)
+    return t
+
+
+def ocr(ckpt, img, out_img):
+    reader = ReaderLite()
+    overrides={"eval_cider":False, "beam":8, "max_len_b":128, "patch_image_size":480, "orig_patch_image_size":224, "no_repeat_ngram_size":0, "seed":7}
+    models, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+        utils.split_paths(ckpt),
+        arg_overrides=overrides
+    )
+
+    # Move models to GPU
+    for model in models:
+        model.eval()
+        if use_fp16:
+            model.half()
+        if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
+            model.cuda()
+        model.prepare_for_inference_(cfg)
+
+    # Initialize generator
+    generator = task.build_generator(models, cfg.generation)
+
+    bos_item = torch.LongTensor([task.src_dict.bos()])
+    eos_item = torch.LongTensor([task.src_dict.eos()])
+    pad_idx = task.src_dict.pad()
+
+    orig_image = Image.open(img)
+    results = get_images(img, reader)
+    box_list, image_list = zip(*results)
+    draw_boxes(orig_image, box_list)
+    orig_image.save(out_img)
+
+    ocr_result = []
+    for box, image in zip(box_list, image_list):
+        image = Image.fromarray(image)
+        sample = construct_sample(task, image, cfg.task.patch_image_size)
+        sample = utils.move_to_cuda(sample) if use_cuda else sample
+        sample = utils.apply_to_sample(apply_half, sample) if use_fp16 else sample
+
+        with torch.no_grad():
+            result, scores = eval_step(task, generator, models, sample)
+        ocr_result.append(result[0]['ocr'].replace(' ', ''))
+
+    result = '\n'.join(ocr_result)
+    return result
+
+
+title = "OFA-OCR"
+description = "Gradio Demo for OFA-OCR. Upload your own image or click any one of the examples, and click " \
+              "\"Submit\" and then wait for the generated OCR result.  "
+article = "<p style='text-align: center'><a href='https://github.com/OFA-Sys/OFA' target='_blank'>OFA Github " \
+          "Repo</a></p> "
+examples = [['a.jpg'], ['aurora.jpeg'], ['good_luck.png'], ['pokemons.jpg'], ['donuts.jpg']]
+io = gr.Interface(fn=ocr, inputs=gr.inputs.Image(type='pil'), outputs=gr.outputs.Textbox(label="Caption"),
+                  title=title, description=description, article=article, examples=examples,
+                  allow_flagging=False, allow_screenshot=False)
+io.launch(cache_examples=True)
+

checkpoints.md

@@ -0,0 +1,37 @@
+# Checkpoints
+
+We provide links for you to download our checkpoints, including pretrained and finetuned models on different tasks. If you would like to use OFA with Transformers, please download checkpoints at [https://huggingface.co/OFA-Sys](https://huggingface.co/OFA-Sys), and check the code in the branch `feature/add_transformers`. 
+
+## Pretraining
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_huge.pt"> Pre-trained checkpoint (OFA-Huge) </a> (~930M parameters)
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_large.pt"> Pre-trained checkpoint (OFA-Large) </a> (~470M parameters)
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_base.pt"> Pre-trained checkpoint (OFA-Base) </a> (~180M parameters)
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_medium.pt"> Pre-trained checkpoint (OFA-Medium) </a> (~93M parameters)
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_tiny.pt"> Pre-trained checkpoint (OFA-Tiny) </a> (~33M parameters)
+
+## Finetuning (OFA-Huge)
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_huge_best.pt"> Finetuned checkpoint for Caption on COCO </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/vqa_huge_best.pt"> Finetuned checkpoint for VQAv2 </a>
+
+## Finetuning (OFA-Large)
+
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_large_best_clean.pt"> Finetuned checkpoint for Caption on COCO </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_stage1_best.pt"> Finetuned checkpoint for Caption on COCO During Stage1 Finetuning </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcoco_large_best.pt"> Finetuned checkpoint for RefCOCO </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocoplus_large_best.pt"> Finetuned checkpoint for RefCOCO+ </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocog_large_best.pt"> Finetuned checkpoint for RefCOCOg </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/vqa_large_best.pt"> Finetuned checkpoint for VQAv2 </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/snli_ve_large_best.pt"> Finetuned checkpoint for SNLI-VE </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/image_gen_large_best.zip"> Finetuned checkpoint for Text-to-Image Generation on COCO && CLIP checkpoint && VQGAN checkpoint </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/imagenet_1k_large_best.pt"> Finetuned checkpoint for ImageNet-1K </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/gigaword_large_best.pt"> Finetuned checkpoint for Gigaword </a>
+
+
+## Finetuning (OFA-Base)
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_base_best.pt"> Finetuned base checkpoint for Caption on COCO </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcoco_base_best.pt"> Finetuned base checkpoint for RefCOCO </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocoplus_base_best.pt"> Finetuned base checkpoint for RefCOCO+ </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocog_base_best.pt"> Finetuned base checkpoint for RefCOCOg </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/vqa_base_best.pt"> Finetuned base checkpoint for VQAv2 </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/snli_ve_base_best.pt"> Finetuned base checkpoint for SNLI-VE </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/image_gen_base_best.pt"> Finetuned base checkpoint for Text-to-Image Generation on COCO  </a>

checkpoints_cn.md

@@ -0,0 +1,82 @@
+# Checkpoints (OFA-CN)
+
+We provide checkpoints of OFA-CN, which is the Chinese version of OFA. We provide Base-size and Large-size models, including pretrained and finetuned models on image captioning and referring expression comprehension. Note that we translated the texts in the RefCOCO(-/+/g) datasets and finetuned OFA-CN on them. We plan to release the related new datasets in the near future. 
+<br>
+
+## Checkpoints
+Below we provide the links for downloading the Chinese OFA checkpoints.
+
+### Pretraining
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_cn_large.pt"> Pretrained checkpoint (OFA-CN-Large) </a> (~443M parameters)
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_cn_base.pt "> Pretrained checkpoint (OFA-CN-Base) </a> (~160M parameters)
+
+### Finetuning (OFA-Large)
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_cn_large.pt"> Finetuned checkpoint for MUGE Caption (Stage 1) </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcoco_cn_large.pt"> Finetuned checkpoint for RefCOCO-CN </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocoplus_cn_large.pt"> Finetuned checkpoint for RefCOCO+-CN </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocog_cn_large.pt"> Finetuned checkpoint for RefCOCOg-CN </a>
+
+### Finetuning (OFA-Base)
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_cn_base.pt"> Finetuned checkpoint for MUGE Caption (Stage 1) </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcoco_cn_base.pt"> Finetuned checkpoint for RefCOCO-CN </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocoplus_cn_base.pt"> Finetuned checkpoint for RefCOCO+-CN </a>
+* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocog_cn_base.pt"> Finetuned checkpoint for RefCOCOg-CN </a>
+<br>
+
+## Model Card
+Below we provide the basic information of the base-size and large-size OFA-CN. 
+
+<table border="1" width="100%">
+    <tr align="center">
+        <th>Model</th><th>#Params</th><th>Backbone</th><th>Hidden Size</th><th>Intermediate Size</th><th>#Heads</th><th>#Enc. Layers</th><th>#Dec. Layers</th>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Base</sub><td>160M</td><td>ResNet101</td><td>768</td></td><td>3072</td><td>12</td><td>6</td><td>6</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Large</sub></td><td>443M</td><td>ResNet152</td><td>1024</td></td><td>4096</td><td>16</td><td>12</td><td>12</td>
+    </tr>
+    </tr>
+</table>
+<br>
+
+## Results
+Below we provide the results of OFA-CN and the baselines for comparison. 
+
+### [MUGE Caption]("https://tianchi.aliyun.com/muge")
+<table border="1" width="100%">
+    <tr align="center">
+        <td>Model</td><td>BLEU@4</td><td>ROUGE-L</td><td>CIDEr-D</td>
+    </tr>
+    <tr align="center">
+        <td>Trm </td><td>7.33</td><td>51.51</td><td>11.00</td>
+    </tr>
+    <tr align="center">
+        <td>M6</td><td>16.19</td><td>55.06</td><td>30.75</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Base</sub></td><td>26.23</td><td>58.95</td><td>50.70</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Large</sub></td><td><b>27.32</b></td><td><b>59.20</b></td><td><b>53.51</b></td>
+    </tr>
+</table>
+
+### RefCOCO-CN Series
+<table border="1" width="100%">
+    <tr align="center">
+        <td>Model</td><td>RefCOCO(val/testA/testB)</td><td>RefCOCO+(val/testA/testB)</td><td>RefCOCOg(val/test-u)</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Base</sub>(random-init)</td><td>30.13/35.07/25.03</td><td>17.89/20.90/15.83</td><td>20.30/20.45</td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Base</sub></td><td>82.18/86.07/<b>76.68</b></td><td>69.38/77.26/60.14</td><td><b>73.57/72.53</b></td>
+    </tr>
+    <tr align="center">
+        <td>OFA<sub>Large</sub></td><td><b>82.84/86.54</b>/76.50</td><td><b>71.30/78.56/61.85</b></td><td>71.96/71.30</td>
+    </tr>
+</table>
+<br>
+
+

colab.md

@@ -0,0 +1,9 @@
+# Colab Notebooks
+
+We provide Colab notebooks of different downstream tasks for you guys to enjoy OFA. See below.
+
+* [Image Captioning in Huggingface Transformers](https://colab.research.google.com/drive/1Ho81RBV8jysZ7e0FhsSCk_v938QeDuy3?usp=sharing)
+* [Generic Interface](https://colab.research.google.com/drive/1jogyZ-2rdHU3XxZOf3TBfhex1XHqX-1m?usp=sharing#scrollTo=s9Vni6YUZOpC) (using different instructions to perform various tasks with just one model.)
+* [Image Captioning](https://colab.research.google.com/drive/1Q4eNhhhLcgOP4hHqwZwU1ijOlabgve1W?usp=sharing)
+* [Referring Expression Comprehension](https://colab.research.google.com/drive/1AHQNRdaUpRTgr3XySHSlba8aXwBAjwPB?usp=sharing)
+* [Open-Domain Visual Question Answering](https://colab.research.google.com/drive/1lsMsF-Vum3MVyXwSVF5E-Y23rHFvj_3y?usp=sharing)

criterions/__init__.py

@@ -0,0 +1,4 @@
+from .scst_loss import ScstRewardCriterion
+from .label_smoothed_cross_entropy import AdjustLabelSmoothedCrossEntropyCriterion
+from .clip_scst_loss import ClipScstRewardCriterion
+from .label_smoothed_encouraging_loss import AdjustLabelSmoothedEncouragingLossCriterion

criterions/clip_scst_loss.py

@@ -0,0 +1,277 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import math
+from dataclasses import dataclass, field
+from typing import Optional
+from PIL import Image
+from torchvision import transforms
+
+import torch
+import numpy as np
+from fairseq import metrics
+from fairseq.data import data_utils
+from fairseq.criterions import FairseqCriterion, register_criterion
+from fairseq.dataclass import FairseqDataclass
+from fairseq import utils
+from omegaconf import II
+
+from models import clip
+
+
+def custom_to_pil(x):
+    x = x.detach().cpu()
+    x = torch.clamp(x, -1., 1.)
+    x = (x + 1.) / 2.
+    x = x.permute(1, 2, 0).numpy()
+    x = (255 * x).astype(np.uint8)
+    x = Image.fromarray(x)
+    if not x.mode == "RGB":
+        x = x.convert("RGB")
+    return x
+
+
+def scst_loss(lprobs, target, reward, ignore_index=None, reduce=True):
+    loss = -lprobs.gather(dim=-1, index=target.unsqueeze(-1)).squeeze() * reward.unsqueeze(-1)
+    if ignore_index is not None:
+        pad_mask = target.eq(ignore_index)
+        loss.masked_fill_(pad_mask, 0.0)
+        ntokens = (~pad_mask).sum()
+    else:
+        loss = loss.squeeze(-1)
+        ntokens = target.numel()
+    if reduce:
+        loss = loss.sum()
+    return loss, ntokens
+
+
+@dataclass
+class ClipScstRewardCriterionConfig(FairseqDataclass):
+    ignore_prefix_size: int = field(
+        default=0,
+        metadata={"help": "Ignore first N tokens"},
+    )
+    sentence_avg: bool = II("optimization.sentence_avg")
+    constraint_range: Optional[str] = field(
+        default=None,
+        metadata={"help": "constraint range"}
+    )
+
+
+@register_criterion(
+    "clip_scst_reward_criterion", dataclass=ClipScstRewardCriterionConfig
+)
+class ClipScstRewardCriterion(FairseqCriterion):
+    CLIP_REWARD_WEIGHT = 2.5
+
+    def __init__(
+        self,
+        task,
+        sentence_avg,
+        ignore_prefix_size=0,
+        constraint_range=None
+    ):
+        super().__init__(task)
+        self.sentence_avg = sentence_avg
+        self.ignore_prefix_size = ignore_prefix_size
+
+        self.constraint_start = None
+        self.constraint_end = None
+        if constraint_range is not None:
+            constraint_start, constraint_end = constraint_range.split(',')
+            self.constraint_start = int(constraint_start)
+            self.constraint_end = int(constraint_end)
+
+    def forward(self, model, sample, update_num=0, reduce=True):
+        """Compute the loss for the given sample.
+
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        loss, score, ntokens, nsentences = self.compute_loss(model, sample, reduce=reduce)
+
+        sample_size = (
+            nsentences if self.sentence_avg else ntokens
+        )
+        logging_output = {
+            "loss": loss.data,
+            "score": score,
+            "ntokens": ntokens,
+            "nsentences": nsentences,
+            "sample_size": sample_size,
+        }
+        return loss, sample_size, logging_output
+
+    def _calculate_clip_scores(self, gen_res, gt_text, device):
+        '''
+        gen_res: generated images, list of Image
+        gt_text: input captions.
+        device: device for clip model
+        '''
+        batch_size = len(gt_text)
+        gen_res_size = len(gen_res)
+        img_per_seq = gen_res_size // batch_size
+
+        hyp_images = torch.stack(
+            [self.task.clip_preprocess(gen_image) for gen_image in gen_res], dim=0
+        ).to(device)
+
+        clip_input = clip.tokenize([text for text in gt_text]).to(device)
+        with torch.no_grad():
+            image_features = self.task.clip_model.encode_image(hyp_images)
+            text_features = self.task.clip_model.encode_text(clip_input)
+            image_features /= image_features.norm(dim=-1, keepdim=True)
+            text_features /= text_features.norm(dim=-1, keepdim=True)
+            image_features = image_features.view(batch_size, img_per_seq, -1)
+            text_features = text_features.view(batch_size, 1, -1)
+            ti_similarity = image_features @ text_features.transpose(1, 2)
+            ti_similarity = ti_similarity.view(-1)
+
+        scores = self.CLIP_REWARD_WEIGHT * ti_similarity
+        return scores
+
+    def get_generator_out(self, model, sample):
+        model.eval()
+        with torch.no_grad():
+            self.task.scst_generator.model.eval()
+            gen_out = self.task.scst_generator.generate([model], sample)
+
+        gen_target = []
+        gen_res = []
+        gt_text = []
+        for i in range(len(gen_out)):
+            with torch.no_grad():
+                tokens = torch.stack([item['tokens'][:-1] for item in gen_out[i]], dim=0)
+                tokens += -len(self.task.src_dict) + self.task.cfg.code_dict_size + self.task.cfg.num_bins
+                images = self.task.image_tokenizer.decode_code(
+                    tokens.view(-1, self.task.cfg.code_image_size // 8, self.task.cfg.code_image_size // 8)
+                )
+                images = [custom_to_pil(image) for image in images]
+
+            gen_target += [item['tokens'] for item in gen_out[i]]
+            gen_res += images
+            gt_text.append(
+                self.task.bpe.decode(
+                    self.task.tgt_dict.string(
+                        utils.strip_pad(sample['net_input']['src_tokens'][i], self.padding_idx).cpu().int()
+                    )
+                )[38:] # remove task instruction.
+            )
+
+        return gen_target, gen_res, gt_text
+
+    def get_reward_and_scores(self, gen_res, gt_text, device):
+        batch_size = len(gt_text)
+        gen_res_size = len(gen_res)
+        img_per_sample = gen_res_size // batch_size
+
+        scores = self._calculate_clip_scores(gen_res, gt_text, device)
+        sc_ = scores.reshape(batch_size, img_per_sample)
+        baseline = (sc_.sum(1, keepdim=True) - sc_) / (sc_.shape[1] - 1)
+        # sample - baseline
+        reward = scores.reshape(batch_size, img_per_sample)
+        reward = reward - baseline
+        reward = reward.view(-1)
+
+        return reward, scores
+
+    def get_net_output(self, model, sample, gen_target):
+        def merge(sample_list, eos=self.task.tgt_dict.eos(), move_eos_to_beginning=False):
+            return data_utils.collate_tokens(
+                sample_list,
+                pad_idx=self.padding_idx,
+                eos_idx=eos,
+                left_pad=False,
+                move_eos_to_beginning=move_eos_to_beginning,
+            )
+
+        batch_size = len(sample["target"])
+        gen_target_size = len(gen_target)
+        img_per_sample = gen_target_size // batch_size
+
+        model.train()
+        sample_src_tokens = torch.repeat_interleave(
+            sample['net_input']['src_tokens'], img_per_sample, dim=0
+        )
+        sample_src_lengths = torch.repeat_interleave(
+            sample['net_input']['src_lengths'], img_per_sample, dim=0
+        )
+        sample_code_masks = torch.repeat_interleave(
+            sample['net_input']['code_masks'], img_per_sample, dim=0
+        )
+        gen_prev_output_tokens = torch.as_tensor(
+            merge(gen_target, eos=self.task.tgt_dict.bos(), move_eos_to_beginning=True),
+            device=sample["target"].device, dtype=torch.int64
+        )
+        gen_target_tokens = torch.as_tensor(
+            merge(gen_target), device=sample["target"].device, dtype=torch.int64
+        )
+        net_output = model(
+            src_tokens=sample_src_tokens, src_lengths=sample_src_lengths,
+            code_masks=sample_code_masks, prev_output_tokens=gen_prev_output_tokens
+        )
+
+        return net_output, gen_target_tokens
+
+    def get_lprobs_and_target(self, model, net_output, gen_target):
+        if self.constraint_start is not None and self.constraint_end is not None:
+            net_output[0][:, :, 4:self.constraint_start] = -math.inf
+            net_output[0][:, :, self.constraint_end:] = -math.inf
+        lprobs = model.get_normalized_probs(net_output, log_probs=True)
+        if self.ignore_prefix_size > 0:
+            if getattr(lprobs, "batch_first", False):
+                lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
+                gen_target = gen_target[:, self.ignore_prefix_size :].contiguous()
+            else:
+                lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
+                gen_target = gen_target[self.ignore_prefix_size :, :].contiguous()
+        return lprobs, gen_target
+
+    def compute_loss(self, model, sample, reduce=True):
+        gen_target, gen_res, gt_text = self.get_generator_out(model, sample)
+        reward, scores = self.get_reward_and_scores(gen_res, gt_text, device=sample["target"].device)
+        net_output, gen_target_tokens = self.get_net_output(model, sample, gen_target)
+        gen_lprobs, gen_target_tokens = self.get_lprobs_and_target(model, net_output, gen_target_tokens)
+        loss, ntokens = scst_loss(gen_lprobs, gen_target_tokens, reward, ignore_index=self.padding_idx, reduce=reduce)
+        nsentences = gen_target_tokens.size(0)
+
+        return loss, scores.sum(), ntokens, nsentences
+
+    @classmethod
+    def reduce_metrics(cls, logging_outputs) -> None:
+        """Aggregate logging outputs from data parallel training."""
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        score_sum = sum(log.get("score", 0) for log in logging_outputs)
+        ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
+        nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size, sample_size, round=3
+        )
+        metrics.log_scalar(
+            "score", score_sum / nsentences, nsentences, round=3
+        )
+
+        metrics.log_scalar(
+            "ntokens", ntokens, 1, round=3
+        )
+        metrics.log_scalar(
+            "nsentences", nsentences, 1, round=3
+        )
+        metrics.log_scalar(
+            "sample_size", sample_size, 1, round=3
+        )
+
+    @staticmethod
+    def logging_outputs_can_be_summed() -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return True

criterions/label_smoothed_cross_entropy.py

@@ -0,0 +1,343 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import math
+from dataclasses import dataclass, field
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+import numpy as np
+from fairseq import metrics, utils
+from fairseq.criterions import FairseqCriterion, register_criterion
+from fairseq.dataclass import FairseqDataclass
+from omegaconf import II
+
+
+@dataclass
+class AdjustLabelSmoothedCrossEntropyCriterionConfig(FairseqDataclass):
+    label_smoothing: float = field(
+        default=0.0,
+        metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
+    )
+    report_accuracy: bool = field(
+        default=False,
+        metadata={"help": "report accuracy metric"},
+    )
+    ignore_prefix_size: int = field(
+        default=0,
+        metadata={"help": "Ignore first N tokens"},
+    )
+    ignore_eos: bool = field(
+        default=False,
+        metadata={"help": "Ignore eos token"},
+    )
+    sentence_avg: bool = II("optimization.sentence_avg")
+    drop_worst_ratio: float = field(
+        default=0.0,
+        metadata={"help": "ratio for discarding bad samples"},
+    )
+    drop_worst_after: int = field(
+        default=0,
+        metadata={"help": "steps for discarding bad samples"},
+    )
+    use_rdrop: bool = field(
+        default=False, metadata={"help": "use R-Drop"}
+    )
+    reg_alpha: float = field(
+        default=1.0, metadata={"help": "weight for R-Drop"}
+    )
+    sample_patch_num: int = field(
+        default=196, metadata={"help": "sample patches for v1"}
+    )
+    constraint_range: Optional[str] = field(
+        default=None,
+        metadata={"help": "constraint range"}
+    )
+
+
+def construct_rdrop_sample(x):
+    if isinstance(x, dict):
+        for key in x:
+            x[key] = construct_rdrop_sample(x[key])
+        return x
+    elif isinstance(x, torch.Tensor):
+        return x.repeat(2, *([1] * (x.dim()-1)))
+    elif isinstance(x, int):
+        return x * 2
+    elif isinstance(x, np.ndarray):
+        return x.repeat(2)
+    else:
+        raise NotImplementedError
+
+
+def kl_loss(p, q):
+    p_loss = F.kl_div(p, torch.exp(q), reduction='sum')
+    q_loss = F.kl_div(q, torch.exp(p), reduction='sum')
+    loss = (p_loss + q_loss) / 2
+    return loss
+
+
+def label_smoothed_nll_loss(
+        lprobs, target, epsilon, update_num, reduce=True,
+        drop_worst_ratio=0.0, drop_worst_after=0, use_rdrop=False, reg_alpha=1.0,
+        constraint_masks=None, constraint_start=None, constraint_end=None
+):
+    if target.dim() == lprobs.dim() - 1:
+        target = target.unsqueeze(-1)
+    nll_loss = -lprobs.gather(dim=-1, index=target).squeeze(-1)
+    if constraint_masks is not None:
+        smooth_loss = -lprobs.masked_fill(~constraint_masks, 0).sum(dim=-1, keepdim=True).squeeze(-1)
+        eps_i = epsilon / (constraint_masks.sum(1) - 1 + 1e-6)
+    elif constraint_start is not None and constraint_end is not None:
+        constraint_range = [0, 1, 2, 3] + list(range(constraint_start, constraint_end))
+        smooth_loss = -lprobs[:, constraint_range].sum(dim=-1, keepdim=True).squeeze(-1)
+        eps_i = epsilon / (len(constraint_range) - 1 + 1e-6)
+    else:
+        smooth_loss = -lprobs.sum(dim=-1, keepdim=True).squeeze(-1)
+        eps_i = epsilon / (lprobs.size(-1) - 1)
+    loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
+    if drop_worst_ratio > 0 and update_num > drop_worst_after:
+        if use_rdrop:
+            true_batch_size = loss.size(0) // 2
+            _, indices = torch.topk(loss[:true_batch_size], k=int(true_batch_size * (1 - drop_worst_ratio)), largest=False)
+            loss = torch.cat([loss[indices], loss[indices+true_batch_size]])
+            nll_loss = torch.cat([nll_loss[indices], nll_loss[indices+true_batch_size]])
+            lprobs = torch.cat([lprobs[indices], lprobs[indices+true_batch_size]])
+        else:
+            loss, indices = torch.topk(loss, k=int(loss.shape[0] * (1 - drop_worst_ratio)), largest=False)
+            nll_loss = nll_loss[indices]
+            lprobs = lprobs[indices]
+
+    ntokens = loss.numel()
+    nll_loss = nll_loss.sum()
+    loss = loss.sum()
+    if use_rdrop:
+        true_batch_size = lprobs.size(0) // 2
+        p = lprobs[:true_batch_size]
+        q = lprobs[true_batch_size:]
+        if constraint_start is not None and constraint_end is not None:
+            constraint_range = [0, 1, 2, 3] + list(range(constraint_start, constraint_end))
+            p = p[:, constraint_range]
+            q = q[:, constraint_range]
+        loss += kl_loss(p, q) * reg_alpha
+
+    return loss, nll_loss, ntokens
+
+
+@register_criterion(
+    "adjust_label_smoothed_cross_entropy", dataclass=AdjustLabelSmoothedCrossEntropyCriterionConfig
+)
+class AdjustLabelSmoothedCrossEntropyCriterion(FairseqCriterion):
+    def __init__(
+        self,
+        task,
+        sentence_avg,
+        label_smoothing,
+        ignore_prefix_size=0,
+        ignore_eos=False,
+        report_accuracy=False,
+        drop_worst_ratio=0,
+        drop_worst_after=0,
+        use_rdrop=False,
+        reg_alpha=1.0,
+        sample_patch_num=196,
+        constraint_range=None
+    ):
+        super().__init__(task)
+        self.sentence_avg = sentence_avg
+        self.eps = label_smoothing
+        self.ignore_prefix_size = ignore_prefix_size
+        self.ignore_eos = ignore_eos
+        self.report_accuracy = report_accuracy
+        self.drop_worst_ratio = drop_worst_ratio
+        self.drop_worst_after = drop_worst_after
+        self.use_rdrop = use_rdrop
+        self.reg_alpha = reg_alpha
+        self.sample_patch_num = sample_patch_num
+
+        self.constraint_start = None
+        self.constraint_end = None
+        if constraint_range is not None:
+            constraint_start, constraint_end = constraint_range.split(',')
+            self.constraint_start = int(constraint_start)
+            self.constraint_end = int(constraint_end)
+
+    def forward(self, model, sample, update_num=0, reduce=True):
+        """Compute the loss for the given sample.
+
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        if isinstance(sample, list):
+            if self.sample_patch_num > 0:
+                sample[0]['net_input']['sample_patch_num'] = self.sample_patch_num
+            loss_v1, sample_size_v1, logging_output_v1 = self.forward(model, sample[0], update_num, reduce)
+            loss_v2, sample_size_v2, logging_output_v2 = self.forward(model, sample[1], update_num, reduce)
+            loss = loss_v1 / sample_size_v1 + loss_v2 / sample_size_v2
+            sample_size = 1
+            logging_output = {
+                "loss": loss.data,
+                "loss_v1": loss_v1.data,
+                "loss_v2": loss_v2.data,
+                "nll_loss": logging_output_v1["nll_loss"].data / sample_size_v1 + logging_output_v2["nll_loss"].data / sample_size_v2,
+                "ntokens": logging_output_v1["ntokens"] + logging_output_v2["ntokens"],
+                "nsentences": logging_output_v1["nsentences"] + logging_output_v2["nsentences"],
+                "sample_size": 1,
+                "sample_size_v1": sample_size_v1,
+                "sample_size_v2": sample_size_v2,
+            }
+            return loss, sample_size, logging_output
+
+        if self.use_rdrop:
+            construct_rdrop_sample(sample)
+
+        net_output = model(**sample["net_input"])
+        loss, nll_loss, ntokens = self.compute_loss(model, net_output, sample, update_num, reduce=reduce)
+        sample_size = (
+            sample["target"].size(0) if self.sentence_avg else ntokens
+        )
+        logging_output = {
+            "loss": loss.data,
+            "nll_loss": nll_loss.data,
+            "ntokens": sample["ntokens"],
+            "nsentences": sample["nsentences"],
+            "sample_size": sample_size,
+        }
+        if self.report_accuracy:
+            n_correct, total = self.compute_accuracy(model, net_output, sample)
+            logging_output["n_correct"] = utils.item(n_correct.data)
+            logging_output["total"] = utils.item(total.data)
+        return loss, sample_size, logging_output
+
+    def get_lprobs_and_target(self, model, net_output, sample):
+        conf = sample['conf'][:, None, None] if 'conf' in sample and sample['conf'] is not None else 1
+        constraint_masks = None
+        if "constraint_masks" in sample and sample["constraint_masks"] is not None:
+            constraint_masks = sample["constraint_masks"]
+            net_output[0].masked_fill_(~constraint_masks, -math.inf)
+        if self.constraint_start is not None and self.constraint_end is not None:
+            net_output[0][:, :, 4:self.constraint_start] = -math.inf
+            net_output[0][:, :, self.constraint_end:] = -math.inf
+        lprobs = model.get_normalized_probs(net_output, log_probs=True) * conf
+        target = model.get_targets(sample, net_output)
+        if self.ignore_prefix_size > 0:
+            lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
+            target = target[:, self.ignore_prefix_size :].contiguous()
+            if constraint_masks is not None:
+                constraint_masks = constraint_masks[:, self.ignore_prefix_size :, :].contiguous()
+        if self.ignore_eos:
+            bsz, seq_len, embed_dim = lprobs.size()
+            eos_indices = target.eq(self.task.tgt_dict.eos())
+            lprobs = lprobs[~eos_indices].reshape(bsz, seq_len-1, embed_dim)
+            target = target[~eos_indices].reshape(bsz, seq_len-1)
+            if constraint_masks is not None:
+                constraint_masks = constraint_masks[~eos_indices].reshape(bsz, seq_len-1, embed_dim)
+        if constraint_masks is not None:
+            constraint_masks = constraint_masks.view(-1, constraint_masks.size(-1))
+        return lprobs.view(-1, lprobs.size(-1)), target.view(-1), constraint_masks
+
+    def compute_loss(self, model, net_output, sample, update_num, reduce=True):
+        lprobs, target, constraint_masks = self.get_lprobs_and_target(model, net_output, sample)
+        if constraint_masks is not None:
+            constraint_masks = constraint_masks[target != self.padding_idx]
+        lprobs = lprobs[target != self.padding_idx]
+        target = target[target != self.padding_idx]
+        loss, nll_loss, ntokens = label_smoothed_nll_loss(
+            lprobs,
+            target,
+            self.eps,
+            update_num,
+            reduce=reduce,
+            drop_worst_ratio=self.drop_worst_ratio,
+            drop_worst_after=self.drop_worst_after,
+            use_rdrop=self.use_rdrop,
+            reg_alpha=self.reg_alpha,
+            constraint_masks=constraint_masks,
+            constraint_start=self.constraint_start,
+            constraint_end=self.constraint_end
+        )
+        return loss, nll_loss, ntokens
+
+    def compute_accuracy(self, model, net_output, sample):
+        lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
+        mask = target.ne(self.padding_idx)
+        n_correct = torch.sum(
+            lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))
+        )
+        total = torch.sum(mask)
+        return n_correct, total
+
+    @classmethod
+    def reduce_metrics(cls, logging_outputs) -> None:
+        """Aggregate logging outputs from data parallel training."""
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        loss_sum_v1 = sum(log.get("loss_v1", 0) for log in logging_outputs)
+        loss_sum_v2 = sum(log.get("loss_v2", 0) for log in logging_outputs)
+        nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
+        ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
+        nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        sample_size_v1 = sum(log.get("sample_size_v1", 0) for log in logging_outputs)
+        sample_size_v2 = sum(log.get("sample_size_v2", 0) for log in logging_outputs)
+
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size, sample_size, round=3
+        )
+        metrics.log_scalar(
+            "loss_v1", loss_sum_v1 / max(sample_size_v1, 1), max(sample_size_v1, 1), round=3
+        )
+        metrics.log_scalar(
+            "loss_v2", loss_sum_v2 / max(sample_size_v2, 1), max(sample_size_v2, 1), round=3
+        )
+        metrics.log_scalar(
+            "nll_loss", nll_loss_sum / sample_size, ntokens, round=3
+        )
+        metrics.log_derived(
+            "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
+        )
+
+        metrics.log_scalar(
+            "ntokens", ntokens, 1, round=3
+        )
+        metrics.log_scalar(
+            "nsentences", nsentences, 1, round=3
+        )
+        metrics.log_scalar(
+            "sample_size", sample_size, 1, round=3
+        )
+        metrics.log_scalar(
+            "sample_size_v1", sample_size_v1, 1, round=3
+        )
+        metrics.log_scalar(
+            "sample_size_v2", sample_size_v2, 1, round=3
+        )
+
+        total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
+        if total > 0:
+            metrics.log_scalar("total", total)
+            n_correct = utils.item(
+                sum(log.get("n_correct", 0) for log in logging_outputs)
+            )
+            metrics.log_scalar("n_correct", n_correct)
+            metrics.log_derived(
+                "accuracy",
+                lambda meters: round(
+                    meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
+                )
+                if meters["total"].sum > 0
+                else float("nan"),
+            )
+
+    @staticmethod
+    def logging_outputs_can_be_summed() -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return True

criterions/label_smoothed_encouraging_loss.py

@@ -0,0 +1,395 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+from dataclasses import dataclass, field
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+import numpy as np
+from fairseq import metrics, utils
+from fairseq.criterions import FairseqCriterion, register_criterion
+from fairseq.dataclass import FairseqDataclass
+from omegaconf import II
+
+
+@dataclass
+class AdjustLabelSmoothedEncouragingLossConfig(FairseqDataclass):
+    label_smoothing: float = field(
+        default=0.0,
+        metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
+    )
+    report_accuracy: bool = field(
+        default=False,
+        metadata={"help": "report accuracy metric"},
+    )
+    ignore_prefix_size: int = field(
+        default=0,
+        metadata={"help": "Ignore first N tokens"},
+    )
+    ignore_eos: bool = field(
+        default=False,
+        metadata={"help": "Ignore eos token"},
+    )
+    sentence_avg: bool = II("optimization.sentence_avg")
+    drop_worst_ratio: float = field(
+        default=0.0,
+        metadata={"help": "ratio for discarding bad samples"},
+    )
+    drop_worst_after: int = field(
+        default=0,
+        metadata={"help": "steps for discarding bad samples"},
+    )
+    use_rdrop: bool = field(
+        default=False, metadata={"help": "use R-Drop"}
+    )
+    reg_alpha: float = field(
+        default=1.0, metadata={"help": "weight for R-Drop"}
+    )
+    sample_patch_num: int = field(
+        default=196, metadata={"help": "sample patchs for v1"}
+    )
+    constraint_range: Optional[str] = field(
+        default=None,
+        metadata={"help": "constraint range"}
+    )
+    log_end: float = field(
+        default=0.75,
+        metadata={"help": "higher log_end is for cases with higher performance,"
+                          " we recommend 0.75 or 0.5 as your first try."}
+    )
+    drop_best_ratio: float = field(
+        default=0.0,
+        metadata={"help": "ratio for discarding best samples"},
+    )
+    drop_best_after: int = field(
+        default=0,
+        metadata={"help": "steps for discarding best samples"},
+    )
+
+
+
+def construct_rdrop_sample(x):
+    if isinstance(x, dict):
+        for key in x:
+            x[key] = construct_rdrop_sample(x[key])
+        return x
+    elif isinstance(x, torch.Tensor):
+        return x.repeat(2, *([1] * (x.dim()-1)))
+    elif isinstance(x, int):
+        return x * 2
+    elif isinstance(x, np.ndarray):
+        return x.repeat(2)
+    else:
+        raise NotImplementedError
+
+
+def kl_loss(p, q):
+    p_loss = F.kl_div(p, torch.exp(q), reduction='sum')
+    q_loss = F.kl_div(q, torch.exp(p), reduction='sum')
+    loss = (p_loss + q_loss) / 2
+    return loss
+
+
+def label_smoothed_nll_loss(
+        lprobs, target, epsilon, update_num, reduce=True,
+        drop_worst_ratio=0.0, drop_worst_after=0, use_rdrop=False, reg_alpha=1.0,
+        constraint_masks=None, constraint_start=None, constraint_end=None,        drop_best_ratio=0.0,
+        drop_best_after=0,
+):
+    if target.dim() == lprobs.dim() - 1:
+        target = target.unsqueeze(-1)
+    nll_loss = -lprobs.gather(dim=-1, index=target).squeeze(-1)
+    if constraint_masks is not None:
+        smooth_loss = -lprobs.masked_fill(~constraint_masks, 0).sum(dim=-1, keepdim=True).squeeze(-1)
+        eps_i = epsilon / (constraint_masks.sum(1) - 1 + 1e-6)
+    elif constraint_start is not None and constraint_end is not None:
+        constraint_range = [0, 1, 2, 3] + list(range(constraint_start, constraint_end))
+        smooth_loss = -lprobs[:, constraint_range].sum(dim=-1, keepdim=True).squeeze(-1)
+        eps_i = epsilon / (len(constraint_range) - 1 + 1e-6)
+    else:
+        smooth_loss = -lprobs.sum(dim=-1, keepdim=True).squeeze(-1)
+        eps_i = epsilon / (lprobs.size(-1) - 1)
+    loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
+    if drop_worst_ratio > 0 and update_num > drop_worst_after:
+        if use_rdrop:
+            true_batch_size = loss.size(0) // 2
+            _, indices = torch.topk(loss[:true_batch_size], k=int(true_batch_size * (1 - drop_worst_ratio)), largest=False)
+            loss = torch.cat([loss[indices], loss[indices+true_batch_size]])
+            nll_loss = torch.cat([nll_loss[indices], nll_loss[indices+true_batch_size]])
+            lprobs = torch.cat([lprobs[indices], lprobs[indices+true_batch_size]])
+        else:
+            loss, indices = torch.topk(loss, k=int(loss.shape[0] * (1 - drop_worst_ratio)), largest=False)
+            nll_loss = nll_loss[indices]
+            lprobs = lprobs[indices]
+            target = target[indices]
+    if update_num > drop_best_after:
+        loss, indices = torch.topk(loss, k=int(loss.shape[0] * (1 - drop_best_ratio)), largest=True)
+        nll_loss = nll_loss[indices]
+        lprobs = lprobs[indices]
+        target = target[indices]
+
+    ntokens = loss.numel()
+    nll_loss = nll_loss.sum()
+    loss = loss.sum()
+    if use_rdrop:
+        true_batch_size = lprobs.size(0) // 2
+        p = lprobs[:true_batch_size]
+        q = lprobs[true_batch_size:]
+        if constraint_start is not None and constraint_end is not None:
+            constraint_range = [0, 1, 2, 3] + list(range(constraint_start, constraint_end))
+            p = p[:, constraint_range]
+            q = q[:, constraint_range]
+        loss += kl_loss(p, q) * reg_alpha
+
+    return loss, nll_loss, ntokens,lprobs,target
+
+
+@register_criterion(
+    "adjust_label_smoothed_encouraging_loss", dataclass=AdjustLabelSmoothedEncouragingLossConfig
+)
+class AdjustLabelSmoothedEncouragingLossCriterion(FairseqCriterion):
+    def __init__(
+        self,
+        task,
+        sentence_avg,
+        label_smoothing,
+        ignore_prefix_size=0,
+        ignore_eos=False,
+        report_accuracy=False,
+        drop_worst_ratio=0,
+        drop_worst_after=0,
+        use_rdrop=False,
+        reg_alpha=1.0,
+        sample_patch_num=196,
+        constraint_range=None,
+        log_end=0.75,
+        drop_best_ratio=0.0,
+        drop_best_after=0,
+    ):
+        super().__init__(task)
+        self.sentence_avg = sentence_avg
+        self.eps = label_smoothing
+        self.ignore_prefix_size = ignore_prefix_size
+        self.ignore_eos = ignore_eos
+        self.report_accuracy = report_accuracy
+        self.drop_worst_ratio = drop_worst_ratio
+        self.drop_worst_after = drop_worst_after
+        self.use_rdrop = use_rdrop
+        self.reg_alpha = reg_alpha
+        self.sample_patch_num = sample_patch_num
+
+        self.constraint_start = None
+        self.constraint_end = None
+        if constraint_range is not None:
+            constraint_start, constraint_end = constraint_range.split(',')
+            self.constraint_start = int(constraint_start)
+            self.constraint_end = int(constraint_end)
+        self.log_end = log_end
+        self.drop_best_ratio = drop_best_ratio
+        self.drop_best_after = drop_best_after
+        print('el, self.log_end=', self.log_end)
+    # @staticmethod
+    # def add_args(parser):
+    #     """Add criterion-specific arguments to the parser."""
+    #     # fmt: off
+    #     parser.add_argument('--log_end', type=float, default=1.0)
+
+    def forward(self, model, sample, update_num=0, reduce=True):
+        """Compute the loss for the given sample.
+
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        if isinstance(sample, list):
+            if self.sample_patch_num > 0:
+                sample[0]['net_input']['sample_patch_num'] = self.sample_patch_num
+            loss_v1, sample_size_v1, logging_output_v1 = self.forward(model, sample[0], update_num, reduce)
+            loss_v2, sample_size_v2, logging_output_v2 = self.forward(model, sample[1], update_num, reduce)
+            loss = loss_v1 / sample_size_v1 + loss_v2 / sample_size_v2
+            sample_size = 1
+            logging_output = {
+                "loss": loss.data,
+                "loss_v1": loss_v1.data,
+                "loss_v2": loss_v2.data,
+                "nll_loss": logging_output_v1["nll_loss"].data / sample_size_v1 + logging_output_v2["nll_loss"].data / sample_size_v2,
+                "ntokens": logging_output_v1["ntokens"] + logging_output_v2["ntokens"],
+                "nsentences": logging_output_v1["nsentences"] + logging_output_v2["nsentences"],
+                "sample_size": 1,
+                "sample_size_v1": sample_size_v1,
+                "sample_size_v2": sample_size_v2,
+            }
+            return loss, sample_size, logging_output
+
+        if self.use_rdrop:
+            construct_rdrop_sample(sample)
+
+        net_output = model(**sample["net_input"])
+        loss, nll_loss, ntokens = self.compute_loss(model, net_output, sample, update_num, reduce=reduce)
+        sample_size = (
+            sample["target"].size(0) if self.sentence_avg else ntokens
+        )
+        logging_output = {
+            "loss": loss.data,
+            "nll_loss": nll_loss.data,
+            "ntokens": sample["ntokens"],
+            "nsentences": sample["nsentences"],
+            "sample_size": sample_size,
+        }
+        if self.report_accuracy:
+            n_correct, total = self.compute_accuracy(model, net_output, sample)
+            logging_output["n_correct"] = utils.item(n_correct.data)
+            logging_output["total"] = utils.item(total.data)
+        return loss, sample_size, logging_output
+
+    def get_lprobs_and_target(self, model, net_output, sample):
+        conf = sample['conf'][:, None, None] if 'conf' in sample and sample['conf'] is not None else 1
+        constraint_masks = None
+        if "constraint_masks" in sample and sample["constraint_masks"] is not None:
+            constraint_masks = sample["constraint_masks"]
+            net_output[0].masked_fill_(~constraint_masks, -math.inf)
+        if self.constraint_start is not None and self.constraint_end is not None:
+            net_output[0][:, :, 4:self.constraint_start] = -math.inf
+            net_output[0][:, :, self.constraint_end:] = -math.inf
+        lprobs = model.get_normalized_probs(net_output, log_probs=True) * conf
+        target = model.get_targets(sample, net_output)
+        if self.ignore_prefix_size > 0:
+            lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
+            target = target[:, self.ignore_prefix_size :].contiguous()
+            if constraint_masks is not None:
+                constraint_masks = constraint_masks[:, self.ignore_prefix_size :, :].contiguous()
+        if self.ignore_eos:
+            bsz, seq_len, embed_dim = lprobs.size()
+            eos_indices = target.eq(self.task.tgt_dict.eos())
+            lprobs = lprobs[~eos_indices].reshape(bsz, seq_len-1, embed_dim)
+            target = target[~eos_indices].reshape(bsz, seq_len-1)
+            if constraint_masks is not None:
+                constraint_masks = constraint_masks[~eos_indices].reshape(bsz, seq_len-1, embed_dim)
+        if constraint_masks is not None:
+            constraint_masks = constraint_masks.view(-1, constraint_masks.size(-1))
+        return lprobs.view(-1, lprobs.size(-1)), target.view(-1), constraint_masks
+
+    def compute_loss(self, model, net_output, sample, update_num, reduce=True):
+        lprobs, target, constraint_masks = self.get_lprobs_and_target(model, net_output, sample)
+        if constraint_masks is not None:
+            constraint_masks = constraint_masks[target != self.padding_idx]
+        lprobs = lprobs[target != self.padding_idx]
+        target = target[target != self.padding_idx]
+        loss, nll_loss, ntokens, lprobs, target = label_smoothed_nll_loss(
+            lprobs,
+            target,
+            self.eps,
+            update_num,
+            reduce=reduce,
+            drop_worst_ratio=self.drop_worst_ratio,
+            drop_worst_after=self.drop_worst_after,
+            use_rdrop=self.use_rdrop,
+            reg_alpha=self.reg_alpha,
+            constraint_masks=constraint_masks,
+            constraint_start=self.constraint_start,
+            constraint_end=self.constraint_end
+        )
+        # for encouraging loss
+        probs = torch.exp(lprobs)
+        bonus = torch.log(torch.clamp((torch.ones_like(probs) - probs), min=1e-5))  # likelihood bonus
+        log_end = self.log_end
+        if log_end != 1.0:  # e.g. 0.9
+            y_log_end = torch.log(torch.ones_like(probs) - log_end)
+            bonus_after_log_end = 1 / (log_end - torch.ones_like(probs)) * (probs - log_end) + y_log_end
+            # x:log_end, y  torch.log(torch.clamp((torch.ones_like(probs) - probs), min=self.cl_eps))
+            bonus = torch.where(probs > log_end, bonus_after_log_end, bonus)
+        c_loss = F.nll_loss(
+            -bonus,
+            target.view(-1),
+            reduction='sum',
+        )
+        smoothing_c_loss = bonus.sum(dim=-1)
+        smoothing_c_loss = smoothing_c_loss.sum()
+        c_loss = c_loss * (1 - self.eps) + (self.eps / lprobs.size(-1)) * smoothing_c_loss
+        loss = loss + c_loss
+        # end for encouraging loss
+        return loss, nll_loss, ntokens
+
+    def compute_accuracy(self, model, net_output, sample):
+        lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
+        mask = target.ne(self.padding_idx)
+        n_correct = torch.sum(
+            lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))
+        )
+        total = torch.sum(mask)
+        return n_correct, total
+
+    @classmethod
+    def reduce_metrics(cls, logging_outputs) -> None:
+        """Aggregate logging outputs from data parallel training."""
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        loss_sum_v1 = sum(log.get("loss_v1", 0) for log in logging_outputs)
+        loss_sum_v2 = sum(log.get("loss_v2", 0) for log in logging_outputs)
+        nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
+        ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
+        nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        sample_size_v1 = sum(log.get("sample_size_v1", 0) for log in logging_outputs)
+        sample_size_v2 = sum(log.get("sample_size_v2", 0) for log in logging_outputs)
+
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size, sample_size, round=3
+        )
+        metrics.log_scalar(
+            "loss_v1", loss_sum_v1 / max(sample_size_v1, 1), max(sample_size_v1, 1), round=3
+        )
+        metrics.log_scalar(
+            "loss_v2", loss_sum_v2 / max(sample_size_v2, 1), max(sample_size_v2, 1), round=3
+        )
+        metrics.log_scalar(
+            "nll_loss", nll_loss_sum / sample_size, ntokens, round=3
+        )
+        metrics.log_derived(
+            "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
+        )
+
+        metrics.log_scalar(
+            "ntokens", ntokens, 1, round=3
+        )
+        metrics.log_scalar(
+            "nsentences", nsentences, 1, round=3
+        )
+        metrics.log_scalar(
+            "sample_size", sample_size, 1, round=3
+        )
+        metrics.log_scalar(
+            "sample_size_v1", sample_size_v1, 1, round=3
+        )
+        metrics.log_scalar(
+            "sample_size_v2", sample_size_v2, 1, round=3
+        )
+
+        total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
+        if total > 0:
+            metrics.log_scalar("total", total)
+            n_correct = utils.item(
+                sum(log.get("n_correct", 0) for log in logging_outputs)
+            )
+            metrics.log_scalar("n_correct", n_correct)
+            metrics.log_derived(
+                "accuracy",
+                lambda meters: round(
+                    meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
+                )
+                if meters["total"].sum > 0
+                else float("nan"),
+            )
+
+    @staticmethod
+    def logging_outputs_can_be_summed() -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return True

criterions/scst_loss.py

@@ -0,0 +1,281 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import math
+import string
+from dataclasses import dataclass, field
+from collections import OrderedDict
+from typing import Optional
+
+import torch
+from fairseq import metrics, utils
+from fairseq.criterions import FairseqCriterion, register_criterion
+from fairseq.dataclass import FairseqDataclass
+from omegaconf import II
+
+from data import data_utils
+from utils.cider.pyciderevalcap.ciderD.ciderD import CiderD
+
+
+def scst_loss(lprobs, target, reward, ignore_index=None, reduce=True):
+    loss = -lprobs.gather(dim=-1, index=target.unsqueeze(-1)).squeeze() * reward.unsqueeze(-1)
+    if ignore_index is not None:
+        pad_mask = target.eq(ignore_index)
+        loss.masked_fill_(pad_mask, 0.0)
+        ntokens = (~pad_mask).sum()
+    else:
+        loss = loss.squeeze(-1)
+        ntokens = target.numel()
+    if reduce:
+        loss = loss.sum()
+    return loss, ntokens
+
+
+@dataclass
+class ScstRewardCriterionConfig(FairseqDataclass):
+    scst_cider_cached_tokens: str = field(
+        default="coco-train-words.p",
+        metadata={"help": "path to cached cPickle file used to calculate CIDEr scores"},
+    )
+    ignore_prefix_size: int = field(
+        default=0,
+        metadata={"help": "Ignore first N tokens"},
+    )
+    sentence_avg: bool = II("optimization.sentence_avg")
+    constraint_range: Optional[str] = field(
+        default=None,
+        metadata={"help": "constraint range"}
+    )
+
+
+@register_criterion(
+    "scst_reward_criterion", dataclass=ScstRewardCriterionConfig
+)
+class ScstRewardCriterion(FairseqCriterion):
+    CIDER_REWARD_WEIGHT = 1
+
+    def __init__(
+        self,
+        task,
+        scst_cider_cached_tokens,
+        sentence_avg,
+        ignore_prefix_size=0,
+        constraint_range=None
+    ):
+        super().__init__(task)
+        self.scst_cider_scorer = CiderD(df=scst_cider_cached_tokens)
+        self.sentence_avg = sentence_avg
+        self.ignore_prefix_size = ignore_prefix_size
+        self.transtab = str.maketrans({key: None for key in string.punctuation})
+
+        self.constraint_start = None
+        self.constraint_end = None
+        if constraint_range is not None:
+            constraint_start, constraint_end = constraint_range.split(',')
+            self.constraint_start = int(constraint_start)
+            self.constraint_end = int(constraint_end)
+
+    def forward(self, model, sample, update_num=0, reduce=True):
+        """Compute the loss for the given sample.
+
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        loss, score, ntokens, nsentences = self.compute_loss(model, sample, reduce=reduce)
+
+        sample_size = (
+            nsentences if self.sentence_avg else ntokens
+        )
+        logging_output = {
+            "loss": loss.data,
+            "score": score,
+            "ntokens": ntokens,
+            "nsentences": nsentences,
+            "sample_size": sample_size,
+        }
+        return loss, sample_size, logging_output
+
+    def _calculate_eval_scores(self, gen_res, gt_idx, gt_res):
+        '''
+        gen_res: generated captions, list of str
+        gt_idx: list of int, of the same length as gen_res
+        gt_res: ground truth captions, list of list of str.
+            gen_res[i] corresponds to gt_res[gt_idx[i]]
+            Each image can have multiple ground truth captions
+        '''
+        gen_res_size = len(gen_res)
+
+        res = OrderedDict()
+        for i in range(gen_res_size):
+            res[i] = [self._wrap_sentence(gen_res[i].strip().translate(self.transtab))]
+
+        gts = OrderedDict()
+        gt_res_ = [
+            [self._wrap_sentence(gt_res[i][j].strip().translate(self.transtab)) for j in range(len(gt_res[i]))]
+                for i in range(len(gt_res))
+        ]
+        for i in range(gen_res_size):
+            gts[i] = gt_res_[gt_idx[i]]
+
+        res_ = [{'image_id':i, 'caption': res[i]} for i in range(len(res))]
+        _, batch_cider_scores = self.scst_cider_scorer.compute_score(gts, res_)
+        scores = self.CIDER_REWARD_WEIGHT * batch_cider_scores
+        return scores
+
+    @classmethod
+    def _wrap_sentence(self, s):
+        # ensure the sentence ends with <eos> token
+        # in order to keep consisitent with cider_cached_tokens
+        r = s.strip()
+        if r.endswith('.'):
+            r = r[:-1]
+        r += ' <eos>'
+        return r
+
+    def get_generator_out(self, model, sample):
+        def decode(toks):
+            hypo = toks.int().cpu()
+            hypo_str = self.task.tgt_dict.string(hypo)
+            hypo_str = self.task.bpe.decode(hypo_str).strip()
+            return hypo, hypo_str
+
+        model.eval()
+        with torch.no_grad():
+            self.task.scst_generator.model.eval()
+            gen_out = self.task.scst_generator.generate([model], sample)
+
+        gen_target = []
+        gen_res = []
+        gt_res = []
+        for i in range(len(gen_out)):
+            for j in range(len(gen_out[i])):
+                hypo, hypo_str = decode(gen_out[i][j]["tokens"])
+                gen_target.append(hypo)
+                gen_res.append(hypo_str)
+            gt_res.append(
+                decode(utils.strip_pad(sample["target"][i], self.padding_idx))[1].split('&&')
+            )
+
+        return gen_target, gen_res, gt_res
+
+    def get_reward_and_scores(self, gen_res, gt_res, device):
+        batch_size = len(gt_res)
+        gen_res_size = len(gen_res)
+        seq_per_img = gen_res_size // batch_size
+
+        gt_idx = [i // seq_per_img for i in range(gen_res_size)]
+        scores = self._calculate_eval_scores(gen_res, gt_idx, gt_res)
+        sc_ = scores.reshape(batch_size, seq_per_img)
+        baseline = (sc_.sum(1, keepdims=True) - sc_) / (sc_.shape[1] - 1)
+        # sample - baseline
+        reward = scores.reshape(batch_size, seq_per_img)
+        reward = reward - baseline
+        reward = reward.reshape(gen_res_size)
+        reward = torch.as_tensor(reward, device=device, dtype=torch.float64)
+
+        return reward, scores
+
+    def get_net_output(self, model, sample, gen_target):
+        def merge(sample_list, eos=self.task.tgt_dict.eos(), move_eos_to_beginning=False):
+            return data_utils.collate_tokens(
+                sample_list,
+                pad_idx=self.padding_idx,
+                eos_idx=eos,
+                left_pad=False,
+                move_eos_to_beginning=move_eos_to_beginning,
+            )
+
+        batch_size = len(sample["target"])
+        gen_target_size = len(gen_target)
+        seq_per_img = gen_target_size // batch_size
+
+        model.train()
+        sample_src_tokens = torch.repeat_interleave(
+            sample['net_input']['src_tokens'], seq_per_img, dim=0
+        )
+        sample_src_lengths = torch.repeat_interleave(
+            sample['net_input']['src_lengths'], seq_per_img, dim=0
+        )
+        sample_patch_images = torch.repeat_interleave(
+            sample['net_input']['patch_images'], seq_per_img, dim=0
+        )
+        sample_patch_masks = torch.repeat_interleave(
+            sample['net_input']['patch_masks'], seq_per_img, dim=0
+        )
+        gen_prev_output_tokens = torch.as_tensor(
+            merge(gen_target, eos=self.task.tgt_dict.bos(), move_eos_to_beginning=True),
+            device=sample["target"].device, dtype=torch.int64
+        )
+        gen_target_tokens = torch.as_tensor(
+            merge(gen_target), device=sample["target"].device, dtype=torch.int64
+        )
+        net_output = model(
+            src_tokens=sample_src_tokens, src_lengths=sample_src_lengths,
+            patch_images=sample_patch_images, patch_masks=sample_patch_masks,
+            prev_output_tokens=gen_prev_output_tokens
+        )
+
+        return net_output, gen_target_tokens
+
+    def get_lprobs_and_target(self, model, net_output, gen_target):
+        if self.constraint_start is not None and self.constraint_end is not None:
+            net_output[0][:, :, 4:self.constraint_start] = -math.inf
+            net_output[0][:, :, self.constraint_end:] = -math.inf
+        lprobs = model.get_normalized_probs(net_output, log_probs=True)
+        if self.ignore_prefix_size > 0:
+            if getattr(lprobs, "batch_first", False):
+                lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
+                gen_target = gen_target[:, self.ignore_prefix_size :].contiguous()
+            else:
+                lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
+                gen_target = gen_target[self.ignore_prefix_size :, :].contiguous()
+        return lprobs, gen_target
+
+    def compute_loss(self, model, sample, reduce=True):
+        gen_target, gen_res, gt_res = self.get_generator_out(model, sample)
+        reward, scores = self.get_reward_and_scores(gen_res, gt_res, device=sample["target"].device)
+        net_output, gen_target_tokens = self.get_net_output(model, sample, gen_target)
+        gen_lprobs, gen_target_tokens = self.get_lprobs_and_target(model, net_output, gen_target_tokens)
+        loss, ntokens = scst_loss(gen_lprobs, gen_target_tokens, reward, ignore_index=self.padding_idx, reduce=reduce)
+        nsentences = gen_target_tokens.size(0)
+
+        return loss, scores.sum(), ntokens, nsentences
+
+    @classmethod
+    def reduce_metrics(cls, logging_outputs) -> None:
+        """Aggregate logging outputs from data parallel training."""
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        score_sum = sum(log.get("score", 0) for log in logging_outputs)
+        ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
+        nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size, sample_size, round=3
+        )
+        metrics.log_scalar(
+            "score", score_sum / nsentences, nsentences, round=3
+        )
+
+        metrics.log_scalar(
+            "ntokens", ntokens, 1, round=3
+        )
+        metrics.log_scalar(
+            "nsentences", nsentences, 1, round=3
+        )
+        metrics.log_scalar(
+            "sample_size", sample_size, 1, round=3
+        )
+
+    @staticmethod
+    def logging_outputs_can_be_summed() -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return True

data/cv_data/image_classify_dataset.py

@@ -0,0 +1,196 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+from io import BytesIO
+
+import logging
+import warnings
+import functools
+
+import numpy as np
+import torch
+import base64
+from torchvision import transforms
+from timm.data import create_transform
+from utils.vision_helper import RandomAugment
+
+from PIL import Image, ImageFile
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+ImageFile.MAX_IMAGE_PIXELS = None
+Image.MAX_IMAGE_PIXELS = None
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    id = np.array([s["id"] for s in samples])
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
+    patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
+
+    conf = None
+    if samples[0].get("conf", None) is not None:
+        conf = torch.cat([s['conf'] for s in samples], dim=0)
+
+    ref_dict = None
+    if samples[0].get("ref_dict", None) is not None:
+        ref_dict = np.array([s['ref_dict'] for s in samples])
+
+    constraint_masks = None
+    if samples[0].get("constraint_mask", None) is not None:
+        constraint_masks = merge("constraint_mask")
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        )
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "id": id,
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "patch_images": patch_images,
+            "patch_masks": patch_masks,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "conf": conf,
+        "ref_dict": ref_dict,
+        "constraint_masks": constraint_masks,
+        "target": target,
+    }
+
+    return batch
+
+
+class ImageClassifyDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=128,
+        max_tgt_length=30,
+        patch_image_size=224,
+        constraint_trie=None,
+        imagenet_default_mean_and_std=False
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.patch_image_size = patch_image_size
+
+        self.constraint_trie = constraint_trie
+
+        if imagenet_default_mean_and_std:
+            mean = IMAGENET_DEFAULT_MEAN
+            std = IMAGENET_DEFAULT_STD
+        else:
+            mean = [0.5, 0.5, 0.5]
+            std = [0.5, 0.5, 0.5]
+
+        if self.split != 'train':
+            self.patch_resize_transform = transforms.Compose([
+                lambda image: image.convert("RGB"),
+                transforms.Resize([patch_image_size, patch_image_size], interpolation=Image.BICUBIC),
+                transforms.ToTensor(),
+                transforms.Normalize(mean=mean, std=std),
+            ])
+            logger.info("val split, do not use random augmentation.")
+        else:
+            self.patch_resize_transform = create_transform(
+                input_size=patch_image_size,
+                is_training=True,
+                color_jitter=0.4,
+                auto_augment='rand-m9-mstd0.5-inc1',
+                interpolation='bicubic',
+                re_prob=0.25,
+                re_mode='pixel',
+                re_count=1,
+                mean=mean,
+                std=std,
+            )
+            self.patch_resize_transform = transforms.Compose(functools.reduce(lambda x, y:x + y, [
+                [lambda image: image.convert("RGB"),],
+                self.patch_resize_transform.transforms[:2],
+                [self.patch_resize_transform.transforms[2]],
+                [RandomAugment(2, 7, isPIL=True, augs=['Identity', 'AutoContrast', 'Equalize', 'Brightness', 'Sharpness', 'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate']), ],
+                self.patch_resize_transform.transforms[3:],
+            ]))
+            logger.info("train split, use random augmentation.")
+
+    def __getitem__(self, index):
+        image, label_name = self.dataset[index]
+
+        image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
+        patch_image = self.patch_resize_transform(image)
+        patch_mask = torch.tensor([True])
+
+        src_item = self.encode_text(' what does the image describe?')
+        tgt_item = self.encode_text(" {}".format(label_name))
+        ref_dict = {label_name: 1.0}
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        target_item = torch.cat([tgt_item, self.eos_item])
+        prev_output_item = torch.cat([self.bos_item, tgt_item])
+
+        example = {
+            "id": index,
+            "source": src_item,
+            "patch_image": patch_image,
+            "patch_mask": patch_mask,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "ref_dict": ref_dict,
+        }
+        if self.constraint_trie is not None:
+            constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
+            for i in range(len(prev_output_item)):
+                constraint_prefix_token = prev_output_item[:i+1].tolist()
+                constraint_nodes = self.constraint_trie.get_next_layer(constraint_prefix_token)
+                constraint_mask[i][constraint_nodes] = True
+            example["constraint_mask"] = constraint_mask
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/data_utils.py

@@ -0,0 +1,601 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+try:
+    from collections.abc import Iterable
+except ImportError:
+    from collections import Iterable
+import contextlib
+import itertools
+import logging
+import re
+import warnings
+from typing import Optional, Tuple
+
+import numpy as np
+import torch
+
+from fairseq.file_io import PathManager
+from fairseq import utils
+import os
+
+logger = logging.getLogger(__name__)
+
+
+def infer_language_pair(path):
+    """Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx"""
+    src, dst = None, None
+    for filename in PathManager.ls(path):
+        parts = filename.split(".")
+        if len(parts) >= 3 and len(parts[1].split("-")) == 2:
+            return parts[1].split("-")
+    return src, dst
+
+
+def collate_tokens(
+    values,
+    pad_idx,
+    eos_idx=None,
+    left_pad=False,
+    move_eos_to_beginning=False,
+    pad_to_length=None,
+    pad_to_multiple=1,
+    pad_to_bsz=None,
+):
+    """Convert a list of 1d tensors into a padded 2d tensor."""
+    size = max(v.size(0) for v in values)
+    size = size if pad_to_length is None else max(size, pad_to_length)
+    if pad_to_multiple != 1 and size % pad_to_multiple != 0:
+        size = int(((size - 0.1) // pad_to_multiple + 1) * pad_to_multiple)
+
+    def copy_tensor(src, dst):
+        assert dst.numel() == src.numel()
+        if move_eos_to_beginning:
+            if eos_idx is None:
+                # if no eos_idx is specified, then use the last token in src
+                dst[0] = src[-1]
+            else:
+                dst[0] = eos_idx
+            dst[1:] = src[:-1]
+        else:
+            dst.copy_(src)
+
+    if values[0].dim() == 1:
+        res = values[0].new(len(values), size).fill_(pad_idx)
+    elif values[0].dim() == 2:
+        assert move_eos_to_beginning is False
+        res = values[0].new(len(values), size, values[0].size(1)).fill_(pad_idx)
+    else:
+        raise NotImplementedError
+
+    for i, v in enumerate(values):
+        copy_tensor(v, res[i][size - len(v) :] if left_pad else res[i][: len(v)])
+    return res
+
+
+def load_indexed_dataset(
+    path, dictionary=None, dataset_impl=None, combine=False, default="cached"
+):
+    """A helper function for loading indexed datasets.
+
+    Args:
+        path (str): path to indexed dataset (e.g., 'data-bin/train')
+        dictionary (~fairseq.data.Dictionary): data dictionary
+        dataset_impl (str, optional): which dataset implementation to use. If
+            not provided, it will be inferred automatically. For legacy indexed
+            data we use the 'cached' implementation by default.
+        combine (bool, optional): automatically load and combine multiple
+            datasets. For example, if *path* is 'data-bin/train', then we will
+            combine 'data-bin/train', 'data-bin/train1', ... and return a
+            single ConcatDataset instance.
+    """
+    import fairseq.data.indexed_dataset as indexed_dataset
+    from fairseq.data.concat_dataset import ConcatDataset
+
+    datasets = []
+    for k in itertools.count():
+        path_k = path + (str(k) if k > 0 else "")
+        try:
+            path_k = indexed_dataset.get_indexed_dataset_to_local(path_k)
+        except Exception as e:
+            if "StorageException: [404] Path not found" in str(e):
+                logger.warning(f"path_k: {e} not found")
+            else:
+                raise e
+
+        dataset_impl_k = dataset_impl
+        if dataset_impl_k is None:
+            dataset_impl_k = indexed_dataset.infer_dataset_impl(path_k)
+        dataset = indexed_dataset.make_dataset(
+            path_k,
+            impl=dataset_impl_k or default,
+            fix_lua_indexing=True,
+            dictionary=dictionary,
+        )
+        if dataset is None:
+            break
+        logger.info("loaded {:,} examples from: {}".format(len(dataset), path_k))
+        datasets.append(dataset)
+        if not combine:
+            break
+    if len(datasets) == 0:
+        return None
+    elif len(datasets) == 1:
+        return datasets[0]
+    else:
+        return ConcatDataset(datasets)
+
+
+@contextlib.contextmanager
+def numpy_seed(seed, *addl_seeds):
+    """Context manager which seeds the NumPy PRNG with the specified seed and
+    restores the state afterward"""
+    if seed is None:
+        yield
+        return
+    if len(addl_seeds) > 0:
+        seed = int(hash((seed, *addl_seeds)) % 1e6)
+    state = np.random.get_state()
+    np.random.seed(seed)
+    try:
+        yield
+    finally:
+        np.random.set_state(state)
+
+
+def collect_filtered(function, iterable, filtered):
+    """
+    Similar to :func:`filter` but collects filtered elements in ``filtered``.
+
+    Args:
+        function (callable): function that returns ``False`` for elements that
+            should be filtered
+        iterable (iterable): iterable to filter
+        filtered (list): list to store filtered elements
+    """
+    for el in iterable:
+        if function(el):
+            yield el
+        else:
+            filtered.append(el)
+
+
+def _filter_by_size_dynamic(indices, size_fn, max_positions, raise_exception=False):
+    def compare_leq(a, b):
+        return a <= b if not isinstance(a, tuple) else max(a) <= b
+
+    def check_size(idx):
+        if isinstance(max_positions, float) or isinstance(max_positions, int):
+            return size_fn(idx) <= max_positions
+        elif isinstance(max_positions, dict):
+            idx_size = size_fn(idx)
+            assert isinstance(idx_size, dict)
+            intersect_keys = set(max_positions.keys()) & set(idx_size.keys())
+            return all(
+                all(
+                    a is None or b is None or a <= b
+                    for a, b in zip(idx_size[key], max_positions[key])
+                )
+                for key in intersect_keys
+            )
+        else:
+            # For MultiCorpusSampledDataset, will generalize it later
+            if not isinstance(size_fn(idx), Iterable):
+                return all(size_fn(idx) <= b for b in max_positions)
+            return all(
+                a is None or b is None or a <= b
+                for a, b in zip(size_fn(idx), max_positions)
+            )
+
+    ignored = []
+    itr = collect_filtered(check_size, indices, ignored)
+    indices = np.fromiter(itr, dtype=np.int64, count=-1)
+    return indices, ignored
+
+
+def filter_by_size(indices, dataset, max_positions, raise_exception=False):
+    """
+    [deprecated] Filter indices based on their size.
+    Use `FairseqDataset::filter_indices_by_size` instead.
+
+    Args:
+        indices (List[int]): ordered list of dataset indices
+        dataset (FairseqDataset): fairseq dataset instance
+        max_positions (tuple): filter elements larger than this size.
+            Comparisons are done component-wise.
+        raise_exception (bool, optional): if ``True``, raise an exception if
+            any elements are filtered (default: False).
+    """
+    warnings.warn(
+        "data_utils.filter_by_size is deprecated. "
+        "Use `FairseqDataset::filter_indices_by_size` instead.",
+        stacklevel=2,
+    )
+    if isinstance(max_positions, float) or isinstance(max_positions, int):
+        if hasattr(dataset, "sizes") and isinstance(dataset.sizes, np.ndarray):
+            ignored = indices[dataset.sizes[indices] > max_positions].tolist()
+            indices = indices[dataset.sizes[indices] <= max_positions]
+        elif (
+            hasattr(dataset, "sizes")
+            and isinstance(dataset.sizes, list)
+            and len(dataset.sizes) == 1
+        ):
+            ignored = indices[dataset.sizes[0][indices] > max_positions].tolist()
+            indices = indices[dataset.sizes[0][indices] <= max_positions]
+        else:
+            indices, ignored = _filter_by_size_dynamic(
+                indices, dataset.size, max_positions
+            )
+    else:
+        indices, ignored = _filter_by_size_dynamic(indices, dataset.size, max_positions)
+
+    if len(ignored) > 0 and raise_exception:
+        raise Exception(
+            (
+                "Size of sample #{} is invalid (={}) since max_positions={}, "
+                "skip this example with --skip-invalid-size-inputs-valid-test"
+            ).format(ignored[0], dataset.size(ignored[0]), max_positions)
+        )
+    if len(ignored) > 0:
+        logger.warning(
+            (
+                "{} samples have invalid sizes and will be skipped, "
+                "max_positions={}, first few sample ids={}"
+            ).format(len(ignored), max_positions, ignored[:10])
+        )
+    return indices
+
+
+def filter_paired_dataset_indices_by_size(src_sizes, tgt_sizes, indices, max_sizes):
+    """Filter a list of sample indices. Remove those that are longer
+        than specified in max_sizes.
+
+    Args:
+        indices (np.array): original array of sample indices
+        max_sizes (int or list[int] or tuple[int]): max sample size,
+            can be defined separately for src and tgt (then list or tuple)
+
+    Returns:
+        np.array: filtered sample array
+        list: list of removed indices
+    """
+    if max_sizes is None:
+        return indices, []
+    if type(max_sizes) in (int, float):
+        max_src_size, max_tgt_size = max_sizes, max_sizes
+    else:
+        max_src_size, max_tgt_size = max_sizes
+    if tgt_sizes is None:
+        ignored = indices[src_sizes[indices] > max_src_size]
+    else:
+        ignored = indices[
+            (src_sizes[indices] > max_src_size) | (tgt_sizes[indices] > max_tgt_size)
+        ]
+    if len(ignored) > 0:
+        if tgt_sizes is None:
+            indices = indices[src_sizes[indices] <= max_src_size]
+        else:
+            indices = indices[
+                (src_sizes[indices] <= max_src_size)
+                & (tgt_sizes[indices] <= max_tgt_size)
+            ]
+    return indices, ignored.tolist()
+
+
+def batch_by_size(
+    indices,
+    num_tokens_fn,
+    num_tokens_vec=None,
+    max_tokens=None,
+    max_sentences=None,
+    required_batch_size_multiple=1,
+    fixed_shapes=None,
+):
+    """
+    Yield mini-batches of indices bucketed by size. Batches may contain
+    sequences of different lengths.
+
+    Args:
+        indices (List[int]): ordered list of dataset indices
+        num_tokens_fn (callable): function that returns the number of tokens at
+            a given index
+        num_tokens_vec (List[int], optional): precomputed vector of the number
+            of tokens for each index in indices (to enable faster batch generation)
+        max_tokens (int, optional): max number of tokens in each batch
+            (default: None).
+        max_sentences (int, optional): max number of sentences in each
+            batch (default: None).
+        required_batch_size_multiple (int, optional): require batch size to
+            be less than N or a multiple of N (default: 1).
+        fixed_shapes (List[Tuple[int, int]], optional): if given, batches will
+            only be created with the given shapes. *max_sentences* and
+            *required_batch_size_multiple* will be ignored (default: None).
+    """
+    try:
+        from fairseq.data.data_utils_fast import (
+            batch_by_size_fn,
+            batch_by_size_vec,
+            batch_fixed_shapes_fast,
+        )
+    except ImportError:
+        raise ImportError(
+            "Please build Cython components with: "
+            "`python setup.py build_ext --inplace`"
+        )
+    except ValueError:
+        raise ValueError(
+            "Please build (or rebuild) Cython components with `python setup.py build_ext --inplace`."
+        )
+
+    # added int() to avoid TypeError: an integer is required
+    max_tokens = (
+        int(max_tokens) if max_tokens is not None else -1
+    )
+    max_sentences = max_sentences if max_sentences is not None else -1
+    bsz_mult = required_batch_size_multiple
+
+    if not isinstance(indices, np.ndarray):
+        indices = np.fromiter(indices, dtype=np.int64, count=-1)
+
+    if num_tokens_vec is not None and not isinstance(num_tokens_vec, np.ndarray):
+        num_tokens_vec = np.fromiter(num_tokens_vec, dtype=np.int64, count=-1)
+
+    if fixed_shapes is None:
+        if num_tokens_vec is None:
+            return batch_by_size_fn(
+                indices,
+                num_tokens_fn,
+                max_tokens,
+                max_sentences,
+                bsz_mult,
+            )
+        else:
+            return batch_by_size_vec(
+                indices,
+                num_tokens_vec,
+                max_tokens,
+                max_sentences,
+                bsz_mult,
+            )
+
+    else:
+        fixed_shapes = np.array(fixed_shapes, dtype=np.int64)
+        sort_order = np.lexsort(
+            [
+                fixed_shapes[:, 1].argsort(),  # length
+                fixed_shapes[:, 0].argsort(),  # bsz
+            ]
+        )
+        fixed_shapes_sorted = fixed_shapes[sort_order]
+        return batch_fixed_shapes_fast(indices, num_tokens_fn, fixed_shapes_sorted)
+
+
+def post_process(sentence: str, symbol: str):
+    if symbol == "sentencepiece":
+        sentence = sentence.replace(" ", "").replace("\u2581", " ").strip()
+    elif symbol == "wordpiece":
+        sentence = sentence.replace(" ", "").replace("_", " ").strip()
+    elif symbol == "letter":
+        sentence = sentence.replace(" ", "").replace("|", " ").strip()
+    elif symbol == "silence":
+        import re
+        sentence = sentence.replace("<SIL>", "")
+        sentence = re.sub(' +', ' ', sentence).strip()
+    elif symbol == "_EOW":
+        sentence = sentence.replace(" ", "").replace("_EOW", " ").strip()
+    elif symbol in {"subword_nmt", "@@ ", "@@"}:
+        if symbol == "subword_nmt":
+            symbol = "@@ "
+        sentence = (sentence + " ").replace(symbol, "").rstrip()
+    elif symbol == "none":
+        pass
+    elif symbol is not None:
+        raise NotImplementedError(f"Unknown post_process option: {symbol}")
+    return sentence
+
+
+def compute_mask_indices(
+    shape: Tuple[int, int],
+    padding_mask: Optional[torch.Tensor],
+    mask_prob: float,
+    mask_length: int,
+    mask_type: str = "static",
+    mask_other: float = 0.0,
+    min_masks: int = 0,
+    no_overlap: bool = False,
+    min_space: int = 0,
+) -> np.ndarray:
+    """
+    Computes random mask spans for a given shape
+
+    Args:
+        shape: the the shape for which to compute masks.
+            should be of size 2 where first element is batch size and 2nd is timesteps
+        padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
+        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
+            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
+            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
+        mask_type: how to compute mask lengths
+            static = fixed size
+            uniform = sample from uniform distribution [mask_other, mask_length*2]
+            normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
+            poisson = sample from possion distribution with lambda = mask length
+        min_masks: minimum number of masked spans
+        no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
+        min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
+    """
+
+    bsz, all_sz = shape
+    mask = np.full((bsz, all_sz), False)
+
+    all_num_mask = int(
+        # add a random number for probabilistic rounding
+        mask_prob * all_sz / float(mask_length)
+        + np.random.rand()
+    )
+
+    all_num_mask = max(min_masks, all_num_mask)
+
+    mask_idcs = []
+    for i in range(bsz):
+        if padding_mask is not None:
+            sz = all_sz - padding_mask[i].long().sum().item()
+            num_mask = int(
+                # add a random number for probabilistic rounding
+                mask_prob * sz / float(mask_length)
+                + np.random.rand()
+            )
+            num_mask = max(min_masks, num_mask)
+        else:
+            sz = all_sz
+            num_mask = all_num_mask
+
+        if mask_type == "static":
+            lengths = np.full(num_mask, mask_length)
+        elif mask_type == "uniform":
+            lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
+        elif mask_type == "normal":
+            lengths = np.random.normal(mask_length, mask_other, size=num_mask)
+            lengths = [max(1, int(round(x))) for x in lengths]
+        elif mask_type == "poisson":
+            lengths = np.random.poisson(mask_length, size=num_mask)
+            lengths = [int(round(x)) for x in lengths]
+        else:
+            raise Exception("unknown mask selection " + mask_type)
+
+        if sum(lengths) == 0:
+            lengths[0] = min(mask_length, sz - 1)
+
+        if no_overlap:
+            mask_idc = []
+
+            def arrange(s, e, length, keep_length):
+                span_start = np.random.randint(s, e - length)
+                mask_idc.extend(span_start + i for i in range(length))
+
+                new_parts = []
+                if span_start - s - min_space >= keep_length:
+                    new_parts.append((s, span_start - min_space + 1))
+                if e - span_start - keep_length - min_space > keep_length:
+                    new_parts.append((span_start + length + min_space, e))
+                return new_parts
+
+            parts = [(0, sz)]
+            min_length = min(lengths)
+            for length in sorted(lengths, reverse=True):
+                lens = np.fromiter(
+                    (e - s if e - s >= length + min_space else 0 for s, e in parts),
+                    np.int,
+                )
+                l_sum = np.sum(lens)
+                if l_sum == 0:
+                    break
+                probs = lens / np.sum(lens)
+                c = np.random.choice(len(parts), p=probs)
+                s, e = parts.pop(c)
+                parts.extend(arrange(s, e, length, min_length))
+            mask_idc = np.asarray(mask_idc)
+        else:
+            min_len = min(lengths)
+            if sz - min_len <= num_mask:
+                min_len = sz - num_mask - 1
+
+            mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
+
+            mask_idc = np.asarray(
+                [
+                    mask_idc[j] + offset
+                    for j in range(len(mask_idc))
+                    for offset in range(lengths[j])
+                ]
+            )
+
+        mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
+
+    min_len = min([len(m) for m in mask_idcs])
+    for i, mask_idc in enumerate(mask_idcs):
+        if len(mask_idc) > min_len:
+            mask_idc = np.random.choice(mask_idc, min_len, replace=False)
+        mask[i, mask_idc] = True
+
+    return mask
+
+
+def get_mem_usage():
+    try:
+        import psutil
+
+        mb = 1024 * 1024
+        return f"used={psutil.virtual_memory().used / mb}Mb; avail={psutil.virtual_memory().available / mb}Mb"
+    except ImportError:
+        return "N/A"
+
+
+# lens: torch.LongTensor
+# returns: torch.BoolTensor
+def lengths_to_padding_mask(lens):
+    bsz, max_lens = lens.size(0), torch.max(lens).item()
+    mask = torch.arange(max_lens).to(lens.device).view(1, max_lens)
+    mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens)
+    return mask
+
+
+# lens: torch.LongTensor
+# returns: torch.BoolTensor
+def lengths_to_mask(lens):
+    return ~lengths_to_padding_mask(lens)
+
+
+def get_buckets(sizes, num_buckets):
+    buckets = np.unique(
+        np.percentile(
+            sizes,
+            np.linspace(0, 100, num_buckets + 1),
+            interpolation='lower',
+        )[1:]
+    )
+    return buckets
+
+
+def get_bucketed_sizes(orig_sizes, buckets):
+    sizes = np.copy(orig_sizes)
+    assert np.min(sizes) >= 0
+    start_val = -1
+    for end_val in buckets:
+        mask = (sizes > start_val) & (sizes <= end_val)
+        sizes[mask] = end_val
+        start_val = end_val
+    return sizes
+
+
+
+def _find_extra_valid_paths(dataset_path: str) -> set:
+    paths = utils.split_paths(dataset_path)
+    all_valid_paths = set()
+    for sub_dir in paths:
+        contents = PathManager.ls(sub_dir)
+        valid_paths = [c for c in contents if re.match("valid*[0-9].*", c) is not None]
+        all_valid_paths |= {os.path.basename(p) for p in valid_paths}
+    # Remove .bin, .idx etc
+    roots = {os.path.splitext(p)[0] for p in all_valid_paths}
+    return roots
+
+
+def raise_if_valid_subsets_unintentionally_ignored(train_cfg) -> None:
+    """Raises if there are paths matching 'valid*[0-9].*' which are not combined or ignored."""
+    if (
+        train_cfg.dataset.ignore_unused_valid_subsets
+        or train_cfg.dataset.combine_valid_subsets
+        or train_cfg.dataset.disable_validation
+        or not hasattr(train_cfg.task, "data")
+    ):
+        return
+    other_paths = _find_extra_valid_paths(train_cfg.task.data)
+    specified_subsets = train_cfg.dataset.valid_subset.split(",")
+    ignored_paths = [p for p in other_paths if p not in specified_subsets]
+    if ignored_paths:
+        advice = "Set --combine-val to combine them or --ignore-unused-valid-subsets to ignore them."
+        msg = f"Valid paths {ignored_paths} will be ignored. {advice}"
+        raise ValueError(msg)

data/file_dataset.py

@@ -0,0 +1,107 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import os
+import torch
+import pickle
+
+
+class FileDataset:
+    def __init__(self, file_path, selected_col_ids=None, dtypes=None, separator="\t", cached_index=False):
+        self.file_path = file_path
+        assert os.path.exists(self.file_path), "Error: The local datafile {} not exists!".format(self.file_path)
+
+        self.separator = separator
+        if selected_col_ids is None:
+            # default to all fields
+            self.selected_col_ids = list(
+                range(len(open(self.file_path).readline().rstrip("\n").split(self.separator))))
+        else:
+            self.selected_col_ids = [int(col_id) for col_id in selected_col_ids.split(",")]
+        if dtypes is None:
+            # default to str
+            self.dtypes = [str for col_id in self.selected_col_ids]
+        else:
+            self.dtypes = [eval(col_dtype) for col_dtype in dtypes.split(",")]
+            assert len(self.dtypes) == len(self.selected_col_ids)
+
+        self.data_cnt = 0
+        try:
+            self.slice_id = torch.distributed.get_rank()
+            self.slice_count = torch.distributed.get_world_size()
+        except Exception:
+            self.slice_id = 0
+            self.slice_count = 1
+        self.cached_index = cached_index
+        self._init_seek_index()
+        self._reader = self._get_reader()
+        print("file {} slice_id {} row count {} total row count {}".format(
+            self.file_path, self.slice_id, self.row_count, self.total_row_count)
+        )
+
+    def _init_seek_index(self):
+        if self.cached_index:
+            cache_path = "{}.index".format(self.file_path)
+            assert os.path.exists(cache_path), "cache file {} not exists!".format(cache_path)
+            self.total_row_count, self.lineid_to_offset = pickle.load(open(cache_path, "rb"))
+            print("local datafile {} slice_id {} use cached row_count and line_idx-to-offset mapping".format(
+                self.file_path, self.slice_id))
+        else:
+            # make an iteration over the file to get row_count and line_idx-to-offset mapping
+            fp = open(self.file_path, "r")
+            print("local datafile {} slice_id {} begin to initialize row_count and line_idx-to-offset mapping".format(
+                self.file_path, self.slice_id))
+            self.total_row_count = 0
+            offset = 0
+            self.lineid_to_offset = []
+            for line in fp:
+                self.lineid_to_offset.append(offset)
+                self.total_row_count += 1
+                offset += len(line.encode('utf-8'))
+        self._compute_start_pos_and_row_count()
+        print("local datafile {} slice_id {} finished initializing row_count and line_idx-to-offset mapping".format(
+            self.file_path, self.slice_id))
+
+    def _compute_start_pos_and_row_count(self):
+        self.row_count = self.total_row_count // self.slice_count
+        if self.slice_id < self.total_row_count - self.row_count * self.slice_count:
+            self.row_count += 1
+            self.start_pos = self.row_count * self.slice_id
+        else:
+            self.start_pos = self.row_count * self.slice_id + (self.total_row_count - self.row_count * self.slice_count)
+
+    def _get_reader(self):
+        fp = open(self.file_path, "r")
+        fp.seek(self.lineid_to_offset[self.start_pos])
+        return fp
+
+    def _seek(self, offset=0):
+        try:
+            print("slice_id {} seek offset {}".format(self.slice_id, self.start_pos + offset))
+            self._reader.seek(self.lineid_to_offset[self.start_pos + offset])
+            self.data_cnt = offset
+        except Exception:
+            print("slice_id {} seek offset {}".format(self.slice_id, offset))
+            self._reader.seek(self.lineid_to_offset[offset])
+            self.data_cnt = offset
+
+    def __del__(self):
+        self._reader.close()
+
+    def __len__(self):
+        return self.row_count
+
+    def get_total_row_count(self):
+        return self.total_row_count
+
+    def __getitem__(self, index):
+        if self.data_cnt == self.row_count:
+            print("reach the end of datafile, start a new reader")
+            self.data_cnt = 0
+            self._reader = self._get_reader()
+        column_l = self._reader.readline().rstrip("\n").split(self.separator)
+        self.data_cnt += 1
+        column_l = [dtype(column_l[col_id]) for col_id, dtype in zip(self.selected_col_ids, self.dtypes)]
+        return column_l

data/mm_data/caption_dataset.py

@@ -0,0 +1,160 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+from io import BytesIO
+
+import logging
+import warnings
+import string
+
+import numpy as np
+import torch
+import base64
+from torchvision import transforms
+
+from PIL import Image, ImageFile
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+ImageFile.MAX_IMAGE_PIXELS = None
+Image.MAX_IMAGE_PIXELS = None
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    id = np.array([s["id"] for s in samples])
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
+    patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor([s["target"].ne(pad_idx).long().sum() for s in samples])
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "id": id,
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "patch_images": patch_images,
+            "patch_masks": patch_masks,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "target": target,
+    }
+
+    return batch
+
+
+class CaptionDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=128,
+        max_tgt_length=30,
+        patch_image_size=224,
+        imagenet_default_mean_and_std=False,
+        scst=False
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.patch_image_size = patch_image_size
+        self.scst = scst
+
+        self.transtab = str.maketrans({key: None for key in string.punctuation})
+
+        if imagenet_default_mean_and_std:
+            mean = IMAGENET_DEFAULT_MEAN
+            std = IMAGENET_DEFAULT_STD
+        else:
+            mean = [0.5, 0.5, 0.5]
+            std = [0.5, 0.5, 0.5]
+
+        self.patch_resize_transform = transforms.Compose([
+            lambda image: image.convert("RGB"),
+            transforms.Resize((patch_image_size, patch_image_size), interpolation=Image.BICUBIC),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std),
+        ])
+
+        if type(bpe).__name__ == 'GPT2BPE':
+            self.prompt = " what does the image describe?"
+        elif type(bpe).__name__ == 'BertBPE':
+            self.prompt = "图片描述了什么内容?"
+
+    def __getitem__(self, index):
+        uniq_id, image, caption = self.dataset[index]
+
+        image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
+        patch_image = self.patch_resize_transform(image)
+        patch_mask = torch.tensor([True])
+
+        if self.split == 'train' and not self.scst:
+            caption = caption.translate(self.transtab).strip()
+            caption_token_list = caption.strip().split()
+            tgt_caption = ' '.join(caption_token_list[:self.max_tgt_length])
+        else:
+            caption = ' '.join(caption.strip().split())
+            caption_list = [cap.translate(self.transtab).strip() for cap in caption.strip().split('&&')]
+            tgt_caption = '&&'.join(caption_list)
+        src_item = self.encode_text(self.prompt)
+        tgt_item = self.encode_text(" {}".format(tgt_caption))
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        target_item = torch.cat([tgt_item, self.eos_item])
+        prev_output_item = torch.cat([self.bos_item, tgt_item])
+
+        example = {
+            "id": uniq_id,
+            "source": src_item,
+            "patch_image": patch_image,
+            "patch_mask": patch_mask,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item
+        }
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/mm_data/image_gen_dataset.py

@@ -0,0 +1,171 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+from io import BytesIO
+
+import logging
+import warnings
+import base64
+import random
+
+import numpy as np
+import torch
+
+from PIL import Image, ImageFile
+from itertools import chain
+from data.ofa_dataset import OFADataset
+from data import data_utils
+
+from PIL import Image
+from io import BytesIO
+import base64
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+ImageFile.MAX_IMAGE_PIXELS = None
+Image.MAX_IMAGE_PIXELS = None
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+
+def collate(
+        samples,
+        pad_idx,
+        eos_idx,
+        left_pad_source=False,
+        left_pad_target=False,
+):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key, left_pad, move_eos_to_beginning=False):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx,
+            left_pad,
+            move_eos_to_beginning,
+        )
+
+    id = np.array([s["id"] for s in samples])
+    src_tokens = merge("source", left_pad=left_pad_source)
+    # sort by descending source length
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    code_images = np.array([s["code_image"] for s in samples])
+    code_masks = torch.cat([sample['code_mask'] for sample in samples])
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target", left_pad=left_pad_target)
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        )
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens", left_pad=left_pad_target)
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "id": id,
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "code_masks": code_masks,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "code_images": code_images,
+        "target": target
+    }
+
+    return batch
+
+
+def preprocess_vqgan(x):
+    x = 2. * x - 1.
+    return x
+
+
+class ImageGenDataset(OFADataset):
+    def __init__(
+            self,
+            split,
+            dataset,
+            bpe,
+            src_dict,
+            tgt_dict=None,
+            max_src_length=128,
+            code_dict_size=8192,
+            code_image_size=256,
+            num_bins=1000
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+
+        self.code_dict_size = code_dict_size
+        self.num_codes = (code_image_size // 8) ** 2
+        self.num_bins = num_bins
+
+        slice_id = self.dataset.slice_id
+        empty_img = Image.new('RGB', (code_image_size, code_image_size))
+        empty_img.save(f'temp_{slice_id}.png')
+        img = Image.open(f'temp_{slice_id}.png')
+        img_buffer = BytesIO()
+        img.save(img_buffer, format=img.format)
+        byte_data = img_buffer.getvalue()
+        self.empty_image_base64 = base64.urlsafe_b64encode(byte_data)
+
+    def __getitem__(self, index):
+
+        data = self.dataset[index]
+        if len(data) == 2:
+            uniq_id, text = data
+            image_code = [0] * 1024
+            image = self.empty_image_base64
+        elif len(data) == 3:
+            uniq_id, text, image_code = data
+            image_code = [int(num) for num in image_code.strip().split()]
+            image = self.empty_image_base64
+        elif len(data) == 4:
+            uniq_id, image, text, image_code = data
+            image_code = [int(num) for num in image_code.strip().split()]
+        else:
+            raise NotImplementedError
+        code_mask = torch.tensor([True])
+        image_code = torch.LongTensor(image_code)
+        tgt_item = image_code + len(self.src_dict) - self.code_dict_size - self.num_bins
+        target_item = torch.cat([tgt_item, self.eos_item])
+        prev_output_item = torch.cat([self.bos_item, tgt_item])
+
+        caption_token_list = text.strip().split()
+        caption = ' '.join(caption_token_list[:self.max_src_length])
+        src_item = self.encode_text(
+            " what is the complete image? caption: {}".format(caption),
+            append_bos=True,
+            append_eos=True
+        )
+        example = {
+            "id": uniq_id,
+            "source": src_item,
+            "code_mask": code_mask,
+            "code_image": image,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item
+        }
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/mm_data/ocr_dataset.py

@@ -0,0 +1,204 @@
+# Copyright 2022 The OFA-Sys Team.
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license
+# found in the LICENSE file in the root directory.
+
+from io import BytesIO
+
+import logging
+import warnings
+import random
+import functools
+
+import torch
+import base64
+from torchvision import transforms
+from torchvision.transforms import InterpolationMode
+from torchvision.transforms import functional as F
+
+from PIL import Image, ImageFile
+
+from zhconv import convert
+import unicodedata
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+ImageFile.MAX_IMAGE_PIXELS = None
+Image.MAX_IMAGE_PIXELS = None
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    id = np.array([s["id"] for s in samples])
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
+    patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor([s["target"].ne(pad_idx).long().sum() for s in samples])
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "id": id,
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "patch_images": patch_images,
+            "patch_masks": patch_masks,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "target": target,
+    }
+
+    return batch
+
+
+def ocr_resize(img, patch_image_size, is_document=False):
+    img = img.convert("RGB")
+    width, height = img.size
+
+    if is_document:
+        new_height, new_width = 64, 1920
+    else:
+        if width >= height:
+            new_width = max(64, patch_image_size)
+            new_height = max(64, int(patch_image_size * (height / width)))
+            top = random.randint(0, patch_image_size - new_height)
+            bottom = patch_image_size - new_height - top
+            left, right = 0, 0
+        else:
+            new_height = max(64, patch_image_size)
+            new_width = max(64, int(patch_image_size * (width / height)))
+            left = random.randint(0, patch_image_size - new_width)
+            right = patch_image_size - new_width - left
+            top, bottom = 0, 0
+
+    img_new = F.resize(
+        img,
+        [new_height, new_width],
+        interpolation=InterpolationMode.BICUBIC,
+    )
+
+    if is_document:
+        img_split = transforms.ToTensor()(img_new).chunk(4, dim=-1)
+        img_new = transforms.ToPILImage()(torch.cat(img_split, dim=-2))
+        new_width, new_height = img_new.size
+        top = random.randint(0, patch_image_size - new_height)
+        bottom = patch_image_size - new_height - top
+        left, right = 0, 0
+
+    img_new = F.pad(img_new, padding=[left, top, right, bottom], padding_mode="edge")
+    assert img_new.size == (patch_image_size, patch_image_size)
+
+    return img_new
+
+
+class OcrDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=80,
+        max_tgt_length=30,
+        patch_image_size=224,
+        imagenet_default_mean_and_std=False,
+        is_document=False,
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.patch_image_size = patch_image_size
+
+        if imagenet_default_mean_and_std:
+            mean = IMAGENET_DEFAULT_MEAN
+            std = IMAGENET_DEFAULT_STD
+        else:
+            mean = [0.5, 0.5, 0.5]
+            std = [0.5, 0.5, 0.5]
+
+        self.patch_resize_transform = transforms.Compose(
+            [
+                lambda image: ocr_resize(
+                    image, patch_image_size, is_document=is_document
+                ),
+                transforms.ToTensor(),
+                transforms.Normalize(mean=mean, std=std),
+            ]
+        )
+
+        self.bpe = bpe
+        if type(bpe).__name__ == 'GPT2BPE':
+            self.prompt = " what are the texts on the image?"
+        elif type(bpe).__name__ == 'BertBPE':
+            self.prompt = "图片上的文字是什么?"
+
+    def __getitem__(self, index):
+        uniq_id, image, caption = self.dataset[index]
+
+        image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
+        patch_image = self.patch_resize_transform(image)
+        patch_mask = torch.tensor([True])
+
+        caption = unicodedata.normalize("NFKC", convert(caption, "zh-hans"))
+        if type(self.bpe).__name__ == 'GPT2BPE':
+            caption_token_list = caption.lower().strip().split()
+            tgt_caption = ' '.join(caption_token_list[:self.max_tgt_length])
+        elif type(self.bpe).__name__ == 'BertBPE':
+            tgt_caption = caption[: self.max_tgt_length].lower()
+        src_item = self.encode_text(self.prompt)
+        tgt_item = self.encode_text(" {}".format(tgt_caption))
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        target_item = torch.cat([tgt_item, self.eos_item])
+        prev_output_item = torch.cat([self.bos_item, tgt_item])
+
+        example = {
+            "id": uniq_id,
+            "source": src_item,
+            "patch_image": patch_image,
+            "patch_mask": patch_mask,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+        }
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data required for the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/mm_data/refcoco_dataset.py

@@ -0,0 +1,174 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+from io import BytesIO
+
+import logging
+import warnings
+
+import numpy as np
+import torch
+import base64
+import utils.transforms as T
+
+from PIL import Image, ImageFile
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+ImageFile.MAX_IMAGE_PIXELS = None
+Image.MAX_IMAGE_PIXELS = None
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    id = np.array([s["id"] for s in samples])
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
+    patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
+
+    w_resize_ratios = torch.stack([s["w_resize_ratio"] for s in samples], dim=0)
+    h_resize_ratios = torch.stack([s["h_resize_ratio"] for s in samples], dim=0)
+    region_coords = torch.stack([s['region_coord'] for s in samples], dim=0)
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor([s["target"].ne(pad_idx).long().sum() for s in samples])
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "id": id,
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "patch_images": patch_images,
+            "patch_masks": patch_masks,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "target": target,
+        "w_resize_ratios": w_resize_ratios,
+        "h_resize_ratios": h_resize_ratios,
+        "region_coords": region_coords
+    }
+
+    return batch
+
+
+class RefcocoDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=80,
+        max_tgt_length=30,
+        patch_image_size=512,
+        imagenet_default_mean_and_std=False,
+        num_bins=1000,
+        max_image_size=512
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.patch_image_size = patch_image_size
+        self.num_bins = num_bins
+
+        if imagenet_default_mean_and_std:
+            mean = IMAGENET_DEFAULT_MEAN
+            std = IMAGENET_DEFAULT_STD
+        else:
+            mean = [0.5, 0.5, 0.5]
+            std = [0.5, 0.5, 0.5]
+
+        # for positioning
+        self.positioning_transform = T.Compose([
+            T.RandomResize([patch_image_size], max_size=patch_image_size),
+            T.ToTensor(),
+            T.Normalize(mean=mean, std=std, max_image_size=max_image_size)
+        ])
+
+        if type(bpe).__name__ == 'GPT2BPE':
+            self.prompt = ' which region does the text " {} " describe?'
+        elif type(bpe).__name__ == 'BertBPE':
+            self.prompt = '这段文字" {} "描述的是哪个区域？'
+
+    def __getitem__(self, index):
+        uniq_id, base64_str, text, region_coord = self.dataset[index]
+
+        image = Image.open(BytesIO(base64.urlsafe_b64decode(base64_str))).convert("RGB")
+        w, h = image.size
+        boxes_target = {"boxes": [], "labels": [], "area": [], "size": torch.tensor([h, w])}
+        x0, y0, x1, y1 = region_coord.strip().split(',')
+        region = torch.tensor([float(x0), float(y0), float(x1), float(y1)])
+        boxes_target["boxes"] = torch.tensor([[float(x0), float(y0), float(x1), float(y1)]])
+        boxes_target["labels"] = np.array([0])
+        boxes_target["area"] = torch.tensor([(float(x1) - float(x0)) * (float(y1) - float(y0))])
+
+        patch_image, patch_boxes = self.positioning_transform(image, boxes_target)
+        resize_h, resize_w = patch_boxes["size"][0], patch_boxes["size"][1]
+        patch_mask = torch.tensor([True])
+        quant_x0 = "<bin_{}>".format(int((patch_boxes["boxes"][0][0] * (self.num_bins - 1)).round()))
+        quant_y0 = "<bin_{}>".format(int((patch_boxes["boxes"][0][1] * (self.num_bins - 1)).round()))
+        quant_x1 = "<bin_{}>".format(int((patch_boxes["boxes"][0][2] * (self.num_bins - 1)).round()))
+        quant_y1 = "<bin_{}>".format(int((patch_boxes["boxes"][0][3] * (self.num_bins - 1)).round()))
+        region_coord = "{} {} {} {}".format(quant_x0, quant_y0, quant_x1, quant_y1)
+        src_caption = self.pre_caption(text, self.max_src_length)
+        src_item = self.encode_text(self.prompt.format(src_caption))
+        tgt_item = self.encode_text(region_coord, use_bpe=False)
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        target_item = torch.cat([tgt_item, self.eos_item])
+        prev_output_item = torch.cat([self.bos_item, tgt_item])
+
+        example = {
+            "id": uniq_id,
+            "source": src_item,
+            "patch_image": patch_image,
+            "patch_mask": patch_mask,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "w_resize_ratio": resize_w / w,
+            "h_resize_ratio": resize_h / h,
+            "region_coord": region
+        }
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/mm_data/snli_ve_dataset.py

@@ -0,0 +1,203 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+from io import BytesIO
+
+import logging
+import warnings
+
+import numpy as np
+import torch
+import base64
+from torchvision import transforms
+
+from PIL import Image, ImageFile
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+ImageFile.MAX_IMAGE_PIXELS = None
+Image.MAX_IMAGE_PIXELS = None
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    id = np.array([s["id"] for s in samples])
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
+    patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
+
+    ref_dict = None
+    if samples[0].get("ref_dict", None) is not None:
+        ref_dict = np.array([s['ref_dict'] for s in samples])
+
+    constraint_masks = None
+    if samples[0].get("constraint_mask", None) is not None:
+        constraint_masks = merge("constraint_mask")
+
+    decoder_prompts = None
+    if samples[0].get("decoder_prompt", None) is not None:
+        decoder_prompts = np.array([s['decoder_prompt'].tolist() for s in samples])
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        )
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "id": id,
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "patch_images": patch_images,
+            "patch_masks": patch_masks,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "ref_dict": ref_dict,
+        "constraint_masks": constraint_masks,
+        "decoder_prompts": decoder_prompts,
+        "target": target
+    }
+
+    return batch
+
+
+class SnliVeDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=80,
+        max_tgt_length=30,
+        patch_image_size=224,
+        add_caption=False,
+        constraint_trie=None,
+        imagenet_default_mean_and_std=False,
+        prompt_type="none"
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.patch_image_size = patch_image_size
+
+        self.add_caption = add_caption
+        self.constraint_trie = constraint_trie
+        self.prompt_type = prompt_type
+
+        if imagenet_default_mean_and_std:
+            mean = IMAGENET_DEFAULT_MEAN
+            std = IMAGENET_DEFAULT_STD
+        else:
+            mean = [0.5, 0.5, 0.5]
+            std = [0.5, 0.5, 0.5]
+
+        self.patch_resize_transform = transforms.Compose([
+            lambda image: image.convert("RGB"),
+            transforms.Resize((patch_image_size, patch_image_size), interpolation=Image.BICUBIC),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std),
+        ])
+
+    def __getitem__(self, index):
+        uniq_id, image, hypothesis, caption, label = self.dataset[index]
+        if label == 'contradiction':
+            label = 'no'
+        elif label == 'entailment':
+            label = 'yes'
+        elif label == 'neutral':
+            label = 'maybe'
+        else:
+            raise NotImplementedError
+
+        image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
+        patch_image = self.patch_resize_transform(image)
+        patch_mask = torch.tensor([True])
+
+        hypothesis = self.pre_caption(hypothesis, self.max_src_length)
+        src_item = self.encode_text(' does the image describe " {} "?'.format(hypothesis))
+        tgt_item = self.encode_text(" {}".format(label))
+        ref_dict = {label: 1.0}
+
+        if self.add_caption:
+            caption = self.pre_caption(caption, self.max_src_length)
+            src_item = self.encode_text(' can image and text1 " {} " imply text2 " {} "?'.format(caption, hypothesis))
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        if self.prompt_type == 'none':
+            prev_output_item = torch.cat([self.bos_item, tgt_item])
+            target_item = torch.cat([prev_output_item[1:], self.eos_item])
+            decoder_prompt = self.bos_item
+        elif self.prompt_type == 'src':
+            prev_output_item = torch.cat([src_item, tgt_item])
+            target_item = torch.cat([prev_output_item[1:], self.eos_item])
+            decoder_prompt = src_item
+        elif self.prompt_type == 'prev_output':
+            prev_output_item = torch.cat([src_item[:-1], tgt_item])
+            target_item = torch.cat([prev_output_item[1:], self.eos_item])
+            decoder_prompt = src_item[:-1]
+        else:
+            raise NotImplementedError
+        target_item[:-len(tgt_item)-1] = self.tgt_dict.pad()
+
+        example = {
+            "id": uniq_id,
+            "source": src_item,
+            "patch_image": patch_image,
+            "patch_mask": patch_mask,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "decoder_prompt": decoder_prompt,
+            "ref_dict": ref_dict,
+        }
+        if self.constraint_trie is not None:
+            constraint_mask = torch.zeros((len(target_item), len(self.tgt_dict))).bool()
+            start_idx = len(target_item) - len(tgt_item) - 1
+            for i in range(len(target_item)-len(tgt_item)-1, len(target_item)):
+                constraint_prefix_token = [self.tgt_dict.bos()] + target_item[start_idx:i].tolist()
+                constraint_nodes = self.constraint_trie.get_next_layer(constraint_prefix_token)
+                constraint_mask[i][constraint_nodes] = True
+            example["constraint_mask"] = constraint_mask
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/mm_data/vqa_gen_dataset.py

@@ -0,0 +1,218 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+from io import BytesIO
+
+import logging
+import warnings
+
+import numpy as np
+import torch
+import base64
+from torchvision import transforms
+
+from PIL import Image, ImageFile
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+ImageFile.MAX_IMAGE_PIXELS = None
+Image.MAX_IMAGE_PIXELS = None
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    id = np.array([s["id"] for s in samples])
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
+    patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
+
+    conf = None
+    if samples[0].get("conf", None) is not None:
+        conf = torch.cat([s['conf'] for s in samples], dim=0)
+
+    ref_dict = None
+    if samples[0].get("ref_dict", None) is not None:
+        ref_dict = np.array([s['ref_dict'] for s in samples])
+
+    constraint_masks = None
+    if samples[0].get("constraint_mask", None) is not None:
+        constraint_masks = merge("constraint_mask")
+
+    decoder_prompts = None
+    if samples[0].get("decoder_prompt", None) is not None:
+        decoder_prompts = np.array([s['decoder_prompt'].tolist() for s in samples])
+
+    prefix_tokens = None
+    if samples[0].get("decoder_prompt", None) is not None:
+        prefix_tokens = merge("decoder_prompt")
+        prefix_tokens = prefix_tokens[:, 1:]
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        )
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "id": id,
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "patch_images": patch_images,
+            "patch_masks": patch_masks,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "conf": conf,
+        "ref_dict": ref_dict,
+        "constraint_masks": constraint_masks,
+        "decoder_prompts": decoder_prompts,
+        "target": target,
+        "prefix_tokens": prefix_tokens
+    }
+
+    return batch
+
+
+class VqaGenDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=128,
+        max_object_length=30,
+        max_tgt_length=30,
+        patch_image_size=224,
+        add_object=False,
+        constraint_trie=None,
+        imagenet_default_mean_and_std=False,
+        prompt_type="none"
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_object_length = max_object_length
+        self.max_tgt_length = max_tgt_length
+        self.patch_image_size = patch_image_size
+
+        self.add_object = add_object
+        self.constraint_trie = constraint_trie
+        self.prompt_type = prompt_type
+
+        if imagenet_default_mean_and_std:
+            mean = IMAGENET_DEFAULT_MEAN
+            std = IMAGENET_DEFAULT_STD
+        else:
+            mean = [0.5, 0.5, 0.5]
+            std = [0.5, 0.5, 0.5]
+
+        self.patch_resize_transform = transforms.Compose([
+            lambda image: image.convert("RGB"),
+            transforms.Resize((patch_image_size, patch_image_size), interpolation=Image.BICUBIC),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std),
+        ])
+
+    def __getitem__(self, index):
+        item = self.dataset[index]
+        if len(item) == 5:
+            uniq_id, image, question, ref, predict_objects = item
+        else:
+            uniq_id, image, question, ref, predict_objects, caption = item
+
+        image = Image.open(BytesIO(base64.urlsafe_b64decode(image)))
+        patch_image = self.patch_resize_transform(image)
+        patch_mask = torch.tensor([True])
+
+        question = self.pre_question(question, self.max_src_length)
+        question = question + '?' if not question.endswith('?') else question
+        src_item = self.encode_text(' {}'.format(question))
+
+        ref_dict = {item.split('|!+')[1]: float(item.split('|!+')[0]) for item in ref.split('&&')}
+        answer = max(ref_dict, key=ref_dict.get)
+        conf = torch.tensor([ref_dict[answer]])
+        tgt_item = self.encode_text(" {}".format(answer))
+
+        if self.add_object and predict_objects is not None:
+            predict_object_seq = ' '.join(predict_objects.strip().split('&&')[:self.max_object_length])
+            predict_object_item = self.encode_text(" object: {}".format(predict_object_seq))
+            src_item = torch.cat([src_item, predict_object_item])
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        if self.prompt_type == 'none':
+            prev_output_item = torch.cat([self.bos_item, tgt_item])
+            target_item = torch.cat([prev_output_item[1:], self.eos_item])
+            decoder_prompt = self.bos_item
+        elif self.prompt_type == 'src':
+            prev_output_item = torch.cat([src_item, tgt_item])
+            target_item = torch.cat([prev_output_item[1:], self.eos_item])
+            decoder_prompt = src_item
+        elif self.prompt_type == 'prev_output':
+            prev_output_item = torch.cat([src_item[:-1], tgt_item])
+            target_item = torch.cat([prev_output_item[1:], self.eos_item])
+            decoder_prompt = src_item[:-1]
+        else:
+            raise NotImplementedError
+        target_item[:-len(tgt_item)-1] = self.tgt_dict.pad()
+
+        example = {
+            "id": uniq_id,
+            "source": src_item,
+            "patch_image": patch_image,
+            "patch_mask": patch_mask,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "decoder_prompt": decoder_prompt,
+            "ref_dict": ref_dict,
+            "conf": conf,
+        }
+        if self.constraint_trie is not None:
+            constraint_mask = torch.zeros((len(target_item), len(self.tgt_dict))).bool()
+            start_idx = len(target_item) - len(tgt_item) - 1
+            for i in range(len(target_item)-len(tgt_item)-1, len(target_item)):
+                constraint_prefix_token = [self.tgt_dict.bos()] + target_item[start_idx:i].tolist()
+                constraint_nodes = self.constraint_trie.get_next_layer(constraint_prefix_token)
+                constraint_mask[i][constraint_nodes] = True
+            example["constraint_mask"] = constraint_mask
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/nlg_data/summary_dataset.py

@@ -0,0 +1,131 @@
+# Copyright 2022 The OFA-Sys Team.
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license
+# found in the LICENSE file in the root directory.
+
+import logging
+import warnings
+import torch
+import numpy as np
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        )
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    target_strs = np.array([s["target_str"] for s in samples])
+
+    batch = {
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "target": target,
+        "target_strs": target_strs
+    }
+
+    return batch
+
+
+class SummaryDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        code_dict_size=8192,
+        num_bins=1000,
+        max_src_length=512,
+        max_tgt_length=128,
+        noise_ratio=0.0
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.code_dict_size = code_dict_size
+        self.num_bins = num_bins
+        self.noise_ratio = noise_ratio
+
+        if type(bpe).__name__ == 'GPT2BPE':
+            self.prompt = ' what is the summary of article " {} "?'
+        elif type(bpe).__name__ == 'BertBPE':
+            self.prompt = "{} 请用一个句子简单总结上文："
+
+    def __getitem__(self, index):
+        source, target = self.dataset[index]
+        target_str = target.lower()
+
+        source = self.pre_caption(source, max_words=self.max_src_length)
+        target = self.pre_caption(target, max_words=self.max_tgt_length)
+        source = source.replace('<unk>', 'unk')
+        target = target.replace('<unk>', 'unk')
+
+        src_item = self.encode_text(
+            self.prompt.format(source),
+            length=self.max_src_length
+        )
+        tgt_item = self.encode_text('{}'.format(target))
+        noise_tgt_item = self.add_noise_to_tgt(tgt_item.clone(), self.noise_ratio)
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        target_item = torch.cat([tgt_item, self.eos_item])
+        prev_output_item = torch.cat([self.bos_item, noise_tgt_item])
+
+        example = {
+            "source": src_item,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "target_str": target_str
+        }
+        return example
+
+    def add_noise_to_tgt(self, target, p):
+        noise_indices = torch.FloatTensor(target.size(0)).uniform_() < p
+        target[noise_indices] = torch.randint(
+            4, len(self.src_dict) - self.code_dict_size - self.num_bins, size=(noise_indices.sum(),)
+        )
+        return target
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/nlu_data/cola_dataset.py

@@ -0,0 +1,138 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import logging
+import warnings
+import torch
+import numpy as np
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    ref_dict = None
+    if samples[0].get("ref_dict", None) is not None:
+        ref_dict = np.array([s['ref_dict'] for s in samples])
+
+    constraint_masks = None
+    if samples[0].get("constraint_mask", None) is not None:
+        constraint_masks = merge("constraint_mask")
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        )
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "ref_dict": ref_dict,
+        "constraint_masks": constraint_masks,
+        "target": target,
+    }
+
+    return batch
+
+
+class COLADataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=512,
+        max_tgt_length=30,
+        constraint_trie=None,
+        prompt_type="none"
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.constraint_trie = constraint_trie
+        self.prompt_type = prompt_type
+
+    def __getitem__(self, index):
+        sentence, label = self.dataset[index]
+        if label == '0':
+            label = 'no'
+        elif label == '1':
+            label = 'yes'
+        else:
+            raise NotImplementedError
+
+        sentence = ' '.join(sentence.lower().strip().split()[:self.max_src_length])
+        src_item = self.encode_text(' is the text " {} " grammatically correct?'.format(sentence))
+        tgt_item = self.encode_text(" {}".format(label))
+        assert tgt_item.size(0) == 1
+        ref_dict = {label: 1.0}
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        if self.prompt_type == 'none':
+            prev_output_item = self.bos_item
+            target_item = tgt_item
+        elif self.prompt_type == 'src':
+            prev_output_item = src_item.clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        elif self.prompt_type == 'prev_output':
+            prev_output_item = src_item[:-1].clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        else:
+            raise NotImplementedError
+        target_item[:-1] = self.tgt_dict.pad()
+
+        example = {
+            "source": src_item,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "ref_dict": ref_dict,
+        }
+        if self.constraint_trie is not None:
+            constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
+            constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
+            constraint_mask[-1][constraint_nodes] = True
+            example["constraint_mask"] = constraint_mask
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/nlu_data/mnli_dataset.py

@@ -0,0 +1,143 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import logging
+import warnings
+import torch
+import numpy as np
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    ref_dict = None
+    if samples[0].get("ref_dict", None) is not None:
+        ref_dict = np.array([s['ref_dict'] for s in samples])
+
+    constraint_masks = None
+    if samples[0].get("constraint_mask", None) is not None:
+        constraint_masks = merge("constraint_mask")
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        )
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "ref_dict": ref_dict,
+        "constraint_masks": constraint_masks,
+        "target": target,
+    }
+
+    return batch
+
+
+class MNLIDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=512,
+        max_tgt_length=30,
+        constraint_trie=None,
+        prompt_type="none"
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.constraint_trie = constraint_trie
+        self.prompt_type = prompt_type
+
+    def __getitem__(self, index):
+        sentence1, sentence2, label = self.dataset[index]
+        if label == '0':
+            label = 'maybe'
+        elif label == '1':
+            label = 'yes'
+        elif label == '2':
+            label = 'no'
+        else:
+            raise NotImplementedError
+
+        sentence1 = ' '.join(sentence1.lower().strip().split()[:self.max_src_length])
+        sentence2 = ' '.join(sentence2.lower().strip().split()[:self.max_src_length])
+        src_item = self.encode_text(
+            ' can text1 " {} " imply text2 " {} "?'.format(sentence1, sentence2)
+        )
+        tgt_item = self.encode_text(" {}".format(label))
+        assert tgt_item.size(0) == 1
+        ref_dict = {label: 1.0}
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        if self.prompt_type == 'none':
+            prev_output_item = self.bos_item
+            target_item = tgt_item
+        elif self.prompt_type == 'src':
+            prev_output_item = src_item.clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        elif self.prompt_type == 'prev_output':
+            prev_output_item = src_item[:-1].clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        else:
+            raise NotImplementedError
+        target_item[:-1] = self.tgt_dict.pad()
+
+        example = {
+            "source": src_item,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "ref_dict": ref_dict,
+        }
+        if self.constraint_trie is not None:
+            constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
+            constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
+            constraint_mask[-1][constraint_nodes] = True
+            example["constraint_mask"] = constraint_mask
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/nlu_data/mrpc_dataset.py

@@ -0,0 +1,141 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import logging
+import warnings
+import torch
+import numpy as np
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    ref_dict = None
+    if samples[0].get("ref_dict", None) is not None:
+        ref_dict = np.array([s['ref_dict'] for s in samples])
+
+    constraint_masks = None
+    if samples[0].get("constraint_mask", None) is not None:
+        constraint_masks = merge("constraint_mask")
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        )
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "ref_dict": ref_dict,
+        "constraint_masks": constraint_masks,
+        "target": target,
+    }
+
+    return batch
+
+
+class MRPCDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=512,
+        max_tgt_length=30,
+        constraint_trie=None,
+        prompt_type="none"
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.constraint_trie = constraint_trie
+        self.prompt_type = prompt_type
+
+    def __getitem__(self, index):
+        sentence1, sentence2, label = self.dataset[index]
+        if label == '0':
+            label = 'no'
+        elif label == '1':
+            label = 'yes'
+        else:
+            raise NotImplementedError
+
+        sentence1 = ' '.join(sentence1.lower().strip().split()[:self.max_src_length])
+        sentence2 = ' '.join(sentence2.lower().strip().split()[:self.max_src_length])
+        src_item = self.encode_text(
+            ' does text1 " {} " and text2 " {} " have the same semantics?'.format(sentence1, sentence2),
+        )
+        tgt_item = self.encode_text(" {}".format(label))
+        assert tgt_item.size(0) == 1
+        ref_dict = {label: 1.0}
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        if self.prompt_type == 'none':
+            prev_output_item = self.bos_item
+            target_item = tgt_item
+        elif self.prompt_type == 'src':
+            prev_output_item = src_item.clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        elif self.prompt_type == 'prev_output':
+            prev_output_item = src_item[:-1].clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        else:
+            raise NotImplementedError
+        target_item[:-1] = self.tgt_dict.pad()
+
+        example = {
+            "source": src_item,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "ref_dict": ref_dict,
+        }
+        if self.constraint_trie is not None:
+            constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
+            constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
+            constraint_mask[-1][constraint_nodes] = True
+            example["constraint_mask"] = constraint_mask
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/nlu_data/qnli_dataset.py

@@ -0,0 +1,141 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import logging
+import warnings
+import torch
+import numpy as np
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    ref_dict = None
+    if samples[0].get("ref_dict", None) is not None:
+        ref_dict = np.array([s['ref_dict'] for s in samples])
+
+    constraint_masks = None
+    if samples[0].get("constraint_mask", None) is not None:
+        constraint_masks = merge("constraint_mask")
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        )
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "ref_dict": ref_dict,
+        "constraint_masks": constraint_masks,
+        "target": target,
+    }
+
+    return batch
+
+
+class QNLIDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=512,
+        max_tgt_length=30,
+        constraint_trie=None,
+        prompt_type="none"
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.constraint_trie = constraint_trie
+        self.prompt_type = prompt_type
+
+    def __getitem__(self, index):
+        question, sentence, label = self.dataset[index]
+        if label == '0' or label == 'not_entailment':
+            label = 'no'
+        elif label == '1' or label == 'entailment':
+            label = 'yes'
+        else:
+            raise NotImplementedError
+
+        question = ' '.join(question.lower().strip().split()[:self.max_src_length])
+        sentence = ' '.join(sentence.lower().strip().split()[:self.max_src_length])
+        src_item = self.encode_text(
+            ' does " {} " contain the answer to question " {} "?'.format(sentence, question)
+        )
+        tgt_item = self.encode_text(" {}".format(label))
+        assert tgt_item.size(0) == 1
+        ref_dict = {label: 1.0}
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        if self.prompt_type == 'none':
+            prev_output_item = self.bos_item
+            target_item = tgt_item
+        elif self.prompt_type == 'src':
+            prev_output_item = src_item.clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        elif self.prompt_type == 'prev_output':
+            prev_output_item = src_item[:-1].clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        else:
+            raise NotImplementedError
+        target_item[:-1] = self.tgt_dict.pad()
+
+        example = {
+            "source": src_item,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "ref_dict": ref_dict,
+        }
+        if self.constraint_trie is not None:
+            constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
+            constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
+            constraint_mask[-1][constraint_nodes] = True
+            example["constraint_mask"] = constraint_mask
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/nlu_data/qqp_dataset.py

@@ -0,0 +1,141 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import logging
+import warnings
+import torch
+import numpy as np
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    ref_dict = None
+    if samples[0].get("ref_dict", None) is not None:
+        ref_dict = np.array([s['ref_dict'] for s in samples])
+
+    constraint_masks = None
+    if samples[0].get("constraint_mask", None) is not None:
+        constraint_masks = merge("constraint_mask")
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        )
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "ref_dict": ref_dict,
+        "constraint_masks": constraint_masks,
+        "target": target,
+    }
+
+    return batch
+
+
+class QQPDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=512,
+        max_tgt_length=30,
+        constraint_trie=None,
+        prompt_type="none"
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.constraint_trie = constraint_trie
+        self.prompt_type = prompt_type
+
+    def __getitem__(self, index):
+        question1, question2, label = self.dataset[index]
+        if label == '0':
+            label = 'no'
+        elif label == '1':
+            label = 'yes'
+        else:
+            raise NotImplementedError
+
+        question1 = ' '.join(question1.lower().strip().split()[:self.max_src_length])
+        question2 = ' '.join(question2.lower().strip().split()[:self.max_src_length])
+        src_item = self.encode_text(
+            ' is question " {} " and question " {} " equivalent?'.format(question1, question2)
+        )
+        tgt_item = self.encode_text(" {}".format(label))
+        assert tgt_item.size(0) == 1
+        ref_dict = {label: 1.0}
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        if self.prompt_type == 'none':
+            prev_output_item = self.bos_item
+            target_item = tgt_item
+        elif self.prompt_type == 'src':
+            prev_output_item = src_item.clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        elif self.prompt_type == 'prev_output':
+            prev_output_item = src_item[:-1].clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        else:
+            raise NotImplementedError
+        target_item[:-1] = self.tgt_dict.pad()
+
+        example = {
+            "source": src_item,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "ref_dict": ref_dict,
+        }
+        if self.constraint_trie is not None:
+            constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
+            constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
+            constraint_mask[-1][constraint_nodes] = True
+            example["constraint_mask"] = constraint_mask
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/nlu_data/rte_dataset.py

@@ -0,0 +1,141 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import logging
+import warnings
+import torch
+import numpy as np
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    ref_dict = None
+    if samples[0].get("ref_dict", None) is not None:
+        ref_dict = np.array([s['ref_dict'] for s in samples])
+
+    constraint_masks = None
+    if samples[0].get("constraint_mask", None) is not None:
+        constraint_masks = merge("constraint_mask")
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        )
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "ref_dict": ref_dict,
+        "constraint_masks": constraint_masks,
+        "target": target,
+    }
+
+    return batch
+
+
+class RTEDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=512,
+        max_tgt_length=30,
+        constraint_trie=None,
+        prompt_type="none"
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.constraint_trie = constraint_trie
+        self.prompt_type = prompt_type
+
+    def __getitem__(self, index):
+        sentence1, sentence2, label = self.dataset[index]
+        if label == 'not_entailment':
+            label = 'no'
+        elif label == 'entailment':
+            label = 'yes'
+        else:
+            raise NotImplementedError
+
+        sentence1 = ' '.join(sentence1.lower().strip().split()[:self.max_src_length])
+        sentence2 = ' '.join(sentence2.lower().strip().split()[:self.max_src_length])
+        src_item = self.encode_text(
+            ' can text1 " {} " imply text2 " {} "?'.format(sentence1, sentence2),
+        )
+        tgt_item = self.encode_text(" {}".format(label))
+        assert tgt_item.size(0) == 1
+        ref_dict = {label: 1.0}
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        if self.prompt_type == 'none':
+            prev_output_item = self.bos_item
+            target_item = tgt_item
+        elif self.prompt_type == 'src':
+            prev_output_item = src_item.clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        elif self.prompt_type == 'prev_output':
+            prev_output_item = src_item[:-1].clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        else:
+            raise NotImplementedError
+        target_item[:-1] = self.tgt_dict.pad()
+
+        example = {
+            "source": src_item,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "ref_dict": ref_dict,
+        }
+        if self.constraint_trie is not None:
+            constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
+            constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
+            constraint_mask[-1][constraint_nodes] = True
+            example["constraint_mask"] = constraint_mask
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/nlu_data/sst2_dataset.py

@@ -0,0 +1,138 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import logging
+import warnings
+import torch
+import numpy as np
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    ref_dict = None
+    if samples[0].get("ref_dict", None) is not None:
+        ref_dict = np.array([s['ref_dict'] for s in samples])
+
+    constraint_masks = None
+    if samples[0].get("constraint_mask", None) is not None:
+        constraint_masks = merge("constraint_mask")
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor(
+            [s["target"].ne(pad_idx).long().sum() for s in samples]
+        )
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "ref_dict": ref_dict,
+        "constraint_masks": constraint_masks,
+        "target": target,
+    }
+
+    return batch
+
+
+class SST2Dataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=512,
+        max_tgt_length=30,
+        constraint_trie=None,
+        prompt_type="none"
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.constraint_trie = constraint_trie
+        self.prompt_type = prompt_type
+
+    def __getitem__(self, index):
+        sentence, label = self.dataset[index]
+        if label == '0':
+            label = 'negative'
+        elif label == '1':
+            label = 'positive'
+        else:
+            raise NotImplementedError
+
+        sentence = ' '.join(sentence.lower().strip().split()[:self.max_src_length])
+        src_item = self.encode_text(' is the sentiment of text " {} " positive or negative?'.format(sentence))
+        tgt_item = self.encode_text(" {}".format(label))
+        assert tgt_item.size(0) == 1
+        ref_dict = {label: 1.0}
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        if self.prompt_type == 'none':
+            prev_output_item = self.bos_item
+            target_item = tgt_item
+        elif self.prompt_type == 'src':
+            prev_output_item = src_item.clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        elif self.prompt_type == 'prev_output':
+            prev_output_item = src_item[:-1].clone()
+            target_item = torch.cat([prev_output_item[1:], tgt_item])
+        else:
+            raise NotImplementedError
+        target_item[:-1] = self.tgt_dict.pad()
+
+        example = {
+            "source": src_item,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "ref_dict": ref_dict,
+        }
+        if self.constraint_trie is not None:
+            constraint_mask = torch.zeros((len(prev_output_item), len(self.tgt_dict))).bool()
+            constraint_nodes = self.constraint_trie.get_next_layer(self.bos_item.tolist())
+            constraint_mask[-1][constraint_nodes] = True
+            example["constraint_mask"] = constraint_mask
+        return example
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge a list of samples to form a mini-batch.
+        Args:
+            samples (List[dict]): samples to collate
+        Returns:
+            dict: a mini-batch containing the data of the task
+        """
+        return collate(samples, pad_idx=self.pad, eos_idx=self.eos)

data/ofa_dataset.py

@@ -0,0 +1,79 @@
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import logging
+import re
+import torch.utils.data
+from fairseq.data import FairseqDataset
+
+logger = logging.getLogger(__name__)
+
+
+class OFADataset(FairseqDataset):
+    def __init__(self, split, dataset, bpe, src_dict, tgt_dict):
+        self.split = split
+        self.dataset = dataset
+        self.bpe = bpe
+        self.src_dict = src_dict
+        self.tgt_dict = tgt_dict
+
+        self.bos = src_dict.bos()
+        self.eos = src_dict.eos()
+        self.pad = src_dict.pad()
+        self.bos_item = torch.LongTensor([self.bos])
+        self.eos_item = torch.LongTensor([self.eos])
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def encode_text(self, text, length=None, append_bos=False, append_eos=False, use_bpe=True):
+        s = self.tgt_dict.encode_line(
+            line=self.bpe.encode(text) if use_bpe else text,
+            add_if_not_exist=False,
+            append_eos=False
+        ).long()
+        if length is not None:
+            s = s[:length]
+        if append_bos:
+            s = torch.cat([self.bos_item, s])
+        if append_eos:
+            s = torch.cat([s, self.eos_item])
+        return s
+
+    def pre_question(self, question, max_ques_words=None):
+        question = question.lower().lstrip(",.!?*#:;~").replace('-', ' ').replace('/', ' ')
+
+        question = re.sub(
+            r"\s{2,}",
+            ' ',
+            question,
+        )
+        question = question.rstrip('\n')
+        question = question.strip(' ')
+
+        # truncate question
+        question_words = question.split(' ')
+        if max_ques_words is not None and len(question_words) > max_ques_words:
+            question = ' '.join(question_words[:max_ques_words])
+
+        return question
+
+    def pre_caption(self, caption, max_words=None):
+        caption = caption.lower().lstrip(",.!?*#:;~").replace('-', ' ').replace('/', ' ').replace('<person>', 'person')
+
+        caption = re.sub(
+            r"\s{2,}",
+            ' ',
+            caption,
+        )
+        caption = caption.rstrip('\n')
+        caption = caption.strip(' ')
+
+        # truncate caption
+        caption_words = caption.split(' ')
+        if max_words is not None and len(caption_words) > max_words:
+            caption = ' '.join(caption_words[:max_words])
+
+        return caption

data/pretrain_data/unify_dataset.py

@@ -0,0 +1,636 @@
+# Copyright 2022 The OFA-Sys Team.
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license
+# found in the LICENSE file in the root directory.
+
+from io import BytesIO
+
+import math
+import logging
+import random
+import warnings
+
+import numpy as np
+import torch
+import base64
+from torchvision import transforms
+
+from PIL import Image, ImageFile
+
+from data import data_utils
+from data.ofa_dataset import OFADataset
+from utils.vision_helper import RandomAugment
+import utils.transforms as T
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+ImageFile.MAX_IMAGE_PIXELS = None
+Image.MAX_IMAGE_PIXELS = None
+
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
+
+
+def get_whole_word_mask(bpe, dictionary):
+    if bpe is not None:
+
+        def is_beginning_of_word(i):
+            if i < dictionary.nspecial:
+                # special elements are always considered beginnings
+                return True
+            tok = dictionary[i]
+            if tok.startswith("madeupword"):
+                return True
+            try:
+                return bpe.is_beginning_of_word(tok)
+            except ValueError:
+                return True
+
+        mask_whole_words = torch.ByteTensor(
+            list(map(is_beginning_of_word, range(len(dictionary))))
+        )
+        return mask_whole_words
+    return None
+
+
+def collate(samples, pad_idx, eos_idx):
+    if len(samples) == 0:
+        return {}
+
+    def merge(key):
+        return data_utils.collate_tokens(
+            [s[key] for s in samples],
+            pad_idx,
+            eos_idx=eos_idx,
+        )
+
+    id = np.array([s["id"] for s in samples])
+    src_tokens = merge("source")
+    src_lengths = torch.LongTensor([s["source"].ne(pad_idx).long().sum() for s in samples])
+
+    patch_images = torch.stack([sample['patch_image'] for sample in samples], dim=0)
+    patch_masks = torch.cat([sample['patch_mask'] for sample in samples])
+
+    code_masks = None
+    if samples[0].get("code_mask", None) is not None:
+        code_masks = torch.cat([sample['code_mask'] for sample in samples])
+
+    conf = torch.cat([s['conf'] for s in samples], dim=0)
+
+    prev_output_tokens = None
+    target = None
+    if samples[0].get("target", None) is not None:
+        target = merge("target")
+        tgt_lengths = torch.LongTensor([s["target"].ne(pad_idx).long().sum() for s in samples])
+        ntokens = tgt_lengths.sum().item()
+
+        if samples[0].get("prev_output_tokens", None) is not None:
+            prev_output_tokens = merge("prev_output_tokens")
+    else:
+        ntokens = src_lengths.sum().item()
+
+    batch = {
+        "id": id,
+        "nsentences": len(samples),
+        "ntokens": ntokens,
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+            "patch_images": patch_images,
+            "patch_masks": patch_masks,
+            "code_masks": code_masks,
+            "prev_output_tokens": prev_output_tokens
+        },
+        "target": target,
+        "conf": conf
+    }
+
+    return batch
+
+
+class UnifyDataset(OFADataset):
+    def __init__(
+        self,
+        split,
+        dataset,
+        bpe,
+        src_dict,
+        tgt_dict=None,
+        max_src_length=128,
+        max_tgt_length=30,
+        seed=7,
+        code_dict_size=8192,
+        num_bins=1000,
+        patch_image_size=384,
+        code_image_size=128,
+        pure_text_dataset=None,
+        pure_image_dataset=None,
+        detection_dataset=None,
+        all_object_list=None,
+        all_caption_list=None,
+        type2ans_dict=None,
+        ans2type_dict=None,
+        max_image_size=512,
+        mask_ratio=0.3,
+        random_ratio=0.0,
+        keep_ratio=0.0,
+        mask_length="span-poisson",
+        poisson_lambda=3.0,
+        replace_length=1
+    ):
+        super().__init__(split, dataset, bpe, src_dict, tgt_dict)
+        self.max_src_length = max_src_length
+        self.max_tgt_length = max_tgt_length
+        self.seed = seed
+        self.code_dict_size = code_dict_size
+        self.num_bins = num_bins
+        self.patch_image_size = patch_image_size
+        self.code_image_size = code_image_size
+
+        self.pure_text_dataset = pure_text_dataset
+        self.pure_image_dataset = pure_image_dataset
+        self.detection_dataset = detection_dataset
+        self.epoch = 0
+
+        self.all_object_list = all_object_list
+        self.all_caption_list = all_caption_list
+        self.type2ans_dict = type2ans_dict
+        self.ans2type_dict = ans2type_dict
+
+        self.mask_ratio = mask_ratio
+        self.random_ratio = random_ratio
+        self.keep_ratio = keep_ratio
+        self.mask_length = mask_length
+        self.poisson_lambda = poisson_lambda
+        self.replace_length = replace_length
+        if self.replace_length not in [-1, 0, 1]:
+            raise ValueError(f"invalid arg: replace_length={self.replace_length}")
+        if self.mask_length not in ["subword", "word", "span-poisson"]:
+            raise ValueError(f"invalid arg: mask-length={self.mask_length}")
+        if self.mask_length == "subword" and self.replace_length not in [0, 1]:
+            raise ValueError(f"if using subwords, use replace-length=1 or 0")
+
+        self.mask_idx = src_dict.index("<mask>")
+        self.mask_whole_word = (
+            get_whole_word_mask(self.bpe, self.src_dict)
+            if self.mask_length != "subword"
+            else None
+        )
+        self.mask_span_distribution = None
+        if self.mask_length == "span-poisson":
+            _lambda = self.poisson_lambda
+            lambda_to_the_k = 1
+            e_to_the_minus_lambda = math.exp(-_lambda)
+            k_factorial = 1
+            ps = []
+            for k in range(0, 128):
+                ps.append(e_to_the_minus_lambda * lambda_to_the_k / k_factorial)
+                lambda_to_the_k *= _lambda
+                k_factorial *= k + 1
+                if ps[-1] < 0.0000001:
+                    break
+            ps = torch.FloatTensor(ps)
+            self.mask_span_distribution = torch.distributions.Categorical(ps)
+
+        self.pos_tgt_item = self.encode_text(" yes")
+        self.neg_tgt_item = self.encode_text(" no")
+
+        self.mask_left = self.mask_top = int(0.5 * self.code_image_size)
+        self.mask_right = self.mask_bottom = int(1.5 * self.code_image_size)
+        self.mask_ids = [
+            i*self.code_image_size*2+j
+            for i in range(self.code_image_size*2) for j in range(self.code_image_size*2)
+            if not (self.mask_left <= i < self.mask_right and self.mask_top <= j < self.mask_bottom)
+        ]
+
+        scales = np.arange(patch_image_size, 481).tolist()
+
+        # for image-text pair
+        self.patch_resize_transform = transforms.Compose([
+            T.RandomResize(scales, max_size=672),
+            transforms.CenterCrop(patch_image_size),
+            RandomAugment(2, 7, isPIL=True, augs=['Identity', 'AutoContrast', 'Equalize', 'Brightness', 'Sharpness',
+                                                  'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate']),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+        ])
+        # for pure image
+        self.patch_crop_transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+        ])
+        # for detection
+        self.detection_transform = T.Compose([
+            T.RandomHorizontalFlip(),
+            T.LargeScaleJitter(output_size=self.code_image_size*2, aug_scale_min=1.0, aug_scale_max=1.5),
+            T.ToTensor(),
+            T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], max_image_size=max_image_size)
+        ])
+        # for visual grounding
+        self.visual_grounding_transform = T.Compose([
+            T.RandomResize(scales, max_size=672),
+            T.ObjectCenterCrop((patch_image_size, patch_image_size)),
+            T.ToTensor(),
+            T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], max_image_size=max_image_size)
+        ])
+
+    def set_epoch(self, epoch, **unused):
+        self.epoch = epoch
+
+    def get_negative_caption(self, caption, gt_objects):
+        prob = random.random()
+        if gt_objects is not None and gt_objects != '' and prob > 0.6:
+            gt_object = random.choice(gt_objects.strip().split('&&'))
+            negative_object = random.choice(self.all_object_list[:-1])
+            negative_object = self.all_object_list[-1] if negative_object == gt_object else negative_object
+            negative_caption = caption.replace(gt_object, negative_object)
+        else:
+            negative_caption = random.choice(self.all_caption_list)
+        return negative_caption
+
+    def get_negative_answer(self, answer, conf):
+        prob = random.random()
+        if conf > (prob + 0.1) and answer in self.ans2type_dict:
+            negative_answer_type = self.ans2type_dict[answer]
+            if negative_answer_type == 'how many' and answer.isdigit() and prob > 0.5:
+                negative_answer = int(answer) + random.choice([-1, 1]) if answer != 0 else 1
+            else:
+                negative_answer_list = self.type2ans_dict[negative_answer_type]
+                negative_answer = random.choice(negative_answer_list[:-1])
+                negative_answer = negative_answer_list[-1] if negative_answer == answer else negative_answer
+            return negative_answer
+
+        negative_answer_list = self.type2ans_dict['other']
+        negative_answer = random.choice(negative_answer_list[:-1])
+        negative_answer = negative_answer_list[-1] if negative_answer == answer else negative_answer
+        return negative_answer
+
+    def process_image_text_pair(self, index):
+        uniq_id, image, caption, question, refs, gt_objects, dataset_name, type = self.dataset[index]
+
+        image = Image.open(BytesIO(base64.urlsafe_b64decode(image))).convert("RGB")
+        patch_image = self.patch_resize_transform(image) if type != 'visual_grounding' else None
+        patch_mask = torch.tensor([True])
+        conf = torch.tensor([1.0])
+        if type == 'caption':
+            tgt_caption = self.pre_caption(caption, self.max_tgt_length)
+            pos_src_caption = self.pre_caption(caption, self.max_src_length)
+            neg_src_caption = self.pre_caption(self.get_negative_caption(caption, gt_objects), self.max_src_length)
+            src_item = self.encode_text(" what does the image describe?")
+            tgt_item = self.encode_text(" {}".format(tgt_caption))
+            pos_src_item = self.encode_text(' does the image describe " {} "?'.format(pos_src_caption))
+            neg_src_item = self.encode_text(' does the image describe " {} "?'.format(neg_src_caption))
+        elif type == 'qa':
+            question = self.pre_question(question, self.max_src_length)
+            ref_dict = {item.split('|!+')[1]: float(item.split('|!+')[0]) for item in refs.split('&&')}
+            answer = max(ref_dict, key=ref_dict.get)
+            conf = ref_dict[answer]
+            src_item = self.encode_text(" {}".format(question))
+            tgt_item = self.encode_text(" {}".format(answer))
+            conf = torch.tensor([conf])
+            pos_src_item = self.encode_text(' what is the answer to question " {} ". is " {} "?'.format(question, answer))
+            neg_src_item = self.encode_text(
+                ' what is the answer to question " {} ". is " {} "?'.format(question, self.get_negative_answer(answer, conf))
+            )
+        elif type == 'visual_grounding':
+            conf = torch.tensor([1.0])
+            w, h = image.size
+            boxes_target = {"boxes": [], "labels": [], "area": [], "size": torch.tensor([h, w])}
+            x0, y0, x1, y1 = refs.strip().split(',')
+            boxes_target["boxes"] = torch.tensor([[float(x0), float(y0), float(x1), float(y1)]])
+            boxes_target["labels"] = np.array([0])
+            boxes_target["area"] = torch.tensor([(float(x1) - float(x0)) * (float(y1) - float(y0))])
+            patch_image, boxes_target = self.visual_grounding_transform(image, boxes_target)
+            quant_x0 = "<bin_{}>".format(int((boxes_target["boxes"][0][0] * (self.num_bins - 1)).round()))
+            quant_y0 = "<bin_{}>".format(int((boxes_target["boxes"][0][1] * (self.num_bins - 1)).round()))
+            quant_x1 = "<bin_{}>".format(int((boxes_target["boxes"][0][2] * (self.num_bins - 1)).round()))
+            quant_y1 = "<bin_{}>".format(int((boxes_target["boxes"][0][3] * (self.num_bins - 1)).round()))
+            region_coord = "{} {} {} {}".format(quant_x0, quant_y0, quant_x1, quant_y1)
+            src_caption = self.pre_caption(caption, self.max_src_length)
+            src_item = self.encode_text(' which region does the text " {} " describe?'.format(src_caption))
+            tgt_item = self.encode_text(region_coord, use_bpe=False)
+        else:
+            logger.info('type {} is not implemented'.format(type))
+            raise NotImplementedError
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        target_item = torch.cat([tgt_item, self.eos_item])
+        prev_output_item = torch.cat([self.bos_item, tgt_item])
+        pos_src_item = torch.cat([self.bos_item, pos_src_item, self.eos_item]) if type != 'visual_grounding' else None
+        neg_src_item = torch.cat([self.bos_item, neg_src_item, self.eos_item]) if type != 'visual_grounding' else None
+
+        if type == 'caption' and dataset_name == 'cc12m':
+            target_item[:2] = self.src_dict.pad()
+            target_item[-1] = self.eos_item
+
+        example = {
+            "id": uniq_id,
+            "source": src_item,
+            "patch_image": patch_image,
+            "patch_mask": patch_mask,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "conf": conf,
+        }
+
+        examples = [example]
+        prob = random.random()
+        if type == 'visual_grounding':
+            region_example = example.copy()
+            region_prefix_item = self.encode_text('  what does the region describe? region:')
+            region_coord_item = self.encode_text('{}'.format(region_coord), use_bpe=False)
+            region_src_item = torch.cat([region_prefix_item, region_coord_item])
+            region_tgt_item = self.encode_text(' {}'.format(self.pre_caption(caption, self.max_tgt_length)))
+            region_example["source"] = torch.cat([self.bos_item, region_src_item, self.eos_item])
+            region_example["target"] = torch.cat([region_tgt_item, self.eos_item])
+            region_example["prev_output_tokens"] = torch.cat([self.bos_item, region_tgt_item])
+            region_example["conf"] = torch.tensor([1.0])
+            examples.append(region_example)
+        elif prob >= 0.5 and self.split == 'train':
+            pos_example = example.copy()
+            pos_example["source"] = pos_src_item
+            pos_example["target"] = torch.cat([self.pos_tgt_item, self.eos_item])
+            pos_example["prev_output_tokens"] = torch.cat([self.bos_item, self.pos_tgt_item])
+            examples.append(pos_example)
+        elif self.split == 'train':
+            neg_example = example.copy()
+            neg_example["source"] = neg_src_item
+            neg_example["target"] = torch.cat([self.neg_tgt_item, self.eos_item])
+            neg_example["prev_output_tokens"] = torch.cat([self.bos_item, self.neg_tgt_item])
+            examples.append(neg_example)
+        return examples
+
+    def process_pure_text(self, index):
+        patch_image = torch.zeros((3, self.code_image_size*2, self.code_image_size*2))
+        patch_mask = torch.tensor([False])
+        code_mask = torch.tensor([False])
+        conf = torch.tensor([2.0])
+
+        examples = []
+        for _ in range(2):
+            uniq_id, text = self.pure_text_dataset[index]
+            text = text.strip().lower()
+            text_item = self.encode_text(" {}".format(text), length=512)
+            text_item = text_item[-256:]
+            text_item = torch.cat([self.bos_item, text_item, self.eos_item])
+            mask_text_item = self.add_whole_word_mask(text_item.clone(), self.mask_ratio)
+            prefix_item = self.encode_text(' what is the complete text of " "?')
+            src_item = torch.cat([prefix_item[:-2], mask_text_item[1:-1], prefix_item[-2:]])
+            tgt_item = text_item[1:-1]
+            src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+            target_item = torch.cat([tgt_item, self.eos_item])
+            prev_output_item = torch.cat([self.bos_item, tgt_item])
+            example = {
+                "id": uniq_id,
+                "source": src_item,
+                "patch_image": patch_image,
+                "patch_mask": patch_mask,
+                "code_mask": code_mask,
+                "target": target_item,
+                "prev_output_tokens": prev_output_item,
+                "conf": conf,
+            }
+            examples.append(example)
+
+        return examples
+
+    def process_pure_image(self, index):
+        image_id, image, code = self.pure_image_dataset[index]
+        image = Image.open(BytesIO(base64.urlsafe_b64decode(image))).convert("RGB")
+        patch_image = self.patch_crop_transform(image)
+        patch_image[:, self.mask_top:self.mask_bottom, self.mask_left:self.mask_right] = 0
+        patch_mask = torch.tensor([True])
+        src_item = self.encode_text(" what is the image in the middle part?")
+        image_code = torch.LongTensor([int(num) for num in code.strip().split()])
+        tgt_item = image_code + len(self.src_dict) - self.code_dict_size - self.num_bins
+        code_mask = torch.tensor([True])
+        conf = torch.tensor([2.0])
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        target_item = torch.cat([tgt_item, self.eos_item])
+        prev_output_item = torch.cat([self.bos_item, tgt_item])
+
+        example = {
+            "id": image_id,
+            "source": src_item,
+            "patch_image": patch_image,
+            "patch_mask": patch_mask,
+            "code_mask": code_mask,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "conf": conf,
+        }
+        return [example]
+
+    def process_detection(self, index):
+        image_id, image, label = self.detection_dataset[index]
+        image = Image.open(BytesIO(base64.urlsafe_b64decode(image))).convert("RGB")
+
+        w, h = image.size
+        boxes_target = {"boxes": [], "labels": [], "area": [], "size": torch.tensor([h, w])}
+        label_list = label.strip().split('&&')
+        for label in label_list:
+            x0, y0, x1, y1, cat_id, cat = label.strip().split(',', 5)
+            boxes_target["boxes"].append([float(x0), float(y0), float(x1), float(y1)])
+            boxes_target["labels"].append(cat)
+            boxes_target["area"].append((float(x1) - float(x0)) * (float(y1) - float(y0)))
+        boxes_target["boxes"] = torch.tensor(boxes_target["boxes"])
+        boxes_target["labels"] = np.array(boxes_target["labels"])
+        boxes_target["area"] = torch.tensor(boxes_target["area"])
+
+        patch_image, boxes_target = self.detection_transform(image, boxes_target)
+        patch_mask = torch.tensor([True])
+        code_mask = torch.tensor([False])
+        conf = torch.tensor([2.0])
+
+        quant_boxes = []
+        for i, box in enumerate(boxes_target["boxes"]):
+            quant_boxes.extend(["<bin_{}>".format(int((pos * (self.num_bins - 1)).round())) for pos in box[:4]])
+            quant_boxes.append(self.bpe.encode(' {}'.format(boxes_target["labels"][i])))
+        src_item = self.encode_text(' what are the objects in the image?')
+        tgt_item = self.encode_text(' '.join(quant_boxes), use_bpe=False)
+
+        src_item = torch.cat([self.bos_item, src_item, self.eos_item])
+        target_item = torch.cat([tgt_item, self.eos_item])
+        prev_output_item = torch.cat([self.bos_item, tgt_item])
+
+        example = {
+            "id": image_id,
+            "source": src_item,
+            "patch_image": patch_image,
+            "patch_mask": patch_mask,
+            "code_mask": code_mask,
+            "target": target_item,
+            "prev_output_tokens": prev_output_item,
+            "conf": conf,
+        }
+        return [example]
+
+    def __getitem__(self, index):
+        with data_utils.numpy_seed(self.seed, self.epoch):
+            pair_samples = self.process_image_text_pair(index)
+            extra_samples = []
+            if self.split == 'train' and self.dataset.data_cnt % 8 == 0:
+                extra_samples += self.process_pure_text(0) if self.pure_text_dataset else []
+                extra_samples += self.process_pure_image(0) if self.pure_image_dataset else []
+                extra_samples += self.process_detection(0) if self.detection_dataset else []
+        return pair_samples, extra_samples
+
+    def word_starts(self, source):
+        if self.mask_whole_word is not None:
+            is_word_start = self.mask_whole_word.gather(0, source)
+        else:
+            is_word_start = torch.ones(source.size())
+        is_word_start[0] = 0
+        is_word_start[-1] = 0
+        return is_word_start
+
+    def add_whole_word_mask(self, source, p):
+        is_word_start = self.word_starts(source)
+        num_to_mask = int(math.ceil(is_word_start.float().sum() * p))
+        num_inserts = 0
+        if num_to_mask == 0:
+            return source
+
+        if self.mask_span_distribution is not None:
+            lengths = self.mask_span_distribution.sample(sample_shape=(num_to_mask,))
+
+            # Make sure we have enough to mask
+            cum_length = torch.cumsum(lengths, 0)
+            while cum_length[-1] < num_to_mask:
+                lengths = torch.cat(
+                    [
+                        lengths,
+                        self.mask_span_distribution.sample(sample_shape=(num_to_mask,)),
+                    ],
+                    dim=0,
+                )
+                cum_length = torch.cumsum(lengths, 0)
+
+            # Trim to masking budget
+            i = 0
+            while cum_length[i] < num_to_mask:
+                i += 1
+            lengths[i] = num_to_mask - (0 if i == 0 else cum_length[i - 1])
+            num_to_mask = i + 1
+            lengths = lengths[:num_to_mask]
+
+            # Handle 0-length mask (inserts) separately
+            lengths = lengths[lengths > 0]
+            num_inserts = num_to_mask - lengths.size(0)
+            num_to_mask -= num_inserts
+            if num_to_mask == 0:
+                return self.add_insertion_noise(source, num_inserts / source.size(0))
+
+            assert (lengths > 0).all()
+        else:
+            lengths = torch.ones((num_to_mask,)).long()
+        assert is_word_start[-1] == 0
+        word_starts = is_word_start.nonzero(as_tuple=False)
+        indices = word_starts[
+            torch.randperm(word_starts.size(0))[:num_to_mask]
+        ].squeeze(1)
+        mask_random = torch.FloatTensor(num_to_mask).uniform_() < self.random_ratio
+
+        source_length = source.size(0)
+        assert source_length - 1 not in indices
+        to_keep = torch.ones(source_length, dtype=torch.bool)
+        is_word_start[
+            -1
+        ] = 255  # acts as a long length, so spans don't go over the end of doc
+        if self.replace_length == 0:
+            to_keep[indices] = 0
+        else:
+            # keep index, but replace it with [MASK]
+            source[indices] = self.mask_idx
+            source[indices[mask_random]] = torch.randint(
+                4, len(self.tgt_dict) - self.code_dict_size - self.num_bins, size=(mask_random.sum(),)
+            )
+
+        if self.mask_span_distribution is not None:
+            assert len(lengths.size()) == 1
+            assert lengths.size() == indices.size()
+            lengths -= 1
+            while indices.size(0) > 0:
+                assert lengths.size() == indices.size()
+                lengths -= is_word_start[indices + 1].long()
+                uncompleted = lengths >= 0
+                indices = indices[uncompleted] + 1
+                mask_random = mask_random[uncompleted]
+                lengths = lengths[uncompleted]
+                if self.replace_length != -1:
+                    # delete token
+                    to_keep[indices] = 0
+                else:
+                    # keep index, but replace it with [MASK]
+                    source[indices] = self.mask_idx
+                    source[indices[mask_random]] = torch.randint(
+                        4, len(self.tgt_dict) - self.code_dict_size - self.num_bins, size=(mask_random.sum(),)
+                    )
+        else:
+            # A bit faster when all lengths are 1
+            while indices.size(0) > 0:
+                uncompleted = is_word_start[indices + 1] == 0
+                indices = indices[uncompleted] + 1
+                mask_random = mask_random[uncompleted]
+                if self.replace_length != -1:
+                    # delete token
+                    to_keep[indices] = 0
+                else:
+                    # keep index, but replace it with [MASK]
+                    source[indices] = self.mask_idx
+                    source[indices[mask_random]] = torch.randint(
+                        4, len(self.tgt_dict) - self.code_dict_size - self.num_bins, size=(mask_random.sum(),)
+                    )
+
+                assert source_length - 1 not in indices
+
+        source = source[to_keep]
+
+        if num_inserts > 0:
+            source = self.add_insertion_noise(source, num_inserts / source.size(0))
+
+        return source
+
+    def add_insertion_noise(self, tokens, p):
+        if p == 0.0:
+            return tokens
+
+        num_tokens = len(tokens)
+        n = int(math.ceil(num_tokens * p))
+
+        noise_indices = torch.randperm(num_tokens + n - 2)[:n] + 1
+        noise_mask = torch.zeros(size=(num_tokens + n,), dtype=torch.bool)
+        noise_mask[noise_indices] = 1
+        result = torch.LongTensor(n + len(tokens)).fill_(-1)
+
+        num_random = int(math.ceil(n * self.random_ratio))
+        result[noise_indices[num_random:]] = self.mask_idx
+        result[noise_indices[:num_random]] = torch.randint(
+            low=4, high=len(self.tgt_dict)-self.code_dict_size-self.num_bins, size=(num_random,)
+        )
+
+        result[~noise_mask] = tokens
+
+        assert (result >= 0).all()
+        return result
+
+    def collater(self, samples, pad_to_length=None):
+        """Merge samples of different tasks to form two mini-batches.
+        Args:
+            samples (List[Tuple]): samples to collate
+        Returns:
+            Tuple[dict]: two mini-batch containing the data of different tasks
+        """
+
+        samples_v1 = []   # containing image-text pairs
+        samples_v2 = []   # containing detection data, text data and image data
+        for sample_tuple in samples:
+            samples_v1 += sample_tuple[0]
+            samples_v2 += sample_tuple[1]
+        if samples_v2 != []:
+            res_v1 = collate(samples_v1, pad_idx=self.src_dict.pad(), eos_idx=self.eos)
+            res_v2 = collate(samples_v2, pad_idx=self.src_dict.pad(), eos_idx=self.eos)
+            return res_v1, res_v2
+        else:
+            res_v1 = collate(samples_v1, pad_idx=self.src_dict.pad(), eos_idx=self.eos)
+            return res_v1

datasets.md

@@ -0,0 +1,44 @@
+# Datasets
+
+We provide links to download our preprocessed dataset. If you would like to process the data on your own, we will soon provide scripts for you to do so. 
+
+## Pretraining
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/pretrain_data/pretrain_data_examples.zip"> A small subset of the pretraining data </a>
+
+ The pretraining datasets used in OFA are all publicly available. Here we provide the public links to these data, it is recommended that you download the data from the links first, and then process the downloaded dataset into a similar format as the examples we provided.
+-   _CC12M_:  https://github.com/google-research-datasets/conceptual-12m
+-   _CC3M_: https://github.com/google-research-datasets/conceptual-captions
+-   _SBU_: https://www.cs.virginia.edu/~vicente/sbucaptions
+-   _COCO_: https://cocodataset.org/#home
+-   _VG_: https://visualgenome.org/
+-   _VQAv2_: https://visualqa.org/
+- _GQA_: https://cs.stanford.edu/people/dorarad/gqa/about.html
+- _RefCOCO_/_RefCOCO+_/RefCOCOg: https://github.com/lichengunc/refer
+-   _OpenImages_: https://storage.googleapis.com/openimages/web/index.html
+-   _Object365_: https://www.objects365.org/overview.html
+-   _YFCC100M (subset)_: https://github.com/openai/CLIP/blob/main/data/yfcc100m.md
+-   _ImageNet-21K_: https://image-net.org/index.php
+-   _Pile_: https://pile.eleuther.ai
+
+## Vision & Language Tasks
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/caption_data/caption_data.zip"> Dataset for Caption </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/refcoco_data/refcoco_data.zip"> Dataset for RefCOCO </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/refcocoplus_data/refcocoplus_data.zip"> Dataset for RefCOCO+ </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/refcocog_data/refcocog_data.zip"> Dataset for RefCOCOg </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/vqa_data/vqa_data.zip"> Dataset for VQAv2 </a> (we have also provided chunked parts of the dataset files for more convenient downloading, please refer to <a href="https://github.com/OFA-Sys/OFA/issues/68#issuecomment-1096837349">issue #68</a>)
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/snli_ve_data/snli_ve_data.zip"> Dataset for SNLI-VE </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/coco_image_gen_data/coco_image_gen.zip"> Dataset for Text-to-Image Genearion </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/coco_image_gen_data/coco_image_gen_origin_id.zip"> Dataset for Text-to-Image Genearion (with original id) </a>
+
+## Vision Tasks
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/imagenet_1k_data/imagenet_1k_data.zip"> Dataset for ImageNet-1K </a>
+
+## Language Tasks
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/cola_data.zip"> Dataset for COLA </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/mnli_data.zip"> Dataset for MNLI </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/mrpc_data.zip"> Dataset for MRPC </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/qnli_data.zip"> Dataset for QNLI </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/qqp_data.zip"> Dataset for QQP </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/rte_data.zip"> Dataset for RTE </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/glue_data/sst2_data.zip"> Dataset for SST2 </a>
+ * <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/datasets/gigaword_data/gigaword_data.zip"> Dataset for Gigaword </a>

evaluate.py

@@ -0,0 +1,160 @@
+#!/usr/bin/env python3 -u
+# Copyright 2022 The OFA-Sys Team. 
+# All rights reserved.
+# This source code is licensed under the Apache 2.0 license 
+# found in the LICENSE file in the root directory.
+
+import logging
+import os
+import sys
+
+import numpy as np
+import torch
+from fairseq import distributed_utils, options, tasks, utils
+from fairseq.dataclass.utils import convert_namespace_to_omegaconf
+from fairseq.logging import progress_bar
+from fairseq.utils import reset_logging
+from omegaconf import DictConfig
+
+from utils import checkpoint_utils
+from utils.eval_utils import eval_step, merge_results
+from utils.zero_shot_utils import zero_shot_step
+
+logging.basicConfig(
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=os.environ.get("LOGLEVEL", "INFO").upper(),
+    stream=sys.stdout,
+)
+logger = logging.getLogger("ofa.evaluate")
+
+
+def apply_half(t):
+    if t.dtype is torch.float32:
+        return t.to(dtype=torch.half)
+    return t
+
+
+def main(cfg: DictConfig, **kwargs):
+    utils.import_user_module(cfg.common)
+
+    reset_logging()
+    logger.info(cfg)
+
+    assert (
+            cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
+    ), "Must specify batch size either with --max-tokens or --batch-size"
+
+    # Fix seed for stochastic decoding
+    if cfg.common.seed is not None and not cfg.generation.no_seed_provided:
+        np.random.seed(cfg.common.seed)
+        utils.set_torch_seed(cfg.common.seed)
+
+    use_fp16 = cfg.common.fp16
+    use_cuda = torch.cuda.is_available() and not cfg.common.cpu
+
+    if use_cuda:
+        torch.cuda.set_device(cfg.distributed_training.device_id)
+
+    # Load ensemble
+    overrides = eval(cfg.common_eval.model_overrides)
+    # Deal with beam-search / all-candidate VQA eval
+    if cfg.task._name == "vqa_gen":
+        overrides['val_inference_type'] = "beamsearch" if kwargs['beam_search_vqa_eval'] else "allcand"
+
+    logger.info("loading model(s) from {}".format(cfg.common_eval.path))
+    if kwargs["zero_shot"]:
+        task = tasks.setup_task(cfg.task)
+        models, saved_cfg = checkpoint_utils.load_model_ensemble(
+            utils.split_paths(cfg.common_eval.path),
+            arg_overrides=overrides,
+            task=task,
+            suffix=cfg.checkpoint.checkpoint_suffix,
+            strict=(cfg.checkpoint.checkpoint_shard_count == 1),
+            num_shards=cfg.checkpoint.checkpoint_shard_count,
+        )
+    else:
+        models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+            utils.split_paths(cfg.common_eval.path),
+            arg_overrides=overrides,
+            suffix=cfg.checkpoint.checkpoint_suffix,
+            strict=(cfg.checkpoint.checkpoint_shard_count == 1),
+            num_shards=cfg.checkpoint.checkpoint_shard_count,
+        )
+
+    # loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config
+    task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task)
+
+    # Move models to GPU
+    for model, ckpt_path in zip(models, utils.split_paths(cfg.common_eval.path)):
+        if kwargs['ema_eval']:
+            logger.info("loading EMA weights from {}".format(ckpt_path))
+            model.load_state_dict(checkpoint_utils.load_ema_from_checkpoint(ckpt_path)['model'])
+        model.eval()
+        if use_fp16:
+            model.half()
+        if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
+            model.cuda()
+        model.prepare_for_inference_(cfg)
+
+    # Load dataset (possibly sharded)
+    itr = task.get_batch_iterator(
+        dataset=task.dataset(cfg.dataset.gen_subset),
+        max_tokens=cfg.dataset.max_tokens,
+        max_sentences=cfg.dataset.batch_size,
+        max_positions=utils.resolve_max_positions(
+            task.max_positions(), *[m.max_positions() for m in models]
+        ),
+        ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
+        required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
+        seed=cfg.common.seed,
+        num_shards=cfg.distributed_training.distributed_world_size,
+        shard_id=cfg.distributed_training.distributed_rank,
+        num_workers=cfg.dataset.num_workers,
+        data_buffer_size=cfg.dataset.data_buffer_size,
+    ).next_epoch_itr(shuffle=False)
+    progress = progress_bar.progress_bar(
+        itr,
+        log_format=cfg.common.log_format,
+        log_interval=cfg.common.log_interval,
+        default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
+    )
+
+    # Initialize generator
+    generator = task.build_generator(models, cfg.generation)
+
+    results = []
+    score_sum = torch.FloatTensor([0]).cuda()
+    score_cnt = torch.FloatTensor([0]).cuda()
+    for sample in progress:
+        if "net_input" not in sample:
+            continue
+        sample = utils.move_to_cuda(sample) if use_cuda else sample
+        sample = utils.apply_to_sample(apply_half, sample) if cfg.common.fp16 else sample
+        with torch.no_grad():
+            if kwargs["zero_shot"]:
+                result, scores = zero_shot_step(task, generator, models, sample)
+            else:
+                result, scores = eval_step(task, generator, models, sample, **kwargs)
+        results += result
+        score_sum += sum(scores) if scores is not None else 0
+        score_cnt += len(scores) if scores is not None else 0
+        progress.log({"sentences": sample["nsentences"]})
+
+    merge_results(task, cfg, logger, score_cnt, score_sum, results)
+
+
+def cli_main():
+    parser = options.get_generation_parser()
+    parser.add_argument("--ema-eval", action='store_true', help="Use EMA weights to make evaluation.")
+    parser.add_argument("--beam-search-vqa-eval", action='store_true', help="Use beam search for vqa evaluation (faster inference speed but sub-optimal result), if not specified, we compute scores for each answer in the candidate set, which is slower but can obtain best result.")
+    parser.add_argument("--zero-shot", action='store_true')
+    args = options.parse_args_and_arch(parser)
+    cfg = convert_namespace_to_omegaconf(args)
+    distributed_utils.call_main(
+        cfg, main, ema_eval=args.ema_eval, beam_search_vqa_eval=args.beam_search_vqa_eval, zero_shot=args.zero_shot
+    )
+
+
+if __name__ == "__main__":
+    cli_main()

fairseq/.github/ISSUE_TEMPLATE.md

@@ -0,0 +1,3 @@
+## 👉 [Please follow one of these issue templates](https://github.com/pytorch/fairseq/issues/new/choose) 👈
+
+Note: to keep the backlog clean and actionable, issues may be immediately closed if they do not follow one of the above issue templates.

fairseq/.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,43 @@
+---
+name: 🐛 Bug Report
+about: Submit a bug report to help us improve
+labels: 'bug, needs triage'
+---
+
+## 🐛 Bug
+
+<!-- A clear and concise description of what the bug is. -->
+
+### To Reproduce
+
+Steps to reproduce the behavior (**always include the command you ran**):
+
+1. Run cmd '....'
+2. See error
+
+<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
+
+
+#### Code sample
+<!-- Ideally attach a minimal code sample to reproduce the decried issue.
+Minimal means having the shortest code but still preserving the bug. -->
+
+### Expected behavior
+
+<!-- A clear and concise description of what you expected to happen. -->
+
+### Environment
+
+ - fairseq Version (e.g., 1.0 or main):
+ - PyTorch Version (e.g., 1.0)
+ - OS (e.g., Linux):
+ - How you installed fairseq (`pip`, source):
+ - Build command you used (if compiling from source):
+ - Python version:
+ - CUDA/cuDNN version:
+ - GPU models and configuration:
+ - Any other relevant information:
+
+### Additional context
+
+<!-- Add any other context about the problem here. -->

fairseq/.github/ISSUE_TEMPLATE/documentation.md

@@ -0,0 +1,15 @@
+---
+name: 📚 Documentation/Typos
+about: Report an issue related to documentation or a typo
+labels: 'documentation, needs triage'
+---
+
+## 📚 Documentation
+
+For typos and doc fixes, please go ahead and:
+
+1. Create an issue.
+2. Fix the typo.
+3. Submit a PR.
+
+Thanks!

fairseq/.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,24 @@
+---
+name: 🚀 Feature Request
+about: Submit a proposal/request for a new feature
+labels: 'enhancement, help wanted, needs triage'
+---
+
+## 🚀 Feature Request
+<!-- A clear and concise description of the feature proposal -->
+
+### Motivation
+
+<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
+
+### Pitch
+
+<!-- A clear and concise description of what you want to happen. -->
+
+### Alternatives
+
+<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
+
+### Additional context
+
+<!-- Add any other context or screenshots about the feature request here. -->

fairseq/.github/ISSUE_TEMPLATE/how-to-question.md

@@ -0,0 +1,33 @@
+---
+name: ❓ Questions/Help
+about: If you have questions, please first search existing issues and docs
+labels: 'question, needs triage'
+---
+
+## ❓ Questions and Help
+
+### Before asking:
+1. search the issues.
+2. search the docs.
+
+<!-- If you still can't find what you need: -->
+
+#### What is your question?
+
+#### Code
+
+<!-- Please paste a code snippet if your question requires it! -->
+
+#### What have you tried?
+
+#### What's your environment?
+
+ - fairseq Version (e.g., 1.0 or main):
+ - PyTorch Version (e.g., 1.0)
+ - OS (e.g., Linux):
+ - How you installed fairseq (`pip`, source):
+ - Build command you used (if compiling from source):
+ - Python version:
+ - CUDA/cuDNN version:
+ - GPU models and configuration:
+ - Any other relevant information:

fairseq/.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,16 @@
+# Before submitting
+
+- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
+- [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/main/CONTRIBUTING.md)?
+- [ ] Did you make sure to update the docs?
+- [ ] Did you write any new necessary tests?
+
+## What does this PR do?
+Fixes # (issue).
+
+## PR review
+Anyone in the community is free to review the PR once the tests have passed.
+If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
+
+## Did you have fun?
+Make sure you had fun coding 🙃

fairseq/.github/stale.yml

@@ -0,0 +1,30 @@
+# Configuration for probot-stale - https://github.com/probot/stale
+# Mostly copied from github.com/facebook/react/blob/master/.github/stale.yml
+# Number of days of inactivity before an issue becomes stale
+daysUntilStale: 90
+# Number of days of inactivity before a stale issue is closed
+daysUntilClose: 7
+# Issues with these labels will never be considered stale
+exemptLabels:
+  - bug
+# Label to use when marking an issue as stale
+staleLabel: stale
+issues:
+  # Comment to post when marking an issue as stale.
+  markComment: >
+    This issue has been automatically marked as stale.
+    **If this issue is still affecting you, please leave any comment** (for example, "bump"), and we'll keep it open.
+    We are sorry that we haven't been able to prioritize it yet. If you have any new additional information, please include it with your comment!
+  # Comment to post when closing a stale issue.
+  closeComment: >
+    Closing this issue after a prolonged period of inactivity. If this issue is still present in the latest release, please create a new issue with up-to-date information. Thank you!
+pulls:
+  # Comment to post when marking a pull request as stale.
+  markComment: >
+    This pull request has been automatically marked as stale.
+    **If this pull request is still relevant, please leave any comment** (for example, "bump"), and we'll keep it open.
+    We are sorry that we haven't been able to prioritize reviewing it yet. Your contribution is very much appreciated.
+  # Comment to post when closing a stale pull request.
+  closeComment: >
+    Closing this pull request after a prolonged period of inactivity. If this issue is still present in the latest release, please ask for this pull request to be reopened. Thank you!
+

fairseq/.github/workflows/build.yml

@@ -0,0 +1,55 @@
+name: build
+
+on:
+  # Trigger the workflow on push to main or any pull request
+  push:
+    branches:
+      - main
+  pull_request:
+
+jobs:
+  build:
+
+    strategy:
+      max-parallel: 4
+      matrix:
+        platform: [ubuntu-latest, macos-latest]
+        python-version: [3.6, 3.7]
+
+    runs-on: ${{ matrix.platform }}
+
+    steps:
+    - uses: actions/checkout@v2
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Conditionally install pytorch
+      if: matrix.platform == 'windows-latest'
+      run: pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html
+
+    - name: Install locally
+      run: |
+        python -m pip install --upgrade pip
+        git submodule update --init --recursive
+        python setup.py build_ext --inplace
+        python -m pip install --editable .
+
+    - name: Install optional test requirements
+      run: |
+        python -m pip install iopath transformers pyarrow
+        python -m pip install git+https://github.com/facebookresearch/fairscale.git@main
+
+    - name: Lint with flake8
+      run: |
+        pip install flake8
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --extend-exclude fairseq/model_parallel/megatron
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --extend-exclude fairseq/model_parallel/megatron
+
+    - name: Run tests
+      run: |
+          python setup.py test

fairseq/.github/workflows/build_wheels.yml

@@ -0,0 +1,41 @@
+name: build_wheels
+
+on:
+  push:
+    branches:
+      - v[0-9]+.[0-9]+.[x0-9]+
+    tags:
+      - v*
+
+jobs:
+  build_wheels:
+    name: Build wheels on ${{ matrix.os }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest]
+
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Install Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.7'
+
+      - name: Install cibuildwheel
+        run: |
+          python -m pip install cibuildwheel
+
+      - name: Build wheels for CPython
+        run: |
+          python -m cibuildwheel --output-dir dist
+        env:
+          CIBW_BUILD: "cp36-*64 cp37-*64 cp38-*64"
+          CIBW_MANYLINUX_X86_64_IMAGE: manylinux1
+          CIBW_BEFORE_BUILD: git submodule update --init --recursive && pip install .
+
+      - uses: actions/upload-artifact@v2
+        with:
+          name: wheels
+          path: ./dist/*.whl

fairseq/.gitignore

@@ -0,0 +1,136 @@
+# JetBrains PyCharm IDE
+.idea/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# macOS dir files
+.DS_Store
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Checkpoints
+checkpoints
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# Generated files
+/fairseq/temporal_convolution_tbc
+/fairseq/modules/*_layer/*_forward.cu
+/fairseq/modules/*_layer/*_backward.cu
+/fairseq/version.py
+
+# data
+data-bin/
+
+# reranking
+/examples/reranking/rerank_data
+
+# Cython-generated C++ source files
+/fairseq/data/data_utils_fast.cpp
+/fairseq/data/token_block_utils_fast.cpp
+
+# VSCODE
+.vscode/ftp-sync.json
+.vscode/settings.json
+
+# Experimental Folder
+experimental/*
+
+# Weights and Biases logs
+wandb/

fairseq/.gitmodules

@@ -0,0 +1,4 @@
+[submodule "fairseq/model_parallel/megatron"]
+    path = fairseq/model_parallel/megatron
+    url = https://github.com/ngoyal2707/Megatron-LM
+    branch = fairseq

fairseq/CODE_OF_CONDUCT.md

@@ -0,0 +1,77 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+  advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <conduct@pytorch.org>. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq
+