| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # Copyright 2020 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # This script creates a super tiny model that is useful inside tests, when we just want to test that | |
| # the machinery works, without needing to the check the quality of the outcomes. | |
| # | |
| # This version creates a tiny model through reduction of a normal pre-trained model, but keeping the | |
| # full vocab, merges file, and thus also resulting in a larger model due to a large vocab size. | |
| # This gives ~3MB in total for all files. | |
| # | |
| # If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated | |
| # | |
| # | |
| # It will be used then as "stas/tiny-wmt19-en-de" | |
| # Build | |
| from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration | |
| mname = "facebook/wmt19-en-de" | |
| tokenizer = FSMTTokenizer.from_pretrained(mname) | |
| # get the correct vocab sizes, etc. from the master model | |
| config = FSMTConfig.from_pretrained(mname) | |
| config.update(dict( | |
| d_model=4, | |
| encoder_layers=1, decoder_layers=1, | |
| encoder_ffn_dim=4, decoder_ffn_dim=4, | |
| encoder_attention_heads=1, decoder_attention_heads=1)) | |
| tiny_model = FSMTForConditionalGeneration(config) | |
| print(f"num of params {tiny_model.num_parameters()}") | |
| # Test | |
| batch = tokenizer(["Making tiny model"], return_tensors="pt") | |
| outputs = tiny_model(**batch) | |
| print("test output:", len(outputs.logits[0])) | |
| # Save | |
| mname_tiny = "tiny-wmt19-en-de" | |
| tiny_model.half() # makes it smaller | |
| tiny_model.save_pretrained(mname_tiny) | |
| tokenizer.save_pretrained(mname_tiny) | |
| print(f"Generated {mname_tiny}") | |
| # Upload | |
| # transformers-cli upload tiny-wmt19-en-de | |