# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import re import shutil import sys import tempfile import unittest import black git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, "utils")) import check_copies # noqa: E402 # This is the reference code that will be used in the tests. # If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated. REFERENCE_CODE = """ def __init__(self, config): super().__init__() self.transform = BertPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states """ class CopyCheckTester(unittest.TestCase): def setUp(self): self.transformer_dir = tempfile.mkdtemp() os.makedirs(os.path.join(self.transformer_dir, "models/bert/")) check_copies.TRANSFORMER_PATH = self.transformer_dir shutil.copy( os.path.join(git_repo_path, "src/transformers/models/bert/modeling_bert.py"), os.path.join(self.transformer_dir, "models/bert/modeling_bert.py"), ) def tearDown(self): check_copies.TRANSFORMER_PATH = "src/transformers" shutil.rmtree(self.transformer_dir) def check_copy_consistency(self, comment, class_name, class_code, overwrite_result=None): code = comment + f"\nclass {class_name}(nn.Module):\n" + class_code if overwrite_result is not None: expected = comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result mode = black.Mode(target_versions={black.TargetVersion.PY35}, line_length=119) code = black.format_str(code, mode=mode) fname = os.path.join(self.transformer_dir, "new_code.py") with open(fname, "w", newline="\n") as f: f.write(code) if overwrite_result is None: self.assertTrue(len(check_copies.is_copy_consistent(fname)) == 0) else: check_copies.is_copy_consistent(f.name, overwrite=True) with open(fname, "r") as f: self.assertTrue(f.read(), expected) def test_find_code_in_transformers(self): code = check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead") self.assertEqual(code, REFERENCE_CODE) def test_is_copy_consistent(self): # Base copy consistency self.check_copy_consistency( "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead", "BertLMPredictionHead", REFERENCE_CODE + "\n", ) # With no empty line at the end self.check_copy_consistency( "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead", "BertLMPredictionHead", REFERENCE_CODE, ) # Copy consistency with rename self.check_copy_consistency( "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel", "TestModelLMPredictionHead", re.sub("Bert", "TestModel", REFERENCE_CODE), ) # Copy consistency with a really long name long_class_name = "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason" self.check_copy_consistency( f"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}", f"{long_class_name}LMPredictionHead", re.sub("Bert", long_class_name, REFERENCE_CODE), ) # Copy consistency with overwrite self.check_copy_consistency( "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel", "TestModelLMPredictionHead", REFERENCE_CODE, overwrite_result=re.sub("Bert", "TestModel", REFERENCE_CODE), ) def test_convert_to_localized_md(self): localized_readme = check_copies.LOCALIZED_READMES["README_zh-hans.md"] md_list = ( "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the" " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for" " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong" " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1." " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace)," " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and" " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same" " method has been applied to compress GPT2 into" " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into" " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation)," " Multilingual BERT into" " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German" " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**" " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders" " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang" " Luong, Quoc V. Le, Christopher D. Manning." ) localized_md_list = ( "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the" " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of" " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian" " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n" ) converted_md_list_sample = ( "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the" " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of" " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian" " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1." " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文" " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and" " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same" " method has been applied to compress GPT2 into" " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into" " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation)," " Multilingual BERT into" " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German" " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自" " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather" " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le," " Christopher D. Manning 发布。\n" ) num_models_equal, converted_md_list = check_copies.convert_to_localized_md( md_list, localized_md_list, localized_readme["format_model_list"] ) self.assertFalse(num_models_equal) self.assertEqual(converted_md_list, converted_md_list_sample) num_models_equal, converted_md_list = check_copies.convert_to_localized_md( md_list, converted_md_list, localized_readme["format_model_list"] ) # Check whether the number of models is equal to README.md after conversion. self.assertTrue(num_models_equal) link_changed_md_list = ( "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the" " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for" " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong" " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut." ) link_unchanged_md_list = ( "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and" " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of" " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian" " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n" ) converted_md_list_sample = ( "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the" " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of" " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian" " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n" ) num_models_equal, converted_md_list = check_copies.convert_to_localized_md( link_changed_md_list, link_unchanged_md_list, localized_readme["format_model_list"] ) # Check if the model link is synchronized. self.assertEqual(converted_md_list, converted_md_list_sample)