[Test refactor 1/5] Per-folder tests reorganization (#15725)
* Per-folder tests reorganization Co-authored-by: sgugger <sylvain.gugger@gmail.com> Co-authored-by: Stas Bekman <stas@stason.org>
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Lysandre Debut
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tests/utils/test_utils_check_copies.py
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tests/utils/test_utils_check_copies.py
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import re
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import shutil
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import sys
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import tempfile
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import unittest
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import black
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git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
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sys.path.append(os.path.join(git_repo_path, "utils"))
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import check_copies # noqa: E402
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# This is the reference code that will be used in the tests.
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# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
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REFERENCE_CODE = """ def __init__(self, config):
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super().__init__()
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self.transform = BertPredictionHeadTransform(config)
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# The output weights are the same as the input embeddings, but there is
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# an output-only bias for each token.
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self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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self.bias = nn.Parameter(torch.zeros(config.vocab_size))
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# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
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self.decoder.bias = self.bias
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def forward(self, hidden_states):
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hidden_states = self.transform(hidden_states)
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hidden_states = self.decoder(hidden_states)
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return hidden_states
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"""
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class CopyCheckTester(unittest.TestCase):
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def setUp(self):
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self.transformer_dir = tempfile.mkdtemp()
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os.makedirs(os.path.join(self.transformer_dir, "models/bert/"))
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check_copies.TRANSFORMER_PATH = self.transformer_dir
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shutil.copy(
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os.path.join(git_repo_path, "src/transformers/models/bert/modeling_bert.py"),
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os.path.join(self.transformer_dir, "models/bert/modeling_bert.py"),
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)
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def tearDown(self):
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check_copies.TRANSFORMER_PATH = "src/transformers"
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shutil.rmtree(self.transformer_dir)
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def check_copy_consistency(self, comment, class_name, class_code, overwrite_result=None):
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code = comment + f"\nclass {class_name}(nn.Module):\n" + class_code
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if overwrite_result is not None:
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expected = comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
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mode = black.Mode(target_versions={black.TargetVersion.PY35}, line_length=119)
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code = black.format_str(code, mode=mode)
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fname = os.path.join(self.transformer_dir, "new_code.py")
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with open(fname, "w", newline="\n") as f:
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f.write(code)
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if overwrite_result is None:
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self.assertTrue(len(check_copies.is_copy_consistent(fname)) == 0)
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else:
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check_copies.is_copy_consistent(f.name, overwrite=True)
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with open(fname, "r") as f:
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self.assertTrue(f.read(), expected)
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def test_find_code_in_transformers(self):
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code = check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead")
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self.assertEqual(code, REFERENCE_CODE)
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def test_is_copy_consistent(self):
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# Base copy consistency
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self.check_copy_consistency(
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"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead",
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"BertLMPredictionHead",
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REFERENCE_CODE + "\n",
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)
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# With no empty line at the end
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self.check_copy_consistency(
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"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead",
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"BertLMPredictionHead",
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REFERENCE_CODE,
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)
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# Copy consistency with rename
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self.check_copy_consistency(
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"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel",
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"TestModelLMPredictionHead",
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re.sub("Bert", "TestModel", REFERENCE_CODE),
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)
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# Copy consistency with a really long name
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long_class_name = "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
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self.check_copy_consistency(
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f"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}",
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f"{long_class_name}LMPredictionHead",
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re.sub("Bert", long_class_name, REFERENCE_CODE),
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)
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# Copy consistency with overwrite
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self.check_copy_consistency(
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"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel",
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"TestModelLMPredictionHead",
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REFERENCE_CODE,
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overwrite_result=re.sub("Bert", "TestModel", REFERENCE_CODE),
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)
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def test_convert_to_localized_md(self):
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localized_readme = check_copies.LOCALIZED_READMES["README_zh-hans.md"]
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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/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/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."
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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"
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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/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/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"
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num_models_equal, converted_md_list = check_copies.convert_to_localized_md(
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md_list, localized_md_list, localized_readme["format_model_list"]
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)
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self.assertFalse(num_models_equal)
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self.assertEqual(converted_md_list, converted_md_list_sample)
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num_models_equal, converted_md_list = check_copies.convert_to_localized_md(
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md_list, converted_md_list, localized_readme["format_model_list"]
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)
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# Check whether the number of models is equal to README.md after conversion.
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self.assertTrue(num_models_equal)
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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."
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link_unchanged_md_list = "1. **[ALBERT](https://huggingface.co/transformers/master/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"
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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"
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num_models_equal, converted_md_list = check_copies.convert_to_localized_md(
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link_changed_md_list, link_unchanged_md_list, localized_readme["format_model_list"]
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)
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# Check if the model link is synchronized.
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self.assertEqual(converted_md_list, converted_md_list_sample)
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