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https://github.com/huggingface/transformers.git
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* Add files * Init * Add TimmWrapperModel * Fix up * Some fixes * Fix up * Remove old file * Sort out import orders * Fix some model loading * Compatible with pipeline and trainer * Fix up * Delete test_timm_model_1/config.json * Remove accidentally commited files * Delete src/transformers/models/modeling_timm_wrapper.py * Remove empty imports; fix transformations applied * Tidy up * Add image classifcation model to special cases * Create pretrained model; enable device_map='auto' * Enable most tests; fix init order * Sort imports * [run-slow] timm_wrapper * Pass num_classes into timm.create_model * Remove train transforms from image processor * Update timm creation with pretrained=False * Fix gamma/beta issue for timm models * Fixing gamma and beta renaming for timm models * Simplify config and model creation * Remove attn_implementation diff * Fixup * Docstrings * Fix warning msg text according to test case * Fix device_map auto * Set dtype and device for pixel_values in forward * Enable output hidden states * Enable tests for hidden_states and model parallel * Remove default scriptable arg * Refactor inner model * Update timm version * Fix _find_mismatched_keys function * Change inheritance for Classification model (fix weights loading with device_map) * Minor bugfix * Disable save pretrained for image processor * Rename hook method for loaded keys correction * Rename state dict keys on save, remove `timm_model` prefix, make checkpoint compatible with `timm` * Managing num_labels <-> num_classes attributes * Enable loading checkpoints in Trainer to resume training * Update error message for output_hidden_states * Add output hidden states test * Decouple base and classification models * Add more test cases * Add save-load-to-timm test * Fix test name * Fixup * Add do_pooling * Add test for do_pooling * Fix doc * Add tests for TimmWrapperModel * Add validation for `num_classes=0` in timm config + test for DINO checkpoint * Adjust atol for test * Fix docs * dev-ci * dev-ci * Add tests for image processor * Update docs * Update init to new format * Update docs in configuration * Fix some docs in image processor * Improve docs for modeling * fix for is_timm_checkpoint * Update code examples * Fix header * Fix typehint * Increase tolerance a bit * Fix Path * Fixing model parallel tests * Disable "parallel" tests * Add comment for metadata * Refactor AutoImageProcessor for timm wrapper loading * Remove custom test_model_outputs_equivalence * Add require_timm decorator * Fix comment * Make image processor work with older timm versions and tensor input * Save config instead of whole model in image processor tests * Add docstring for `image_processor_filename` * Sanitize kwargs for timm image processor * Fix doc style * Update check for tensor input * Update normalize * Remove _load_timm_model function --------- Co-authored-by: Amy Roberts <22614925+amyeroberts@users.noreply.github.com>
101 lines
3.6 KiB
Python
101 lines
3.6 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team.
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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 inspect
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import re
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from transformers.utils import direct_transformers_import
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# All paths are set with the intent you should run this script from the root of the repo with the command
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# python utils/check_config_docstrings.py
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PATH_TO_TRANSFORMERS = "src/transformers"
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# This is to make sure the transformers module imported is the one in the repo.
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transformers = direct_transformers_import(PATH_TO_TRANSFORMERS)
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CONFIG_MAPPING = transformers.models.auto.configuration_auto.CONFIG_MAPPING
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# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
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# For example, `[google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)`
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_re_checkpoint = re.compile(r"\[(.+?)\]\((https://huggingface\.co/.+?)\)")
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CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = {
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"DecisionTransformerConfig",
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"EncoderDecoderConfig",
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"MusicgenConfig",
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"RagConfig",
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"SpeechEncoderDecoderConfig",
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"TimmBackboneConfig",
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"TimmWrapperConfig",
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"VisionEncoderDecoderConfig",
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"VisionTextDualEncoderConfig",
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"LlamaConfig",
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"GraniteConfig",
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"GraniteMoeConfig",
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}
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def get_checkpoint_from_config_class(config_class):
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checkpoint = None
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# source code of `config_class`
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config_source = inspect.getsource(config_class)
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checkpoints = _re_checkpoint.findall(config_source)
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# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
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# For example, `('google-bert/bert-base-uncased', 'https://huggingface.co/google-bert/bert-base-uncased')`
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for ckpt_name, ckpt_link in checkpoints:
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# allow the link to end with `/`
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if ckpt_link.endswith("/"):
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ckpt_link = ckpt_link[:-1]
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# verify the checkpoint name corresponds to the checkpoint link
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ckpt_link_from_name = f"https://huggingface.co/{ckpt_name}"
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if ckpt_link == ckpt_link_from_name:
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checkpoint = ckpt_name
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break
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return checkpoint
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def check_config_docstrings_have_checkpoints():
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configs_without_checkpoint = []
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for config_class in list(CONFIG_MAPPING.values()):
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# Skip deprecated models
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if "models.deprecated" in config_class.__module__:
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continue
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checkpoint = get_checkpoint_from_config_class(config_class)
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name = config_class.__name__
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if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
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configs_without_checkpoint.append(name)
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if len(configs_without_checkpoint) > 0:
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message = "\n".join(sorted(configs_without_checkpoint))
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raise ValueError(
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f"The following configurations don't contain any valid checkpoint:\n{message}\n\n"
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"The requirement is to include a link pointing to one of the models of this architecture in the "
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"docstring of the config classes listed above. The link should have be a markdown format like "
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"[myorg/mymodel](https://huggingface.co/myorg/mymodel)."
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)
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if __name__ == "__main__":
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check_config_docstrings_have_checkpoints()
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