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| # coding=utf-8 | |
| # Copyright 2018 The HuggingFace Inc. team. | |
| # | |
| # 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. | |
| """ Convert pytorch checkpoints to TensorFlow""" | |
| import argparse | |
| import os | |
| from . import ( | |
| ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| BART_PRETRAINED_MODEL_ARCHIVE_LIST, | |
| BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, | |
| DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, | |
| DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, | |
| ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, | |
| LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| T5_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| AlbertConfig, | |
| BartConfig, | |
| BertConfig, | |
| CamembertConfig, | |
| CTRLConfig, | |
| DistilBertConfig, | |
| DPRConfig, | |
| ElectraConfig, | |
| FlaubertConfig, | |
| GPT2Config, | |
| LayoutLMConfig, | |
| LxmertConfig, | |
| OpenAIGPTConfig, | |
| RobertaConfig, | |
| T5Config, | |
| TFAlbertForPreTraining, | |
| TFBartForConditionalGeneration, | |
| TFBartForSequenceClassification, | |
| TFBertForPreTraining, | |
| TFBertForQuestionAnswering, | |
| TFBertForSequenceClassification, | |
| TFCamembertForMaskedLM, | |
| TFCTRLLMHeadModel, | |
| TFDistilBertForMaskedLM, | |
| TFDistilBertForQuestionAnswering, | |
| TFDPRContextEncoder, | |
| TFDPRQuestionEncoder, | |
| TFDPRReader, | |
| TFElectraForPreTraining, | |
| TFFlaubertWithLMHeadModel, | |
| TFGPT2LMHeadModel, | |
| TFLayoutLMForMaskedLM, | |
| TFLxmertForPreTraining, | |
| TFLxmertVisualFeatureEncoder, | |
| TFOpenAIGPTLMHeadModel, | |
| TFRobertaForCausalLM, | |
| TFRobertaForMaskedLM, | |
| TFRobertaForSequenceClassification, | |
| TFT5ForConditionalGeneration, | |
| TFTransfoXLLMHeadModel, | |
| TFWav2Vec2Model, | |
| TFXLMRobertaForMaskedLM, | |
| TFXLMWithLMHeadModel, | |
| TFXLNetLMHeadModel, | |
| TransfoXLConfig, | |
| Wav2Vec2Config, | |
| Wav2Vec2Model, | |
| XLMConfig, | |
| XLMRobertaConfig, | |
| XLNetConfig, | |
| is_torch_available, | |
| load_pytorch_checkpoint_in_tf2_model, | |
| ) | |
| from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging | |
| if is_torch_available(): | |
| import numpy as np | |
| import torch | |
| from . import ( | |
| AlbertForPreTraining, | |
| BartForConditionalGeneration, | |
| BertForPreTraining, | |
| BertForQuestionAnswering, | |
| BertForSequenceClassification, | |
| CamembertForMaskedLM, | |
| CTRLLMHeadModel, | |
| DistilBertForMaskedLM, | |
| DistilBertForQuestionAnswering, | |
| DPRContextEncoder, | |
| DPRQuestionEncoder, | |
| DPRReader, | |
| ElectraForPreTraining, | |
| FlaubertWithLMHeadModel, | |
| GPT2LMHeadModel, | |
| LayoutLMForMaskedLM, | |
| LxmertForPreTraining, | |
| LxmertVisualFeatureEncoder, | |
| OpenAIGPTLMHeadModel, | |
| RobertaForMaskedLM, | |
| RobertaForSequenceClassification, | |
| T5ForConditionalGeneration, | |
| TransfoXLLMHeadModel, | |
| XLMRobertaForMaskedLM, | |
| XLMWithLMHeadModel, | |
| XLNetLMHeadModel, | |
| ) | |
| logging.set_verbosity_info() | |
| MODEL_CLASSES = { | |
| "bart": ( | |
| BartConfig, | |
| TFBartForConditionalGeneration, | |
| TFBartForSequenceClassification, | |
| BartForConditionalGeneration, | |
| BART_PRETRAINED_MODEL_ARCHIVE_LIST, | |
| ), | |
| "bert": ( | |
| BertConfig, | |
| TFBertForPreTraining, | |
| BertForPreTraining, | |
| BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "bert-large-uncased-whole-word-masking-finetuned-squad": ( | |
| BertConfig, | |
| TFBertForQuestionAnswering, | |
| BertForQuestionAnswering, | |
| BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "bert-large-cased-whole-word-masking-finetuned-squad": ( | |
| BertConfig, | |
| TFBertForQuestionAnswering, | |
| BertForQuestionAnswering, | |
| BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "bert-base-cased-finetuned-mrpc": ( | |
| BertConfig, | |
| TFBertForSequenceClassification, | |
| BertForSequenceClassification, | |
| BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "dpr": ( | |
| DPRConfig, | |
| TFDPRQuestionEncoder, | |
| TFDPRContextEncoder, | |
| TFDPRReader, | |
| DPRQuestionEncoder, | |
| DPRContextEncoder, | |
| DPRReader, | |
| DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, | |
| DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, | |
| DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, | |
| ), | |
| "gpt2": ( | |
| GPT2Config, | |
| TFGPT2LMHeadModel, | |
| GPT2LMHeadModel, | |
| GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "xlnet": ( | |
| XLNetConfig, | |
| TFXLNetLMHeadModel, | |
| XLNetLMHeadModel, | |
| XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "xlm": ( | |
| XLMConfig, | |
| TFXLMWithLMHeadModel, | |
| XLMWithLMHeadModel, | |
| XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "xlm-roberta": ( | |
| XLMRobertaConfig, | |
| TFXLMRobertaForMaskedLM, | |
| XLMRobertaForMaskedLM, | |
| XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "transfo-xl": ( | |
| TransfoXLConfig, | |
| TFTransfoXLLMHeadModel, | |
| TransfoXLLMHeadModel, | |
| TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "openai-gpt": ( | |
| OpenAIGPTConfig, | |
| TFOpenAIGPTLMHeadModel, | |
| OpenAIGPTLMHeadModel, | |
| OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "roberta": ( | |
| RobertaConfig, | |
| TFRobertaForCausalLM, | |
| TFRobertaForMaskedLM, | |
| RobertaForMaskedLM, | |
| ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "layoutlm": ( | |
| LayoutLMConfig, | |
| TFLayoutLMForMaskedLM, | |
| LayoutLMForMaskedLM, | |
| LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, | |
| ), | |
| "roberta-large-mnli": ( | |
| RobertaConfig, | |
| TFRobertaForSequenceClassification, | |
| RobertaForSequenceClassification, | |
| ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "camembert": ( | |
| CamembertConfig, | |
| TFCamembertForMaskedLM, | |
| CamembertForMaskedLM, | |
| CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "flaubert": ( | |
| FlaubertConfig, | |
| TFFlaubertWithLMHeadModel, | |
| FlaubertWithLMHeadModel, | |
| FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "distilbert": ( | |
| DistilBertConfig, | |
| TFDistilBertForMaskedLM, | |
| DistilBertForMaskedLM, | |
| DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "distilbert-base-distilled-squad": ( | |
| DistilBertConfig, | |
| TFDistilBertForQuestionAnswering, | |
| DistilBertForQuestionAnswering, | |
| DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "lxmert": ( | |
| LxmertConfig, | |
| TFLxmertForPreTraining, | |
| LxmertForPreTraining, | |
| LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "lxmert-visual-feature-encoder": ( | |
| LxmertConfig, | |
| TFLxmertVisualFeatureEncoder, | |
| LxmertVisualFeatureEncoder, | |
| LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "ctrl": ( | |
| CTRLConfig, | |
| TFCTRLLMHeadModel, | |
| CTRLLMHeadModel, | |
| CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "albert": ( | |
| AlbertConfig, | |
| TFAlbertForPreTraining, | |
| AlbertForPreTraining, | |
| ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "t5": ( | |
| T5Config, | |
| TFT5ForConditionalGeneration, | |
| T5ForConditionalGeneration, | |
| T5_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "electra": ( | |
| ElectraConfig, | |
| TFElectraForPreTraining, | |
| ElectraForPreTraining, | |
| ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| "wav2vec2": ( | |
| Wav2Vec2Config, | |
| TFWav2Vec2Model, | |
| Wav2Vec2Model, | |
| WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, | |
| ), | |
| } | |
| def convert_pt_checkpoint_to_tf( | |
| model_type, pytorch_checkpoint_path, config_file, tf_dump_path, compare_with_pt_model=False, use_cached_models=True | |
| ): | |
| if model_type not in MODEL_CLASSES: | |
| raise ValueError(f"Unrecognized model type, should be one of {list(MODEL_CLASSES.keys())}.") | |
| config_class, model_class, pt_model_class, aws_config_map = MODEL_CLASSES[model_type] | |
| # Initialise TF model | |
| if config_file in aws_config_map: | |
| config_file = cached_file(config_file, CONFIG_NAME, force_download=not use_cached_models) | |
| config = config_class.from_json_file(config_file) | |
| config.output_hidden_states = True | |
| config.output_attentions = True | |
| print(f"Building TensorFlow model from configuration: {config}") | |
| tf_model = model_class(config) | |
| # Load weights from tf checkpoint | |
| if pytorch_checkpoint_path in aws_config_map.keys(): | |
| pytorch_checkpoint_path = cached_file( | |
| pytorch_checkpoint_path, WEIGHTS_NAME, force_download=not use_cached_models | |
| ) | |
| # Load PyTorch checkpoint in tf2 model: | |
| tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path) | |
| if compare_with_pt_model: | |
| tfo = tf_model(tf_model.dummy_inputs, training=False) # build the network | |
| state_dict = torch.load(pytorch_checkpoint_path, map_location="cpu") | |
| pt_model = pt_model_class.from_pretrained( | |
| pretrained_model_name_or_path=None, config=config, state_dict=state_dict | |
| ) | |
| with torch.no_grad(): | |
| pto = pt_model(**pt_model.dummy_inputs) | |
| np_pt = pto[0].numpy() | |
| np_tf = tfo[0].numpy() | |
| diff = np.amax(np.abs(np_pt - np_tf)) | |
| print(f"Max absolute difference between models outputs {diff}") | |
| assert diff <= 2e-2, f"Error, model absolute difference is >2e-2: {diff}" | |
| # Save pytorch-model | |
| print(f"Save TensorFlow model to {tf_dump_path}") | |
| tf_model.save_weights(tf_dump_path, save_format="h5") | |
| def convert_all_pt_checkpoints_to_tf( | |
| args_model_type, | |
| tf_dump_path, | |
| model_shortcut_names_or_path=None, | |
| config_shortcut_names_or_path=None, | |
| compare_with_pt_model=False, | |
| use_cached_models=False, | |
| remove_cached_files=False, | |
| only_convert_finetuned_models=False, | |
| ): | |
| if args_model_type is None: | |
| model_types = list(MODEL_CLASSES.keys()) | |
| else: | |
| model_types = [args_model_type] | |
| for j, model_type in enumerate(model_types, start=1): | |
| print("=" * 100) | |
| print(f" Converting model type {j}/{len(model_types)}: {model_type}") | |
| print("=" * 100) | |
| if model_type not in MODEL_CLASSES: | |
| raise ValueError(f"Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys())}.") | |
| config_class, model_class, pt_model_class, aws_model_maps, aws_config_map = MODEL_CLASSES[model_type] | |
| if model_shortcut_names_or_path is None: | |
| model_shortcut_names_or_path = list(aws_model_maps.keys()) | |
| if config_shortcut_names_or_path is None: | |
| config_shortcut_names_or_path = model_shortcut_names_or_path | |
| for i, (model_shortcut_name, config_shortcut_name) in enumerate( | |
| zip(model_shortcut_names_or_path, config_shortcut_names_or_path), start=1 | |
| ): | |
| print("-" * 100) | |
| if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name: | |
| if not only_convert_finetuned_models: | |
| print(f" Skipping finetuned checkpoint {model_shortcut_name}") | |
| continue | |
| model_type = model_shortcut_name | |
| elif only_convert_finetuned_models: | |
| print(f" Skipping not finetuned checkpoint {model_shortcut_name}") | |
| continue | |
| print( | |
| f" Converting checkpoint {i}/{len(aws_config_map)}: {model_shortcut_name} - model_type {model_type}" | |
| ) | |
| print("-" * 100) | |
| if config_shortcut_name in aws_config_map: | |
| config_file = cached_file(config_shortcut_name, CONFIG_NAME, force_download=not use_cached_models) | |
| else: | |
| config_file = config_shortcut_name | |
| if model_shortcut_name in aws_model_maps: | |
| model_file = cached_file(model_shortcut_name, WEIGHTS_NAME, force_download=not use_cached_models) | |
| else: | |
| model_file = model_shortcut_name | |
| if os.path.isfile(model_shortcut_name): | |
| model_shortcut_name = "converted_model" | |
| convert_pt_checkpoint_to_tf( | |
| model_type=model_type, | |
| pytorch_checkpoint_path=model_file, | |
| config_file=config_file, | |
| tf_dump_path=os.path.join(tf_dump_path, model_shortcut_name + "-tf_model.h5"), | |
| compare_with_pt_model=compare_with_pt_model, | |
| ) | |
| if remove_cached_files: | |
| os.remove(config_file) | |
| os.remove(model_file) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| # Required parameters | |
| parser.add_argument( | |
| "--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file." | |
| ) | |
| parser.add_argument( | |
| "--model_type", | |
| default=None, | |
| type=str, | |
| help=( | |
| f"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and " | |
| "convert all the models from AWS." | |
| ), | |
| ) | |
| parser.add_argument( | |
| "--pytorch_checkpoint_path", | |
| default=None, | |
| type=str, | |
| help=( | |
| "Path to the PyTorch checkpoint path or shortcut name to download from AWS. " | |
| "If not given, will download and convert all the checkpoints from AWS." | |
| ), | |
| ) | |
| parser.add_argument( | |
| "--config_file", | |
| default=None, | |
| type=str, | |
| help=( | |
| "The config json file corresponding to the pre-trained model. \n" | |
| "This specifies the model architecture. If not given and " | |
| "--pytorch_checkpoint_path is not given or is a shortcut name " | |
| "use the configuration associated to the shortcut name on the AWS" | |
| ), | |
| ) | |
| parser.add_argument( | |
| "--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions." | |
| ) | |
| parser.add_argument( | |
| "--use_cached_models", | |
| action="store_true", | |
| help="Use cached models if possible instead of updating to latest checkpoint versions.", | |
| ) | |
| parser.add_argument( | |
| "--remove_cached_files", | |
| action="store_true", | |
| help="Remove pytorch models after conversion (save memory when converting in batches).", | |
| ) | |
| parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.") | |
| args = parser.parse_args() | |
| # if args.pytorch_checkpoint_path is not None: | |
| # convert_pt_checkpoint_to_tf(args.model_type.lower(), | |
| # args.pytorch_checkpoint_path, | |
| # args.config_file if args.config_file is not None else args.pytorch_checkpoint_path, | |
| # args.tf_dump_path, | |
| # compare_with_pt_model=args.compare_with_pt_model, | |
| # use_cached_models=args.use_cached_models) | |
| # else: | |
| convert_all_pt_checkpoints_to_tf( | |
| args.model_type.lower() if args.model_type is not None else None, | |
| args.tf_dump_path, | |
| model_shortcut_names_or_path=[args.pytorch_checkpoint_path] | |
| if args.pytorch_checkpoint_path is not None | |
| else None, | |
| config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None, | |
| compare_with_pt_model=args.compare_with_pt_model, | |
| use_cached_models=args.use_cached_models, | |
| remove_cached_files=args.remove_cached_files, | |
| only_convert_finetuned_models=args.only_convert_finetuned_models, | |
| ) | |