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| import torch | |
| import logging | |
| from transformers.modeling_utils import cached_path, WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME | |
| logger = logging.getLogger(__name__) | |
| def get_checkpoint_from_transformer_cache( | |
| archive_file, pretrained_model_name_or_path, pretrained_model_archive_map, | |
| cache_dir, force_download, proxies, resume_download, | |
| ): | |
| try: | |
| resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, | |
| proxies=proxies, resume_download=resume_download) | |
| except EnvironmentError: | |
| if pretrained_model_name_or_path in pretrained_model_archive_map: | |
| msg = "Couldn't reach server at '{}' to download pretrained weights.".format( | |
| archive_file) | |
| else: | |
| msg = "Model name '{}' was not found in model name list ({}). " \ | |
| "We assumed '{}' was a path or url to model weight files named one of {} but " \ | |
| "couldn't find any such file at this path or url.".format( | |
| pretrained_model_name_or_path, | |
| ', '.join(pretrained_model_archive_map.keys()), | |
| archive_file, | |
| [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME]) | |
| raise EnvironmentError(msg) | |
| if resolved_archive_file == archive_file: | |
| logger.info("loading weights file {}".format(archive_file)) | |
| else: | |
| logger.info("loading weights file {} from cache at {}".format( | |
| archive_file, resolved_archive_file)) | |
| return torch.load(resolved_archive_file, map_location='cpu') | |
| def hf_roberta_to_hf_bert(state_dict): | |
| logger.info(" * Convert Huggingface RoBERTa format to Huggingface BERT format * ") | |
| new_state_dict = {} | |
| for key in state_dict: | |
| value = state_dict[key] | |
| if key == 'roberta.embeddings.position_embeddings.weight': | |
| value = value[2:] | |
| if key == 'roberta.embeddings.token_type_embeddings.weight': | |
| continue | |
| if key.startswith('roberta'): | |
| key = 'bert.' + key[8:] | |
| elif key.startswith('lm_head'): | |
| if 'layer_norm' in key or 'dense' in key: | |
| key = 'cls.predictions.transform.' + key[8:] | |
| else: | |
| key = 'cls.predictions.' + key[8:] | |
| key = key.replace('layer_norm', 'LayerNorm') | |
| new_state_dict[key] = value | |
| return new_state_dict | |
| def hf_distilbert_to_hf_bert(state_dict): | |
| logger.info(" * Convert Huggingface DistilBERT format to Huggingface BERT format * ") | |
| new_state_dict = {} | |
| for key in state_dict: | |
| value = state_dict[key] | |
| if key == 'roberta.embeddings.position_embeddings.weight': | |
| value = value[2:] | |
| if key == 'roberta.embeddings.token_type_embeddings.weight': | |
| continue | |
| if key.startswith('roberta'): | |
| key = 'bert.' + key[8:] | |
| elif key.startswith('lm_head'): | |
| if 'layer_norm' in key or 'dense' in key: | |
| key = 'cls.predictions.transform.' + key[8:] | |
| else: | |
| key = 'cls.predictions.' + key[8:] | |
| key = key.replace('layer_norm', 'LayerNorm') | |
| new_state_dict[key] = value | |
| return new_state_dict | |
| def hf_bert_to_hf_bert(state_dict): | |
| # NOTE: all cls states are used for prediction, | |
| # we predict the index so omit all pretrained states for prediction. | |
| new_state_dict = {} | |
| for key in state_dict: | |
| value = state_dict[key] | |
| if key.startswith('cls'): | |
| # NOTE: all cls states are used for prediction, | |
| # we predict the index so omit all pretrained states for prediction. | |
| continue | |
| new_state_dict[key] = value | |
| return new_state_dict | |
| def hf_layoutlm_to_hf_bert(state_dict): | |
| logger.info(" * Convert Huggingface LayoutLM format to Huggingface BERT format * ") | |
| new_state_dict = {} | |
| for key in state_dict: | |
| value = state_dict[key] | |
| if key.startswith('layoutlm'): | |
| key = 'bert.' + key[9:] | |
| elif key.startswith('cls'): | |
| # NOTE: all cls states are used for prediction, | |
| # we predict the index so omit all pretrained states for prediction. | |
| continue | |
| new_state_dict[key] = value | |
| return new_state_dict | |
| state_dict_convert = { | |
| 'bert': hf_bert_to_hf_bert, | |
| 'unilm': hf_bert_to_hf_bert, | |
| 'minilm': hf_bert_to_hf_bert, | |
| 'layoutlm': hf_layoutlm_to_hf_bert, | |
| 'roberta': hf_roberta_to_hf_bert, | |
| 'xlm-roberta': hf_roberta_to_hf_bert, | |
| 'distilbert': hf_distilbert_to_hf_bert, | |
| } | |