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