mirror of
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90 lines
4.2 KiB
Python
90 lines
4.2 KiB
Python
# coding=utf-8
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# Copyright 2019-present, 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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"""
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Preprocessing script before training the distilled model.
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Specific to RoBERTa -> DistilRoBERTa and GPT2 -> DistilGPT2.
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"""
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from transformers import BertForMaskedLM, RobertaForMaskedLM, GPT2LMHeadModel
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import torch
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import argparse
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned Distillation")
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parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
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parser.add_argument("--model_name", default='roberta-large', type=str)
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parser.add_argument("--dump_checkpoint", default='serialization_dir/tf_roberta_048131723.pth', type=str)
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parser.add_argument("--vocab_transform", action='store_true')
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args = parser.parse_args()
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if args.model_type == 'roberta':
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model = RobertaForMaskedLM.from_pretrained(args.model_name)
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prefix = 'roberta'
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elif args.model_type == 'gpt2':
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model = GPT2LMHeadModel.from_pretrained(args.model_name)
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prefix = 'transformer'
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state_dict = model.state_dict()
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compressed_sd = {}
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### Embeddings ###
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if args.model_type == 'gpt2':
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for param_name in ['wte.weight', 'wpe.weight']:
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compressed_sd[f'{prefix}.{param_name}'] = state_dict[f'{prefix}.{param_name}']
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else:
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for w in ['word_embeddings', 'position_embeddings', 'token_type_embeddings']:
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param_name = f'{prefix}.embeddings.{w}.weight'
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compressed_sd[param_name] = state_dict[param_name]
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for w in ['weight', 'bias']:
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param_name = f'{prefix}.embeddings.LayerNorm.{w}'
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compressed_sd[param_name] = state_dict[param_name]
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### Transformer Blocks ###
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std_idx = 0
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for teacher_idx in [0, 2, 4, 7, 9, 11]:
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if args.model_type == 'gpt2':
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for layer in ['ln_1', 'attn.c_attn', 'attn.c_proj', 'ln_2', 'mlp.c_fc', 'mlp.c_proj']:
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for w in ['weight', 'bias']:
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compressed_sd[f'{prefix}.h.{std_idx}.{layer}.{w}'] = \
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state_dict[f'{prefix}.h.{teacher_idx}.{layer}.{w}']
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compressed_sd[f'{prefix}.h.{std_idx}.attn.bias'] = state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
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else:
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for layer in ['attention.self.query', 'attention.self.key', 'attention.self.value',
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'attention.output.dense', 'attention.output.LayerNorm',
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'intermediate.dense', 'output.dense', 'output.LayerNorm']:
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for w in ['weight', 'bias']:
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compressed_sd[f'{prefix}.encoder.layer.{std_idx}.{layer}.{w}'] = \
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state_dict[f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}']
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std_idx += 1
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### Language Modeling Head ###s
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if args.model_type == 'roberta':
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for layer in ['lm_head.decoder.weight', 'lm_head.bias']:
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compressed_sd[f'{layer}'] = state_dict[f'{layer}']
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if args.vocab_transform:
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for w in ['weight', 'bias']:
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compressed_sd[f'lm_head.dense.{w}'] = state_dict[f'lm_head.dense.{w}']
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compressed_sd[f'lm_head.layer_norm.{w}'] = state_dict[f'lm_head.layer_norm.{w}']
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elif args.model_type == 'gpt2':
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for w in ['weight', 'bias']:
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compressed_sd[f'{prefix}.ln_f.{w}'] = state_dict[f'{prefix}.ln_f.{w}']
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compressed_sd[f'lm_head.weight'] = state_dict[f'lm_head.weight']
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print(f'N layers selected for distillation: {std_idx}')
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print(f'Number of params transfered for distillation: {len(compressed_sd.keys())}')
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print(f'Save transfered checkpoint to {args.dump_checkpoint}.')
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torch.save(compressed_sd, args.dump_checkpoint)
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