75 lines
3.0 KiB
Python
75 lines
3.0 KiB
Python
# coding=utf-8
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# Copyright 2018 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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"""Convert OpenAI GPT checkpoint."""
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from __future__ import absolute_import, division, print_function
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import argparse
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from io import open
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import torch
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from transformers import (CONFIG_NAME, WEIGHTS_NAME,
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GPT2Config,
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GPT2Model,
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load_tf_weights_in_gpt2)
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import logging
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logging.basicConfig(level=logging.INFO)
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def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):
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# Construct model
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if gpt2_config_file == "":
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config = GPT2Config()
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else:
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config = GPT2Config.from_json_file(gpt2_config_file)
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model = GPT2Model(config)
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# Load weights from numpy
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load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path)
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# Save pytorch-model
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pytorch_weights_dump_path = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
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pytorch_config_dump_path = pytorch_dump_folder_path + '/' + CONFIG_NAME
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print("Save PyTorch model to {}".format(pytorch_weights_dump_path))
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torch.save(model.state_dict(), pytorch_weights_dump_path)
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print("Save configuration file to {}".format(pytorch_config_dump_path))
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with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
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f.write(config.to_json_string())
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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## Required parameters
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parser.add_argument("--gpt2_checkpoint_path",
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default = None,
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type = str,
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required = True,
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help = "Path to the TensorFlow checkpoint path.")
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parser.add_argument("--pytorch_dump_folder_path",
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default = None,
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type = str,
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required = True,
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help = "Path to the output PyTorch model.")
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parser.add_argument("--gpt2_config_file",
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default = "",
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type = str,
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help = "An optional config json file corresponding to the pre-trained OpenAI model. \n"
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"This specifies the model architecture.")
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args = parser.parse_args()
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convert_gpt2_checkpoint_to_pytorch(args.gpt2_checkpoint_path,
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args.gpt2_config_file,
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args.pytorch_dump_folder_path) |