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