gpt2-common-sense-reasoning / model /convert_gpt2_original_tf_checkpoint_to_pytorch.py

add weights and tokenizers

282c159 almost 3 years ago

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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."""


	import argparse

	import torch

	from transformers import GPT2Config, GPT2Model, load_tf_weights_in_gpt2
	from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
	from transformers.utils import logging


	logging.set_verbosity_info()


	def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):
	# Construct model
	if gpt2_config_file == "":
	config = GPT2Config()
	else:
	config = GPT2Config.from_json_file(gpt2_config_file)
	model = GPT2Model(config)

	# Load weights from numpy
	load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_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(f"Save PyTorch model to {pytorch_weights_dump_path}")
	torch.save(model.state_dict(), pytorch_weights_dump_path)
	print(f"Save configuration file to {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(
	"--gpt2_checkpoint_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(
	"--gpt2_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_gpt2_checkpoint_to_pytorch(args.gpt2_checkpoint_path, args.gpt2_config_file, args.pytorch_dump_folder_path)