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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)
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