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transformers_4_35_0
/models
/albert
/convert_albert_original_tf_checkpoint_to_pytorch.py
# 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 ALBERT checkpoint.""" | |
import argparse | |
import torch | |
from ...utils import logging | |
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert | |
logging.set_verbosity_info() | |
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, albert_config_file, pytorch_dump_path): | |
# Initialise PyTorch model | |
config = AlbertConfig.from_json_file(albert_config_file) | |
print(f"Building PyTorch model from configuration: {config}") | |
model = AlbertForPreTraining(config) | |
# Load weights from tf checkpoint | |
load_tf_weights_in_albert(model, config, tf_checkpoint_path) | |
# Save pytorch-model | |
print(f"Save PyTorch model to {pytorch_dump_path}") | |
torch.save(model.state_dict(), pytorch_dump_path) | |
if __name__ == "__main__": | |
parser = argparse.ArgumentParser() | |
# Required parameters | |
parser.add_argument( | |
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." | |
) | |
parser.add_argument( | |
"--albert_config_file", | |
default=None, | |
type=str, | |
required=True, | |
help=( | |
"The config json file corresponding to the pre-trained ALBERT model. \n" | |
"This specifies the model architecture." | |
), | |
) | |
parser.add_argument( | |
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." | |
) | |
args = parser.parse_args() | |
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path) | |