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# coding=utf-8 | |
# Copyright 2020 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 Seq2Seq TF Hub checkpoint.""" | |
import argparse | |
from . import ( | |
BertConfig, | |
BertGenerationConfig, | |
BertGenerationDecoder, | |
BertGenerationEncoder, | |
load_tf_weights_in_bert_generation, | |
logging, | |
) | |
logging.set_verbosity_info() | |
def convert_tf_checkpoint_to_pytorch(tf_hub_path, pytorch_dump_path, is_encoder_named_decoder, vocab_size, is_encoder): | |
# Initialise PyTorch model | |
bert_config = BertConfig.from_pretrained( | |
"bert-large-cased", | |
vocab_size=vocab_size, | |
max_position_embeddings=512, | |
is_decoder=True, | |
add_cross_attention=True, | |
) | |
bert_config_dict = bert_config.to_dict() | |
del bert_config_dict["type_vocab_size"] | |
config = BertGenerationConfig(**bert_config_dict) | |
if is_encoder: | |
model = BertGenerationEncoder(config) | |
else: | |
model = BertGenerationDecoder(config) | |
print(f"Building PyTorch model from configuration: {config}") | |
# Load weights from tf checkpoint | |
load_tf_weights_in_bert_generation( | |
model, | |
tf_hub_path, | |
model_class="bert", | |
is_encoder_named_decoder=is_encoder_named_decoder, | |
is_encoder=is_encoder, | |
) | |
# Save pytorch-model | |
print(f"Save PyTorch model and config to {pytorch_dump_path}") | |
model.save_pretrained(pytorch_dump_path) | |
if __name__ == "__main__": | |
parser = argparse.ArgumentParser() | |
# Required parameters | |
parser.add_argument( | |
"--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." | |
) | |
parser.add_argument( | |
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." | |
) | |
parser.add_argument( | |
"--is_encoder_named_decoder", | |
action="store_true", | |
help="If decoder has to be renamed to encoder in PyTorch model.", | |
) | |
parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.") | |
parser.add_argument("--vocab_size", default=50358, type=int, help="Vocab size of model") | |
args = parser.parse_args() | |
convert_tf_checkpoint_to_pytorch( | |
args.tf_hub_path, | |
args.pytorch_dump_path, | |
args.is_encoder_named_decoder, | |
args.vocab_size, | |
is_encoder=args.is_encoder, | |
) | |