Bert（英文版）-- 使用Tensorflow生成pytorch_model.bin_tensorflow加载pytorch-model.bin

Tensorflow生成pytorch_model.bin

参考该链接PyTorch pretrained BigGAN
convert_bert_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 BERT checkpoint."""


import argparse
import logging

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert


logging.basicConfig(level=logging.INFO)


def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
    # Initialise PyTorch model
    config = BertConfig.from_json_file(bert_config_file)
    print("Building PyTorch model from configuration: {}".format(str(config)))
    model = BertForPreTraining(config)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert(model, config, tf_checkpoint_path)

    # Save pytorch-model
    print("Save PyTorch model to {}".format(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(
        "--bert_config_file",
        default=None,
        type=str,
        required=True,
        help="The config json file corresponding to the pre-trained BERT 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.bert_config_file, args.pytorch_dump_path)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61

打开命令行，需要安装tensorflow、pytorch
我使用的是英文Bert cased_L-12_H-768_A-12
输入如下两行命令，在/home/username/桌面/词向量/cased_L-12_H-768_A-12文件夹下生成pytorch_model.bin

$ export BERT_BASE_DIR=/home/username/桌面/词向量/cased_L-12_H-768_A-12
$ python convert_bert_original_tf_checkpoint_to_pytorch.py --tf_checkpoint_path $BERT_BASE_DIR/bert_model.ckpt --bert_config_file $BERT_BASE_DIR/bert_config.json --pytorch_dump_path $BERT_BASE_DIR/pytorch_model.bin
1
2

安装成功图片