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- # coding=utf-8
- # Copyright 2018 The HugginFace 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."""
-
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import os
- import re
- import argparse
- import tensorflow as tf
- import torch
- import numpy as np
-
- from modeling import BertConfig, BertForPreTraining
-
- def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
- config_path = os.path.abspath(bert_config_file)
- tf_path = os.path.abspath(tf_checkpoint_path)
- print("Converting TensorFlow checkpoint from {} with config at {}".format(tf_path, config_path))
- # Load weights from TF model
- init_vars = tf.train.list_variables(tf_path)
- names = []
- arrays = []
- for name, shape in init_vars:
- print("Loading TF weight {} with shape {}".format(name, shape))
- array = tf.train.load_variable(tf_path, name)
- names.append(name)
- arrays.append(array)
-
- # Initialise PyTorch model
- config = BertConfig.from_json_file(bert_config_file)
- print("Building PyTorch model from configuration: {}".format(str(config)))
- model = BertForPreTraining(config)
-
- for name, array in zip(names, arrays):
- name = name.split('/')
- # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
- # which are not required for using pretrained model
- if any(n in ["adam_v", "adam_m", "global_step"] for n in name):
- print("Skipping {}".format("/".join(name)))
- continue
- pointer = model
- for m_name in name:
- if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
- l = re.split(r'_(\d+)', m_name)
- else:
- l = [m_name]
- if l[0] == 'kernel' or l[0] == 'gamma':
- pointer = getattr(pointer, 'weight')
- elif l[0] == 'output_bias' or l[0] == 'beta':
- pointer = getattr(pointer, 'bias')
- elif l[0] == 'output_weights':
- pointer = getattr(pointer, 'weight')
- else:
- pointer = getattr(pointer, l[0])
- if len(l) >= 2:
- num = int(l[1])
- pointer = pointer[num]
- if m_name[-11:] == '_embeddings':
- pointer = getattr(pointer, 'weight')
- elif m_name == 'kernel':
- array = np.transpose(array)
- try:
- assert pointer.shape == array.shape
- except AssertionError as e:
- e.args += (pointer.shape, array.shape)
- raise
- print("Initialize PyTorch weight {}".format(name))
- pointer.data = torch.from_numpy(array)
-
- # 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 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)
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