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110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
# coding=utf-8
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# Copyright 2018 The HugginFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Convert BERT checkpoint."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import re
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import argparse
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import tensorflow as tf
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import torch
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import numpy as np
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from modeling import BertConfig, BertModel
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def convert(config_path, ckpt_path, out_path=None):
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# Initialise PyTorch model
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config = BertConfig.from_json_file(config_path)
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model = BertModel(config)
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# Load weights from TF model
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path = ckpt_path
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print("Converting TensorFlow checkpoint from {}".format(path))
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init_vars = tf.train.list_variables(path)
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names = []
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arrays = []
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for name, shape in init_vars:
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print("Loading {} with shape {}".format(name, shape))
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array = tf.train.load_variable(path, name)
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print("Numpy array shape {}".format(array.shape))
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names.append(name)
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arrays.append(array)
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for name, array in zip(names, arrays):
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name = name[5:] # skip "bert/"
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print("Loading {}".format(name))
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name = name.split('/')
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if name[0] in ['redictions', 'eq_relationship']:
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print("Skipping")
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continue
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pointer = model
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for m_name in name:
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if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
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l = re.split(r'_(\d+)', m_name)
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else:
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l = [m_name]
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if l[0] == 'kernel':
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pointer = getattr(pointer, 'weight')
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else:
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pointer = getattr(pointer, l[0])
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if len(l) >= 2:
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num = int(l[1])
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pointer = pointer[num]
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if m_name[-11:] == '_embeddings':
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pointer = getattr(pointer, 'weight')
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elif m_name == 'kernel':
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array = np.transpose(array)
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try:
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assert pointer.shape == array.shape
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except AssertionError as e:
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e.args += (pointer.shape, array.shape)
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raise
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pointer.data = torch.from_numpy(array)
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# Save pytorch-model
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if out_path is not None:
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torch.save(model.state_dict(), out_path)
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return model
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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## Required parameters
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parser.add_argument("--tf_checkpoint_path",
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default=None,
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type=str,
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required=True,
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help="Path the TensorFlow checkpoint path.")
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parser.add_argument("--bert_config_file",
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default=None,
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type=str,
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required=True,
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help="The config json file corresponding to the pre-trained BERT model. \n"
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"This specifies the model architecture.")
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parser.add_argument("--pytorch_dump_path",
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default=None,
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type=str,
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required=False,
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help="Path to the output PyTorch model.")
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args = parser.parse_args()
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print(args)
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convert(args.bert_config_file, args.tf_checkpoint_path, args.pytorch_dump_path)
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