wanghua
5 years ago
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""
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mindspore and tensorflow checkpoint transfer tools
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"""
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import argparse
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import tensorflow as tf
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from mindspore.common.tensor import Tensor
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from mindspore.train.serialization import load_checkpoint, save_checkpoint
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from ms2tf_config import param_name_dict as ms2tf_param_dict
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def convert_ms_2_tf(tf_ckpt_path, ms_ckpt_path, new_ckpt_path):
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"""
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convert ms checkpoint to tf checkpoint
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"""
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# load MS checkpoint
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ms_param_dict = load_checkpoint(ms_ckpt_path)
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for name in ms_param_dict.keys():
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if isinstance(ms_param_dict[name].data, Tensor):
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ms_param_dict[name] = ms_param_dict[name].data.asnumpy()
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convert_count = 0
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with tf.Session() as sess:
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# convert ms shape to tf
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print("start convert parameter ...")
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new_var_list = []
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for var_name, shape in tf.contrib.framework.list_variables(tf_ckpt_path):
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if var_name in ms2tf_param_dict:
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ms_name = ms2tf_param_dict[var_name]
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new_tensor = tf.convert_to_tensor(ms_param_dict[ms_name])
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if len(shape) == 2:
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if tuple(shape) != new_tensor.shape or new_tensor.shape[0] == new_tensor.shape[1]:
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new_tensor = tf.transpose(new_tensor, (1, 0))
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if new_tensor.shape != tuple(shape):
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raise ValueError("shape is not matched after transpose!! {}, {}"
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.format(str(new_tensor.shape), str(tuple(shape))))
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if new_tensor.shape != tuple(shape):
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raise ValueError("shape is not matched after transpose!! {}, {}"
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.format(str(new_tensor.shape), str(tuple(shape))))
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var = tf.Variable(new_tensor, name=var_name)
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convert_count = convert_count + 1
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else:
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var = tf.Variable(tf.contrib.framework.load_variable(tf_ckpt_path, var_name), name=var_name)
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new_var_list.append(var)
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print('convert value num: ', convert_count, " of ", len(ms2tf_param_dict))
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# saving tf checkpoint
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print("start saving ...")
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saver = tf.train.Saver(var_list=new_var_list)
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sess.run(tf.global_variables_initializer())
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saver.save(sess, new_ckpt_path)
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print("tf checkpoint was save in :", new_ckpt_path)
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return True
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def convert_tf_2_ms(tf_ckpt_path, ms_ckpt_path, new_ckpt_path):
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"""
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convert tf checkpoint to ms checkpoint
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"""
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tf2ms_param_dict = dict(zip(ms2tf_param_dict.values(), ms2tf_param_dict.keys()))
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# load MS checkpoint
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ms_param_dict = load_checkpoint(ms_ckpt_path)
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new_params_list = []
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session = tf.compat.v1.Session()
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count = 0
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for ms_name in tf2ms_param_dict.keys():
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count += 1
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param_dict = {}
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tf_name = tf2ms_param_dict[ms_name]
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data = tf.train.load_variable(tf_ckpt_path, tf_name)
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ms_shape = ms_param_dict[ms_name].data.shape
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tf_shape = data.shape
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if len(ms_shape) == 2:
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if ms_shape != tf_shape or ms_shape[0] == ms_shape[1]:
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data = tf.transpose(data, (1, 0))
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data = data.eval(session=session)
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param_dict['name'] = ms_name
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param_dict['data'] = Tensor(data)
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new_params_list.append(param_dict)
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print("start saving checkpoint ...")
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save_checkpoint(new_params_list, new_ckpt_path)
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print("ms checkpoint was save in :", new_ckpt_path)
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return True
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def main():
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"""
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tf checkpoint transfer to ms or ms checkpoint transfer to tf
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"""
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parser = argparse.ArgumentParser(description='checkpoint transfer.')
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parser.add_argument("--tf_ckpt_path", type=str, default='./tf-bert/bs64k_32k_ckpt_model.ckpt-28252',
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help="TensorFlow checkpoint dir, default is: './tf-bert/bs64k_32k_ckpt_model.ckpt-28252'.")
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parser.add_argument("--ms_ckpt_path", type=str, default='./ms-bert/large_en.ckpt',
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help="MindSpore checkpoint dir, default is: './ms-bert/large_en.ckpt'.")
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parser.add_argument("--new_ckpt_path", type=str, default='./new_ckpt/new_bert_large_en.ckpt',
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help="New checkpoint dir, default is: './new_ckpt/new_bert_large_en.ckpt'.")
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parser.add_argument("--transfer_option", type=str, default='ms2tf',
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help="option of transfer ms2tf or tf2ms, default is ms2tf.")
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args_opt = parser.parse_args()
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if args_opt.transfer_option == 'ms2tf':
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print("start ms2tf option ...")
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tf_ckpt_path = args_opt.tf_ckpt_path
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ms_ckpt_path = args_opt.ms_ckpt_path
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new_ckpt_path = args_opt.new_ckpt_path
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convert_ms_2_tf(tf_ckpt_path, ms_ckpt_path, new_ckpt_path)
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elif args_opt.transfer_option == 'tf2ms':
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print("start tf2ms option ...")
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tf_ckpt_path = args_opt.tf_ckpt_path
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ms_ckpt_path = args_opt.ms_ckpt_path
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new_ckpt_path = args_opt.new_ckpt_path
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convert_tf_2_ms(tf_ckpt_path, ms_ckpt_path, new_ckpt_path)
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else:
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print("ERROR: '--transfer_option' please select 0 or 1")
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if __name__ == "__main__":
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main()
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@ -1,22 +0,0 @@
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# Mindspore and Tensorflow checkpoint transfer tools
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# How to use
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## 1. For Mindspore to Tensorflow
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```
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python ms_and_tf_checkpoint_transfer_for_bert_large.py \
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--transfer_option='ms2tf' \
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--ms_ckpt_path='/data/ms-bert/checkpoint_bert-1500_100.ckpt' \
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--tf_ckpt_path='/data/tf-bert/bs64k_32k_ckpt_model.ckpt' \
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--new_ckpt_path='/data/ms2tf/tf_bert_large_1500-100.ckpt'
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```
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## 2. For Tensorflow to Mindspore
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```
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python ms_and_tf_checkpoint_transfer_for_bert_large.py \
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--transfer_option='tf2ms' \
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--tf_ckpt_path='/data/tf-bert/tf_bert_large_1500-100.ckpt' \
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--ms_ckpt_path='/data/ms-bert/checkpoint_bert-1500_100.ckpt' \
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--new_ckpt_path='/data/tf2ms/ms_bert_large_1500-100.ckpt'
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```
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# Note
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Please note that both tf2ms and ms2tf require two inputs, one output, one of the two inputs is the checkpoint to be converted, and the other is the target checkpoint to be referred to. Because there are many types of bert models, the meaning of the target checkpoint is to prevent you from using different checkpoints for conversion errors.
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