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convert_params.py
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# 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 Google official BERT models to Fluid parameters."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import argparse
import collections
from utils.args import print_arguments
import tensorflow as tf
import paddle.fluid as fluid
from tensorflow.python import pywrap_tensorflow
def parse_args():
parser = argparse.ArgumentParser(__doc__)
parser.add_argument(
"--init_tf_checkpoint",
type=str,
required=True,
help="Initial TF checkpoint (a pre-trained BERT model).")
parser.add_argument(
"--fluid_params_dir",
type=str,
required=True,
help="The directory to store converted Fluid parameters.")
args = parser.parse_args()
return args
def parse(init_checkpoint):
tf_fluid_param_name_map = collections.OrderedDict()
tf_param_name_shape_map = collections.OrderedDict()
init_vars = tf.train.list_variables(init_checkpoint)
for (var_name, var_shape) in init_vars:
fluid_param_name = ''
if var_name.startswith('bert/'):
key = var_name[5:]
if (key.startswith('embeddings/')):
if (key.endswith('LayerNorm/gamma')):
fluid_param_name = 'pre_encoder_layer_norm_scale'
elif (key.endswith('LayerNorm/beta')):
fluid_param_name = 'pre_encoder_layer_norm_bias'
elif (key.endswith('position_embeddings')):
fluid_param_name = 'pos_embedding'
elif (key.endswith('word_embeddings')):
fluid_param_name = 'word_embedding'
elif (key.endswith('token_type_embeddings')):
fluid_param_name = 'sent_embedding'
else:
print("ignored param: %s" % var_name)
elif (key.startswith('encoder/')):
key = key[8:]
layer_num = int(key[key.find('_') + 1:key.find('/')])
suffix = "encoder_layer_" + str(layer_num)
if key.endswith('attention/output/LayerNorm/beta'):
fluid_param_name = suffix + '_post_att_layer_norm_bias'
elif key.endswith('attention/output/LayerNorm/gamma'):
fluid_param_name = suffix + '_post_att_layer_norm_scale'
elif key.endswith('attention/output/dense/bias'):
fluid_param_name = suffix + '_multi_head_att_output_fc.b_0'
elif key.endswith('attention/output/dense/kernel'):
fluid_param_name = suffix + '_multi_head_att_output_fc.w_0'
elif key.endswith('attention/self/key/bias'):
fluid_param_name = suffix + '_multi_head_att_key_fc.b_0'
elif key.endswith('attention/self/key/kernel'):
fluid_param_name = suffix + '_multi_head_att_key_fc.w_0'
elif key.endswith('attention/self/query/bias'):
fluid_param_name = suffix + '_multi_head_att_query_fc.b_0'
elif key.endswith('attention/self/query/kernel'):
fluid_param_name = suffix + '_multi_head_att_query_fc.w_0'
elif key.endswith('attention/self/value/bias'):
fluid_param_name = suffix + '_multi_head_att_value_fc.b_0'
elif key.endswith('attention/self/value/kernel'):
fluid_param_name = suffix + '_multi_head_att_value_fc.w_0'
elif key.endswith('intermediate/dense/bias'):
fluid_param_name = suffix + '_ffn_fc_0.b_0'
elif key.endswith('intermediate/dense/kernel'):
fluid_param_name = suffix + '_ffn_fc_0.w_0'
elif key.endswith('output/LayerNorm/beta'):
fluid_param_name = suffix + '_post_ffn_layer_norm_bias'
elif key.endswith('output/LayerNorm/gamma'):
fluid_param_name = suffix + '_post_ffn_layer_norm_scale'
elif key.endswith('output/dense/bias'):
fluid_param_name = suffix + '_ffn_fc_1.b_0'
elif key.endswith('output/dense/kernel'):
fluid_param_name = suffix + '_ffn_fc_1.w_0'
else:
print("ignored param: %s" % var_name)
elif (key.startswith('pooler/')):
if key.endswith('dense/bias'):
fluid_param_name = 'pooled_fc.b_0'
elif key.endswith('dense/kernel'):
fluid_param_name = 'pooled_fc.w_0'
else:
print("ignored param: %s" % var_name)
else:
print("ignored param: %s" % var_name)
elif var_name.startswith('cls/'):
if var_name == 'cls/predictions/output_bias':
fluid_param_name = 'mask_lm_out_fc.b_0'
elif var_name == 'cls/predictions/transform/LayerNorm/beta':
fluid_param_name = 'mask_lm_trans_layer_norm_bias'
elif var_name == 'cls/predictions/transform/LayerNorm/gamma':
fluid_param_name = 'mask_lm_trans_layer_norm_scale'
elif var_name == 'cls/predictions/transform/dense/bias':
fluid_param_name = 'mask_lm_trans_fc.b_0'
elif var_name == 'cls/predictions/transform/dense/kernel':
fluid_param_name = 'mask_lm_trans_fc.w_0'
elif var_name == 'cls/seq_relationship/output_bias':
fluid_param_name = 'next_sent_fc.b_0'
elif var_name == 'cls/seq_relationship/output_weights':
fluid_param_name = 'next_sent_fc.w_0'
elif var_name == 'cls/squad/output_weights':
fluid_param_name = 'cls_squad_out_w'
elif var_name == 'cls/squad/output_bias':
fluid_param_name = 'cls_squad_out_b'
else:
print("ignored param: %s" % var_name)
else:
print("ignored param: %s" % var_name)
if fluid_param_name is not '':
tf_fluid_param_name_map[var_name] = fluid_param_name
tf_param_name_shape_map[var_name] = var_shape
fluid_param_name = ''
return tf_fluid_param_name_map, tf_param_name_shape_map
def convert(args):
tf_fluid_param_name_map, tf_param_name_shape_map = parse(
args.init_tf_checkpoint)
program = fluid.Program()
global_block = program.global_block()
for param in tf_fluid_param_name_map:
global_block.create_parameter(
name=tf_fluid_param_name_map[param],
shape=tf_param_name_shape_map[param],
dtype='float32',
initializer=fluid.initializer.Constant(value=0.0))
place = fluid.core.CPUPlace()
exe = fluid.Executor(place)
exe.run(program)
print('---------------------- Converted Parameters -----------------------')
print('###### [TF param name] --> [Fluid param name] [param shape] ######')
print('-------------------------------------------------------------------')
reader = pywrap_tensorflow.NewCheckpointReader(args.init_tf_checkpoint)
for param in tf_fluid_param_name_map:
value = reader.get_tensor(param)
if param == 'cls/seq_relationship/output_weights':
value = np.transpose(value)
if param == 'cls/squad/output_weights':
value = np.transpose(value)
fluid.global_scope().find_var(tf_fluid_param_name_map[
param]).get_tensor().set(value, place)
print(param, ' --> ', tf_fluid_param_name_map[param], ' ', value.shape)
fluid.io.save_params(exe, args.fluid_params_dir, main_program=program)
if __name__ == '__main__':
args = parse_args()
print_arguments(args)
convert(args)