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f3507ef | 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 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | # Copyright 2023 The TensorFlow 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.
"""Converts pre-trained encoder into a fffner encoder checkpoint."""
import os
from absl import app
import numpy as np
import tensorflow as tf, tf_keras
import tensorflow_hub as hub
from official.projects.fffner.fffner import FFFNerEncoderConfig
from official.projects.fffner.fffner_encoder import FFFNerEncoder
def _get_tensorflow_bert_model_and_config(tfhub_handle_encoder):
"""Gets the BERT model name-parameters pairs and configurations."""
bert_model = hub.KerasLayer(tfhub_handle_encoder)
bert_model_weights_name = [w.name for w in bert_model.weights]
bert_model_weights = bert_model.get_weights()
named_parameters = {
n: p for n, p in zip(bert_model_weights_name, bert_model_weights)
}
config = {}
config["num_attention_heads"], _, config["hidden_size"] = named_parameters[
"transformer/layer_0/self_attention/attention_output/kernel:0"].shape
_, config["intermediate_size"] = named_parameters[
"transformer/layer_0/intermediate/kernel:0"].shape
num_hidden_layers = 0
while f"transformer/layer_{num_hidden_layers}/self_attention/query/kernel:0" in named_parameters:
num_hidden_layers += 1
config["num_hidden_layers"] = num_hidden_layers
config["vocab_size"], _ = named_parameters[
"word_embeddings/embeddings:0"].shape
config["max_position_embeddings"], _ = named_parameters[
"position_embedding/embeddings:0"].shape
config["type_vocab_size"], _ = named_parameters[
"type_embeddings/embeddings:0"].shape
return named_parameters, config
def _create_fffner_model(bert_config):
"""Creates a Longformer model."""
encoder_cfg = FFFNerEncoderConfig()
encoder = FFFNerEncoder(
vocab_size=bert_config["vocab_size"],
hidden_size=bert_config["hidden_size"],
num_layers=bert_config["num_hidden_layers"],
num_attention_heads=bert_config["num_attention_heads"],
inner_dim=bert_config["intermediate_size"],
max_sequence_length=bert_config["max_position_embeddings"],
type_vocab_size=bert_config["type_vocab_size"],
initializer=tf_keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
output_range=encoder_cfg.output_range,
embedding_width=bert_config["hidden_size"],
norm_first=encoder_cfg.norm_first)
return encoder
# pylint: disable=protected-access
def convert(encoder, bert_model):
"""Convert a Tensorflow transformers bert encoder to the one in the codebase.
"""
num_layers = encoder._config["num_layers"]
num_attention_heads = encoder._config["num_attention_heads"]
hidden_size = encoder._config["hidden_size"]
head_size = hidden_size // num_attention_heads
assert head_size * num_attention_heads == hidden_size
encoder._embedding_layer.set_weights(
[bert_model["word_embeddings/embeddings:0"]])
encoder._embedding_norm_layer.set_weights([
bert_model["embeddings/layer_norm/gamma:0"],
bert_model["embeddings/layer_norm/beta:0"]
])
encoder._type_embedding_layer.set_weights(
[bert_model["type_embeddings/embeddings:0"]])
encoder._position_embedding_layer.set_weights(
[bert_model["position_embedding/embeddings:0"]])
for layer_num in range(num_layers):
encoder._transformer_layers[
layer_num]._attention_layer._key_dense.set_weights([
bert_model[
f"transformer/layer_{layer_num}/self_attention/key/kernel:0"],
bert_model[
f"transformer/layer_{layer_num}/self_attention/key/bias:0"]
])
encoder._transformer_layers[
layer_num]._attention_layer._query_dense.set_weights([
bert_model[
f"transformer/layer_{layer_num}/self_attention/query/kernel:0"],
bert_model[
f"transformer/layer_{layer_num}/self_attention/query/bias:0"]
])
encoder._transformer_layers[
layer_num]._attention_layer._value_dense.set_weights([
bert_model[
f"transformer/layer_{layer_num}/self_attention/value/kernel:0"],
bert_model[
f"transformer/layer_{layer_num}/self_attention/value/bias:0"]
])
encoder._transformer_layers[layer_num]._attention_layer._output_dense.set_weights([
bert_model[
f"transformer/layer_{layer_num}/self_attention/attention_output/kernel:0"],
bert_model[
f"transformer/layer_{layer_num}/self_attention/attention_output/bias:0"]
])
encoder._transformer_layers[layer_num]._attention_layer_norm.set_weights([
bert_model[
f"transformer/layer_{layer_num}/self_attention_layer_norm/gamma:0"],
bert_model[
f"transformer/layer_{layer_num}/self_attention_layer_norm/beta:0"]
])
encoder._transformer_layers[layer_num]._intermediate_dense.set_weights([
bert_model[f"transformer/layer_{layer_num}/intermediate/kernel:0"],
bert_model[f"transformer/layer_{layer_num}/intermediate/bias:0"]
])
encoder._transformer_layers[layer_num]._output_dense.set_weights([
bert_model[f"transformer/layer_{layer_num}/output/kernel:0"],
bert_model[f"transformer/layer_{layer_num}/output/bias:0"]
])
encoder._transformer_layers[layer_num]._output_layer_norm.set_weights([
bert_model[f"transformer/layer_{layer_num}/output_layer_norm/gamma:0"],
bert_model[f"transformer/layer_{layer_num}/output_layer_norm/beta:0"]
])
def convert_checkpoint(output_path, tfhub_handle_encoder):
"""Converts and save the checkpoint."""
output_dir, _ = os.path.split(output_path)
tf.io.gfile.makedirs(output_dir)
bert_model, bert_config = _get_tensorflow_bert_model_and_config(
tfhub_handle_encoder)
encoder = _create_fffner_model(bert_config)
sequence_length = 128
batch_size = 2
word_id_data = np.random.randint(
10, size=(batch_size, sequence_length), dtype=np.int32)
mask_data = np.random.randint(
2, size=(batch_size, sequence_length), dtype=np.int32)
type_id_data = np.random.randint(
2, size=(batch_size, sequence_length), dtype=np.int32)
is_entity_token_pos = np.zeros((batch_size, 1), dtype=np.int32)
entity_type_token_pos = np.ones((batch_size, 1), dtype=np.int32)
inputs = {
"input_word_ids": word_id_data,
"input_mask": mask_data,
"input_type_ids": type_id_data,
"is_entity_token_pos": is_entity_token_pos,
"entity_type_token_pos": entity_type_token_pos,
}
encoder(inputs)
convert(encoder, bert_model)
tf.train.Checkpoint(encoder=encoder).write(output_path)
def main(_):
convert_checkpoint(
output_path="tf-bert-uncased",
tfhub_handle_encoder="https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/3"
)
if __name__ == "__main__":
app.run(main)
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