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# 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.
"""A script to export BERT as a TF-Hub SavedModel.
This script is **DEPRECATED** for exporting BERT encoder models;
see the error message in by main() for details.
"""
from typing import Text
# Import libraries
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf, tf_keras
from official.legacy.bert import bert_models
from official.legacy.bert import configs
FLAGS = flags.FLAGS
flags.DEFINE_string("bert_config_file", None,
"Bert configuration file to define core bert layers.")
flags.DEFINE_string("model_checkpoint_path", None,
"File path to TF model checkpoint.")
flags.DEFINE_string("export_path", None, "TF-Hub SavedModel destination path.")
flags.DEFINE_string("vocab_file", None,
"The vocabulary file that the BERT model was trained on.")
flags.DEFINE_bool(
"do_lower_case", None, "Whether to lowercase. If None, "
"do_lower_case will be enabled if 'uncased' appears in the "
"name of --vocab_file")
flags.DEFINE_enum("model_type", "encoder", ["encoder", "squad"],
"What kind of BERT model to export.")
def create_bert_model(bert_config: configs.BertConfig) -> tf_keras.Model:
"""Creates a BERT keras core model from BERT configuration.
Args:
bert_config: A `BertConfig` to create the core model.
Returns:
A keras model.
"""
# Adds input layers just as placeholders.
input_word_ids = tf_keras.layers.Input(
shape=(None,), dtype=tf.int32, name="input_word_ids")
input_mask = tf_keras.layers.Input(
shape=(None,), dtype=tf.int32, name="input_mask")
input_type_ids = tf_keras.layers.Input(
shape=(None,), dtype=tf.int32, name="input_type_ids")
transformer_encoder = bert_models.get_transformer_encoder(
bert_config, sequence_length=None)
sequence_output, pooled_output = transformer_encoder(
[input_word_ids, input_mask, input_type_ids])
# To keep consistent with legacy hub modules, the outputs are
# "pooled_output" and "sequence_output".
return tf_keras.Model(
inputs=[input_word_ids, input_mask, input_type_ids],
outputs=[pooled_output, sequence_output]), transformer_encoder
def export_bert_tfhub(bert_config: configs.BertConfig,
model_checkpoint_path: Text,
hub_destination: Text,
vocab_file: Text,
do_lower_case: bool = None):
"""Restores a tf_keras.Model and saves for TF-Hub."""
# If do_lower_case is not explicit, default to checking whether "uncased" is
# in the vocab file name
if do_lower_case is None:
do_lower_case = "uncased" in vocab_file
logging.info("Using do_lower_case=%s based on name of vocab_file=%s",
do_lower_case, vocab_file)
core_model, encoder = create_bert_model(bert_config)
checkpoint = tf.train.Checkpoint(
model=encoder, # Legacy checkpoints.
encoder=encoder)
checkpoint.restore(model_checkpoint_path).assert_existing_objects_matched()
core_model.vocab_file = tf.saved_model.Asset(vocab_file)
core_model.do_lower_case = tf.Variable(do_lower_case, trainable=False)
core_model.save(hub_destination, include_optimizer=False, save_format="tf")
def export_bert_squad_tfhub(bert_config: configs.BertConfig,
model_checkpoint_path: Text,
hub_destination: Text,
vocab_file: Text,
do_lower_case: bool = None):
"""Restores a tf_keras.Model for BERT with SQuAD and saves for TF-Hub."""
# If do_lower_case is not explicit, default to checking whether "uncased" is
# in the vocab file name
if do_lower_case is None:
do_lower_case = "uncased" in vocab_file
logging.info("Using do_lower_case=%s based on name of vocab_file=%s",
do_lower_case, vocab_file)
span_labeling, _ = bert_models.squad_model(bert_config, max_seq_length=None)
checkpoint = tf.train.Checkpoint(model=span_labeling)
checkpoint.restore(model_checkpoint_path).assert_existing_objects_matched()
span_labeling.vocab_file = tf.saved_model.Asset(vocab_file)
span_labeling.do_lower_case = tf.Variable(do_lower_case, trainable=False)
span_labeling.save(hub_destination, include_optimizer=False, save_format="tf")
def main(_):
bert_config = configs.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.model_type == "encoder":
deprecation_note = (
"nlp/bert/export_tfhub is **DEPRECATED** for exporting BERT encoder "
"models. Please switch to nlp/tools/export_tfhub for exporting BERT "
"(and other) encoders with dict inputs/outputs conforming to "
"https://www.tensorflow.org/hub/common_saved_model_apis/text#transformer-encoders"
)
logging.error(deprecation_note)
print("\n\nNOTICE:", deprecation_note, "\n")
export_bert_tfhub(bert_config, FLAGS.model_checkpoint_path,
FLAGS.export_path, FLAGS.vocab_file, FLAGS.do_lower_case)
elif FLAGS.model_type == "squad":
export_bert_squad_tfhub(bert_config, FLAGS.model_checkpoint_path,
FLAGS.export_path, FLAGS.vocab_file,
FLAGS.do_lower_case)
else:
raise ValueError("Unsupported model_type %s." % FLAGS.model_type)
if __name__ == "__main__":
app.run(main)
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