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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.
"""Examples of SavedModel export for tf-serving."""
from absl import app
from absl import flags
import tensorflow as tf, tf_keras
from official.legacy.bert import bert_models
from official.legacy.bert import configs
flags.DEFINE_integer(
"sequence_length", None, "Sequence length to parse the tf.Example. If "
"sequence_length > 0, add a signature for serialized "
"tf.Example and define the parsing specification by the "
"sequence_length.")
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,
"Destination folder to export the serving SavedModel.")
FLAGS = flags.FLAGS
class BertServing(tf_keras.Model):
"""Bert transformer encoder model for serving."""
def __init__(self, bert_config, name_to_features=None, name="serving_model"):
super(BertServing, self).__init__(name=name)
self.encoder = bert_models.get_transformer_encoder(
bert_config, sequence_length=None)
self.name_to_features = name_to_features
def call(self, inputs):
input_word_ids = inputs["input_ids"]
input_mask = inputs["input_mask"]
input_type_ids = inputs["segment_ids"]
encoder_outputs, _ = self.encoder(
[input_word_ids, input_mask, input_type_ids])
return encoder_outputs
def serve_body(self, input_ids, input_mask=None, segment_ids=None):
if segment_ids is None:
# Requires CLS token is the first token of inputs.
segment_ids = tf.zeros_like(input_ids)
if input_mask is None:
# The mask has 1 for real tokens and 0 for padding tokens.
input_mask = tf.where(
tf.equal(input_ids, 0), tf.zeros_like(input_ids),
tf.ones_like(input_ids))
inputs = dict(
input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids)
return self.call(inputs)
@tf.function
def serve(self, input_ids, input_mask=None, segment_ids=None):
outputs = self.serve_body(input_ids, input_mask, segment_ids)
# Returns a dictionary to control SignatureDef output signature.
return {"outputs": outputs[-1]}
@tf.function
def serve_examples(self, inputs):
features = tf.io.parse_example(inputs, self.name_to_features)
for key in list(features.keys()):
t = features[key]
if t.dtype == tf.int64:
t = tf.cast(t, tf.int32)
features[key] = t
return self.serve(
features["input_ids"],
input_mask=features["input_mask"] if "input_mask" in features else None,
segment_ids=features["segment_ids"]
if "segment_ids" in features else None)
@classmethod
def export(cls, model, export_dir):
if not isinstance(model, cls):
raise ValueError("Invalid model instance: %s, it should be a %s" %
(model, cls))
signatures = {
"serving_default":
model.serve.get_concrete_function(
input_ids=tf.TensorSpec(
shape=[None, None], dtype=tf.int32, name="inputs")),
}
if model.name_to_features:
signatures[
"serving_examples"] = model.serve_examples.get_concrete_function(
tf.TensorSpec(shape=[None], dtype=tf.string, name="examples"))
tf.saved_model.save(model, export_dir=export_dir, signatures=signatures)
def main(_):
sequence_length = FLAGS.sequence_length
if sequence_length is not None and sequence_length > 0:
name_to_features = {
"input_ids": tf.io.FixedLenFeature([sequence_length], tf.int64),
"input_mask": tf.io.FixedLenFeature([sequence_length], tf.int64),
"segment_ids": tf.io.FixedLenFeature([sequence_length], tf.int64),
}
else:
name_to_features = None
bert_config = configs.BertConfig.from_json_file(FLAGS.bert_config_file)
serving_model = BertServing(
bert_config=bert_config, name_to_features=name_to_features)
checkpoint = tf.train.Checkpoint(model=serving_model.encoder)
checkpoint.restore(FLAGS.model_checkpoint_path
).assert_existing_objects_matched().run_restore_ops()
BertServing.export(serving_model, FLAGS.export_path)
if __name__ == "__main__":
flags.mark_flag_as_required("bert_config_file")
flags.mark_flag_as_required("model_checkpoint_path")
flags.mark_flag_as_required("export_path")
app.run(main)
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