File size: 3,461 Bytes
18ddfe2 |
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 |
# Copyright 2019 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 the ALBERT core model as a TF-Hub SavedModel."""
from __future__ import absolute_import
from __future__ import division
# from __future__ import google_type_annotations
from __future__ import print_function
from absl import app
from absl import flags
import tensorflow as tf
from typing import Text
from official.nlp.albert import configs
from official.nlp.bert import bert_models
FLAGS = flags.FLAGS
flags.DEFINE_string("albert_config_file", None,
"Albert configuration file to define core albert 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(
"sp_model_file", None,
"The sentence piece model file that the ALBERT model was trained on.")
def create_albert_model(
albert_config: configs.AlbertConfig) -> tf.keras.Model:
"""Creates an ALBERT keras core model from ALBERT configuration.
Args:
albert_config: An `AlbertConfig` 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(
albert_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_albert_tfhub(albert_config: configs.AlbertConfig,
model_checkpoint_path: Text, hub_destination: Text,
sp_model_file: Text):
"""Restores a tf.keras.Model and saves for TF-Hub."""
core_model, encoder = create_albert_model(albert_config)
checkpoint = tf.train.Checkpoint(model=encoder)
checkpoint.restore(model_checkpoint_path).assert_consumed()
core_model.sp_model_file = tf.saved_model.Asset(sp_model_file)
core_model.save(hub_destination, include_optimizer=False, save_format="tf")
def main(_):
albert_config = configs.AlbertConfig.from_json_file(
FLAGS.albert_config_file)
export_albert_tfhub(albert_config, FLAGS.model_checkpoint_path,
FLAGS.export_path, FLAGS.sp_model_file)
if __name__ == "__main__":
app.run(main)
|