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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.
"""Common utils for tasks."""
from typing import Any, Callable
import orbit
import tensorflow as tf, tf_keras
import tensorflow_hub as hub
def get_encoder_from_hub(hub_model_path: str) -> tf_keras.Model:
"""Gets an encoder from hub.
Args:
hub_model_path: The path to the tfhub model.
Returns:
A tf_keras.Model.
"""
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')
hub_layer = hub.KerasLayer(hub_model_path, trainable=True)
output_dict = {}
dict_input = dict(
input_word_ids=input_word_ids,
input_mask=input_mask,
input_type_ids=input_type_ids)
output_dict = hub_layer(dict_input)
return tf_keras.Model(inputs=dict_input, outputs=output_dict)
def predict(predict_step_fn: Callable[[Any], Any],
aggregate_fn: Callable[[Any, Any], Any], dataset: tf.data.Dataset):
"""Runs prediction.
Args:
predict_step_fn: A callable such as `def predict_step(inputs)`, where
`inputs` are input tensors.
aggregate_fn: A callable such as `def aggregate_fn(state, value)`, where
`value` is the outputs from `predict_step_fn`.
dataset: A `tf.data.Dataset` object.
Returns:
The aggregated predictions.
"""
@tf.function
def predict_step(iterator):
"""Predicts on distributed devices."""
outputs = tf.distribute.get_strategy().run(
predict_step_fn, args=(next(iterator),))
return tf.nest.map_structure(
tf.distribute.get_strategy().experimental_local_results, outputs)
loop_fn = orbit.utils.create_loop_fn(predict_step)
# Set `num_steps` to -1 to exhaust the dataset.
outputs = loop_fn(
iter(dataset), num_steps=-1, state=None, reduce_fn=aggregate_fn) # pytype: disable=wrong-arg-types
return outputs
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