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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.
"""Functions and classes related to training performance."""
from absl import logging
import tensorflow as tf, tf_keras
def configure_optimizer(optimizer,
use_float16=False,
loss_scale=None,
use_graph_rewrite=None):
"""Configures optimizer object with performance options."""
if use_graph_rewrite is not None:
logging.warning('`use_graph_rewrite` is deprecated inside '
'`configure_optimizer`. Please remove the usage.')
del use_graph_rewrite
if use_float16:
if loss_scale in (None, 'dynamic'):
optimizer = tf_keras.mixed_precision.LossScaleOptimizer(optimizer)
else:
# loss_scale is a number. We interpret that as a fixed loss scale.
optimizer = tf_keras.mixed_precision.LossScaleOptimizer(
optimizer, dynamic=False, initial_scale=loss_scale)
return optimizer
def set_mixed_precision_policy(dtype, loss_scale=None):
"""Sets the global `tf_keras.mixed_precision.Policy`."""
# TODO(b/191894773): Remove loss_scale argument
assert loss_scale is None, (
'The loss_scale argument must be None. The argument exists for '
'historical reasons and will be removed soon.')
if dtype == tf.float16:
tf_keras.mixed_precision.set_global_policy('mixed_float16')
elif dtype == tf.bfloat16:
tf_keras.mixed_precision.set_global_policy('mixed_bfloat16')
elif dtype == tf.float32:
tf_keras.mixed_precision.set_global_policy('float32')
else:
raise ValueError('Unexpected dtype: %s' % dtype)
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