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optimizer.py
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optimizer.py
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# Copyright 2020 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.
# ==============================================================================
"""Optimizer and learning rate scheduler."""
import tensorflow as tf
from official.modeling.hyperparams import params_dict
class LearningRateSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
"""Learning rate schedule."""
def __init__(self, initial_learning_rate, hidden_size, warmup_steps):
"""Initialize configuration of the learning rate schedule.
Args:
initial_learning_rate: A float, the initial learning rate.
hidden_size: An integer, the model dimension in the hidden layers.
warmup_steps: An integer, the number of steps required for linear warmup.
"""
super(LearningRateSchedule, self).__init__()
self.initial_learning_rate = initial_learning_rate
self.hidden_size = hidden_size
self.warmup_steps = tf.cast(warmup_steps, tf.float32)
def __call__(self, global_step):
"""Calculate learning rate with linear warmup and rsqrt decay.
Args:
global_step: An integer, the current global step used for learning rate
calculation.
Returns:
A float, the learning rate needs to be used for current global step.
"""
with tf.name_scope('learning_rate_schedule'):
global_step = tf.cast(global_step, tf.float32)
learning_rate = self.initial_learning_rate
learning_rate *= (self.hidden_size**-0.5)
# Apply linear warmup
learning_rate *= tf.minimum(1.0, global_step / self.warmup_steps)
# Apply rsqrt decay
learning_rate /= tf.sqrt(tf.maximum(global_step, self.warmup_steps))
return learning_rate
def get_config(self):
"""Get the configuration of the learning rate schedule."""
return {
'initial_learning_rate': self.initial_learning_rate,
'hidden_size': self.hidden_size,
'warmup_steps': self.warmup_steps,
}
def create_optimizer(params: params_dict.ParamsDict):
"""Creates optimizer."""
lr_schedule = LearningRateSchedule(params.learning_rate, params.hidden_size,
params.learning_rate_warmup_steps)
return tf.keras.optimizers.Adam(
learning_rate=lr_schedule,
beta_1=params.adam_beta1,
beta_2=params.adam_beta2,
epsilon=params.adam_epsilon)