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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.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| # from __future__ import google_type_annotations | |
| from __future__ import print_function | |
| 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) | |