mirror of https://github.com/tensorflow/models.git
76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
# Copyright 2025 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Optimizer and learning rate scheduler."""
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import tensorflow as tf, tf_keras
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from official.modeling.hyperparams import params_dict
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class LearningRateSchedule(tf_keras.optimizers.schedules.LearningRateSchedule):
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"""Learning rate schedule."""
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def __init__(self, initial_learning_rate, hidden_size, warmup_steps):
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"""Initialize configuration of the learning rate schedule.
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Args:
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initial_learning_rate: A float, the initial learning rate.
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hidden_size: An integer, the model dimension in the hidden layers.
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warmup_steps: An integer, the number of steps required for linear warmup.
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"""
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super(LearningRateSchedule, self).__init__()
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self.initial_learning_rate = initial_learning_rate
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self.hidden_size = hidden_size
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self.warmup_steps = tf.cast(warmup_steps, tf.float32)
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def __call__(self, global_step):
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"""Calculate learning rate with linear warmup and rsqrt decay.
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Args:
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global_step: An integer, the current global step used for learning rate
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calculation.
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Returns:
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A float, the learning rate needs to be used for current global step.
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"""
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with tf.name_scope('learning_rate_schedule'):
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global_step = tf.cast(global_step, tf.float32)
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learning_rate = self.initial_learning_rate
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learning_rate *= (self.hidden_size**-0.5)
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# Apply linear warmup
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learning_rate *= tf.minimum(1.0, global_step / self.warmup_steps)
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# Apply rsqrt decay
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learning_rate /= tf.sqrt(tf.maximum(global_step, self.warmup_steps))
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return learning_rate
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def get_config(self):
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"""Get the configuration of the learning rate schedule."""
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return {
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'initial_learning_rate': self.initial_learning_rate,
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'hidden_size': self.hidden_size,
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'warmup_steps': self.warmup_steps,
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}
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def create_optimizer(params: params_dict.ParamsDict):
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"""Creates optimizer."""
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lr_schedule = LearningRateSchedule(params.learning_rate, params.hidden_size,
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params.learning_rate_warmup_steps)
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return tf_keras.optimizers.Adam(
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learning_rate=lr_schedule,
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beta_1=params.adam_beta1,
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beta_2=params.adam_beta2,
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epsilon=params.adam_epsilon)
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