49 lines
1.8 KiB
Python
Executable file
49 lines
1.8 KiB
Python
Executable file
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# ==============================================================================
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# Copyright 2018 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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# ==============================================================================
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from __future__ import print_function
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import tensorflow as tf
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__all__ = ['FixedLossScalerOptimizer']
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class FixedLossScalerOptimizer(tf.compat.v1.train.Optimizer):
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"""An optimizer that scales loss and un-scales gradients for FP16 training."""
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def __init__(self, optimizer, scale=None, name="LossScalingOptimizer", use_locking=False):
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super(FixedLossScalerOptimizer, self).__init__(name=name, use_locking=use_locking)
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self._optimizer = optimizer
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self._scale = float(scale) if scale is not None else 1.0
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def compute_gradients(self, loss, var_list=None, *args, **kwargs):
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if self._scale != 1.0:
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loss = tf.scalar_mul(self._scale, loss)
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gradvar = self._optimizer.compute_gradients(loss, var_list, *args, **kwargs)
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gradvar = [(tf.scalar_mul(1. / self._scale, g), v) for g, v in gradvar]
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return gradvar
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def apply_gradients(self, *args, **kwargs):
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return self._optimizer.apply_gradients(*args, **kwargs)
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