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