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Paddle/python/paddle/distributed/fleet/meta_optimizers/amp_optimizer.py

68 lines
2.8 KiB

# Copyright (c) 2020 PaddlePaddle 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
import paddle.fluid.contrib.mixed_precision as mixed_precision
from .meta_optimizer_base import MetaOptimizerBase
class AMPOptimizer(MetaOptimizerBase):
def __init__(self, optimizer):
super(AMPOptimizer, self).__init__(optimizer)
self.inner_opt = optimizer
self.amp_opt = None
# we do not allow meta optimizer to be inner optimizer currently
self.meta_optimizers_white_list = [
"LarsOptimizer", "LambOptimizer", "RecomputeOptimizer",
"LocalSGDOptimizer", "GradientMergeOptimizer",
"GraphExecutionOptimizer"
]
self.meta_optimizers_black_list = ["DGCOptimizer"]
def _set_basic_info(self, loss, role_maker, user_defined_optimizer,
user_defined_strategy):
super(AMPOptimizer, self)._set_basic_info(
loss, role_maker, user_defined_optimizer, user_defined_strategy)
def _can_apply(self):
if self.user_defined_strategy.amp:
return True
return False
def _disable_strategy(self, dist_strategy):
dist_strategy.amp = False
dist_strategy.amp_configs = {}
def minimize_impl(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None):
if self.amp_opt is None:
config = self.user_defined_strategy.amp_configs
custom_white_list = set(config['custom_white_list'])
custom_black_list = set(config['custom_black_list'])
custom_black_varnames = set(config['custom_black_varnames'])
amp_lists = mixed_precision.AutoMixedPrecisionLists(
custom_white_list, custom_black_list, custom_black_varnames)
self.amp_opt = mixed_precision.decorate(
self.inner_opt, amp_lists, config['init_loss_scaling'],
config['incr_every_n_steps'], config['decr_every_n_nan_or_inf'],
config['incr_ratio'], config['decr_ratio'],
config['use_dynamic_loss_scaling'])
optimize_ops, params_grads = \
self.amp_opt.minimize(loss, startup_program,
parameter_list, no_grad_set)
return optimize_ops, params_grads