wehub-resource-sync
140 lines
4.8 KiB
Python
Executable File
140 lines
4.8 KiB
Python
Executable File
# Copyright (c) 2020 PaddlePaddle 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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import paddle.static.amp as mixed_precision
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from .meta_optimizer_base import MetaOptimizerBase
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__all__ = []
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class AMPOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super().__init__(optimizer)
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self.inner_opt = optimizer
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self.wrapped_opt = None
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# we do not allow meta optimizer to be inner optimizer currently
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self.meta_optimizers_white_list = [
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"LarsOptimizer",
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"LambOptimizer",
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"RecomputeOptimizer",
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]
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self.meta_optimizers_black_list = ["DGCOptimizer"]
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def _set_basic_info(
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self, loss, role_maker, user_defined_optimizer, user_defined_strategy
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):
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super()._set_basic_info(
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loss, role_maker, user_defined_optimizer, user_defined_strategy
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)
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def _init_wrapped_opt(self):
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if self.wrapped_opt is not None:
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return
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config = self.user_defined_strategy.amp_configs
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custom_white_list = set(config['custom_white_list'])
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custom_black_list = set(config['custom_black_list'])
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custom_black_varnames = set(config['custom_black_varnames'])
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amp_lists = mixed_precision.AutoMixedPrecisionLists(
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custom_white_list, custom_black_list, custom_black_varnames
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)
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self.wrapped_opt = mixed_precision.decorate(
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self.inner_opt,
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amp_lists,
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config['init_loss_scaling'],
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config['incr_every_n_steps'],
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config['decr_every_n_nan_or_inf'],
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config['incr_ratio'],
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config['decr_ratio'],
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config['use_dynamic_loss_scaling'],
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config['use_pure_fp16'],
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config['use_fp16_guard'],
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)
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# if worker_num > 1, all cards will communication with each other,
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# add is_distributed to optimize amp, overlap communication and
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# computation by split the check_finite_and_unscale op.
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is_distributed = self.role_maker._worker_num() > 1
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if self.user_defined_strategy.sharding:
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# FIXME(wangxi). sharding failed when split check_finite_and_unscale
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# FIXME(JZ-LIANG). To support Sharding-Megatron-AMP, Megatron should follow Sharding's behavior that to disable is_distributed.
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is_distributed = False
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self.wrapped_opt._set_distributed(is_distributed)
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def _can_apply(self):
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if not self.role_maker._is_collective:
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return False
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if self.user_defined_strategy.amp:
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return True
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return False
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def _disable_strategy(self, dist_strategy):
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dist_strategy.amp = False
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dist_strategy.amp_configs = {}
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def _enable_strategy(self, dist_strategy, context):
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dist_strategy.amp = True
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dist_strategy.amp_configs = {
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"init_loss_scaling": 32768.0,
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"incr_every_n_steps": 1000,
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"decr_every_n_nan_or_inf": 2,
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"incr_ratio": 2.0,
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"decr_ratio": 0.8,
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"use_dynamic_loss_scaling": True,
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}
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def backward(
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self,
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loss,
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startup_program=None,
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parameter_list=None,
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no_grad_set=None,
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callbacks=None,
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):
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# maybe inner_opt of other meta optimizer
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self._init_wrapped_opt()
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return self.wrapped_opt.backward(
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loss, startup_program, parameter_list, no_grad_set, callbacks
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)
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def apply_gradients(self, params_grads):
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return self.wrapped_opt.apply_gradients(params_grads=params_grads)
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def apply_optimize(self, loss, startup_program, params_grads):
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return self.wrapped_opt.apply_optimize(
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loss, startup_program=startup_program, params_grads=params_grads
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)
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def minimize_impl(
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self, loss, startup_program=None, parameter_list=None, no_grad_set=None
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):
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self._init_wrapped_opt()
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optimize_ops, params_grads = self.wrapped_opt.minimize(
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loss, startup_program, parameter_list, no_grad_set
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)
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return optimize_ops, params_grads
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def amp_init(
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self, place, scope=None, test_program=None, use_fp16_test=False
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):
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return self.wrapped_opt.amp_init(
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place, scope, test_program, use_fp16_test
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)
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def get_loss_scaling(self):
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return self.wrapped_opt.get_loss_scaling()
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