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114 lines
4.3 KiB
114 lines
4.3 KiB
# 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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from paddle.fluid.optimizer import AdamOptimizer
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from paddle.fluid.optimizer import LambOptimizer as LAMB
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from .meta_optimizer_base import MetaOptimizerBase
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import logging
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class LambOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super(LambOptimizer, self).__init__(optimizer)
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self.inner_opt = optimizer
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self.lamb_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 = ["GraphExecutionOptimizer"]
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self.meta_optimizers_black_list = []
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def _set_basic_info(self, loss, role_maker, user_defined_optimizer,
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user_defined_strategy):
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super(LambOptimizer, self)._set_basic_info(
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loss, role_maker, user_defined_optimizer, user_defined_strategy)
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opt = self.inner_opt
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if not isinstance(opt, AdamOptimizer):
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return
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configs = self.user_defined_strategy.lamb_configs
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if len(configs['exclude_from_weight_decay']) == 0:
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_exclude_from_weight_decay_fn = None
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else:
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def exclude_fn(param):
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exclude_list = configs['exclude_from_weight_decay']
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for name in exclude_list:
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if param.name.endswith(name):
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return True
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return False
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_exclude_from_weight_decay_fn = exclude_fn
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self.lamb_opt = LAMB(
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learning_rate=opt._learning_rate,
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lamb_weight_decay=configs['lamb_weight_decay'],
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beta1=opt._beta1,
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beta2=opt._beta2,
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epsilon=opt._epsilon,
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parameter_list=opt._parameter_list,
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regularization=opt.regularization,
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grad_clip=opt._grad_clip,
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exclude_from_weight_decay_fn=_exclude_from_weight_decay_fn,
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name=opt._name)
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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.lamb:
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if not isinstance(self.inner_opt, AdamOptimizer):
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logging.warn(
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"lamb need the inner optimizer to be AdamOptimizer optimizer but got {}.".
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format(self.inner_opt.type))
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return False
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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.lamb = False
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dist_strategy.lamb_configs = {}
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def _enable_strategy(self, dist_strategy, context):
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dist_strategy.lamb = True
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dist_strategy.lamb_configs = {
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"lamb_weight_decay": 0.01,
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"exclude_from_weight_decay": []
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}
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def backward(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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return self.lamb_opt.backward(loss, startup_program, parameter_list,
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no_grad_set, callbacks)
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# the following function will be used by AMP if both LARS and AMP are turn on together.
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def apply_gradients(self, params_grads):
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return self.lamb_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.lamb_opt.apply_optimize(
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loss, startup_program=startup_program, params_grads=params_grads)
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def minimize_impl(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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optimize_ops, params_grads = \
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self.lamb_opt.minimize(loss, startup_program,
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parameter_list, no_grad_set)
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return optimize_ops, params_grads
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