# 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
# limitations under the License.
"""
Optimizer
"""
import re
import paddle.fluid as fluid
import paddle.fluid.layers as layers
[docs]class AdamW(fluid.optimizer.AdamaxOptimizer):
"""AdamW object for dygraph."""
def __init__(self, *args, **kwargs):
weight_decay = kwargs.pop('weight_decay', None)
var_name_to_exclude = kwargs.pop('var_name_to_exclude', '.*layer_norm_scale|.*layer_norm_bias|.*b_0')
super(AdamW, self).__init__(*args, **kwargs)
self.wd = weight_decay
self.pat = re.compile(var_name_to_exclude)
[docs] def apply_optimize(self, loss, startup_program, params_grads):
"""Update params with weight decay."""
super(AdamW, self).apply_optimize(loss, startup_program, params_grads)
for p, g in params_grads:
if not self.pat.match(p.name):
layers.assign(p * (1. - self.wd * self._learning_rate), p)