432 lines
17 KiB
Python
432 lines
17 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import inspect
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from paddle import optimizer as optim
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from ppcls.utils import logger
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from functools import partial
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class SGD(object):
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"""
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Args:
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learning_rate (float|Tensor|LearningRateDecay, optional): The learning rate used to update ``Parameter``.
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It can be a float value, a ``Tensor`` with a float type or a LearningRateDecay. The default value is 0.001.
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parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. \
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This parameter is required in dygraph mode. \
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The default value is None in static mode, at this time all parameters will be updated.
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weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \
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It canbe a float value as coeff of L2 regularization or \
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:ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`.
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If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \
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the regularization setting here in optimizer will be ignored for this parameter. \
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Otherwise, the regularization setting here in optimizer will take effect. \
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Default None, meaning there is no regularization.
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grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
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some derived class of ``GradientClipBase`` . There are three cliping strategies
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( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
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:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
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name (str, optional): The default value is None. Normally there is no need for user
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to set this property.
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"""
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def __init__(self,
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learning_rate=0.001,
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weight_decay=None,
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grad_clip=None,
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multi_precision=False,
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name=None):
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self.learning_rate = learning_rate
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self.weight_decay = weight_decay
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self.grad_clip = grad_clip
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self.multi_precision = multi_precision
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self.name = name
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def __call__(self, model_list):
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# model_list is None in static graph
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parameters = sum([m.parameters() for m in model_list],
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[]) if model_list else None
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argspec = inspect.getargspec(optim.SGD.__init__).args
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if 'multi_precision' in argspec:
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opt = optim.SGD(learning_rate=self.learning_rate,
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parameters=parameters,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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multi_precision=self.multi_precision,
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name=self.name)
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else:
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opt = optim.SGD(learning_rate=self.learning_rate,
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parameters=parameters,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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name=self.name)
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return opt
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class Momentum(object):
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"""
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Simple Momentum optimizer with velocity state.
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Args:
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learning_rate (float|Variable) - The learning rate used to update parameters.
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Can be a float value or a Variable with one float value as data element.
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momentum (float) - Momentum factor.
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regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
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"""
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def __init__(self,
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learning_rate,
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momentum,
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weight_decay=None,
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grad_clip=None,
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use_nesterov=False,
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multi_precision=True,
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no_weight_decay_name=None):
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super().__init__()
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self.learning_rate = learning_rate
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self.momentum = momentum
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self.weight_decay = weight_decay
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self.grad_clip = grad_clip
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self.multi_precision = multi_precision
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self.use_nesterov = use_nesterov
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self.no_weight_decay_name_list = no_weight_decay_name.split(
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) if no_weight_decay_name else []
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def __call__(self, model_list):
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# model_list is None in static graph
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parameters = None
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if len(self.no_weight_decay_name_list) > 0:
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params_with_decay = []
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params_without_decay = []
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for m in model_list:
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params = [p for n, p in m.named_parameters() \
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if not any(nd in n for nd in self.no_weight_decay_name_list)]
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params_with_decay.extend(params)
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params = [p for n, p in m.named_parameters() \
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if any(nd in n for nd in self.no_weight_decay_name_list)]
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params_without_decay.extend(params)
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parameters = [{
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"params": params_with_decay,
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"weight_decay": self.weight_decay
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}, {
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"params": params_without_decay,
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"weight_decay": 0.0
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}]
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else:
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parameters = sum([m.parameters() for m in model_list],
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[]) if model_list else None
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opt = optim.Momentum(
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learning_rate=self.learning_rate,
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momentum=self.momentum,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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multi_precision=self.multi_precision,
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use_nesterov=self.use_nesterov,
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parameters=parameters)
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if hasattr(opt, '_use_multi_tensor'):
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opt = optim.Momentum(
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learning_rate=self.learning_rate,
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momentum=self.momentum,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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multi_precision=self.multi_precision,
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parameters=parameters,
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use_nesterov=self.use_nesterov,
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use_multi_tensor=True)
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return opt
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class Adam(object):
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def __init__(self,
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learning_rate=0.001,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-08,
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parameter_list=None,
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weight_decay=None,
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grad_clip=None,
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name=None,
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lazy_mode=False,
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multi_precision=False):
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self.learning_rate = learning_rate
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self.beta1 = beta1
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self.beta2 = beta2
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self.epsilon = epsilon
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self.parameter_list = parameter_list
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self.learning_rate = learning_rate
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self.weight_decay = weight_decay
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self.grad_clip = grad_clip
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self.name = name
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self.lazy_mode = lazy_mode
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self.multi_precision = multi_precision
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def __call__(self, model_list):
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# model_list is None in static graph
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parameters = sum([m.parameters() for m in model_list],
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[]) if model_list else None
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opt = optim.Adam(
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learning_rate=self.learning_rate,
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beta1=self.beta1,
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beta2=self.beta2,
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epsilon=self.epsilon,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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name=self.name,
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lazy_mode=self.lazy_mode,
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multi_precision=self.multi_precision,
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parameters=parameters)
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return opt
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class RMSProp(object):
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"""
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Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning rate method.
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Args:
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learning_rate (float|Variable) - The learning rate used to update parameters.
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Can be a float value or a Variable with one float value as data element.
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momentum (float) - Momentum factor.
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rho (float) - rho value in equation.
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epsilon (float) - avoid division by zero, default is 1e-6.
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regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
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"""
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def __init__(self,
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learning_rate,
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momentum=0.0,
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rho=0.95,
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epsilon=1e-6,
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weight_decay=None,
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grad_clip=None,
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multi_precision=False):
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super().__init__()
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self.learning_rate = learning_rate
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self.momentum = momentum
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self.rho = rho
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self.epsilon = epsilon
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self.weight_decay = weight_decay
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self.grad_clip = grad_clip
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def __call__(self, model_list):
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# model_list is None in static graph
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parameters = sum([m.parameters() for m in model_list],
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[]) if model_list else None
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opt = optim.RMSProp(
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learning_rate=self.learning_rate,
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momentum=self.momentum,
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rho=self.rho,
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epsilon=self.epsilon,
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weight_decay=self.weight_decay,
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grad_clip=self.grad_clip,
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parameters=parameters)
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return opt
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class AdamW(object):
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def __init__(self,
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learning_rate=0.001,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-8,
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weight_decay=None,
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multi_precision=False,
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grad_clip=None,
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no_weight_decay_name=None,
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one_dim_param_no_weight_decay=False,
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**args):
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super().__init__()
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self.learning_rate = learning_rate
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self.beta1 = beta1
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self.beta2 = beta2
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self.epsilon = epsilon
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self.grad_clip = grad_clip
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self.weight_decay = weight_decay
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self.multi_precision = multi_precision
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self.no_weight_decay_name_list = no_weight_decay_name.split(
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) if no_weight_decay_name else []
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self.one_dim_param_no_weight_decay = one_dim_param_no_weight_decay
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def __call__(self, model_list):
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# model_list is None in static graph
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parameters = sum([m.parameters() for m in model_list],
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[]) if model_list else None
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# TODO(gaotingquan): model_list is None when in static graph, "no_weight_decay" not work.
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if model_list is None:
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if self.one_dim_param_no_weight_decay or len(
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self.no_weight_decay_name_list) != 0:
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msg = "\"AdamW\" does not support setting \"no_weight_decay\" in static graph. Please use dynamic graph."
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logger.error(Exception(msg))
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raise Exception(msg)
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self.no_weight_decay_param_name_list = [
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p.name for model in model_list for n, p in model.named_parameters()
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if any(nd in n for nd in self.no_weight_decay_name_list)
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] if model_list else []
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if self.one_dim_param_no_weight_decay:
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self.no_weight_decay_param_name_list += [
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p.name
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for model in model_list for n, p in model.named_parameters()
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if len(p.shape) == 1
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] if model_list else []
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opt = optim.AdamW(
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learning_rate=self.learning_rate,
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beta1=self.beta1,
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beta2=self.beta2,
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epsilon=self.epsilon,
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parameters=parameters,
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weight_decay=self.weight_decay,
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multi_precision=self.multi_precision,
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grad_clip=self.grad_clip,
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apply_decay_param_fun=self._apply_decay_param_fun)
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return opt
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def _apply_decay_param_fun(self, name):
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return name not in self.no_weight_decay_param_name_list
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class AdamWDL(object):
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"""
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The AdamWDL optimizer is implemented based on the AdamW Optimization with dynamic lr setting.
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Generally it's used for transformer model.
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"""
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def __init__(self,
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learning_rate=0.001,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-8,
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weight_decay=None,
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multi_precision=False,
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grad_clip=None,
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layerwise_decay=None,
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filter_bias_and_bn=True,
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**args):
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self.learning_rate = learning_rate
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self.beta1 = beta1
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self.beta2 = beta2
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self.epsilon = epsilon
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self.grad_clip = grad_clip
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self.weight_decay = weight_decay
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self.multi_precision = multi_precision
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self.layerwise_decay = layerwise_decay
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self.filter_bias_and_bn = filter_bias_and_bn
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class AdamWDLImpl(optim.AdamW):
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def __init__(self,
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learning_rate=0.001,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-8,
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parameters=None,
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weight_decay=0.01,
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apply_decay_param_fun=None,
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grad_clip=None,
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lazy_mode=False,
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multi_precision=False,
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layerwise_decay=1.0,
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n_layers=12,
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name_dict=None,
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name=None):
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if not isinstance(layerwise_decay, float) and \
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not isinstance(layerwise_decay, fluid.framework.Variable):
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raise TypeError("coeff should be float or Tensor.")
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self.layerwise_decay = layerwise_decay
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self.name_dict = name_dict
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self.n_layers = n_layers
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self.set_param_lr_func = partial(
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self._layerwise_lr_decay, layerwise_decay, name_dict, n_layers)
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super().__init__(
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learning_rate=learning_rate,
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parameters=parameters,
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beta1=beta1,
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beta2=beta2,
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epsilon=epsilon,
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grad_clip=grad_clip,
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name=name,
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apply_decay_param_fun=apply_decay_param_fun,
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weight_decay=weight_decay,
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lazy_mode=lazy_mode,
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multi_precision=multi_precision,
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lr_ratio=self.set_param_lr_func)
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# Layerwise decay
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def _layerwise_lr_decay(self, decay_rate, name_dict, n_layers, param):
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"""
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Args:
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decay_rate (float):
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The layer-wise decay ratio.
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name_dict (dict):
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The keys of name_dict is dynamic name of model while the value
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of name_dict is static name.
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Use model.named_parameters() to get name_dict.
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n_layers (int):
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Total number of layers in the transformer encoder.
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"""
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ratio = 1.0
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static_name = name_dict[param.name]
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if "blocks" in static_name:
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idx = static_name.find("blocks.")
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layer = int(static_name[idx:].split(".")[1])
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ratio = decay_rate**(n_layers - layer)
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elif "embed" in static_name:
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ratio = decay_rate**(n_layers + 1)
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# param.optimize_attr["learning_rate"] *= ratio
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return ratio
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def __call__(self, model_list):
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model = model_list[0]
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if self.weight_decay and self.filter_bias_and_bn:
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skip = {}
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if hasattr(model, 'no_weight_decay'):
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skip = model.no_weight_decay()
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decay_dict = {
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param.name: not (len(param.shape) == 1 or
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name.endswith(".bias") or name in skip)
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for name, param in model.named_parameters()
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if not 'teacher' in name
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}
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parameters = [
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param for param in model.parameters()
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if 'teacher' not in param.name
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]
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weight_decay = 0.
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else:
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parameters = model.parameters()
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opt_args = dict(
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learning_rate=self.learning_rate, weight_decay=self.weight_decay)
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opt_args['parameters'] = parameters
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if decay_dict is not None:
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opt_args['apply_decay_param_fun'] = lambda n: decay_dict[n]
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opt_args['epsilon'] = self.epsilon
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opt_args['beta1'] = self.beta1
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opt_args['beta2'] = self.beta2
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if self.layerwise_decay and self.layerwise_decay < 1.0:
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opt_args['layerwise_decay'] = self.layerwise_decay
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name_dict = dict()
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for n, p in model.named_parameters():
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name_dict[p.name] = n
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opt_args['name_dict'] = name_dict
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opt_args['n_layers'] = model.get_num_layers()
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optimizer = self.AdamWDLImpl(**opt_args)
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return optimizer
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