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