# 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.
import inspect
import paddle
from paddle import optimizer as optim
from ppcls.utils import logger
from functools import partial
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.getfullargspec(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,
use_nesterov=False,
multi_precision=True,
no_weight_decay_name=None,
one_dim_param_no_weight_decay=False):
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
self.use_nesterov = use_nesterov
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 = None
if model_list:
# TODO(gaotingquan): to avoid cause issues for unset no_weight_decay models
if len(self.no_weight_decay_name_list) > 0:
params_with_decay = []
params_without_decay = []
for m in model_list:
for n, p in m.named_parameters():
if any(nd in n for nd in self.no_weight_decay_name_list) \
or (self.one_dim_param_no_weight_decay and len(p.shape) == 1):
params_without_decay.append(p)
else:
params_with_decay.append(p)
parameters = [{
"params": params_with_decay,
"weight_decay": self.weight_decay
}, {
"params": params_without_decay,
"weight_decay": 0.0
}]
else:
parameters = sum([m.parameters() for m in model_list], [])
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,
use_nesterov=self.use_nesterov,
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_nesterov=self.use_nesterov,
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,
no_weight_decay_name=None,
one_dim_param_no_weight_decay=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
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 = None
if model_list:
params_with_decay = []
params_without_decay = []
for m in model_list:
for n, p in m.named_parameters():
if any(nd in n for nd in self.no_weight_decay_name_list) \
or (self.one_dim_param_no_weight_decay and len(p.shape) == 1):
params_without_decay.append(p)
else:
params_with_decay.append(p)
if params_without_decay:
parameters = [{
"params": params_with_decay,
"weight_decay": self.weight_decay
}, {
"params": params_without_decay,
"weight_decay": 0.0
}]
else:
parameters = params_with_decay
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, paddle.static.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._coeff = weight_decay
self._lr_to_coeff = dict()
self.set_param_lr_func = partial(
self._layerwise_lr_decay, layerwise_decay, name_dict, n_layers)
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,)
# 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 any([
key in static_name
for key in ["embed", "token", "conv1", "ln_pre"]
]):
ratio = decay_rate**(n_layers + 1)
# param.optimize_attr["learning_rate"] *= ratio
return ratio
def _append_decoupled_weight_decay(self, block, param_and_grad):
"""
Add decoupled weight decay op.
parameter = parameter - parameter * coeff * lr
Args:
block: block in which variable is to be created
param_and_grad: (parameters, gradients) pairs,
the parameters need to decay.
Raises:
Exception: The type of coeff and parameter is not consistent.
"""
if isinstance(param_and_grad, dict):
param_and_grad = self._update_param_group(param_and_grad)
param, grad = param_and_grad
if self._apply_decay_param_fun is not None and not self._apply_decay_param_fun(param.name):
return
if isinstance(self._learning_rate, float):
learning_rate = self._learning_rate
else:
# NOTE. We add this function to the _append_optimize_op(),
# for we must make sure _create_param_lr() be called after
# optimizer._create_global_learning_rate().
learning_rate = self._create_param_lr(param_and_grad)
with block.program._optimized_guard([param, grad]), paddle.static.name_scope("weight decay"):
self._params_name.add(param.name)
# If it has been calculated, the result will be reused.
# NOTE(wangxi): In dygraph mode, apply_gradient will be executed
# every step, so need clear _lr_to_coeff every step,
# we do this in _create_optimization_pass
decay_coeff = self._lr_to_coeff.get(learning_rate, None)
if decay_coeff is None:
# NOTE(wangxi): for pipeline to set device:all
with paddle.static.device_guard(None):
decay_coeff = 1.0 - learning_rate * self._coeff
self._lr_to_coeff[learning_rate] = decay_coeff
find_master = self._multi_precision and param.dtype == paddle.float16
if find_master:
master_weight = self._master_weights[param.name]
scaled_param = master_weight * decay_coeff
paddle.assign(scaled_param, output=master_weight)
else:
scaled_param = param * decay_coeff
paddle.assign(scaled_param, output=param)
def _append_optimize_op(self, block, param_and_grad):
if self.set_param_lr_func is None:
return super()._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"]
ratio = self.set_param_lr_func(param_and_grad[0])
param_and_grad[0].optimize_attr["learning_rate"] *= ratio
# excute Adam op
res = super()._append_optimize_op(block, param_and_grad)
param_and_grad[0].optimize_attr["learning_rate"] = prev_lr
return res
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