wehub-resource-sync
595 lines
25 KiB
Python
595 lines
25 KiB
Python
# 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.
|
|
|
|
from __future__ import annotations
|
|
|
|
import warnings
|
|
from typing import TYPE_CHECKING
|
|
|
|
import paddle
|
|
from paddle import _C_ops, pir
|
|
from paddle.framework import in_dynamic_or_pir_mode
|
|
from paddle.regularizer import L2Decay
|
|
|
|
from ..base import core, framework
|
|
from .optimizer import Optimizer
|
|
|
|
if TYPE_CHECKING:
|
|
from collections.abc import Sequence
|
|
|
|
from typing_extensions import NotRequired
|
|
|
|
from paddle import Tensor
|
|
from paddle.nn.clip import GradientClipBase
|
|
from paddle.regularizer import WeightDecayRegularizer
|
|
|
|
from .lr import LRScheduler
|
|
from .optimizer import _ParameterConfig
|
|
|
|
class _MomentumParameterConfig(_ParameterConfig):
|
|
momentum: NotRequired[float]
|
|
use_nesterov: NotRequired[bool]
|
|
rescale_grad: NotRequired[float]
|
|
regularization_method: NotRequired[str]
|
|
regularization_coeff: NotRequired[float]
|
|
|
|
|
|
__all__ = []
|
|
|
|
|
|
class Momentum(Optimizer):
|
|
r"""
|
|
|
|
Simple Momentum optimizer with velocity state
|
|
|
|
This optimizer has a flag for Nestrov Momentum.
|
|
|
|
The update equations are as follows:
|
|
|
|
.. math::
|
|
|
|
& velocity = mu * velocity + gradient
|
|
|
|
& if (use\_nesterov):
|
|
|
|
&\quad param = param - (gradient + mu * velocity) * learning\_rate
|
|
|
|
& else:
|
|
|
|
&\quad param = param - learning\_rate * velocity
|
|
|
|
Parameters:
|
|
|
|
learning_rate (float|Tensor|LRScheduler, optional): The learning rate used to update ``Parameter``.
|
|
It can be a float value, a ``Tensor`` with a float type or a LRScheduler. The default value is 0.001.
|
|
momentum (float): Momentum factor. The default value is 0.9.
|
|
parameters (list|tuple|None, optional): List|Tuple of ``Tensor`` to update to minimize ``loss``. \
|
|
This parameter is required in dygraph mode. And you can specify different options for \
|
|
different parameter groups such as the learning rate, weight decay, etc, \
|
|
then the parameters are list of dict. Note that the learning_rate in parameter groups \
|
|
represents the scale of base learning_rate. \
|
|
The default value is None in static graph mode, at this time all parameters will be updated.
|
|
use_nesterov(bool, optional): Enables Nesterov momentum. The default value is False.
|
|
weight_decay (int|float|WeightDecayRegularizer|None, optional): The strategy of regularization. \
|
|
It can be a int or float value as coeff of L2 regularization or \
|
|
:ref:`api_paddle_regularizer_L1Decay`, :ref:`api_paddle_regularizer_L2Decay`.
|
|
If a parameter has set regularizer using :ref:`api_paddle_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|None, optional): Gradient clipping strategy, it's an instance of
|
|
some derived class of ``GradientClipBase`` . There are three clipping strategies
|
|
( :ref:`api_paddle_nn_ClipGradByGlobalNorm` , :ref:`api_paddle_nn_ClipGradByNorm` ,
|
|
:ref:`api_paddle_nn_ClipGradByValue` ). Default None, meaning there is no gradient clipping.
|
|
multi_precision (bool, optional): Whether to use multi-precision during weight updating. Default is false.
|
|
rescale_grad (float, optional): Multiply the gradient with `rescale_grad` before updating. \
|
|
Often choose to be ``1.0/batch_size``.
|
|
use_multi_tensor (bool, optional): Whether to use multi-tensor strategy to update all parameters at once . Default is false.
|
|
name (str|None, optional): The default value is None. Normally there is no need for user
|
|
to set this property. For more information, please refer to
|
|
:ref:`api_guide_Name` .
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
|
|
>>> inp = paddle.uniform([10, 10], dtype="float32", min=-0.1, max=0.1)
|
|
>>> linear = paddle.nn.Linear(10, 10)
|
|
>>> inp = paddle.to_tensor(inp)
|
|
>>> out = linear(inp)
|
|
>>> loss = paddle.mean(out)
|
|
>>> momentum = paddle.optimizer.Momentum(
|
|
... learning_rate=0.1,
|
|
... parameters=linear.parameters(),
|
|
... weight_decay=0.01
|
|
... )
|
|
>>> back = out.backward()
|
|
>>> momentum.step()
|
|
>>> momentum.clear_grad()
|
|
|
|
>>> # Note that the learning_rate of linear_2 is 0.01.
|
|
>>> linear_1 = paddle.nn.Linear(10, 10)
|
|
>>> linear_2 = paddle.nn.Linear(10, 10)
|
|
>>> inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
|
|
>>> out = linear_1(inp)
|
|
>>> out = linear_2(out)
|
|
>>> loss = paddle.mean(out)
|
|
>>> momentum = paddle.optimizer.Momentum(
|
|
... learning_rate=0.1,
|
|
... parameters=[{ # type: ignore
|
|
... 'params': linear_1.parameters()
|
|
... }, {
|
|
... 'params': linear_2.parameters(),
|
|
... 'weight_decay': 0.001,
|
|
... 'learning_rate': 0.1
|
|
... }],
|
|
... weight_decay=0.01,
|
|
... momentum=0.9
|
|
... )
|
|
>>> out.backward()
|
|
>>> momentum.step()
|
|
>>> momentum.clear_grad()
|
|
|
|
"""
|
|
|
|
_velocity_acc_str = "velocity"
|
|
|
|
def __init__(
|
|
self,
|
|
learning_rate: float | Tensor | LRScheduler = 0.001,
|
|
momentum: float = 0.9,
|
|
parameters: (
|
|
Sequence[Tensor] | Sequence[_MomentumParameterConfig] | None
|
|
) = None,
|
|
use_nesterov: bool = False,
|
|
weight_decay: float | WeightDecayRegularizer | None = None,
|
|
grad_clip: GradientClipBase | None = None,
|
|
multi_precision: bool = False,
|
|
rescale_grad: float = 1.0,
|
|
use_multi_tensor: bool = False,
|
|
name: str | None = None,
|
|
) -> None:
|
|
if learning_rate is None:
|
|
raise ValueError("learning_rate is not set")
|
|
if momentum is None:
|
|
raise ValueError("momentum is not set")
|
|
|
|
if isinstance(weight_decay, int):
|
|
weight_decay = float(weight_decay)
|
|
predicate = lambda regular: isinstance(regular, (L2Decay, float))
|
|
if isinstance(parameters, list):
|
|
if isinstance(parameters[0], dict):
|
|
for param_group in parameters:
|
|
decay = (
|
|
param_group['weight_decay']
|
|
if 'weight_decay' in param_group
|
|
else weight_decay
|
|
)
|
|
reg_method, reg_coeff = self._update_regularization(decay)
|
|
param_group['regularization_method'] = reg_method
|
|
param_group['regularization_coeff'] = reg_coeff
|
|
py_regular = None if predicate(decay) else decay
|
|
param_group['weight_decay'] = py_regular
|
|
|
|
py_regular = None if predicate(weight_decay) else weight_decay
|
|
super().__init__(
|
|
learning_rate=learning_rate,
|
|
parameters=parameters,
|
|
weight_decay=py_regular,
|
|
grad_clip=grad_clip,
|
|
name=name,
|
|
)
|
|
self.type = "momentum"
|
|
self._momentum = momentum
|
|
self._use_nesterov = bool(use_nesterov)
|
|
(
|
|
self._regularization_method,
|
|
self._regularization_coeff,
|
|
) = self._update_regularization(weight_decay)
|
|
self._multi_precision = multi_precision
|
|
self._rescale_grad = rescale_grad
|
|
self._master_weights = {}
|
|
|
|
self._default_dict = {
|
|
'momentum': momentum,
|
|
'use_nesterov': use_nesterov,
|
|
'rescale_grad': rescale_grad,
|
|
'regularization_method': self._regularization_method,
|
|
'regularization_coeff': self._regularization_coeff,
|
|
}
|
|
self._use_multi_tensor = use_multi_tensor
|
|
if self._use_multi_tensor:
|
|
self._param_dict = self._create_multi_tensor_dict()
|
|
self._velocity_dict = self._create_multi_tensor_dict()
|
|
self._master_weight_dict = self._create_multi_tensor_dict()
|
|
self._master_weight_dict['FP32_DenseTensor'] = None
|
|
self._regularization_method_dict = self._create_multi_tensor_dict()
|
|
self._regularization_coeff_dict = self._create_multi_tensor_dict()
|
|
|
|
def _update_regularization(self, weight_decay):
|
|
reg_method = ""
|
|
reg_coeff = 0.0
|
|
|
|
if isinstance(weight_decay, L2Decay):
|
|
reg_method = "l2_decay"
|
|
reg_coeff = weight_decay._coeff
|
|
if isinstance(weight_decay, float):
|
|
reg_method = "l2_decay"
|
|
reg_coeff = weight_decay
|
|
return reg_method, reg_coeff
|
|
|
|
def _create_accumulators(self, block, parameters):
|
|
'''
|
|
if framework.in_dynamic_mode():
|
|
return
|
|
'''
|
|
assert isinstance(block, (framework.Block, paddle.pir.Block))
|
|
|
|
if isinstance(parameters, dict):
|
|
parameters = self._update_param_group(parameters)
|
|
|
|
for p in parameters:
|
|
if p.name in self._already_create_accumulator:
|
|
continue
|
|
if self._multi_precision and self._is_dtype_fp16_or_bf16(p.dtype):
|
|
master_p = self._create_master_weight(p)
|
|
self._add_accumulator(self._velocity_acc_str, master_p)
|
|
self._already_create_accumulator.add(p.name)
|
|
continue
|
|
if (
|
|
self._is_dtype_fp16_or_bf16(p.dtype)
|
|
and not self._multi_precision
|
|
):
|
|
warnings.warn(
|
|
"Accumulating with FP16/BF16 in optimizer can lead to poor accuracy or slow convergence."
|
|
"Consider using multi_precision=True option of the Momentum optimizer."
|
|
)
|
|
self._add_accumulator(self._velocity_acc_str, p)
|
|
self._already_create_accumulator.add(p.name)
|
|
|
|
def _create_regularization_of_grad(self, param, grad, regularization=None):
|
|
"""Create and add backward regularization Operators
|
|
|
|
Function helper of append_regularization_ops.
|
|
"""
|
|
# If ParamAttr is set to L2Decay, we skip doing regularization here. And then we fused
|
|
# L2Decay with momentum which can refer to _append_optimize_op below.
|
|
if hasattr(param, 'regularizer') and isinstance(
|
|
param.regularizer, L2Decay
|
|
):
|
|
return grad
|
|
return super()._create_regularization_of_grad(
|
|
param, grad, regularization
|
|
)
|
|
|
|
def _append_optimize_op(self, block, param_and_grad):
|
|
if not isinstance(block, (framework.Block, pir.Block)):
|
|
raise TypeError("block is not instance of Block.")
|
|
if isinstance(param_and_grad, dict):
|
|
param_and_grad = self._update_param_group(param_and_grad)
|
|
|
|
velocity_acc = self._get_accumulator_master(
|
|
self._velocity_acc_str, param_and_grad[0]
|
|
)
|
|
lr = self._create_param_lr(param_and_grad)
|
|
|
|
# For fusion of momentum and l2decay
|
|
param = param_and_grad[0]
|
|
regularization_method = self._regularization_method
|
|
regularization_coeff = self._regularization_coeff
|
|
if hasattr(param, 'regularizer'):
|
|
# we skip param's l2decay before, so fuse it with momentum here.
|
|
if isinstance(param.regularizer, L2Decay):
|
|
regularization_method = "l2_decay"
|
|
regularization_coeff = param.regularizer._coeff
|
|
# the param's regularization has been done before, we avoid do l2decay in momentum.
|
|
elif param.regularizer is not None:
|
|
regularization_method = ""
|
|
regularization_coeff = 0.0
|
|
|
|
find_master = self._multi_precision and self._is_dtype_fp16_or_bf16(
|
|
param_and_grad[0].dtype
|
|
)
|
|
master_weight = (
|
|
self._master_weights[param_and_grad[0].name]
|
|
if find_master
|
|
else None
|
|
)
|
|
|
|
if in_dynamic_or_pir_mode():
|
|
if isinstance(param_and_grad, dict):
|
|
self._update_regularization(param_and_grad['weight_decay'])
|
|
return _C_ops.momentum_(
|
|
param_and_grad[0],
|
|
param_and_grad[1],
|
|
velocity_acc,
|
|
lr,
|
|
master_weight,
|
|
self._momentum,
|
|
self._use_nesterov,
|
|
regularization_method,
|
|
regularization_coeff,
|
|
find_master,
|
|
self._rescale_grad,
|
|
)
|
|
else:
|
|
attrs = {
|
|
"mu": self._momentum,
|
|
"use_nesterov": self._use_nesterov,
|
|
"regularization_method": regularization_method,
|
|
"regularization_coeff": regularization_coeff,
|
|
"multi_precision": find_master,
|
|
"rescale_grad": self._rescale_grad,
|
|
}
|
|
|
|
inputs = {
|
|
"Param": [param_and_grad[0]],
|
|
"Grad": [param_and_grad[1]],
|
|
"Velocity": [velocity_acc],
|
|
"LearningRate": [lr],
|
|
}
|
|
|
|
outputs = {
|
|
"ParamOut": [param_and_grad[0]],
|
|
"VelocityOut": [velocity_acc],
|
|
}
|
|
|
|
if find_master:
|
|
inputs["MasterParam"] = master_weight
|
|
outputs["MasterParamOut"] = master_weight
|
|
|
|
# create the momentum optimize op
|
|
momentum_op = block.append_op(
|
|
type=self.type,
|
|
inputs=inputs,
|
|
outputs=outputs,
|
|
attrs=attrs,
|
|
stop_gradient=True,
|
|
)
|
|
|
|
return momentum_op
|
|
|
|
def _multi_tensor_init(self, target_block, parameters, param_group_idx):
|
|
"""
|
|
All parameters used for optimizer (such as: parameters, master_weight, velocity_acc for momentum) calculations are grouped into a python list by data type (float16, bf16, float32).
|
|
This function will be overridden in the corresponding optimizer file.
|
|
|
|
Args:
|
|
target_block: the block in which the loss tensor is present
|
|
parameters: list of parameter tensors for the optimizer
|
|
"""
|
|
self._create_accumulators(target_block, parameters)
|
|
for param in parameters:
|
|
velocity_acc = self._get_accumulator_master(
|
|
self._velocity_acc_str, param
|
|
)
|
|
regularization_method = self._regularization_method
|
|
regularization_coeff = self._regularization_coeff
|
|
if hasattr(param, 'regularizer'):
|
|
# we skip param's l2decay before, so fuse it with momentum here.
|
|
if isinstance(param.regularizer, L2Decay):
|
|
regularization_method = "l2_decay"
|
|
regularization_coeff = param.regularizer._coeff
|
|
elif param.regularizer is not None:
|
|
regularization_method = ""
|
|
regularization_coeff = 0.0
|
|
if param.dtype == paddle.float32:
|
|
self._param_dict['FP32_DenseTensor'][param_group_idx].append(
|
|
param
|
|
)
|
|
self._velocity_dict['FP32_DenseTensor'][param_group_idx].append(
|
|
velocity_acc
|
|
)
|
|
# fp32 no master weight
|
|
self._regularization_method_dict['FP32_DenseTensor'][
|
|
param_group_idx
|
|
].append(regularization_method)
|
|
self._regularization_coeff_dict['FP32_DenseTensor'][
|
|
param_group_idx
|
|
].append(regularization_coeff)
|
|
elif self._is_dtype_fp16_or_bf16(param.dtype):
|
|
self._param_dict['FP16_DenseTensor'][param_group_idx].append(
|
|
param
|
|
)
|
|
self._velocity_dict['FP16_DenseTensor'][param_group_idx].append(
|
|
velocity_acc
|
|
)
|
|
if self._multi_precision:
|
|
self._master_weight_dict['FP16_DenseTensor'][
|
|
param_group_idx
|
|
].append(self._master_weights[param.name])
|
|
else:
|
|
self._master_weight_dict['FP16_DenseTensor'][
|
|
param_group_idx
|
|
] = None
|
|
self._regularization_method_dict['FP16_DenseTensor'][
|
|
param_group_idx
|
|
].append(regularization_method)
|
|
self._regularization_coeff_dict['FP16_DenseTensor'][
|
|
param_group_idx
|
|
].append(regularization_coeff)
|
|
else:
|
|
raise ValueError(
|
|
"Now multi_tensor_momentum only support fp32, fp16 or bf16 parameters and grad is DENSE_TENSOR."
|
|
)
|
|
|
|
def _append_optimize_multi_tensor_op(
|
|
self,
|
|
target_block,
|
|
parameters_and_grads,
|
|
param_group_idx,
|
|
):
|
|
"""
|
|
For Multi Tensor, append optimize merged_operator to block.
|
|
"""
|
|
assert isinstance(target_block, framework.Block)
|
|
|
|
grad_dict = {'FP32_DenseTensor': [], 'FP16_DenseTensor': []}
|
|
lr_dict = {'FP32_DenseTensor': [], 'FP16_DenseTensor': []}
|
|
|
|
if isinstance(parameters_and_grads, list):
|
|
for param_and_grad in parameters_and_grads:
|
|
if param_and_grad[1] is None:
|
|
continue
|
|
if param_and_grad[0].stop_gradient is False:
|
|
if (
|
|
param_and_grad[0].dtype == paddle.float32
|
|
and param_and_grad[1].type
|
|
== core.VarDesc.VarType.DENSE_TENSOR
|
|
):
|
|
grad_dict['FP32_DenseTensor'].append(param_and_grad[1])
|
|
lr = self._create_param_lr(param_and_grad)
|
|
lr_dict['FP32_DenseTensor'].append(lr)
|
|
elif (
|
|
self._is_dtype_fp16_or_bf16(param_and_grad[0].dtype)
|
|
and param_and_grad[1].type
|
|
== core.VarDesc.VarType.DENSE_TENSOR
|
|
):
|
|
grad_dict['FP16_DenseTensor'].append(param_and_grad[1])
|
|
lr = self._create_param_lr(param_and_grad)
|
|
lr_dict['FP16_DenseTensor'].append(lr)
|
|
else:
|
|
for param_and_grad in parameters_and_grads['params']:
|
|
if param_and_grad[1] is None:
|
|
continue
|
|
if param_and_grad[0].stop_gradient is False:
|
|
param_grad_dict = {}
|
|
param_grad_dict['params'] = param_and_grad
|
|
param_grad_dict.update(
|
|
{
|
|
k: v
|
|
for k, v in parameters_and_grads.items()
|
|
if k != 'params'
|
|
}
|
|
)
|
|
param_and_grad = self._update_param_group(param_grad_dict)
|
|
if (
|
|
param_and_grad[0].dtype == paddle.float32
|
|
and param_and_grad[1].type
|
|
== core.VarDesc.VarType.DENSE_TENSOR
|
|
):
|
|
grad_dict['FP32_DenseTensor'].append(param_and_grad[1])
|
|
lr = self._create_param_lr(param_and_grad)
|
|
lr_dict['FP32_DenseTensor'].append(lr)
|
|
elif (
|
|
self._is_dtype_fp16_or_bf16(param_and_grad[0].dtype)
|
|
and param_and_grad[1].type
|
|
== core.VarDesc.VarType.DENSE_TENSOR
|
|
):
|
|
grad_dict['FP16_DenseTensor'].append(param_and_grad[1])
|
|
lr = self._create_param_lr(param_and_grad)
|
|
lr_dict['FP16_DenseTensor'].append(lr)
|
|
|
|
multi_tensor_list = ['FP32_DenseTensor', 'FP16_DenseTensor']
|
|
for key in multi_tensor_list:
|
|
if len(self._param_dict[key][param_group_idx]) > 0:
|
|
find_master = (
|
|
self._multi_precision and key == 'FP16_DenseTensor'
|
|
)
|
|
|
|
master_weight = self._master_weight_dict[key]
|
|
master_weight = (
|
|
master_weight[param_group_idx]
|
|
if master_weight is not None
|
|
else None
|
|
)
|
|
|
|
if in_dynamic_or_pir_mode():
|
|
found_inf = self._get_auxiliary_var('found_inf')
|
|
if found_inf:
|
|
if isinstance(
|
|
found_inf, (core.eager.Tensor, paddle.pir.Value)
|
|
):
|
|
self._set_auxiliary_var('found_inf', True)
|
|
else:
|
|
if isinstance(
|
|
found_inf, (core.eager.Tensor, paddle.pir.Value)
|
|
):
|
|
self._set_auxiliary_var('found_inf', False)
|
|
_, _, _ = _C_ops.merged_momentum_(
|
|
self._param_dict[key][param_group_idx],
|
|
grad_dict[key],
|
|
self._velocity_dict[key][param_group_idx],
|
|
lr_dict[key],
|
|
master_weight,
|
|
self._momentum,
|
|
self._use_nesterov,
|
|
self._regularization_method_dict[key][
|
|
param_group_idx
|
|
],
|
|
self._regularization_coeff_dict[key][
|
|
param_group_idx
|
|
],
|
|
find_master,
|
|
self._rescale_grad,
|
|
)
|
|
else:
|
|
inputs = {
|
|
"Param": self._param_dict[key][param_group_idx],
|
|
"Grad": grad_dict[key],
|
|
"Velocity": self._velocity_dict[key][param_group_idx],
|
|
"LearningRate": lr_dict[key],
|
|
}
|
|
outputs = {
|
|
"ParamOut": self._param_dict[key][param_group_idx],
|
|
"VelocityOut": self._velocity_dict[key][
|
|
param_group_idx
|
|
],
|
|
}
|
|
attrs = {
|
|
"mu": self._momentum,
|
|
"use_nesterov": self._use_nesterov,
|
|
"regularization_method": self._regularization_method_dict[
|
|
key
|
|
][param_group_idx],
|
|
"regularization_coeff": self._regularization_coeff_dict[
|
|
key
|
|
][param_group_idx],
|
|
}
|
|
if find_master:
|
|
inputs["MasterParam"] = self._master_weight_dict[key][
|
|
param_group_idx
|
|
]
|
|
outputs["MasterParamOut"] = self._master_weight_dict[
|
|
key
|
|
][param_group_idx]
|
|
attrs["multi_precision"] = find_master
|
|
target_block.append_op(
|
|
type="merged_momentum",
|
|
inputs=inputs,
|
|
outputs=outputs,
|
|
attrs=attrs,
|
|
stop_gradient=True,
|
|
)
|
|
|
|
def _update_param_group(self, parameters):
|
|
self._momentum = parameters.get(
|
|
'momentum', self._default_dict['momentum']
|
|
)
|
|
self._use_nesterov = parameters.get(
|
|
'use_nesterov', self._default_dict['use_nesterov']
|
|
)
|
|
self._rescale_grad = parameters.get(
|
|
'rescale_grad', self._default_dict['rescale_grad']
|
|
)
|
|
self._regularization_method = parameters.get(
|
|
'regularization_method', self._default_dict['regularization_method']
|
|
)
|
|
self._regularization_coeff = parameters.get(
|
|
'regularization_coeff', self._default_dict['regularization_coeff']
|
|
)
|
|
parameters = parameters.get('params')
|
|
return parameters
|