chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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 annotations
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import warnings
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from typing import TYPE_CHECKING
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import paddle
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from paddle import _C_ops, pir
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from paddle.framework import in_dynamic_or_pir_mode
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from paddle.regularizer import L2Decay
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from ..base import core, framework
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from .optimizer import Optimizer
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if TYPE_CHECKING:
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from collections.abc import Sequence
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from typing_extensions import NotRequired
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from paddle import Tensor
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from paddle.nn.clip import GradientClipBase
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from paddle.regularizer import WeightDecayRegularizer
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from .lr import LRScheduler
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from .optimizer import _ParameterConfig
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class _MomentumParameterConfig(_ParameterConfig):
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momentum: NotRequired[float]
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use_nesterov: NotRequired[bool]
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rescale_grad: NotRequired[float]
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regularization_method: NotRequired[str]
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regularization_coeff: NotRequired[float]
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__all__ = []
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class Momentum(Optimizer):
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r"""
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Simple Momentum optimizer with velocity state
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This optimizer has a flag for Nestrov Momentum.
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The update equations are as follows:
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.. math::
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& velocity = mu * velocity + gradient
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& if (use\_nesterov):
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&\quad param = param - (gradient + mu * velocity) * learning\_rate
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& else:
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&\quad param = param - learning\_rate * velocity
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Parameters:
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learning_rate (float|Tensor|LRScheduler, 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 LRScheduler. The default value is 0.001.
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momentum (float): Momentum factor. The default value is 0.9.
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parameters (list|tuple|None, optional): List|Tuple of ``Tensor`` to update to minimize ``loss``. \
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This parameter is required in dygraph mode. And you can specify different options for \
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different parameter groups such as the learning rate, weight decay, etc, \
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then the parameters are list of dict. Note that the learning_rate in parameter groups \
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represents the scale of base learning_rate. \
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The default value is None in static graph mode, at this time all parameters will be updated.
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use_nesterov(bool, optional): Enables Nesterov momentum. The default value is False.
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weight_decay (int|float|WeightDecayRegularizer|None, optional): The strategy of regularization. \
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It can be a int or float value as coeff of L2 regularization or \
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:ref:`api_paddle_regularizer_L1Decay`, :ref:`api_paddle_regularizer_L2Decay`.
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If a parameter has set regularizer using :ref:`api_paddle_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|None, optional): Gradient clipping strategy, it's an instance of
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some derived class of ``GradientClipBase`` . There are three clipping strategies
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( :ref:`api_paddle_nn_ClipGradByGlobalNorm` , :ref:`api_paddle_nn_ClipGradByNorm` ,
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:ref:`api_paddle_nn_ClipGradByValue` ). Default None, meaning there is no gradient clipping.
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multi_precision (bool, optional): Whether to use multi-precision during weight updating. Default is false.
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rescale_grad (float, optional): Multiply the gradient with `rescale_grad` before updating. \
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Often choose to be ``1.0/batch_size``.
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use_multi_tensor (bool, optional): Whether to use multi-tensor strategy to update all parameters at once . Default is false.
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name (str|None, optional): The default value is None. Normally there is no need for user
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to set this property. For more information, please refer to
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:ref:`api_guide_Name` .
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> inp = paddle.uniform([10, 10], dtype="float32", min=-0.1, max=0.1)
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>>> linear = paddle.nn.Linear(10, 10)
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>>> inp = paddle.to_tensor(inp)
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>>> out = linear(inp)
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>>> loss = paddle.mean(out)
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>>> momentum = paddle.optimizer.Momentum(
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... learning_rate=0.1,
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... parameters=linear.parameters(),
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... weight_decay=0.01
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... )
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>>> back = out.backward()
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>>> momentum.step()
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>>> momentum.clear_grad()
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>>> # Note that the learning_rate of linear_2 is 0.01.
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>>> linear_1 = paddle.nn.Linear(10, 10)
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>>> linear_2 = paddle.nn.Linear(10, 10)
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>>> inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
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>>> out = linear_1(inp)
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>>> out = linear_2(out)
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>>> loss = paddle.mean(out)
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>>> momentum = paddle.optimizer.Momentum(
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... learning_rate=0.1,
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... parameters=[{ # type: ignore
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... 'params': linear_1.parameters()
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... }, {
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... 'params': linear_2.parameters(),
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... 'weight_decay': 0.001,
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... 'learning_rate': 0.1
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... }],
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... weight_decay=0.01,
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... momentum=0.9
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... )
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>>> out.backward()
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>>> momentum.step()
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>>> momentum.clear_grad()
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"""
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_velocity_acc_str = "velocity"
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def __init__(
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self,
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learning_rate: float | Tensor | LRScheduler = 0.001,
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momentum: float = 0.9,
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parameters: (
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Sequence[Tensor] | Sequence[_MomentumParameterConfig] | None
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) = None,
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use_nesterov: bool = False,
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weight_decay: float | WeightDecayRegularizer | None = None,
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grad_clip: GradientClipBase | None = None,
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multi_precision: bool = False,
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rescale_grad: float = 1.0,
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use_multi_tensor: bool = False,
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name: str | None = None,
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) -> None:
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if learning_rate is None:
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raise ValueError("learning_rate is not set")
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if momentum is None:
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raise ValueError("momentum is not set")
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if isinstance(weight_decay, int):
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weight_decay = float(weight_decay)
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predicate = lambda regular: isinstance(regular, (L2Decay, float))
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if isinstance(parameters, list):
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if isinstance(parameters[0], dict):
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for param_group in parameters:
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decay = (
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param_group['weight_decay']
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if 'weight_decay' in param_group
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else weight_decay
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)
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reg_method, reg_coeff = self._update_regularization(decay)
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param_group['regularization_method'] = reg_method
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param_group['regularization_coeff'] = reg_coeff
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py_regular = None if predicate(decay) else decay
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param_group['weight_decay'] = py_regular
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py_regular = None if predicate(weight_decay) else weight_decay
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super().__init__(
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learning_rate=learning_rate,
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parameters=parameters,
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weight_decay=py_regular,
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grad_clip=grad_clip,
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name=name,
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)
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self.type = "momentum"
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self._momentum = momentum
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self._use_nesterov = bool(use_nesterov)
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(
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self._regularization_method,
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self._regularization_coeff,
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) = self._update_regularization(weight_decay)
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self._multi_precision = multi_precision
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self._rescale_grad = rescale_grad
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self._master_weights = {}
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self._default_dict = {
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'momentum': momentum,
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'use_nesterov': use_nesterov,
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'rescale_grad': rescale_grad,
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'regularization_method': self._regularization_method,
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'regularization_coeff': self._regularization_coeff,
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}
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self._use_multi_tensor = use_multi_tensor
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if self._use_multi_tensor:
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self._param_dict = self._create_multi_tensor_dict()
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self._velocity_dict = self._create_multi_tensor_dict()
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self._master_weight_dict = self._create_multi_tensor_dict()
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self._master_weight_dict['FP32_DenseTensor'] = None
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self._regularization_method_dict = self._create_multi_tensor_dict()
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self._regularization_coeff_dict = self._create_multi_tensor_dict()
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def _update_regularization(self, weight_decay):
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reg_method = ""
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reg_coeff = 0.0
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if isinstance(weight_decay, L2Decay):
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reg_method = "l2_decay"
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reg_coeff = weight_decay._coeff
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if isinstance(weight_decay, float):
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reg_method = "l2_decay"
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reg_coeff = weight_decay
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return reg_method, reg_coeff
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def _create_accumulators(self, block, parameters):
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'''
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if framework.in_dynamic_mode():
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return
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'''
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assert isinstance(block, (framework.Block, paddle.pir.Block))
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if isinstance(parameters, dict):
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parameters = self._update_param_group(parameters)
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for p in parameters:
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if p.name in self._already_create_accumulator:
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continue
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if self._multi_precision and self._is_dtype_fp16_or_bf16(p.dtype):
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master_p = self._create_master_weight(p)
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self._add_accumulator(self._velocity_acc_str, master_p)
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self._already_create_accumulator.add(p.name)
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continue
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if (
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self._is_dtype_fp16_or_bf16(p.dtype)
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and not self._multi_precision
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):
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warnings.warn(
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"Accumulating with FP16/BF16 in optimizer can lead to poor accuracy or slow convergence."
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"Consider using multi_precision=True option of the Momentum optimizer."
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)
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self._add_accumulator(self._velocity_acc_str, p)
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self._already_create_accumulator.add(p.name)
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def _create_regularization_of_grad(self, param, grad, regularization=None):
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"""Create and add backward regularization Operators
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Function helper of append_regularization_ops.
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"""
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# If ParamAttr is set to L2Decay, we skip doing regularization here. And then we fused
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# L2Decay with momentum which can refer to _append_optimize_op below.
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if hasattr(param, 'regularizer') and isinstance(
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param.regularizer, L2Decay
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):
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return grad
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return super()._create_regularization_of_grad(
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param, grad, regularization
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)
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def _append_optimize_op(self, block, param_and_grad):
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if not isinstance(block, (framework.Block, pir.Block)):
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raise TypeError("block is not instance of Block.")
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if isinstance(param_and_grad, dict):
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param_and_grad = self._update_param_group(param_and_grad)
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velocity_acc = self._get_accumulator_master(
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self._velocity_acc_str, param_and_grad[0]
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)
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lr = self._create_param_lr(param_and_grad)
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# For fusion of momentum and l2decay
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param = param_and_grad[0]
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regularization_method = self._regularization_method
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regularization_coeff = self._regularization_coeff
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if hasattr(param, 'regularizer'):
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# we skip param's l2decay before, so fuse it with momentum here.
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if isinstance(param.regularizer, L2Decay):
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regularization_method = "l2_decay"
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regularization_coeff = param.regularizer._coeff
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# the param's regularization has been done before, we avoid do l2decay in momentum.
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elif param.regularizer is not None:
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regularization_method = ""
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regularization_coeff = 0.0
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find_master = self._multi_precision and self._is_dtype_fp16_or_bf16(
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param_and_grad[0].dtype
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)
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master_weight = (
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self._master_weights[param_and_grad[0].name]
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if find_master
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else None
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)
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if in_dynamic_or_pir_mode():
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if isinstance(param_and_grad, dict):
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self._update_regularization(param_and_grad['weight_decay'])
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return _C_ops.momentum_(
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param_and_grad[0],
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param_and_grad[1],
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velocity_acc,
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lr,
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master_weight,
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self._momentum,
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self._use_nesterov,
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regularization_method,
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regularization_coeff,
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find_master,
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self._rescale_grad,
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)
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else:
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attrs = {
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"mu": self._momentum,
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"use_nesterov": self._use_nesterov,
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"regularization_method": regularization_method,
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"regularization_coeff": regularization_coeff,
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"multi_precision": find_master,
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"rescale_grad": self._rescale_grad,
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}
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inputs = {
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"Param": [param_and_grad[0]],
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"Grad": [param_and_grad[1]],
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"Velocity": [velocity_acc],
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"LearningRate": [lr],
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}
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outputs = {
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"ParamOut": [param_and_grad[0]],
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"VelocityOut": [velocity_acc],
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}
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if find_master:
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inputs["MasterParam"] = master_weight
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outputs["MasterParamOut"] = master_weight
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# create the momentum optimize op
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momentum_op = block.append_op(
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type=self.type,
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inputs=inputs,
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outputs=outputs,
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attrs=attrs,
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stop_gradient=True,
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)
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return momentum_op
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def _multi_tensor_init(self, target_block, parameters, param_group_idx):
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"""
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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).
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This function will be overridden in the corresponding optimizer file.
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Args:
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target_block: the block in which the loss tensor is present
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parameters: list of parameter tensors for the optimizer
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"""
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self._create_accumulators(target_block, parameters)
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for param in parameters:
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velocity_acc = self._get_accumulator_master(
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self._velocity_acc_str, param
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)
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regularization_method = self._regularization_method
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regularization_coeff = self._regularization_coeff
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if hasattr(param, 'regularizer'):
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# we skip param's l2decay before, so fuse it with momentum here.
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if isinstance(param.regularizer, L2Decay):
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regularization_method = "l2_decay"
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regularization_coeff = param.regularizer._coeff
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elif param.regularizer is not None:
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regularization_method = ""
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regularization_coeff = 0.0
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if param.dtype == paddle.float32:
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self._param_dict['FP32_DenseTensor'][param_group_idx].append(
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param
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)
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self._velocity_dict['FP32_DenseTensor'][param_group_idx].append(
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velocity_acc
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)
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# fp32 no master weight
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self._regularization_method_dict['FP32_DenseTensor'][
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param_group_idx
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].append(regularization_method)
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self._regularization_coeff_dict['FP32_DenseTensor'][
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param_group_idx
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].append(regularization_coeff)
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elif self._is_dtype_fp16_or_bf16(param.dtype):
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self._param_dict['FP16_DenseTensor'][param_group_idx].append(
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param
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)
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self._velocity_dict['FP16_DenseTensor'][param_group_idx].append(
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velocity_acc
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)
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if self._multi_precision:
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self._master_weight_dict['FP16_DenseTensor'][
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param_group_idx
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].append(self._master_weights[param.name])
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else:
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self._master_weight_dict['FP16_DenseTensor'][
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param_group_idx
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] = None
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self._regularization_method_dict['FP16_DenseTensor'][
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param_group_idx
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].append(regularization_method)
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self._regularization_coeff_dict['FP16_DenseTensor'][
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param_group_idx
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].append(regularization_coeff)
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else:
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raise ValueError(
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"Now multi_tensor_momentum only support fp32, fp16 or bf16 parameters and grad is DENSE_TENSOR."
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)
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def _append_optimize_multi_tensor_op(
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self,
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target_block,
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parameters_and_grads,
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param_group_idx,
|
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):
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"""
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For Multi Tensor, append optimize merged_operator to block.
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"""
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assert isinstance(target_block, framework.Block)
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grad_dict = {'FP32_DenseTensor': [], 'FP16_DenseTensor': []}
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lr_dict = {'FP32_DenseTensor': [], 'FP16_DenseTensor': []}
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if isinstance(parameters_and_grads, list):
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for param_and_grad in parameters_and_grads:
|
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if param_and_grad[1] is None:
|
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continue
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||||
if param_and_grad[0].stop_gradient is False:
|
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if (
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param_and_grad[0].dtype == paddle.float32
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||||
and param_and_grad[1].type
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||||
== core.VarDesc.VarType.DENSE_TENSOR
|
||||
):
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grad_dict['FP32_DenseTensor'].append(param_and_grad[1])
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lr = self._create_param_lr(param_and_grad)
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||||
lr_dict['FP32_DenseTensor'].append(lr)
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||||
elif (
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||||
self._is_dtype_fp16_or_bf16(param_and_grad[0].dtype)
|
||||
and param_and_grad[1].type
|
||||
== core.VarDesc.VarType.DENSE_TENSOR
|
||||
):
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||||
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
|
||||
Reference in New Issue
Block a user