chore: import upstream snapshot with attribution
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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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from typing_extensions import NotRequired
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from paddle import _C_ops, pir
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from ..base import framework
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from ..base.framework import in_dynamic_or_pir_mode
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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.optimizer.lr import LRScheduler
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from paddle.regularizer import WeightDecayRegularizer
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from .optimizer import _ParameterConfig
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class _RMSPropParameterConfig(_ParameterConfig):
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epsilon: NotRequired[float]
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momentum: NotRequired[float]
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rho: NotRequired[float]
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centered: NotRequired[bool]
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__all__ = []
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class RMSProp(Optimizer):
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r"""
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Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning
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rate method. The original slides proposed RMSProp: Slide 29 of
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http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf .
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The original equation is as follows:
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.. math::
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r(w, t) & = \rho r(w, t-1) + (1 - \rho)(\nabla Q_{i}(w))^2
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w & = w - \frac{\eta} {\sqrt{r(w,t) + \epsilon}} \nabla Q_{i}(w)
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The first equation calculates moving average of the squared gradient for
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each weight. Then dividing the gradient by :math:`sqrt{v(w,t)}`.
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In some cases, adding a momentum term :math: `\\beta` is beneficial.
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In our implementation, Nesterov momentum is used:
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.. math::
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r(w, t) & = \rho r(w, t-1) + (1 - \rho)(\nabla Q_{i}(w))^2
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v(w, t) & = \beta v(w, t-1) + \frac{\eta} {\sqrt{r(w,t) +
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\epsilon}} \nabla Q_{i}(w)
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w & = w - v(w, t)
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if centered is True:
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.. math::
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r(w, t) & = \rho r(w, t-1) + (1 - \rho)(\nabla Q_{i}(w))^2
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g(w, t) & = \rho g(w, t-1) + (1 - \rho)\nabla Q_{i}(w)
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v(w, t) & = \beta v(w, t-1) + \frac{\eta} {\sqrt{r(w,t) - (g(w, t))^2 +
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\epsilon}} \nabla Q_{i}(w)
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w & = w - v(w, t)
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where, :math:`\rho` is a hyperparameter and typical values are 0.9, 0.95
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and so on. :math:`\beta` is the momentum term. :math:`\epsilon` is a
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smoothing term to avoid division by zero, usually set somewhere in range
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from 1e-4 to 1e-8.
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Parameters:
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learning_rate (float|LRScheduler): The learning rate used to update ``Parameter``.
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It can be a float value or a LRScheduler.
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rho(float, optional): rho is :math:`\rho` in equation, default is 0.95.
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epsilon(float, optional): :math:`\epsilon` in equation is smoothing term to
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avoid division by zero, default is 1e-6.
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momentum(float, optional): :math:`\beta` in equation is the momentum term,
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default is 0.0.
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centered(bool, optional): If True, gradients are normalized by the estimated variance of
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the gradient; if False, by the uncentered second moment. Setting this to
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True may help with training, but is slightly more expensive in terms of
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computation and memory. Defaults to False.
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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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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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name (str|None, optional): Normally there is no need for user to set this property.
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For more information, please refer to :ref:`api_guide_Name`.
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The default value is None.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> inp = paddle.rand([10,10], dtype="float32")
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>>> linear = paddle.nn.Linear(10, 10)
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>>> out = linear(inp)
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>>> loss = paddle.mean(out)
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>>> rmsprop = paddle.optimizer.RMSProp(
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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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>>> out.backward()
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>>> rmsprop.step()
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>>> rmsprop.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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>>> rmsprop = paddle.optimizer.RMSProp(
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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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... )
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>>> out.backward()
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>>> rmsprop.step()
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>>> rmsprop.clear_grad()
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"""
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_momentum_acc_str = "momentum"
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_mean_square_acc_str = "mean_square"
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_mean_grad_acc_str = "mean_grad"
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def __init__(
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self,
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learning_rate: float | LRScheduler,
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rho: float = 0.95,
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epsilon: float = 1.0e-6,
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momentum: float = 0.0,
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centered: bool = False,
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parameters: (
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Sequence[Tensor] | Sequence[_RMSPropParameterConfig] | None
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) = None,
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weight_decay: float | WeightDecayRegularizer | None = None,
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grad_clip: GradientClipBase | None = None,
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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 rho is None:
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raise ValueError("rho is not set.")
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if epsilon is None:
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raise ValueError("epsilon 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 not 0.0 <= epsilon:
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raise ValueError("Invalid value of epsilon, expect epsilon >= 0.")
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if not 0.0 <= momentum:
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raise ValueError("Invalid value of momentum, expect momentum >= 0.")
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if not 0.0 <= rho:
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raise ValueError("Invalid value of rho, expect rho >= 0.")
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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=weight_decay,
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grad_clip=grad_clip,
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name=name,
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)
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self.type = "rmsprop"
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self._rho = rho
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self._epsilon = epsilon
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self._momentum = momentum
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self._centered = centered
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self._multi_precision = False
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self._master_weights = {}
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self._default_dict = {
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'rho': rho,
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'epsilon': epsilon,
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'momentum': momentum,
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'centered': centered,
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}
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def _create_accumulators(self, block, parameters):
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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(parameters, dict):
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parameters = parameters.get('params')
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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._momentum_acc_str, master_p)
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self._add_accumulator(self._mean_square_acc_str, master_p)
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self._add_accumulator(self._mean_grad_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 in optimizer can lead to poor accuracy or slow convergence."
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"Consider using multi_precision=True option of the Lars optimizer."
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)
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self._add_accumulator(self._momentum_acc_str, p)
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self._add_accumulator(self._mean_square_acc_str, p)
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self._add_accumulator(self._mean_grad_acc_str, p)
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self._already_create_accumulator.add(p.name)
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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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momentum_acc = self._get_accumulator_master(
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self._momentum_acc_str, param_and_grad[0]
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)
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mean_square_acc = self._get_accumulator_master(
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self._mean_square_acc_str, param_and_grad[0]
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)
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mean_grad_acc = self._get_accumulator_master(
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self._mean_grad_acc_str, param_and_grad[0]
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)
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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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_C_ops.rmsprop_(
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param_and_grad[0],
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mean_square_acc,
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param_and_grad[1],
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momentum_acc,
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self._create_param_lr(param_and_grad),
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mean_grad_acc,
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master_weight,
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self._epsilon,
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self._rho,
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self._momentum,
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self._centered,
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find_master,
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)
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return None
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else:
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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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"Moment": momentum_acc,
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"MeanSquare": mean_square_acc,
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"MeanGrad": mean_grad_acc,
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"LearningRate": self._create_param_lr(param_and_grad),
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}
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outputs = {
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"ParamOut": param_and_grad[0],
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"MomentOut": momentum_acc,
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"MeanSquareOut": mean_square_acc,
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"MeanGradOut": mean_grad_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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rmsprop_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={
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"epsilon": self._epsilon,
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"decay": self._rho,
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"momentum": self._momentum,
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"centered": self._centered,
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},
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stop_gradient=True,
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)
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return rmsprop_op
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def _update_param_group(self, parameters):
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self._epsilon = parameters.get('epsilon', self._default_dict['epsilon'])
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self._rho = parameters.get('rho', self._default_dict['rho'])
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self._momentum = parameters.get(
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'momentum', self._default_dict['momentum']
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)
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self._centered = parameters.get(
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'centered', self._default_dict['centered']
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)
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parameters = parameters.get('params')
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return parameters
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