chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2024 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 paddle import _C_ops, pir
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from paddle.base.libpaddle import DataType
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from ..base import core, framework
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from ..base.framework import (
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in_dynamic_or_pir_mode,
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in_pir_mode,
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)
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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 _RAdamParameterConfig(_ParameterConfig):
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beta1: NotRequired[float | Tensor]
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beta2: NotRequired[float | Tensor]
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epsilon: NotRequired[float]
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__all__ = []
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class RAdam(Optimizer):
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r"""
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The RAdam optimizer is implemented based on the Adam Optimization
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in paper `On the Variance of the Adaptive Learning Rate and Beyond <https://arxiv.org/abs/1908.03265>`_.
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RAdam improved the initial stability of training by modifying Adam's momentum term.
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.. math::
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\begin{aligned}
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&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
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&\hspace{6mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
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&\hspace{6mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\
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&\hspace{6mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\
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&\hspace{6mm}\rho_t \leftarrow \rho_{\infty} -
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2 t \beta^t_2 /\big(1-\beta_2^t \big) \\
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&\hspace{6mm}\textbf{if} \: \rho_t > 5 \\
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&\hspace{12mm} l_t \leftarrow \frac{\sqrt{ (1-\beta^t_2) }}{ \sqrt{v_t} +\epsilon } \\
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&\hspace{12mm} r_t \leftarrow
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\sqrt{\frac{(\rho_t-4)(\rho_t-2)\rho_{\infty}}{(\rho_{\infty}-4)(\rho_{\infty}-2) \rho_t}} \\
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&\hspace{12mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t} r_t l_t \\
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&\hspace{6mm}\textbf{else} \\
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&\hspace{12mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t} \\
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&\hspace{0mm} \text{ with: } \gamma_t \text{ (lr)}, \: \beta_1,\beta_2 \text{ (betas)}, \: \theta_t \text{ (params)} \\
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&\hspace{0mm} \rho_{\infty} \leftarrow 2/(1-\beta_2) -1
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\end{aligned}
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Args:
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learning_rate (float|LRScheduler, optional): The learning rate used to update ``Parameter``.
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It can be a float value or a LRScheduler. The default value is 0.001.
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parameters (list|tuple|None, optional): List/Tuple of ``Tensor`` names 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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beta1 (float|Tensor, optional): The exponential decay rate for the 1st moment estimates.
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It should be a float number or a 0-D Tensor with shape [] and data type as float32.
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The default value is 0.9.
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beta2 (float|Tensor, optional): The exponential decay rate for the 2nd moment estimates.
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It should be a float number or a 0-D Tensor with shape [] and data type as float32.
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The default value is 0.999.
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epsilon (float, optional): A small float value for numerical stability.
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The default value is 1e-08.
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weight_decay (int|float|Tensor|WeightDecayRegularizer|None, optional): The weight decay coefficient, it can be int, float or Tensor.
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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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Note:
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Currently, RAdam doesn't support sparse parameter optimization.
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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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>>> radam = paddle.optimizer.RAdam(
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... learning_rate=0.1,
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... parameters=linear.parameters()
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... )
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>>> out.backward()
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>>> radam.step()
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>>> radam.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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>>> opt = paddle.optimizer.RAdam(
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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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... 'beta1': 0.8
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... }],
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... weight_decay=0.01,
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... beta1=0.9
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... )
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>>> loss.backward()
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>>> opt.step()
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>>> opt.clear_grad()
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"""
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_beta1_pow_acc_str = "beta1_pow"
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_beta2_pow_acc_str = "beta2_pow"
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_rho_acc_str = "rho"
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_moment1_acc_str = "moment1"
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_moment2_acc_str = "moment2"
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def __init__(
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self,
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learning_rate: float | LRScheduler = 0.001,
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beta1: float | Tensor = 0.9,
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beta2: float | Tensor = 0.999,
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epsilon: float = 1.0e-8,
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parameters: (
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Sequence[Tensor] | Sequence[_RAdamParameterConfig] | None
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) = None,
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weight_decay: float | Tensor | 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 isinstance(learning_rate, (float, int)) and not 0.0 <= learning_rate:
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raise ValueError(
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f"Invalid learning rate: {learning_rate}, expect learning_rate >= 0."
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)
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if not 0.0 <= epsilon:
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raise ValueError(
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f"Invalid epsilon value: {epsilon}, expect epsilon >= 0."
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)
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if not 0.0 <= beta1 < 1.0:
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raise ValueError(
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f"Invalid beta1: {beta1}, expect 0. <= beta1 < 1.0."
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)
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if not 0.0 <= beta2 < 1.0:
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raise ValueError(
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f"Invalid beta2: {beta2}, expect 0. <= beta2 < 1.0."
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)
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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 = "radam"
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self._beta1 = beta1
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self._beta2 = beta2
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self._epsilon = epsilon
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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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'beta1': beta1,
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'beta2': beta2,
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'epsilon': epsilon,
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}
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def _add_moments_pows(self, p):
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acc_dtype = p.dtype
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if self._is_dtype_fp16_or_bf16(acc_dtype):
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acc_dtype = (
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DataType.FLOAT32 if in_pir_mode() else core.VarDesc.VarType.FP32
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)
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self._add_accumulator(
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name=self._beta1_pow_acc_str,
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param=p,
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dtype=acc_dtype,
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fill_value=1.0,
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)
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self._add_accumulator(
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name=self._beta2_pow_acc_str,
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param=p,
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dtype=acc_dtype,
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fill_value=1.0,
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)
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self._add_accumulator(
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name=self._rho_acc_str,
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param=p,
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dtype=acc_dtype,
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fill_value=1.0,
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)
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self._add_accumulator(self._moment1_acc_str, p, dtype=acc_dtype)
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self._add_accumulator(self._moment2_acc_str, p, dtype=acc_dtype)
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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_moments_pows(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_moments_pows(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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beta1_pow_acc = self._get_accumulator_master(
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self._beta1_pow_acc_str, param_and_grad[0]
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)
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beta2_pow_acc = self._get_accumulator_master(
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self._beta2_pow_acc_str, param_and_grad[0]
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)
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rho_acc = self._get_accumulator_master(
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self._rho_acc_str, param_and_grad[0]
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)
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moment1_acc = self._get_accumulator_master(
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self._moment1_acc_str, param_and_grad[0]
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)
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moment2_acc = self._get_accumulator_master(
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self._moment2_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.radam_(
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param_and_grad[0],
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param_and_grad[1],
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self._create_param_lr(param_and_grad),
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beta1_pow_acc,
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beta2_pow_acc,
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rho_acc,
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moment1_acc,
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moment2_acc,
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master_weight,
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self._beta1,
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self._beta2,
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self._epsilon,
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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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"beta1_pow": beta1_pow_acc,
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"beta2_pow": beta2_pow_acc,
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"rho": rho_acc,
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"moment1": moment1_acc,
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"moment2": moment2_acc,
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"learning_rate": self._create_param_lr(param_and_grad),
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}
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outputs = {
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"param_out": param_and_grad[0],
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"beta1_pow_out": beta1_pow_acc,
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"beta2_pow_out": beta2_pow_acc,
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"rho_out": rho_acc,
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"moment1_out": moment1_acc,
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"moment2_out": moment2_acc,
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}
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if find_master:
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inputs["master_param"] = master_weight
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outputs["master_param_out"] = master_weight
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radam_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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"beta1": self._beta1,
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"beta2": self._beta2,
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},
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stop_gradient=True,
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)
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return radam_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._beta1 = parameters.get('beta1', self._default_dict['beta1'])
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self._beta2 = parameters.get('beta2', self._default_dict['beta2'])
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parameters = parameters.get('params')
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return parameters
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