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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from paddle import _C_ops, pir
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from paddle.tensor.creation import to_tensor
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from ..base import framework
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from ..base.dygraph import no_grad
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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 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 .optimizer import _ParameterConfig
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__all__ = []
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class Rprop(Optimizer):
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r"""
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**Notes: This optimizer is only applicable to full-batch training.**
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Optimizer of the Rprop algorithm.Please refer to this for details:
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`A direct adaptive method for faster backpropagation learning : The RPROP algorithm <https://ieeexplore.ieee.org/document/298623>`_.
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.. math::
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\begin{aligned}
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&\hspace{0mm} For\ all\ weights\ and\ biases\{ \\
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&\hspace{5mm} \textbf{if} \: (\frac{\partial E}{\partial w_{ij}}(t-1)*\frac{\partial E}{\partial w_{ij}}(t)> 0)\ \textbf{then} \: \{ \\
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&\hspace{10mm} learning\_rate_{ij}(t)=\mathrm{minimum}(learning\_rate_{ij}(t-1)*\eta^{+},learning\_rate_{max}) \\
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&\hspace{10mm} \Delta w_{ij}(t)=-sign(\frac{\partial E}{\partial w_{ij}}(t))*learning\_rate_{ij}(t) \\
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&\hspace{10mm} w_{ij}(t+1)=w_{ij}(t)+\Delta w_{ij}(t) \\
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&\hspace{5mm} \} \\
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&\hspace{5mm} \textbf{else if} \: (\frac{\partial E}{\partial w_{ij}}(t-1)*\frac{\partial E}{\partial w_{ij}}(t)< 0)\ \textbf{then} \: \{ \\
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&\hspace{10mm} learning\_rate_{ij}(t)=\mathrm{maximum}(learning\_rate_{ij}(t-1)*\eta^{-},learning\_rate_{min}) \\
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&\hspace{10mm} w_{ij}(t+1)=w_{ij}(t) \\
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&\hspace{10mm} \frac{\partial E}{\partial w_{ij}}(t)=0 \\
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&\hspace{5mm} \} \\
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&\hspace{5mm} \textbf{else if} \: (\frac{\partial E}{\partial w_{ij}}(t-1)*\frac{\partial E}{\partial w_{ij}}(t)= 0)\ \textbf{then} \: \{ \\
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&\hspace{10mm} \Delta w_{ij}(t)=-sign(\frac{\partial E}{\partial w_{ij}}(t))*learning\_rate_{ij}(t) \\
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&\hspace{10mm} w_{ij}(t+1)=w_{ij}(t)+\Delta w_{ij}(t) \\
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&\hspace{5mm} \} \\
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&\hspace{0mm} \} \\
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\end{aligned}
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Parameters:
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learning_rate (float|Tensor|LRScheduler, optional): The initial 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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learning_rate_range (tuple, optional): The range of learning rate.
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Learning rate cannot be smaller than the first element of the tuple;
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learning rate cannot be larger than the second element of the tuple.
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The default value is (1e-5, 50).
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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.
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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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etas (tuple, optional): Tuple used to update learning rate.
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The first element of the tuple is the multiplicative decrease factor;
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the second element of the tuple is the multiplicative increase factor.
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The default value is (0.5, 1.2).
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grad_clip (GradientClipBase|None, optional): Gradient clipping strategy, it's an instance of some derived class of ``GradientClipBase`` .
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There are three clipping strategies ( :ref:`api_paddle_nn_ClipGradByGlobalNorm` , :ref:`api_paddle_nn_ClipGradByNorm` , :ref:`api_paddle_nn_ClipGradByValue` ).
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Default None, meaning there is no gradient clipping.
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multi_precision (bool, optional): In mixed precision training scenarios based on GPU,
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this parameter is mainly used to ensure the numerical stability of gradient updates.
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When it is set to True, the optimizer will save a backup of FP32 type parameters with an equal value for FP16 type parameters.
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When updating gradients, first increase the gradient type to FP32, and then assign it to the FP32 type parameter backup.
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Finally, the updated FP32 type value will be converted to FP16 type first,
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and then assigned to the actual FP16 type parameters participating in the calculation.
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The default value is False.
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name (str|None, optional): The default value is None. 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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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> inp = paddle.uniform(min=-0.1, max=0.1, shape=[1, 100], dtype='float32')
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>>> linear = paddle.nn.Linear(100, 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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>>> rprop = paddle.optimizer.Rprop(
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... learning_rate=0.001,
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... learning_rate_range=(0.0001, 0.1),
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... parameters=linear.parameters(),
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... etas=(0.5, 1.2)
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... )
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>>> out.backward()
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>>> rprop.step()
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>>> rprop.clear_grad()
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"""
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_prevs_acc_str = "prevs"
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_learning_rates_acc_str = "learning_rates"
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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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learning_rate_range: tuple[float, float] = (1e-5, 50),
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parameters: Sequence[Tensor] | Sequence[_ParameterConfig] | None = None,
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etas: tuple[float, float] = (0.5, 1.2),
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grad_clip: GradientClipBase | None = None,
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multi_precision: 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 (
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not 0.0
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< learning_rate_range[0]
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<= learning_rate
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<= learning_rate_range[1]
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):
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raise ValueError(
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"'0.0 < learning_rate_range[0] <= learning_rate <= learning_rate_range[1]' must be true"
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)
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if not 0.0 < etas[0] < 1.0 < etas[1]:
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raise ValueError("'0.0 < etas[0] < 1.0 < etas[1]' must be true")
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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=0.0,
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grad_clip=grad_clip,
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name=name,
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)
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self.type = "rprop"
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self._initial_learning_rate = learning_rate
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self._multi_precision = multi_precision
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self._master_weights = {}
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self._learning_rate_range = [learning_rate_range]
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self._etas = [etas]
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self._sign = True
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def _to_tensor(self, block, dtype):
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assert isinstance(block, (framework.Block, pir.Block))
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self._learning_rate_range = to_tensor(
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self._learning_rate_range, dtype=dtype
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)
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self._etas = to_tensor(self._etas, dtype=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 = self._update_param_group(parameters)
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# Create accumulator tensors for first and second moments
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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(
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self._prevs_acc_str,
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master_p,
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p.dtype,
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0,
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)
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self._add_accumulator(
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self._learning_rates_acc_str,
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master_p,
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p.dtype,
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self._initial_learning_rate,
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)
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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 Adam optimizer."
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)
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self._add_accumulator(
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self._prevs_acc_str,
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p,
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p.dtype,
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0,
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)
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self._add_accumulator(
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self._learning_rates_acc_str,
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p,
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p.dtype,
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fill_value=self._initial_learning_rate,
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)
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self._already_create_accumulator.add(p.name)
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@no_grad
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def _append_optimize_op(self, block, param_and_grad):
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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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if self._sign:
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self._to_tensor(block, param_and_grad[0][0].dtype)
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self._sign = False
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prevs = self._get_accumulator_master(
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self._prevs_acc_str, param_and_grad[0]
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)
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learning_rates = self._get_accumulator_master(
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self._learning_rates_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.rprop_(
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param_and_grad[0],
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param_and_grad[1],
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prevs,
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learning_rates,
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master_weight,
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self._learning_rate_range,
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self._etas,
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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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assert isinstance(block, framework.Block)
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# create the optimize op
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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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"prev": prevs,
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"learning_rate": learning_rates,
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"learning_rate_range": self._learning_rate_range,
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"etas": self._etas,
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}
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outputs = {
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"param_out": param_and_grad[0],
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"prev_out": prevs,
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"learning_rate_out": learning_rates,
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}
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attrs = {"multi_precision": find_master}
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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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rprop_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 rprop_op
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def _update_param_group(self, parameters):
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parameters = parameters.get('params')
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return parameters
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