wehub-resource-sync
399 lines
16 KiB
Python
399 lines
16 KiB
Python
# 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 ..base import core, framework
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from ..base.dygraph import no_grad
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from ..base.framework import name_scope
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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 _AdamaxParameterConfig(_ParameterConfig):
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beta1: NotRequired[float | Tensor]
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beta2: NotRequired[float | Tensor]
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epsilon: NotRequired[float | Tensor]
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__all__ = []
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class Adamax(Optimizer):
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r"""
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The Adamax optimizer is implemented based on the Adamax Optimization
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in Section 7 of `Adam paper <https://arxiv.org/abs/1412.6980>`_.
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The Adamax algorithm is a variant of the Adam algorithm based on the infinite norm,
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which makes the learning rate update algorithm more stable and simple.
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The parameter ``param_out`` update rule with gradient ``grad``:
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.. math::
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t & = t + 1
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moment\_out & = {\beta}_1 * moment + (1 - {\beta}_1) * grad
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inf\_norm\_out & = max({\beta}_2 * inf\_norm + \epsilon, |grad|)
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learning\_rate & = \frac{learning\_rate}{1 - {\beta}_1^t}
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param\_out & = param - learning\_rate * \frac{moment\_out}{inf\_norm\_out}
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Related paper: `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_
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The original paper does not have an ``epsilon`` attribute,
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it is added here for numerical stability to prevent the division by 0 error.
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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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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|Tensor, optional): A small float value for numerical stability.
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The default value is 1e-08.
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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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**Notes**:
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**Currently, Adamax 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.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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>>> beta1 = paddle.to_tensor([0.9], dtype="float32")
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>>> beta2 = paddle.to_tensor([0.99], dtype="float32")
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>>> adamax = paddle.optimizer.Adamax(
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... learning_rate=0.1,
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... parameters=linear.parameters(),
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... beta1=beta1,
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... beta2=beta2,
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... weight_decay=0.01,
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... )
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>>> out.backward()
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>>> adamax.step()
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>>> adamax.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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>>> adamax = paddle.optimizer.Adamax(
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... learning_rate=0.1,
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... parameters=[ # type: ignore
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... {
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... 'params': linear_1.parameters(),
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... },
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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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... ],
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... weight_decay=0.01,
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... beta1=0.9,
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... )
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>>> out.backward()
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>>> adamax.step()
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>>> adamax.clear_grad()
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"""
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type: str
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_moment_acc_str = "moment"
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_inf_norm_acc_str = "inf_norm"
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_beta1_pow_acc_str = "beta1_pow_acc"
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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 | Tensor = 1e-8,
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parameters: (
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Sequence[Tensor] | Sequence[_AdamaxParameterConfig] | 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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assert learning_rate is not None
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assert beta1 is not None
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assert beta2 is not None
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assert epsilon is not None
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if not 0 <= beta1 < 1:
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raise ValueError("Invalid value of beta1, expect beta1 in [0,1).")
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if not 0 <= beta2 < 1:
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raise ValueError("Invalid value of beta2, expect beta2 in [0,1).")
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if not 0 <= epsilon:
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raise ValueError("Invalid value of epsilon, expect epsilon >= 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 = "adamax"
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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 = core.VarDesc.VarType.FP32
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self._add_accumulator(self._moment_acc_str, p, dtype=acc_dtype)
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self._add_accumulator(self._inf_norm_acc_str, p, dtype=acc_dtype)
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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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fill_value=self._beta1,
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shape=[1],
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)
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def _create_accumulators(self, block, parameters):
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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 moment and infinity norm
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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/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_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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assert isinstance(block, (framework.Block, pir.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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moment = self._get_accumulator_master(
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self._moment_acc_str, param_and_grad[0]
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)
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inf_norm = self._get_accumulator_master(
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self._inf_norm_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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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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if framework.in_dynamic_or_pir_mode():
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_C_ops.adamax_(
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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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moment,
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inf_norm,
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beta1_pow_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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else:
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# create the adamax 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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"LearningRate": self._create_param_lr(param_and_grad),
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"Moment": moment,
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"InfNorm": inf_norm,
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"Beta1Pow": beta1_pow_acc,
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}
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outputs = {
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"ParamOut": param_and_grad[0],
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"MomentOut": moment,
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"InfNormOut": inf_norm,
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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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attrs = {
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"beta1": self._beta1,
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"beta2": self._beta2,
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"epsilon": self._epsilon,
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"multi_precision": find_master,
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}
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adamax_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 adamax_op
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def _finish_update(self, block, parameters_and_grads):
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"""Update Beta1 Power accumulator"""
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assert isinstance(block, (framework.Block, pir.Block))
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if isinstance(parameters_and_grads, list):
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for param, grad in parameters_and_grads:
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if grad is None or param.stop_gradient is True:
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continue
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if framework.in_dygraph_mode():
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beta1_pow_acc = self._get_accumulator_master(
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self._beta1_pow_acc_str, param
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)
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with no_grad():
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tmp = _C_ops.scale(
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beta1_pow_acc, self._beta1, 0.0, True
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)
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beta1_pow_acc.copy_(tmp, False)
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elif framework.in_pir_mode():
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with param.block.program._optimized_guard([param, grad]):
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beta1_pow_acc = self._get_accumulator_master(
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self._beta1_pow_acc_str, param
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)
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_C_ops.scale_(beta1_pow_acc, self._beta1, 0.0, True)
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else:
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with (
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param.block.program._optimized_guard([param, grad]),
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name_scope('adamax'),
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):
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beta1_pow_acc = self._get_accumulator_master(
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self._beta1_pow_acc_str, param
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)
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block.append_op(
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type="scale",
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inputs={"X": beta1_pow_acc},
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outputs={"Out": beta1_pow_acc},
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attrs={"scale": self._beta1},
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stop_gradient=True,
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)
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else:
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for param, grad in parameters_and_grads['params']:
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if grad is None or param.stop_gradient is True:
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continue
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if framework.in_dygraph_mode():
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beta1_pow_acc = self._get_accumulator_master(
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self._beta1_pow_acc_str, param
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)
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self._beta1 = parameters_and_grads.get(
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'beta1', self._default_dict['beta1']
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)
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with no_grad():
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tmp = _C_ops.scale(
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beta1_pow_acc, self._beta1, 0.0, True
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)
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beta1_pow_acc.copy_(tmp, False)
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else:
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with (
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param.block.program._optimized_guard([param, grad]),
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name_scope('adamax'),
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):
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beta1_pow_acc = self._get_accumulator_master(
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self._beta1_pow_acc_str, param
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)
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self._beta1 = parameters_and_grads.get(
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'beta1', self._default_dict['beta1']
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)
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block.append_op(
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type="scale",
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inputs={"X": beta1_pow_acc},
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outputs={"Out": beta1_pow_acc},
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attrs={"scale": self._beta1},
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stop_gradient=True,
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)
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def _update_param_group(self, parameters):
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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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self._epsilon = parameters.get('epsilon', self._default_dict['epsilon'])
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parameters = parameters.get('params')
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return parameters
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