wehub-resource-sync
274 lines
10 KiB
Python
274 lines
10 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 paddle.framework import (
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in_dynamic_or_pir_mode,
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)
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from ..base import framework
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from .optimizer import Optimizer
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if TYPE_CHECKING:
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from collections.abc import Sequence
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from typing_extensions import NotRequired
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from paddle import Tensor
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from paddle.nn.clip import GradientClipBase
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from paddle.regularizer import WeightDecayRegularizer
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from .optimizer import _ParameterConfig
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class _AdagradParameterConfig(_ParameterConfig):
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epsilon: NotRequired[float]
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initial_accumulator_value: NotRequired[float]
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__all__ = []
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class Adagrad(Optimizer):
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r"""
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The Adaptive Gradient optimizer (Adagrad for short) use an optimization described
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in paper: `Adaptive Subgradient Methods for Online Learning and
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Stochastic Optimization <http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf>`_.
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The parameter ``param_out`` update rule with gradient ``grad``:
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.. math::
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moment\_out &= moment + grad * grad
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param\_out &= param - \frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
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The original paper does not have the ``epsilon`` attribute. It is added here
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in our implementation as also proposed `Per-parameter adaptive learning rate
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methods <http://cs231n.github.io/neural-networks-3/#ada>`_
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for numerical stability to avoid the division by zero error.
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Args:
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learning_rate (float|Tensor): The learning rate used to update ``Parameter``.
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It can be a float value or a ``Variable`` with a float type.
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epsilon (float, optional): A small float value for numerical stability.
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The default value is 1e-06.
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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 canbe 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_base_param_attr_aramAttr` 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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ClipGradByGlobalNorm, ClipGradByNorm and ClipGradByValue. Default None,
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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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initial_accumulator_value (float, optional): Initial value for moment accumulator.
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The default value is 0.0.
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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(shape=[10, 10])
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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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>>> adagrad = paddle.optimizer.Adagrad(
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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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>>> adagrad.step()
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>>> adagrad.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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>>> adagrad = paddle.optimizer.Adagrad(
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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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... },
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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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>>> adagrad.step()
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>>> adagrad.clear_grad()
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"""
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type: str
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initial_accumulator_value: float
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_moment_acc_str = "moment"
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def __init__(
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self,
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learning_rate: float | Tensor,
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epsilon: float = 1.0e-6,
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parameters: (
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Sequence[Tensor] | Sequence[_AdagradParameterConfig] | 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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initial_accumulator_value: float = 0.0,
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) -> None:
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assert learning_rate is not None
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assert epsilon is not None
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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 = "adagrad"
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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.initial_accumulator_value = initial_accumulator_value
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self._default_dict = {
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'epsilon': epsilon,
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'initial_accumulator_value': initial_accumulator_value,
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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 = self._update_param_group(parameters)
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for p in parameters:
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if p.name in self._already_create_accumulator:
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continue
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if self._multi_precision and self._is_dtype_fp16_or_bf16(p.dtype):
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master_p = self._create_master_weight(p)
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self._add_accumulator(
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self._moment_acc_str,
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master_p,
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fill_value=self.initial_accumulator_value,
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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 Momentum optimizer."
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)
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self._add_accumulator(
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self._moment_acc_str,
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p,
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fill_value=self.initial_accumulator_value,
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)
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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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moment_acc = self._get_accumulator_master(
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self._moment_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.adagrad_(
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param_and_grad[0],
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param_and_grad[1],
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moment_acc,
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self._create_param_lr(param_and_grad),
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master_weight if find_master else None,
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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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# Create the adagrad optimizer 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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"Moment": moment_acc,
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"LearningRate": self._create_param_lr(param_and_grad),
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}
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outputs = {"ParamOut": param_and_grad[0], "MomentOut": moment_acc}
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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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adagrad_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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"multi_precision": find_master,
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},
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stop_gradient=True,
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)
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return adagrad_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.initial_accumulator_value = parameters.get(
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'initial_accumulator_value',
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self._default_dict['initial_accumulator_value'],
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)
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parameters = parameters.get('params')
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return parameters
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