chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import warnings
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from typing import TYPE_CHECKING
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import paddle
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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_dygraph_mode, in_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.regularizer import WeightDecayRegularizer
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from .lr import LRScheduler
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from .optimizer import _ParameterConfig
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__all__ = []
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class ASGD(Optimizer):
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r"""
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Optimizer of the ASGD algorithm.Please refer to this for details:
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`Minimizing Finite Sums with the Stochastic Average Gradient <https://hal.science/hal-00860051v2>`_.
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.. math::
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\begin{aligned}
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&\hspace{0mm} d=0,\ y_i=0\ \textbf{for}\ i=1,2,...,n \\
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&\hspace{0mm} \textbf{for}\ \: m=0,1,...\ \textbf{do} \: \\
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&\hspace{5mm} i=m\ \%\ n \\
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&\hspace{5mm} d=d-y_i+f_i{}'(x) \\
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&\hspace{5mm} y_i=f_i{}'(x) \\
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&\hspace{5mm} x=x-learning\_rate(\frac{d}{\mathrm{min}(m+1,\ n)}+\lambda x) \\
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&\hspace{0mm} \textbf{end for} \\
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\end{aligned}
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Parameters:
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learning_rate (float|Tensor|LRScheduler, optional): The 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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batch_num (int, optional): The number of batches needed to complete one epoch.
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Assuming the total number of samples is ``all``,
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it is recommended to set ``batch_num`` to ``all`` / ``batch_size``.
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In situations where the graphics memory is tight,
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it is possible to reduce the batch_num appropriately.
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The default value is 1.
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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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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 :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 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=[10, 10], dtype='float32')
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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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>>> asgd = paddle.optimizer.ASGD(
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... learning_rate=0.001,
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... batch_num=10,
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... parameters=linear.parameters(),
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... weight_decay=0.01
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... )
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>>> out.backward()
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>>> asgd.step()
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>>> asgd.clear_grad()
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"""
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type: str
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_d_acc_str = "d"
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_y_acc_str = "y"
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_m_acc_str = "m"
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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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batch_num: int = 1,
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parameters: Sequence[Tensor] | Sequence[_ParameterConfig] | None = None,
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weight_decay: float | WeightDecayRegularizer | None = None,
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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 should not be none")
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if batch_num is None:
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raise ValueError("batch_num should not be none")
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if not 0 < batch_num:
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raise ValueError("batch_num should be greater than 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 = "asgd"
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self._multi_precision = multi_precision
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self._master_weights = {}
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self._n = batch_num
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self._n_tensor = None
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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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p_new = p
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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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p_new = master_p
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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._d_acc_str,
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p_new,
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p.dtype,
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0,
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)
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# Sometimes p.shape is a tuple, so we need to change it to a list
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self._add_accumulator(
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self._y_acc_str,
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p_new,
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p.dtype,
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0,
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[self._n, *list(p.shape)],
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)
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self._add_accumulator(
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self._m_acc_str,
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p_new,
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"int64",
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0,
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[1],
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)
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self._already_create_accumulator.add(p.name)
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def _assign_accumulator_master(
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self, block, name, param, assign_value, index
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):
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if self._name is not None:
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name = self._name + "_" + name
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find_master = self._multi_precision and self._is_dtype_fp16_or_bf16(
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param.dtype
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)
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target_param = (
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self._master_weights[param.name] if find_master else param
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)
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target_name = target_param.name
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if (
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name not in self._accumulators
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or target_name not in self._accumulators[name]
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):
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raise Exception(
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f"Accumulator {name} does not exist for parameter {target_name}"
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)
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if in_pir_mode():
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if index is None:
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self._accumulators[name][target_name] = paddle.assign(
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assign_value
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)
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else:
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self._accumulators[name][target_name][index] = paddle.assign(
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assign_value
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)
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else:
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assert isinstance(block, framework.Block)
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assign_inputs = {
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"X": assign_value,
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}
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assign_outputs = {
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"Out": self._accumulators[name][target_name],
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}
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block.append_op(
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type="assign",
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inputs=assign_inputs,
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outputs=assign_outputs,
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)
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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._n_tensor is None:
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self._n_tensor = to_tensor(
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[self._n],
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)
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d = self._get_accumulator_master(self._d_acc_str, param_and_grad[0])
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m = self._get_accumulator_master(self._m_acc_str, param_and_grad[0])
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ys = self._get_accumulator_master(self._y_acc_str, param_and_grad[0])
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index = paddle.mod(m, self._n_tensor).item()
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y = paddle.assign(ys[index])
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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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lr = self._create_param_lr(param_and_grad)
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if in_dygraph_mode():
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m.add_(to_tensor([1], dtype=m.dtype))
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_C_ops.asgd_(
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param_and_grad[0],
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param_and_grad[1],
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lr,
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d,
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ys[index],
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paddle.fmin(m, self._n_tensor),
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master_weight,
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find_master,
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)
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return None
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elif in_pir_mode():
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m = paddle.assign(paddle.add(m, to_tensor([1], dtype=m.dtype)))
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self._assign_accumulator_master(
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block, self._m_acc_str, param_and_grad[0], m, None
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)
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# The y in the static graph has one more dimension than the y in the dynamic graph.
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# So we should unify the shape of y in both dynamic and static graph.
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# eg:
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# dynamic graph: y.shape is [2, 2]
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# static graph: y.shape is [1, 2, 2]
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# so we should do
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# static graph: y = y[0]
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y = y[0]
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_C_ops.asgd_(
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param_and_grad[0],
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param_and_grad[1],
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lr,
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d,
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y,
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paddle.fmin(m, self._n_tensor),
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master_weight,
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find_master,
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)
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self._assign_accumulator_master(
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block, self._y_acc_str, param_and_grad[0], y, index
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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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add_inputs = {
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"X": m,
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"Y": to_tensor([1], dtype=m.dtype),
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}
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add_outputs = {
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"Out": m,
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}
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block.append_op(
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type="elementwise_add",
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inputs=add_inputs,
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outputs=add_outputs,
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)
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# The y in the static graph has one more dimension than the y in the dynamic graph.
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# So we should unify the shape of y in both dynamic and static graph.
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# eg:
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# dynamic graph: y.shape is [2, 2]
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# static graph: y.shape is [1, 2, 2]
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# so we should do
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# static graph: y = y[0]
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y = y[0]
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asgd_inputs = {
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"param": param_and_grad[0],
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"grad": param_and_grad[1],
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"learning_rate": lr,
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"d": d,
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"y": y,
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"n": paddle.fmin(m, self._n_tensor),
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}
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asgd_outputs = {
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"param_out": param_and_grad[0],
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"d_out": d,
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"y_out": y,
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}
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asgd_attrs = {"multi_precision": find_master}
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if find_master:
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asgd_inputs["master_param"] = master_weight
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asgd_outputs["master_param_out"] = master_weight
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asgd_op = block.append_op(
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type=self.type,
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inputs=asgd_inputs,
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outputs=asgd_outputs,
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attrs=asgd_attrs,
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stop_gradient=True,
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)
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ys = paddle.static.setitem(ys, index, y)
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self._assign_accumulator_master(
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block, self._y_acc_str, param_and_grad[0], ys, None
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)
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return asgd_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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