wehub-resource-sync
974 lines
41 KiB
Python
974 lines
41 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 collections import defaultdict
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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.base.libpaddle import DataType
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from paddle.pir import Value
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from ..base import core, framework
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from ..base.dygraph import base as imperative_base
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from ..base.framework import (
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Variable,
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in_dygraph_mode,
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in_dynamic_or_pir_mode,
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in_pir_mode,
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)
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from .optimizer import Optimizer
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if TYPE_CHECKING:
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from collections.abc import Callable, 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 _AdamParameterConfig(_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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lazy_mode: NotRequired[bool]
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__all__ = []
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class Adam(Optimizer):
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r"""
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The Adam optimizer uses an optimization described at the end
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of section 2 of `Adam paper <https://arxiv.org/abs/1412.6980>`_ ,
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it can dynamically adjusts the learning rate of each parameter using
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the 1st moment estimates and the 2nd moment estimates of the gradient.
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The parameter ``param_out`` update rule with gradient ``grad``:
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.. math::
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\begin{aligned}
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&\hspace{5mm} t = t + 1 \\
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&\hspace{5mm} moment\_1\_out = {\beta}_1 * moment\_1 + (1 - {\beta}_1) * grad \\
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&\hspace{5mm} moment\_2\_out = {\beta}_2 * moment\_2 + (1 - {\beta}_2) * grad * grad \\
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&\hspace{5mm} learning\_rate = learning\_rate * \frac{\sqrt{1 - {\beta}_2^t}}{1 - {\beta}_1^t} \\
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&\hspace{5mm}\textbf{if} \: \textit{amsgrad}: \\
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&\hspace{15mm} moment\_2\_max\_out = max(moment\_2\_out, moment\_2\_max) \\
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&\hspace{15mm} param\_out = param - learning\_rate * \frac{moment\_1\_out}{\sqrt{moment\_2\_max\_out} + \epsilon} \\
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&\hspace{5mm}\textbf{else}: \: \\
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&\hspace{15mm} param\_out = param - learning\_rate * \frac{moment\_1\_out}{\sqrt{moment\_2\_out} + \epsilon} \\
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\end{aligned}
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Related paper: `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_
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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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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 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 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_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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lazy_mode (bool, optional): The official Adam algorithm has two moving-average accumulators.
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The accumulators are updated at every step. Every element of the two moving-average
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is updated in both dense mode and sparse mode. If the size of parameter is very large,
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then the update may be very slow. The lazy mode only update the element that has
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gradient in current mini-batch, so it will be much more faster. But this mode has
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different semantics with the original Adam algorithm and may lead to different result.
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The default value is False.
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multi_precision (bool, optional): Whether to use multi-precision during weight updating. Default is false.
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use_multi_tensor (bool, optional): Whether to use multi-tensor strategy to update all parameters at once . Default is false.
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amsgrad (bool, optional): Whether to use the AMSGrad variant of this algorithm from the paper
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`On the Convergence of Adam and Beyond <https://openreview.net/forum?id=ryQu7f-RZ>`_. Default is false.
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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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Examples:
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.. code-block:: pycon
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:name: code-example1
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>>> import paddle
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>>> linear = paddle.nn.Linear(10, 10)
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>>> inp = paddle.rand([10,10], dtype="float32")
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>>> out = linear(inp)
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>>> loss = paddle.mean(out)
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>>> adam = paddle.optimizer.Adam(
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... learning_rate=0.1,
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... parameters=linear.parameters()
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... )
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>>> loss.backward()
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>>> adam.step()
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>>> adam.clear_grad()
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.. code-block:: pycon
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:name: code-example2
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>>> # Adam with beta1/beta2 as Tensor and weight_decay as float
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>>> import paddle
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>>> linear = paddle.nn.Linear(10, 10)
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>>> inp = paddle.rand([10,10], dtype="float32")
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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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>>> adam = paddle.optimizer.Adam(
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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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>>> loss.backward()
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>>> adam.step()
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>>> adam.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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>>> adam = paddle.optimizer.Adam(
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... learning_rate=0.1,
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... parameters=[{ # type: ignore
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... 'params': linear_1.parameters()
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... }, {
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... 'params': linear_2.parameters(),
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... 'weight_decay': 0.001,
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... 'learning_rate': 0.1,
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... 'beta1': 0.8
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... }],
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... weight_decay=0.01,
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... beta1=0.9
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... )
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>>> loss.backward()
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>>> adam.step()
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>>> adam.clear_grad()
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"""
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type: str
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_moment1_acc_str = "moment1"
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_moment2_acc_str = "moment2"
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_moment2_acc_max_str = "moment2_max"
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_beta1_pow_acc_str = "beta1_pow_acc"
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_beta2_pow_acc_str = "beta2_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[_AdamParameterConfig] | 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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lazy_mode: bool = False,
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multi_precision: bool = False,
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use_multi_tensor: bool = False,
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amsgrad: bool = False,
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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 isinstance(beta1, (Variable, Value)):
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if not 0 <= beta1 < 1:
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raise ValueError(
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"Invalid value of beta1, expect beta1 in [0,1)."
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)
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if not isinstance(beta2, (Variable, Value)):
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if not 0 <= beta2 < 1:
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raise ValueError(
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"Invalid value of beta2, expect beta2 in [0,1)."
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)
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if not isinstance(epsilon, (Variable, Value)):
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if not 0 <= epsilon:
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raise ValueError(
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"Invalid value of epsilon, expect epsilon >= 0."
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)
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super().__init__(
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learning_rate=learning_rate,
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parameters=parameters,
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weight_decay=weight_decay,
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grad_clip=grad_clip,
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name=name,
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)
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self.type = "adam"
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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._lazy_mode = lazy_mode
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self._multi_precision = multi_precision
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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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'lazy_mode': lazy_mode,
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}
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self._use_multi_tensor = use_multi_tensor
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if self._use_multi_tensor:
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self._param_dict = self._create_multi_tensor_dict()
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self._moment1_dict = self._create_multi_tensor_dict()
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self._moment2_dict = self._create_multi_tensor_dict()
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self._moment2_max_dict = (
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self._create_multi_tensor_dict() if amsgrad else None
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)
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self._beta1_pow_acc_dict = self._create_multi_tensor_dict()
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self._beta2_pow_acc_dict = self._create_multi_tensor_dict()
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self._master_weight_dict = self._create_multi_tensor_dict()
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self._master_weight_dict['FP32_DenseTensor'] = None
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# whether to use AMSGrad
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self._amsgrad = amsgrad
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def get_lr_dtype(self) -> paddle.dtype:
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return paddle.float64
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def _create_regularization_of_grad(self, param, grad, regularization=None):
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from paddle.regularizer import L2Decay
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if (
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regularization is not None
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and isinstance(regularization, L2Decay)
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and paddle.get_flags(['FLAGS_use_accuracy_compatible_kernel']).get(
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'FLAGS_use_accuracy_compatible_kernel', False
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)
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):
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# PyTorch fused Adam: grad += param * weight_decay in the kernel
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# where weight_decay is double. The effective grad is:
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# float32(float64(grad) + float64(param) * float64(wd))
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# Replicate without intermediate float32 truncation.
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wd = float(regularization._coeff) # Python float (float64)
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return (grad.cast('float64') + param.cast('float64') * wd).cast(
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'float32'
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)
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return super()._create_regularization_of_grad(
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param, grad, regularization
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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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if in_pir_mode():
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acc_dtype = DataType.FLOAT32
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else:
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acc_dtype = core.VarDesc.VarType.FP32
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self._add_accumulator(self._moment1_acc_str, p, dtype=acc_dtype)
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self._add_accumulator(self._moment2_acc_str, p, dtype=acc_dtype)
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if self._amsgrad:
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self._add_accumulator(self._moment2_acc_max_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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dtype=acc_dtype,
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fill_value=(
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0.9
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if isinstance(self._beta1, (Variable, Value))
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else self._beta1
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),
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shape=[1],
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type=core.VarDesc.VarType.DENSE_TENSOR,
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device='cpu',
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)
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self._add_accumulator(
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name=self._beta2_pow_acc_str,
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param=p,
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dtype=acc_dtype,
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fill_value=(
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0.999
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if isinstance(self._beta2, (Variable, Value))
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else self._beta2
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),
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shape=[1],
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type=core.VarDesc.VarType.DENSE_TENSOR,
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device='cpu',
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)
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def _create_accumulators(self, block, parameters):
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assert isinstance(block, (framework.Block, paddle.pir.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_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 or 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, paddle.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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moment1 = self._get_accumulator_master(
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self._moment1_acc_str, param_and_grad[0]
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)
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moment2 = self._get_accumulator_master(
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self._moment2_acc_str, param_and_grad[0]
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)
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moment2_max = (
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self._get_accumulator_master(
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self._moment2_acc_max_str, param_and_grad[0]
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)
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if self._amsgrad
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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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beta2_pow_acc = self._get_accumulator_master(
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self._beta2_pow_acc_str, param_and_grad[0]
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)
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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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# create the adam optimize op
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if in_dynamic_or_pir_mode():
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_beta1 = (
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self._beta1
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if not isinstance(self._beta1, Variable)
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else self._beta1.item(0)
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)
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_beta2 = (
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self._beta2
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if not isinstance(self._beta2, Variable)
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else self._beta2.item(0)
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)
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found_inf = (
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self._get_auxiliary_var('found_inf') if in_pir_mode() else None
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)
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_, _, _, _, _, _, _ = _C_ops.adam_(
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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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moment1,
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moment2,
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moment2_max,
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beta1_pow_acc,
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beta2_pow_acc,
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master_weight,
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found_inf,
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_beta1,
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_beta2,
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self._epsilon,
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self._lazy_mode,
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1000,
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find_master,
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False,
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self._amsgrad,
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)
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return None
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else:
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inputs = {
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"Param": [param_and_grad[0]],
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"Grad": [param_and_grad[1]],
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"LearningRate": [lr],
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"Moment1": [moment1],
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"Moment2": [moment2],
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"Beta1Pow": [beta1_pow_acc],
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"Beta2Pow": [beta2_pow_acc],
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}
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# Pass found_inf to adam, to skip update for not only param, but also momentum and beta_pow
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found_inf = self._get_auxiliary_var('found_inf')
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if found_inf:
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inputs['SkipUpdate'] = found_inf
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outputs = {
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"ParamOut": [param_and_grad[0]],
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"Moment1Out": [moment1],
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"Moment2Out": [moment2],
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"Beta1PowOut": [beta1_pow_acc],
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"Beta2PowOut": [beta2_pow_acc],
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}
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attrs = {
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"lazy_mode": self._lazy_mode,
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"min_row_size_to_use_multithread": 1000,
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"multi_precision": find_master,
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"amsgrad": self._amsgrad,
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}
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if isinstance(self._beta1, Variable):
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inputs['Beta1Tensor'] = self._beta1
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else:
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attrs['beta1'] = self._beta1
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if isinstance(self._beta2, Variable):
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inputs['Beta2Tensor'] = self._beta2
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else:
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attrs['beta2'] = self._beta2
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if isinstance(self._epsilon, Variable):
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inputs['EpsilonTensor'] = self._epsilon
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else:
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attrs['epsilon'] = self._epsilon
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|
|
if self._amsgrad:
|
|
inputs['Moment2Max'] = [moment2_max]
|
|
outputs["Moment2MaxOut"] = [moment2_max]
|
|
|
|
if find_master:
|
|
inputs["MasterParam"] = master_weight
|
|
outputs["MasterParamOut"] = master_weight
|
|
|
|
adam_op = block.append_op(
|
|
type=self.type,
|
|
inputs=inputs,
|
|
outputs=outputs,
|
|
attrs=attrs,
|
|
stop_gradient=True,
|
|
)
|
|
|
|
return adam_op
|
|
|
|
@imperative_base.no_grad
|
|
@framework.non_static_only
|
|
def step(
|
|
self, closure: Callable[[], Tensor] | None = None
|
|
) -> Tensor | None:
|
|
"""
|
|
Execute the optimizer and update parameters once.
|
|
|
|
Args:
|
|
closure (Callable|None, optional): A closure that reevaluates the model
|
|
and returns the loss. It should be a callable that takes no arguments
|
|
and returns a Tensor. This is useful for optimizers that need to
|
|
evaluate the loss multiple times (e.g., line search). Default is None.
|
|
|
|
Returns:
|
|
Tensor|None: If closure is provided, returns the loss value computed by
|
|
the closure. Otherwise returns None.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
|
|
>>> x = paddle.rand([2, 13], dtype="float32")
|
|
>>> linear = paddle.nn.Linear(13, 5)
|
|
>>> # This can be any optimizer supported by dygraph.
|
|
>>> adam = paddle.optimizer.Adam(
|
|
... learning_rate=0.01,
|
|
... parameters=linear.parameters(),
|
|
... )
|
|
>>> out = linear(x)
|
|
>>> out.backward()
|
|
>>> adam.step()
|
|
>>> adam.clear_grad()
|
|
|
|
>>> # usage 1: not use closure
|
|
>>> adam.zero_grad()
|
|
>>> output = linear(x)
|
|
>>> loss = paddle.mean(output)
|
|
>>> loss.backward()
|
|
>>> adam.step()
|
|
|
|
>>> # usage 2: use closure
|
|
>>> def closure():
|
|
... adam.zero_grad()
|
|
... output = linear(x)
|
|
... loss = paddle.mean(output)
|
|
... loss.backward()
|
|
... return loss
|
|
>>> step_loss = adam.step(closure)
|
|
"""
|
|
loss = None
|
|
if closure is not None:
|
|
with imperative_base.enable_grad():
|
|
loss = closure()
|
|
|
|
if paddle.base.dygraph.base.in_to_static_mode():
|
|
self._declarative_step()
|
|
return loss
|
|
|
|
if not isinstance(self._parameter_list[0], dict):
|
|
params_grads = []
|
|
for param in self._parameter_list:
|
|
if param.stop_gradient:
|
|
continue
|
|
if param._grad_ivar() is not None:
|
|
grad_var = param._grad_ivar()
|
|
if in_dygraph_mode():
|
|
if (
|
|
hasattr(grad_var, "is_selected_rows")
|
|
and grad_var.is_selected_rows()
|
|
and self.regularization is not None
|
|
):
|
|
raise RuntimeError(
|
|
"Adam don't support weight_decay with sparse parameters, please set it to None."
|
|
)
|
|
else:
|
|
if (
|
|
hasattr(grad_var, "_is_sparse")
|
|
and grad_var._is_sparse()
|
|
and self.regularization is not None
|
|
):
|
|
raise RuntimeError(
|
|
"Adam don't support weight_decay with sparse parameters, please set it to None."
|
|
)
|
|
params_grads.append((param, grad_var))
|
|
|
|
optimize_ops = self._apply_optimize(
|
|
loss=None,
|
|
startup_program=None,
|
|
params_grads=params_grads,
|
|
param_group_idx=0,
|
|
)
|
|
else:
|
|
# optimize parameters in groups
|
|
for idx, param_group in enumerate(self._param_groups):
|
|
params_grads = defaultdict(lambda: [])
|
|
for param in param_group['params']:
|
|
if param.stop_gradient:
|
|
continue
|
|
if param._grad_ivar() is not None:
|
|
grad_var = param._grad_ivar()
|
|
params_grads['params'].append((param, grad_var))
|
|
params_grads.update(
|
|
{k: v for k, v in param_group.items() if k != 'params'}
|
|
)
|
|
self._apply_optimize(
|
|
loss=None,
|
|
startup_program=None,
|
|
params_grads=params_grads,
|
|
param_group_idx=idx,
|
|
)
|
|
return loss
|
|
|
|
def _multi_tensor_init(self, target_block, parameters, param_group_idx):
|
|
"""
|
|
All parameters used for optimizer (such as: parameters, master_weight, velocity_acc for momentum) calculations are grouped into a python list by data type (bfloat16, float16, float32).
|
|
This function will be overridden in the corresponding optimizer file.
|
|
Args:
|
|
target_block: the block in which the loss tensor is present
|
|
parameters: list of parameter tensors for the optimizer
|
|
"""
|
|
self._create_accumulators(target_block, parameters)
|
|
for param in parameters:
|
|
moment1 = self._get_accumulator_master(self._moment1_acc_str, param)
|
|
moment2 = self._get_accumulator_master(self._moment2_acc_str, param)
|
|
moment2_max = (
|
|
self._get_accumulator_master(self._moment2_acc_max_str, param)
|
|
if self._amsgrad
|
|
else None
|
|
)
|
|
beta1_pow_acc = self._get_accumulator_master(
|
|
self._beta1_pow_acc_str, param
|
|
)
|
|
beta2_pow_acc = self._get_accumulator_master(
|
|
self._beta2_pow_acc_str, param
|
|
)
|
|
|
|
if param.dtype == paddle.float32:
|
|
self._param_dict['FP32_DenseTensor'][param_group_idx].append(
|
|
param
|
|
)
|
|
self._moment1_dict['FP32_DenseTensor'][param_group_idx].append(
|
|
moment1
|
|
)
|
|
self._moment2_dict['FP32_DenseTensor'][param_group_idx].append(
|
|
moment2
|
|
)
|
|
if self._amsgrad:
|
|
self._moment2_max_dict['FP32_DenseTensor'][
|
|
param_group_idx
|
|
].append(moment2_max)
|
|
self._beta1_pow_acc_dict['FP32_DenseTensor'][
|
|
param_group_idx
|
|
].append(beta1_pow_acc)
|
|
self._beta2_pow_acc_dict['FP32_DenseTensor'][
|
|
param_group_idx
|
|
].append(beta2_pow_acc)
|
|
elif self._is_dtype_fp16_or_bf16(param.dtype):
|
|
self._param_dict['FP16_DenseTensor'][param_group_idx].append(
|
|
param
|
|
)
|
|
self._moment1_dict['FP16_DenseTensor'][param_group_idx].append(
|
|
moment1
|
|
)
|
|
self._moment2_dict['FP16_DenseTensor'][param_group_idx].append(
|
|
moment2
|
|
)
|
|
if self._amsgrad:
|
|
self._moment2_max_dict['FP16_DenseTensor'][
|
|
param_group_idx
|
|
].append(moment2_max)
|
|
self._beta1_pow_acc_dict['FP16_DenseTensor'][
|
|
param_group_idx
|
|
].append(beta1_pow_acc)
|
|
self._beta2_pow_acc_dict['FP16_DenseTensor'][
|
|
param_group_idx
|
|
].append(beta2_pow_acc)
|
|
if self._multi_precision:
|
|
self._master_weight_dict['FP16_DenseTensor'][
|
|
param_group_idx
|
|
].append(self._master_weights[param.name])
|
|
else:
|
|
self._master_weight_dict['FP16_DenseTensor'] = None
|
|
else:
|
|
raise ValueError(
|
|
"Now multi_tensor_momentum only support fp32, fp16 or bf16 parameters and grad is DENSE_TENSOR."
|
|
)
|
|
|
|
def _append_optimize_multi_tensor_op(
|
|
self,
|
|
target_block,
|
|
parameters_and_grads,
|
|
param_group_idx,
|
|
):
|
|
"""
|
|
For Multi Tensor, append optimize merged_operator to block.
|
|
"""
|
|
assert isinstance(target_block, (framework.Block, pir.Block))
|
|
|
|
grad_dict = {'FP32_DenseTensor': [], 'FP16_DenseTensor': []}
|
|
lr_dict = {'FP32_DenseTensor': [], 'FP16_DenseTensor': []}
|
|
|
|
if isinstance(parameters_and_grads, list):
|
|
if framework.in_dygraph_mode():
|
|
params = [pair[0] for pair in parameters_and_grads]
|
|
grads_types = core.eager.get_grads_types(params)
|
|
for index, tp in enumerate(grads_types):
|
|
if tp == core.DataType.FLOAT32:
|
|
grad_dict['FP32_DenseTensor'].append(
|
|
parameters_and_grads[index][1]
|
|
)
|
|
lr = self._create_param_lr(parameters_and_grads[index])
|
|
lr_dict['FP32_DenseTensor'].append(lr)
|
|
elif (
|
|
tp == core.DataType.FLOAT16
|
|
or tp == core.DataType.BFLOAT16
|
|
):
|
|
grad_dict['FP16_DenseTensor'].append(
|
|
parameters_and_grads[index][1]
|
|
)
|
|
lr = self._create_param_lr(parameters_and_grads[index])
|
|
lr_dict['FP16_DenseTensor'].append(lr)
|
|
elif in_pir_mode():
|
|
for param_and_grad in parameters_and_grads:
|
|
if param_and_grad[1] is None:
|
|
continue
|
|
if param_and_grad[0].stop_gradient is False:
|
|
if (
|
|
param_and_grad[0].dtype == DataType.FLOAT32
|
|
and param_and_grad[1].is_dense_tensor_type()
|
|
):
|
|
grad_dict['FP32_DenseTensor'].append(
|
|
param_and_grad[1]
|
|
)
|
|
lr = self._create_param_lr(param_and_grad)
|
|
lr_dict['FP32_DenseTensor'].append(lr)
|
|
elif (
|
|
self._is_dtype_fp16_or_bf16(param_and_grad[0].dtype)
|
|
and param_and_grad[1].is_dense_tensor_type()
|
|
):
|
|
grad_dict['FP16_DenseTensor'].append(
|
|
param_and_grad[1]
|
|
)
|
|
lr = self._create_param_lr(param_and_grad)
|
|
lr_dict['FP16_DenseTensor'].append(lr)
|
|
else:
|
|
for param_and_grad in parameters_and_grads:
|
|
if param_and_grad[1] is None:
|
|
continue
|
|
if param_and_grad[0].stop_gradient is False:
|
|
if (
|
|
param_and_grad[0].dtype == paddle.float32
|
|
and param_and_grad[1].type
|
|
== core.VarDesc.VarType.DENSE_TENSOR
|
|
):
|
|
grad_dict['FP32_DenseTensor'].append(
|
|
param_and_grad[1]
|
|
)
|
|
lr = self._create_param_lr(param_and_grad)
|
|
lr_dict['FP32_DenseTensor'].append(lr)
|
|
elif (
|
|
self._is_dtype_fp16_or_bf16(param_and_grad[0].dtype)
|
|
and param_and_grad[1].type
|
|
== core.VarDesc.VarType.DENSE_TENSOR
|
|
):
|
|
grad_dict['FP16_DenseTensor'].append(
|
|
param_and_grad[1]
|
|
)
|
|
lr = self._create_param_lr(param_and_grad)
|
|
lr_dict['FP16_DenseTensor'].append(lr)
|
|
else:
|
|
for param_and_grad in parameters_and_grads['params']:
|
|
if param_and_grad[1] is None:
|
|
continue
|
|
if param_and_grad[0].stop_gradient is False:
|
|
param_grad_dict = {}
|
|
param_grad_dict['params'] = param_and_grad
|
|
param_grad_dict.update(
|
|
{
|
|
k: v
|
|
for k, v in parameters_and_grads.items()
|
|
if k != 'params'
|
|
}
|
|
)
|
|
param_and_grad = self._update_param_group(param_grad_dict)
|
|
if in_pir_mode():
|
|
if (
|
|
param_and_grad[0].dtype == DataType.FLOAT32
|
|
and param_and_grad[1].is_dense_tensor_type()
|
|
):
|
|
grad_dict['FP32_DenseTensor'].append(
|
|
param_and_grad[1]
|
|
)
|
|
lr = self._create_param_lr(param_and_grad)
|
|
lr_dict['FP32_DenseTensor'].append(lr)
|
|
elif (
|
|
self._is_dtype_fp16_or_bf16(param_and_grad[0].dtype)
|
|
and param_and_grad[1].is_dense_tensor_type()
|
|
):
|
|
grad_dict['FP16_DenseTensor'].append(
|
|
param_and_grad[1]
|
|
)
|
|
lr = self._create_param_lr(param_and_grad)
|
|
lr_dict['FP16_DenseTensor'].append(lr)
|
|
else:
|
|
if (
|
|
param_and_grad[0].dtype == paddle.float32
|
|
and param_and_grad[1].type
|
|
== core.VarDesc.VarType.DENSE_TENSOR
|
|
):
|
|
grad_dict['FP32_DenseTensor'].append(
|
|
param_and_grad[1]
|
|
)
|
|
lr = self._create_param_lr(param_and_grad)
|
|
lr_dict['FP32_DenseTensor'].append(lr)
|
|
elif (
|
|
self._is_dtype_fp16_or_bf16(param_and_grad[0].dtype)
|
|
and param_and_grad[1].type
|
|
== core.VarDesc.VarType.DENSE_TENSOR
|
|
):
|
|
grad_dict['FP16_DenseTensor'].append(
|
|
param_and_grad[1]
|
|
)
|
|
lr = self._create_param_lr(param_and_grad)
|
|
lr_dict['FP16_DenseTensor'].append(lr)
|
|
|
|
multi_tensor_list = ['FP32_DenseTensor', 'FP16_DenseTensor']
|
|
for key in multi_tensor_list:
|
|
if len(self._param_dict[key][param_group_idx]) > 0:
|
|
find_master = (
|
|
self._multi_precision and key == 'FP16_DenseTensor'
|
|
)
|
|
|
|
_beta1 = (
|
|
self._beta1
|
|
if not isinstance(self._beta1, Variable)
|
|
else self._beta1.item(0)
|
|
)
|
|
_beta2 = (
|
|
self._beta2
|
|
if not isinstance(self._beta2, Variable)
|
|
else self._beta2.item(0)
|
|
)
|
|
|
|
if in_dygraph_mode():
|
|
master_weight = self._master_weight_dict[key]
|
|
master_weight = (
|
|
master_weight[param_group_idx]
|
|
if master_weight is not None
|
|
else None
|
|
)
|
|
found_inf = self._get_auxiliary_var('found_inf')
|
|
if found_inf:
|
|
if isinstance(
|
|
found_inf, (core.eager.Tensor, pir.Value)
|
|
):
|
|
self._set_auxiliary_var('found_inf', True)
|
|
else:
|
|
if isinstance(
|
|
found_inf, (core.eager.Tensor, pir.Value)
|
|
):
|
|
self._set_auxiliary_var('found_inf', False)
|
|
_, _, _, _, _, _, _ = _C_ops.merged_adam_(
|
|
self._param_dict[key][param_group_idx],
|
|
grad_dict[key],
|
|
lr_dict[key],
|
|
self._moment1_dict[key][param_group_idx],
|
|
self._moment2_dict[key][param_group_idx],
|
|
(
|
|
self._moment2_max_dict[key][param_group_idx]
|
|
if self._amsgrad
|
|
else None
|
|
),
|
|
self._beta1_pow_acc_dict[key][param_group_idx],
|
|
self._beta2_pow_acc_dict[key][param_group_idx],
|
|
master_weight,
|
|
_beta1,
|
|
_beta2,
|
|
self._epsilon,
|
|
find_master,
|
|
False,
|
|
self._amsgrad,
|
|
)
|
|
elif in_pir_mode():
|
|
master_weight = self._master_weight_dict[key]
|
|
master_weight = (
|
|
master_weight[param_group_idx]
|
|
if master_weight is not None
|
|
else None
|
|
)
|
|
_, _, _, _, _, _, _ = _C_ops.merged_adam_(
|
|
self._param_dict[key][param_group_idx],
|
|
grad_dict[key],
|
|
lr_dict[key],
|
|
self._moment1_dict[key][param_group_idx],
|
|
self._moment2_dict[key][param_group_idx],
|
|
(
|
|
self._moment2_max_dict[key][param_group_idx]
|
|
if self._amsgrad
|
|
else None
|
|
),
|
|
self._beta1_pow_acc_dict[key][param_group_idx],
|
|
self._beta2_pow_acc_dict[key][param_group_idx],
|
|
master_weight,
|
|
_beta1,
|
|
_beta2,
|
|
self._epsilon,
|
|
find_master,
|
|
False,
|
|
self._amsgrad,
|
|
)
|
|
else:
|
|
inputs = {
|
|
"Param": self._param_dict[key][param_group_idx],
|
|
"Grad": grad_dict[key],
|
|
"LearningRate": lr_dict[key],
|
|
"Moment1": self._moment1_dict[key][param_group_idx],
|
|
"Moment2": self._moment2_dict[key][param_group_idx],
|
|
"Beta1Pow": self._beta1_pow_acc_dict[key][
|
|
param_group_idx
|
|
],
|
|
"Beta2Pow": self._beta2_pow_acc_dict[key][
|
|
param_group_idx
|
|
],
|
|
}
|
|
outputs = {
|
|
"ParamOut": self._param_dict[key][param_group_idx],
|
|
"Moment1Out": self._moment1_dict[key][param_group_idx],
|
|
"Moment2Out": self._moment2_dict[key][param_group_idx],
|
|
"Beta1PowOut": self._beta1_pow_acc_dict[key][
|
|
param_group_idx
|
|
],
|
|
"Beta2PowOut": self._beta2_pow_acc_dict[key][
|
|
param_group_idx
|
|
],
|
|
}
|
|
attrs = {
|
|
"epsilon": self._epsilon,
|
|
"beta1": _beta1,
|
|
"beta2": _beta2,
|
|
"amsgrad": self._amsgrad,
|
|
}
|
|
|
|
if self._amsgrad:
|
|
inputs["Moment2Max"] = self._moment2_max_dict[key][
|
|
param_group_idx
|
|
]
|
|
outputs["Moment2MaxOut"] = self._moment2_max_dict[key][
|
|
param_group_idx
|
|
]
|
|
|
|
if find_master:
|
|
inputs["MasterParam"] = self._master_weight_dict[key][
|
|
param_group_idx
|
|
]
|
|
outputs["MasterParamOut"] = self._master_weight_dict[
|
|
key
|
|
][param_group_idx]
|
|
attrs["multi_precision"] = find_master
|
|
|
|
target_block.append_op(
|
|
type="merged_adam",
|
|
inputs=inputs,
|
|
outputs=outputs,
|
|
attrs=attrs,
|
|
stop_gradient=True,
|
|
)
|
|
|
|
def _update_param_group(self, parameters):
|
|
self._beta1 = parameters.get('beta1', self._default_dict['beta1'])
|
|
self._beta2 = parameters.get('beta2', self._default_dict['beta2'])
|
|
self._epsilon = parameters.get('epsilon', self._default_dict['epsilon'])
|
|
self._lazy_mode = parameters.get(
|
|
'lazy_mode', self._default_dict['lazy_mode']
|
|
)
|
|
parameters = parameters.get('params')
|
|
return parameters
|