wehub-resource-sync
881 lines
35 KiB
Python
881 lines
35 KiB
Python
# Copyright (c) 2021 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 collections.abc import Callable
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from typing import TYPE_CHECKING
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import paddle
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from paddle import pir
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from paddle.base.libpaddle import DataType
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from paddle.distributed.flex_checkpoint.dcp.sharded_weight import (
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ShardedStateDict,
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ShardedWeight,
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create_sharded_weight_with_new_local,
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)
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from paddle.pir import Value
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from .. import _C_ops
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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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Parameter,
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Variable,
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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 ..nn.clip import GradientClipBase
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from .lr import LRScheduler
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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 .adam import _AdamParameterConfig
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__all__ = []
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class AdamW(Optimizer):
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r"""
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The AdamW optimizer is implemented based on the AdamW Optimization
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in paper `DECOUPLED WEIGHT DECAY REGULARIZATION <https://arxiv.org/pdf/1711.05101.pdf>`_.
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it can resolves the problem of L2 regularization failure in the Adam optimizer.
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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} + \lambda * param) \\
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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} + \lambda * param) \\
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\end{aligned}
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Args:
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learning_rate (float|LRScheduler, optional): The learning rate used to update ``Parameter``.
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It can be a float value or a LRScheduler. The default value is 0.001.
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beta1 (float|Tensor, optional): The exponential decay rate for the 1st moment estimates.
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It should be a float number or a 0-D Tensor with shape [] and data type as float32.
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The default value is 0.9.
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beta2 (float|Tensor, optional): The exponential decay rate for the 2nd moment estimates.
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It should be a float number or a 0-D Tensor with shape [] and data type as float32.
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The default value is 0.999.
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epsilon (float|Tensor, optional): A small float value for numerical stability.
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The default value is 1e-08.
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parameters (list|tuple|None, optional): List/Tuple of ``Tensor`` names 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|Tensor, optional): The weight decay coefficient, it can be int, float or Tensor. The default value is 0.01.
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lr_ratio (Callable|None, optional): If it is not None,
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the learning rate will be updated with layer-wise learning rate ratio.
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Otherwise, the learning rate is the original.
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Default: None.
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apply_decay_param_fun (Callable|None, optional): If it is not None,
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only tensors that makes apply_decay_param_fun(Tensor.name)==True
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will be updated with weight decay. It only works when we want to specify tensors.
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Default: None.
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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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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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Notes:
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**Currently, AdamW doesn't support sparse parameter optimization.**
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> 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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>>> opt = paddle.optimizer.AdamW(
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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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>>> opt.step()
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>>> opt.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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>>> opt = paddle.optimizer.AdamW(
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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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>>> opt.step()
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>>> opt.clear_grad()
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"""
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helper: None
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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 | Tensor = 0.01,
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use_lowprecision_moment: bool = False,
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lr_ratio: Callable[[Tensor], float] | None = None,
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apply_decay_param_fun: Callable[[str], bool] | 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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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, Value) and not 0 <= beta1 < 1:
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raise ValueError("Invalid value of beta1, expect beta1 in [0,1).")
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if not isinstance(beta2, Value) and not 0 <= beta2 < 1:
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raise ValueError("Invalid value of beta2, expect beta2 in [0,1).")
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if not isinstance(epsilon, Value) and not 0 <= epsilon:
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raise ValueError("Invalid value of epsilon, expect epsilon >= 0.")
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if not isinstance(weight_decay, (int, float)) and not isinstance(
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weight_decay, (framework.Variable, Value)
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):
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raise TypeError("weight_decay should be int, float or Tensor.")
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if lr_ratio is not None:
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assert isinstance(lr_ratio, Callable)
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if (
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not core.is_compiled_with_cuda()
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and not core.is_compiled_with_xpu()
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and paddle.device.get_device().split(":")[0]
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not in paddle.device.get_all_custom_device_type()
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):
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raise NotImplementedError("'lr_ratio' is unimplemented in CPU.")
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if parameters is not None:
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# paddle.Tensor is also iterable, so here we don't check whether
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# the input is iterable, if the input is paddle.Tensor, the
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# list(paddle.Tensor) will be a error value
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if isinstance(parameters, paddle.Tensor):
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raise TypeError(
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"`parameters` argument given to the optimizer should be "
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f"an iterable of paddle Tensors, but got argument type is `{type(parameters)}`."
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)
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if isinstance(parameters, dict):
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raise TypeError(
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"`parameters` argument should not get dict type, "
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"if parameter groups is needed, please set `parameters`"
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" as list of dict"
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)
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self._parameter_list = list(parameters)
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else:
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self._parameter_list = None
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self._name = name
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if framework.in_dygraph_mode():
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if self._parameter_list is None:
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raise AttributeError(
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"parameters argument given to the Optimizer should not be None in dygraph mode."
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)
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if not isinstance(learning_rate, (float, LRScheduler)):
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raise TypeError(
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f"learning rate should be float or LRScheduler, got {type(learning_rate)} here"
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)
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if grad_clip is not None:
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if not isinstance(grad_clip, GradientClipBase):
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raise TypeError(
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"'grad_clip' should be an instance of GradientClipBase's derived class"
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)
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self._dtype = None
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# Infer the dtype form parameter
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if self._parameter_list:
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if isinstance(self._parameter_list[0], dict):
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for param_group in self._parameter_list:
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assert 'params' in param_group, (
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'params should be set in parameters if parameter groups are optimized in different options'
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)
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self._dtype = self._parameter_list[0]['params'][0].dtype
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else:
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self._dtype = self._parameter_list[0].dtype
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# each program should have a independent learning rate
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# program -> tensor(learning_rate)
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self._learning_rate_map = {}
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# Dictionary of accumulators. Some optimizer subclasses need to
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# allocate and manage extra tensors associated with the parameters
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# to train. These tensors are called accumulators.
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# {accum_name : { parameter_name : accumulator_for_parameter, ...}, ...}
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self._accumulators = defaultdict(lambda: {})
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self.helper = None
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self._opti_name_list = []
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self._accumulators_holder = {}
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self._param_device_map = {}
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self.clear_gradients = self.clear_grad
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self.type = "adamw"
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self._learning_rate = learning_rate
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self._params_name = set()
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self._apply_decay_param_fun = apply_decay_param_fun
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self._weight_decay = float(weight_decay)
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self._use_lowprecision_moment = use_lowprecision_moment
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self._grad_clip = grad_clip
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self._lr_ratio = lr_ratio
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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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# whether to use AMSGrad
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self._amsgrad = amsgrad
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self._default_dict = {
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'weight_decay': float(weight_decay),
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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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'grad_clip': grad_clip,
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}
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self._param_groups = []
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if self._parameter_list and isinstance(self._parameter_list[0], dict):
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for param_group in self._parameter_list:
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self._add_param_group(param_group.copy())
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else:
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self._param_groups = self._parameter_list
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self._use_multi_tensor = None
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self.regularization = None
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self._auxiliary_vars = {}
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self._already_create_accumulator = set()
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self._create_master_grad_states()
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self._use_fusion_storage = False
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self._need_refuse = False
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self.fusion_storage = None
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self._fuse_buffer_version = 0
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self.merged_model_params = None
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def _set_auxiliary_var(self, key, val):
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self._auxiliary_vars[key] = val
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def _get_auxiliary_var(self, key):
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if key in self._auxiliary_vars:
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return self._auxiliary_vars[key]
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else:
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return None
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def get_lr_dtype(self) -> paddle.dtype:
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return paddle.float64
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def _add_param_group(self, param_group):
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"""
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Add a param group to parameter_list.
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Args:
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param_group (dict): The group of Tensors to be optimized with
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different optimization options.
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"""
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params = param_group['params']
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if isinstance(params, (Parameter, pir.core.ParameterMeta)):
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param_group['params'] = [params]
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elif isinstance(params, set):
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raise TypeError(
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"optimizer parameters should be in ordered collections,"
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"but received set, please use list instead."
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)
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else:
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param_group['params'] = list(params)
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# Update optimization options for each groups
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for k, v in self._default_dict.items():
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param_group.setdefault(k, v)
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param_set = set()
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for group in self._param_groups:
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param_set.update(set(group['params']))
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if not param_set.isdisjoint(set(param_group['params'])):
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raise ValueError(
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"some parameters appear in more than one parameter group"
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)
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for param in param_group['params']:
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param.optimize_attr['learning_rate'] = param_group.get(
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'learning_rate', 1.0
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)
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self._param_groups.append(param_group)
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def _add_moments_pows(self, p):
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acc_dtype = p.dtype
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if (
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self._is_dtype_fp16_or_bf16(acc_dtype)
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and not self._use_lowprecision_moment
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):
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acc_dtype = (
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DataType.FLOAT32 if in_pir_mode() else core.VarDesc.VarType.FP32
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)
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if core.is_compiled_with_xpu():
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import os
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xpu_adamw_moment_dtype = os.getenv(
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"xpu_adamw_moment_dtype", default="fp32"
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)
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if xpu_adamw_moment_dtype == "fp16":
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self._add_accumulator(
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self._moment1_acc_str, p, dtype=core.VarDesc.VarType.FP16
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)
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self._add_accumulator(
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self._moment2_acc_str, p, dtype=core.VarDesc.VarType.FP16
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)
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if self._amsgrad:
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self._add_accumulator(
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self._moment2_acc_max_str,
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p,
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dtype=core.VarDesc.VarType.FP16,
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)
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else:
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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(
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self._moment2_acc_max_str, p, dtype=acc_dtype
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)
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else:
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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(
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self._moment2_acc_max_str, p, dtype=acc_dtype
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)
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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, 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):
|
|
assert isinstance(block, (framework.Block, pir.Block))
|
|
if isinstance(param_and_grad, dict):
|
|
param_and_grad = self._update_param_group(param_and_grad)
|
|
param, grad = param_and_grad
|
|
|
|
# Whether we should do weight decay for the parameter.
|
|
with_decay = True
|
|
if (
|
|
self._apply_decay_param_fun is not None
|
|
and not self._apply_decay_param_fun(param.name)
|
|
):
|
|
with_decay = False
|
|
|
|
moment1 = self._get_accumulator_master(
|
|
self._moment1_acc_str, param_and_grad[0]
|
|
)
|
|
moment2 = self._get_accumulator_master(
|
|
self._moment2_acc_str, param_and_grad[0]
|
|
)
|
|
moment2_max = (
|
|
self._get_accumulator_master(
|
|
self._moment2_acc_max_str, param_and_grad[0]
|
|
)
|
|
if self._amsgrad
|
|
else None
|
|
)
|
|
beta1_pow_acc = self._get_accumulator_master(
|
|
self._beta1_pow_acc_str, param_and_grad[0]
|
|
)
|
|
beta2_pow_acc = self._get_accumulator_master(
|
|
self._beta2_pow_acc_str, param_and_grad[0]
|
|
)
|
|
find_master = self._multi_precision and self._is_dtype_fp16_or_bf16(
|
|
param_and_grad[0].dtype
|
|
)
|
|
master_weight = (
|
|
self._master_weights[param_and_grad[0].name]
|
|
if find_master
|
|
else None
|
|
)
|
|
lr = self._create_param_lr(param_and_grad)
|
|
|
|
# create the adamw optimize op
|
|
if in_dynamic_or_pir_mode():
|
|
lr_ratio_ = (
|
|
1.0
|
|
if self._lr_ratio is None
|
|
else self._lr_ratio(param_and_grad[0])
|
|
)
|
|
|
|
_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)
|
|
)
|
|
|
|
found_inf = (
|
|
self._get_auxiliary_var('found_inf') if in_pir_mode() else None
|
|
)
|
|
|
|
_, _, _, _, _, _, _ = _C_ops.adamw_(
|
|
param_and_grad[0],
|
|
param_and_grad[1],
|
|
lr,
|
|
moment1,
|
|
moment2,
|
|
moment2_max,
|
|
beta1_pow_acc,
|
|
beta2_pow_acc,
|
|
master_weight,
|
|
found_inf,
|
|
_beta1,
|
|
_beta2,
|
|
self._epsilon,
|
|
lr_ratio_,
|
|
self._weight_decay,
|
|
with_decay,
|
|
self._lazy_mode,
|
|
1000,
|
|
find_master,
|
|
False,
|
|
self._amsgrad,
|
|
)
|
|
return None
|
|
else:
|
|
inputs = {
|
|
"Param": [param_and_grad[0]],
|
|
"Grad": [param_and_grad[1]],
|
|
"LearningRate": [lr],
|
|
"Moment1": [moment1],
|
|
"Moment2": [moment2],
|
|
"Beta1Pow": [beta1_pow_acc],
|
|
"Beta2Pow": [beta2_pow_acc],
|
|
}
|
|
|
|
# Pass found_inf to adamw, to skip update for not only param, but also momentum and beta_pow
|
|
found_inf = self._get_auxiliary_var('found_inf')
|
|
|
|
if found_inf:
|
|
inputs['SkipUpdate'] = found_inf
|
|
|
|
outputs = {
|
|
"ParamOut": [param_and_grad[0]],
|
|
"Moment1Out": [moment1],
|
|
"Moment2Out": [moment2],
|
|
"Beta1PowOut": [beta1_pow_acc],
|
|
"Beta2PowOut": [beta2_pow_acc],
|
|
}
|
|
attrs = {
|
|
"lazy_mode": self._lazy_mode,
|
|
"min_row_size_to_use_multithread": 1000,
|
|
"multi_precision": find_master,
|
|
"with_decay": with_decay,
|
|
"coeff": self._weight_decay,
|
|
"lr_ratio": (
|
|
1.0
|
|
if self._lr_ratio is None
|
|
else self._lr_ratio(param_and_grad[0])
|
|
),
|
|
"amsgrad": self._amsgrad,
|
|
}
|
|
|
|
if isinstance(self._beta1, Variable):
|
|
inputs['Beta1Tensor'] = self._beta1
|
|
else:
|
|
attrs['beta1'] = self._beta1
|
|
if isinstance(self._beta2, Variable):
|
|
inputs['Beta2Tensor'] = self._beta2
|
|
else:
|
|
attrs['beta2'] = self._beta2
|
|
if isinstance(self._epsilon, Variable):
|
|
inputs['EpsilonTensor'] = self._epsilon
|
|
else:
|
|
attrs['epsilon'] = self._epsilon
|
|
|
|
if self._amsgrad:
|
|
inputs["Moment2Max"] = [moment2_max]
|
|
outputs["Moment2MaxOut"] = [moment2_max]
|
|
|
|
if find_master:
|
|
inputs["MasterParam"] = master_weight
|
|
outputs["MasterParamOut"] = master_weight
|
|
|
|
adamw_op = block.append_op(
|
|
type=self.type,
|
|
inputs=inputs,
|
|
outputs=outputs,
|
|
attrs=attrs,
|
|
stop_gradient=True,
|
|
)
|
|
|
|
return adamw_op
|
|
|
|
def __str__(self):
|
|
return " ".join(["Weight Decay, params:", ",".join(self._params_name)])
|
|
|
|
@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.
|
|
>>> opt = paddle.optimizer.AdamW(
|
|
... learning_rate=0.01,
|
|
... parameters=linear.parameters(),
|
|
... )
|
|
>>> out = linear(x)
|
|
>>> out.backward()
|
|
>>> opt.step()
|
|
>>> opt.clear_grad()
|
|
|
|
>>> # usage 1: not use closure
|
|
>>> opt.zero_grad()
|
|
>>> output = linear(x)
|
|
>>> loss = paddle.mean(output)
|
|
>>> loss.backward()
|
|
>>> opt.step()
|
|
|
|
>>> # usage 2: use closure
|
|
>>> def closure():
|
|
... opt.zero_grad()
|
|
... output = linear(x)
|
|
... loss = paddle.mean(output)
|
|
... loss.backward()
|
|
... return loss
|
|
>>> step_loss = opt.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 framework.in_dygraph_mode():
|
|
if (
|
|
hasattr(grad_var, "is_selected_rows")
|
|
and grad_var.is_selected_rows()
|
|
and self.regularization is not None
|
|
):
|
|
raise RuntimeError(
|
|
"AdamW 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(
|
|
"AdamW 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
|
|
)
|
|
else:
|
|
# optimize parameters in groups
|
|
for param_group in 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()
|
|
if framework.in_dygraph_mode():
|
|
if (
|
|
hasattr(grad_var, "is_selected_rows")
|
|
and grad_var.is_selected_rows()
|
|
and self.regularization is not None
|
|
):
|
|
raise RuntimeError(
|
|
"AdamW 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(
|
|
"AdamW don't support weight_decay with sparse parameters, please set it to None."
|
|
)
|
|
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
|
|
)
|
|
return loss
|
|
|
|
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']
|
|
)
|
|
self._weight_decay = parameters.get(
|
|
'weight_decay', self._default_dict['weight_decay']
|
|
)
|
|
parameters = parameters.get('params')
|
|
|
|
return parameters
|
|
|
|
def sharded_state_dict(
|
|
self,
|
|
model_sharded_state_dict: ShardedStateDict,
|
|
) -> ShardedStateDict:
|
|
"""
|
|
Convert optimizer state dict to a sharded state dict based on model sharding information.
|
|
|
|
Args:
|
|
model_sharded_state_dict (dict): Sharded state dict of the model, containing tensor metadata.
|
|
|
|
Returns:
|
|
dict: A new optimizer state dict where weights are wrapped as ShardedWeight.
|
|
"""
|
|
|
|
_FP32_MASTER = "fp32_master_0"
|
|
_MOMENT_NAME = "moment"
|
|
_optimizer_scalar_name = [
|
|
"beta1_pow_acc_0",
|
|
"beta2_pow_acc_0",
|
|
]
|
|
_optimizer_non_scaler_name = [
|
|
"moment1_0",
|
|
"moment2_0",
|
|
"velocity_0",
|
|
]
|
|
|
|
def _generate_base_static_name(vname):
|
|
if _FP32_MASTER in vname:
|
|
return tuple(vname.split("_" + _FP32_MASTER + "_", 1))
|
|
for name in _optimizer_scalar_name + _optimizer_non_scaler_name:
|
|
if vname.endswith(name):
|
|
return vname[: -(len(name) + 1)], name
|
|
raise ValueError(f"Cannot split variable name: {vname}.")
|
|
|
|
optimizer_sharded_state_dict = {}
|
|
optimizer_state_dict = self.state_dict()
|
|
# Build name mapping and remove non-tensor entries from optimizer state
|
|
static_to_struct_mapping = {}
|
|
model_sharded_state_dict = dict(
|
|
sorted(model_sharded_state_dict.items())
|
|
)
|
|
for k, v in model_sharded_state_dict.items():
|
|
# When shared weights exist, the v.local_tensor.name of shared parameters are identical, but only the first parameter has optimizer states. Therefore, only the key-value pairs of the first occurrence in the shared parameter group need to be retained.
|
|
if v.local_tensor.name not in static_to_struct_mapping:
|
|
static_to_struct_mapping[v.local_tensor.name] = k
|
|
|
|
master_weights = optimizer_state_dict.pop("master_weights", None)
|
|
optimizer_state_dict.pop("LR_Scheduler", None)
|
|
|
|
# Process main optimizer states
|
|
for key, tensor in optimizer_state_dict.items():
|
|
static_name, optim_state_type = _generate_base_static_name(key)
|
|
struct_name = static_to_struct_mapping[static_name]
|
|
sharded_weight = model_sharded_state_dict[struct_name]
|
|
|
|
unified_name = f"{struct_name}.{optim_state_type}"
|
|
|
|
# Determine tensor partitioning scheme
|
|
if _MOMENT_NAME in optim_state_type:
|
|
if tensor.is_dist():
|
|
optimizer_sharded_state_dict[unified_name] = ShardedWeight(
|
|
key=unified_name,
|
|
local_tensor=tensor,
|
|
local_shape=tensor.shape,
|
|
global_shape=tensor.shape,
|
|
global_offset=sharded_weight.global_offset,
|
|
)
|
|
else:
|
|
optimizer_sharded_state_dict[unified_name] = (
|
|
create_sharded_weight_with_new_local(
|
|
unified_name, tensor, sharded_weight
|
|
)
|
|
)
|
|
else: # Non-momentum parameters
|
|
optimizer_sharded_state_dict[unified_name] = ShardedWeight(
|
|
key=unified_name,
|
|
local_tensor=tensor,
|
|
local_shape=(1,),
|
|
global_shape=(1,),
|
|
global_offset=(0,),
|
|
)
|
|
|
|
# Process master weights if using mixed precision
|
|
if master_weights is not None:
|
|
for key, tensor in master_weights.items():
|
|
struct_name = static_to_struct_mapping[key]
|
|
sharded_weight = model_sharded_state_dict[struct_name]
|
|
unified_name = f"{struct_name}.w_0"
|
|
if tensor.is_dist():
|
|
optimizer_sharded_state_dict[unified_name] = ShardedWeight(
|
|
key=unified_name,
|
|
local_tensor=tensor,
|
|
local_shape=tensor.shape,
|
|
global_shape=tensor.shape,
|
|
global_offset=sharded_weight.global_offset,
|
|
)
|
|
else:
|
|
optimizer_sharded_state_dict[unified_name] = (
|
|
create_sharded_weight_with_new_local(
|
|
unified_name, tensor, sharded_weight
|
|
)
|
|
)
|
|
|
|
return optimizer_sharded_state_dict
|