chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2022 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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from typing import TYPE_CHECKING
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from paddle import _C_ops, pir
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from paddle.base.executor import global_scope
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from ..base import core, framework
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from ..base.framework import Variable
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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 .optimizer import _ParameterConfig
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class _LambParameterConfig(_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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lamb_weight_decay: NotRequired[float]
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exclude_from_weight_decay_fn: NotRequired[
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Callable[[Tensor], bool] | None
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]
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__all__ = []
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class Lamb(Optimizer):
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r"""
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LAMB (Layer-wise Adaptive Moments optimizer for Batching training) Optimizer.
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LAMB Optimizer is designed to scale up the batch size of training without losing
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accuracy, which supports adaptive element-wise updating and accurate layer-wise
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correction. For more information, please refer to `Large Batch Optimization for
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Deep Learning: Training BERT in 76 minutes <https://arxiv.org/abs/1904.00962>`_ .
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The updating of parameters follows:
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.. math::
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m_t &= \beta_1 m_{t - 1}+ (1 - \beta_1)g_t
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v_t &= \beta_2 v_{t - 1} + (1 - \beta_2)g_t^2
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m_t &= \frac{m_t}{\beta_1^t}
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v_t &= \frac{v_t}{\beta_2^t}
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r_t &= \frac{m_t}{\sqrt{v_t}+\epsilon}
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w_t &= w_{t-1} -\eta_t \frac{\left \| w_{t-1}\right \|}{\left \| r_t + \lambda w_{t-1}\right \|} (r_t + \lambda w_{t-1})
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where :math:`m` is the 1st moment, and :math:`v` the 2nd moment, :math:`\\eta` the
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learning rate, :math:`\\lambda` the LAMB weight decay rate.
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Args:
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learning_rate (float|Tensor, optional): the learning rate used to update parameters. \
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Can be a float value or a Variable with data type float32. Default 0.001.
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lamb_weight_decay (float, optional): The LAMB weight decay rate. Default 0.01. Remind that weight_decay should be None.
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beta1 (float|Tensor, optional): The exponential decay rate for the 1st moment estimates.
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Default 0.9.
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beta2 (float|Tensor, optional): The exponential decay rate for the 2nd moment estimates.
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Default 0.999.
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epsilon (float|Tensor, optional): A small float value for numerical stability. Default 1e-6.
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parameters (list|tuple|None, optional): Iterable of ``Variable`` 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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grad_clip (GradientClipBase, 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_base_clip_ClipGradByGlobalNorm` , :ref:`api_paddle_base_clip_ClipGradByNorm` ,
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:ref:`api_paddle_base_clip_ClipGradByValue` ). If you want better convergence, it is recommended
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to use :ref:`api_paddle_base_clip_ClipGradByGlobalNorm` . Default None, meaning there is no gradient clipping.
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exclude_from_weight_decay_fn (Callable|None, optional): whether to skip weight decay for a parameter when this function returns True while take the parameter as input.
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multi_precision (bool, optional) - Whether to use it during weight updates multi-precision, Default False。
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always_adapt (bool, optional): whether to use Layer-wise LR adaptation. By default, skip adaptation on parameters that are
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excluded from weight decay, unless always_adapt == True, then always enable LR adaptation.
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name(str|None, optional): For detailed information, please refer to
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:ref:`api_guide_Name` . Usually name is no need to set and None by default.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> inp = paddle.uniform(shape=[10, 10], dtype='float32', min=-0.1, max=0.1)
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>>> linear = paddle.nn.Linear(10, 10)
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>>> out = linear(inp)
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>>> loss = paddle.mean(out)
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>>> beta1 = paddle.to_tensor([0.9], dtype="float32")
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>>> beta2 = paddle.to_tensor([0.85], dtype="float32")
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>>> lamb = paddle.optimizer.Lamb(
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... learning_rate=0.002,
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... beta1=beta1,
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... beta2=beta2,
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... parameters=linear.parameters(),
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... lamb_weight_decay=0.01
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... )
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>>> back = out.backward()
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>>> lamb.step()
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>>> lamb.clear_grad()
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"""
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_moment1_acc_str = "moment1"
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_moment2_acc_str = "moment2"
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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 | Tensor = 0.001,
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lamb_weight_decay: float = 0.01,
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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-6,
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parameters: (
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Sequence[Tensor] | Sequence[_LambParameterConfig] | None
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) = None,
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grad_clip: GradientClipBase | None = None,
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exclude_from_weight_decay_fn: Callable[[Tensor], bool] | None = None,
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multi_precision: bool = False,
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always_adapt: 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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super().__init__(
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learning_rate=learning_rate,
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parameters=parameters,
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weight_decay=None,
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grad_clip=grad_clip,
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name=name,
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)
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self.type = "lamb"
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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._lamb_weight_decay = lamb_weight_decay
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self._exclude_from_weight_decay_fn = exclude_from_weight_decay_fn
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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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'lamb_weight_decay': lamb_weight_decay,
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'exclude_from_weight_decay_fn': exclude_from_weight_decay_fn,
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}
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self._master_weights = {}
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self._used_master_weights = {}
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# TODO(zengjinle): expose API as soon as possible
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self._multi_precision = multi_precision
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self.always_adapt = always_adapt
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def _get_parameter(self, name, scope=None):
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if scope is None:
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scope = global_scope()
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p_t = scope.find_var(name).get_tensor()
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master_name = self._used_master_weights.get(name)
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if master_name is not None:
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master_p_t = scope.find_var(master_name).get_tensor()
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assert master_p_t._dtype() != p_t._dtype()
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assert master_p_t.shape() == p_t.shape()
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else:
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master_p_t = None
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return p_t, master_p_t
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def _create_accumulators(self, block, parameters):
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if not isinstance(block, (framework.Block, pir.Block)):
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raise TypeError("block is not instance of Block.")
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if isinstance(parameters, dict):
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parameters = self._update_param_group(parameters)
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# 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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else:
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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 _add_moments_pows(self, p):
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acc_dtype = p.dtype
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if self._is_dtype_fp16_or_bf16(acc_dtype):
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acc_dtype = core.VarDesc.VarType.FP32
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self._add_accumulator(self._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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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 if isinstance(self._beta1, Variable) 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 if isinstance(self._beta2, Variable) 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 _append_optimize_op(self, block, param_and_grad):
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if not isinstance(block, (framework.Block, pir.Block)):
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raise TypeError("block is not instance of Block.")
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if isinstance(param_and_grad, dict):
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param_and_grad = self._update_param_group(param_and_grad)
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block.program._use_lamb = True
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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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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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if (
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self._exclude_from_weight_decay_fn is not None
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and self._exclude_from_weight_decay_fn(param_and_grad[0])
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):
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weight_decay = 0.0
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else:
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weight_decay = self._lamb_weight_decay
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lr = self._create_param_lr(param_and_grad)
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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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p_name = param_and_grad[0].name
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if find_master:
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master_weight = self._master_weights[p_name]
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self._used_master_weights[p_name] = master_weight.name
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else:
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master_weight = None
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if framework.in_dynamic_or_pir_mode():
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_C_ops.lamb_(
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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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beta1_pow_acc,
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beta2_pow_acc,
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master_weight,
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None,
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weight_decay,
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self._beta1,
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self._beta2,
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self._epsilon,
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self.always_adapt,
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find_master,
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)
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return None
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else:
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# create the lamb optimize op
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inputs = {
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"Param": param_and_grad[0],
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"Grad": param_and_grad[1],
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"LearningRate": 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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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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"beta1": self._beta1,
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"beta2": self._beta2,
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"epsilon": self._epsilon,
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"weight_decay": weight_decay,
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"always_adapt": self.always_adapt,
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"multi_precision": find_master,
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}
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if find_master:
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inputs["MasterParam"] = master_weight
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outputs["MasterParamOut"] = master_weight
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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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lamb_op = block.append_op(
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type=self.type,
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inputs=inputs,
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outputs=outputs,
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attrs=attrs,
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stop_gradient=True,
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)
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return lamb_op
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def _update_param_group(self, parameters):
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self._beta1 = parameters.get('beta1', self._default_dict['beta1'])
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self._beta2 = parameters.get('beta2', self._default_dict['beta2'])
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self._epsilon = parameters.get('epsilon', self._default_dict['epsilon'])
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self._lamb_weight_decay = parameters.get(
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'lamb_weight_decay', self._default_dict['lamb_weight_decay']
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)
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self._exclude_from_weight_decay_fn = parameters.get(
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'exclude_from_weight_decay_fn',
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self._default_dict['exclude_from_weight_decay_fn'],
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)
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parameters = parameters.get('params')
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return parameters
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