chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,273 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import warnings
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from paddle import _C_ops, pir
|
||||
from paddle.framework import (
|
||||
in_dynamic_or_pir_mode,
|
||||
)
|
||||
|
||||
from ..base import framework
|
||||
from .optimizer import Optimizer
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Sequence
|
||||
|
||||
from typing_extensions import NotRequired
|
||||
|
||||
from paddle import Tensor
|
||||
from paddle.nn.clip import GradientClipBase
|
||||
from paddle.regularizer import WeightDecayRegularizer
|
||||
|
||||
from .optimizer import _ParameterConfig
|
||||
|
||||
class _AdagradParameterConfig(_ParameterConfig):
|
||||
epsilon: NotRequired[float]
|
||||
initial_accumulator_value: NotRequired[float]
|
||||
|
||||
|
||||
__all__ = []
|
||||
|
||||
|
||||
class Adagrad(Optimizer):
|
||||
r"""
|
||||
The Adaptive Gradient optimizer (Adagrad for short) use an optimization described
|
||||
in paper: `Adaptive Subgradient Methods for Online Learning and
|
||||
Stochastic Optimization <http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf>`_.
|
||||
|
||||
The parameter ``param_out`` update rule with gradient ``grad``:
|
||||
|
||||
.. math::
|
||||
|
||||
moment\_out &= moment + grad * grad
|
||||
|
||||
param\_out &= param - \frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
|
||||
|
||||
|
||||
The original paper does not have the ``epsilon`` attribute. It is added here
|
||||
in our implementation as also proposed `Per-parameter adaptive learning rate
|
||||
methods <http://cs231n.github.io/neural-networks-3/#ada>`_
|
||||
for numerical stability to avoid the division by zero error.
|
||||
|
||||
Args:
|
||||
learning_rate (float|Tensor): The learning rate used to update ``Parameter``.
|
||||
It can be a float value or a ``Variable`` with a float type.
|
||||
epsilon (float, optional): A small float value for numerical stability.
|
||||
The default value is 1e-06.
|
||||
parameters (list|tuple|None, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``.
|
||||
This parameter is required in dygraph mode. And you can specify different options for
|
||||
different parameter groups such as the learning rate, weight decay, etc,
|
||||
then the parameters are list of dict. Note that the learning_rate in parameter groups
|
||||
represents the scale of base learning_rate.
|
||||
The default value is None in static graph mode, at this time all parameters will be updated.
|
||||
weight_decay (int|float|WeightDecayRegularizer|None, optional): The strategy of regularization.
|
||||
It canbe a int or float value as coeff of L2 regularization or
|
||||
:ref:`api_paddle_regularizer_L1Decay`, :ref:`api_paddle_regularizer_L2Decay`.
|
||||
If a parameter has set regularizer using :ref:`api_paddle_base_param_attr_aramAttr` already,
|
||||
the regularization setting here in optimizer will be ignored for this parameter.
|
||||
Otherwise, the regularization setting here in optimizer will take effect.
|
||||
Default None, meaning there is no regularization.
|
||||
grad_clip (GradientClipBase|None, optional): Gradient clipping strategy, it's an instance of
|
||||
some derived class of ``GradientClipBase`` . There are three clipping strategies,
|
||||
ClipGradByGlobalNorm, ClipGradByNorm and ClipGradByValue. Default None,
|
||||
meaning there is no gradient clipping.
|
||||
name (str|None, optional): Normally there is no need for user to set this property.
|
||||
For more information, please refer to :ref:`api_guide_Name`.
|
||||
The default value is None.
|
||||
initial_accumulator_value (float, optional): Initial value for moment accumulator.
|
||||
The default value is 0.0.
|
||||
|
||||
Examples:
|
||||
.. code-block:: pycon
|
||||
|
||||
>>> import paddle
|
||||
|
||||
>>> inp = paddle.rand(shape=[10, 10])
|
||||
>>> linear = paddle.nn.Linear(10, 10)
|
||||
>>> out = linear(inp)
|
||||
>>> loss = paddle.mean(out)
|
||||
>>> adagrad = paddle.optimizer.Adagrad(
|
||||
... learning_rate=0.1,
|
||||
... parameters=linear.parameters(),
|
||||
... )
|
||||
>>> out.backward()
|
||||
>>> adagrad.step()
|
||||
>>> adagrad.clear_grad()
|
||||
|
||||
>>> # Note that the learning_rate of linear_2 is 0.01.
|
||||
>>> linear_1 = paddle.nn.Linear(10, 10)
|
||||
>>> linear_2 = paddle.nn.Linear(10, 10)
|
||||
>>> inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
|
||||
>>> out = linear_1(inp)
|
||||
>>> out = linear_2(out)
|
||||
>>> loss = paddle.mean(out)
|
||||
>>> adagrad = paddle.optimizer.Adagrad(
|
||||
... learning_rate=0.1,
|
||||
... parameters=[ # type: ignore
|
||||
... {
|
||||
... 'params': linear_1.parameters(),
|
||||
... },
|
||||
... {
|
||||
... 'params': linear_2.parameters(),
|
||||
... 'weight_decay': 0.001,
|
||||
... 'learning_rate': 0.1,
|
||||
... },
|
||||
... ],
|
||||
... weight_decay=0.01,
|
||||
... )
|
||||
>>> out.backward()
|
||||
>>> adagrad.step()
|
||||
>>> adagrad.clear_grad()
|
||||
|
||||
"""
|
||||
|
||||
type: str
|
||||
initial_accumulator_value: float
|
||||
_moment_acc_str = "moment"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
learning_rate: float | Tensor,
|
||||
epsilon: float = 1.0e-6,
|
||||
parameters: (
|
||||
Sequence[Tensor] | Sequence[_AdagradParameterConfig] | None
|
||||
) = None,
|
||||
weight_decay: float | WeightDecayRegularizer | None = None,
|
||||
grad_clip: GradientClipBase | None = None,
|
||||
name: str | None = None,
|
||||
initial_accumulator_value: float = 0.0,
|
||||
) -> None:
|
||||
assert learning_rate is not None
|
||||
assert epsilon is not None
|
||||
super().__init__(
|
||||
learning_rate=learning_rate,
|
||||
parameters=parameters,
|
||||
weight_decay=weight_decay,
|
||||
grad_clip=grad_clip,
|
||||
name=name,
|
||||
)
|
||||
self.type = "adagrad"
|
||||
self._epsilon = epsilon
|
||||
self._multi_precision = False
|
||||
self._master_weights = {}
|
||||
self.initial_accumulator_value = initial_accumulator_value
|
||||
self._default_dict = {
|
||||
'epsilon': epsilon,
|
||||
'initial_accumulator_value': initial_accumulator_value,
|
||||
}
|
||||
|
||||
def _create_accumulators(self, block, parameters):
|
||||
if not isinstance(block, (framework.Block, pir.Block)):
|
||||
raise TypeError("block is not instance of Block.")
|
||||
if isinstance(parameters, dict):
|
||||
parameters = self._update_param_group(parameters)
|
||||
|
||||
for p in parameters:
|
||||
if p.name in self._already_create_accumulator:
|
||||
continue
|
||||
if self._multi_precision and self._is_dtype_fp16_or_bf16(p.dtype):
|
||||
master_p = self._create_master_weight(p)
|
||||
self._add_accumulator(
|
||||
self._moment_acc_str,
|
||||
master_p,
|
||||
fill_value=self.initial_accumulator_value,
|
||||
)
|
||||
self._already_create_accumulator.add(p.name)
|
||||
continue
|
||||
if (
|
||||
self._is_dtype_fp16_or_bf16(p.dtype)
|
||||
and not self._multi_precision
|
||||
):
|
||||
warnings.warn(
|
||||
"Accumulating with FP16/BF16 in optimizer can lead to poor accuracy or slow convergence."
|
||||
"Consider using multi_precision=True option of the Momentum optimizer."
|
||||
)
|
||||
self._add_accumulator(
|
||||
self._moment_acc_str,
|
||||
p,
|
||||
fill_value=self.initial_accumulator_value,
|
||||
)
|
||||
self._already_create_accumulator.add(p.name)
|
||||
|
||||
def _append_optimize_op(self, block, param_and_grad):
|
||||
if not isinstance(block, (framework.Block, pir.Block)):
|
||||
raise TypeError("block is not instance of Block.")
|
||||
if isinstance(param_and_grad, dict):
|
||||
param_and_grad = self._update_param_group(param_and_grad)
|
||||
|
||||
moment_acc = self._get_accumulator_master(
|
||||
self._moment_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
|
||||
)
|
||||
|
||||
if in_dynamic_or_pir_mode():
|
||||
_, _, _ = _C_ops.adagrad_(
|
||||
param_and_grad[0],
|
||||
param_and_grad[1],
|
||||
moment_acc,
|
||||
self._create_param_lr(param_and_grad),
|
||||
master_weight if find_master else None,
|
||||
self._epsilon,
|
||||
find_master,
|
||||
)
|
||||
return None
|
||||
else:
|
||||
# Create the adagrad optimizer op
|
||||
inputs = {
|
||||
"Param": param_and_grad[0],
|
||||
"Grad": param_and_grad[1],
|
||||
"Moment": moment_acc,
|
||||
"LearningRate": self._create_param_lr(param_and_grad),
|
||||
}
|
||||
|
||||
outputs = {"ParamOut": param_and_grad[0], "MomentOut": moment_acc}
|
||||
|
||||
if find_master:
|
||||
inputs["MasterParam"] = master_weight
|
||||
outputs["MasterParamOut"] = master_weight
|
||||
|
||||
adagrad_op = block.append_op(
|
||||
type=self.type,
|
||||
inputs=inputs,
|
||||
outputs=outputs,
|
||||
attrs={
|
||||
"epsilon": self._epsilon,
|
||||
"multi_precision": find_master,
|
||||
},
|
||||
stop_gradient=True,
|
||||
)
|
||||
|
||||
return adagrad_op
|
||||
|
||||
def _update_param_group(self, parameters):
|
||||
self._epsilon = parameters.get('epsilon', self._default_dict['epsilon'])
|
||||
self.initial_accumulator_value = parameters.get(
|
||||
'initial_accumulator_value',
|
||||
self._default_dict['initial_accumulator_value'],
|
||||
)
|
||||
parameters = parameters.get('params')
|
||||
return parameters
|
||||
Reference in New Issue
Block a user