wehub-resource-sync
269 lines
11 KiB
Python
269 lines
11 KiB
Python
# 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
|
|
|
|
import paddle
|
|
from paddle import _C_ops
|
|
from paddle.base.framework import in_dynamic_or_pir_mode
|
|
|
|
from ..base import framework
|
|
from ..base.dygraph import no_grad
|
|
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 .lr import LRScheduler
|
|
from .optimizer import _ParameterConfig
|
|
|
|
class _AdadeltaParameterConfig(_ParameterConfig):
|
|
epsilon: NotRequired[float]
|
|
rho: NotRequired[float]
|
|
|
|
|
|
__all__ = []
|
|
|
|
|
|
class Adadelta(Optimizer):
|
|
r"""
|
|
**Notes: This API does not support sparse parameter optimization.**
|
|
|
|
Adadelta Optimizer. Please refer to this for details:
|
|
`ADADELTA: AN ADAPTIVE LEARNING RATE METHOD <https://arxiv.org/abs/1212.5701>`_.
|
|
|
|
The update is done as follows:
|
|
|
|
.. math::
|
|
|
|
E(g_t^2) &= \rho * E(g_{t-1}^2) + (1-\rho) * g^2
|
|
|
|
learning\_rate &= \sqrt{ ( E(dx_{t-1}^2) + \epsilon ) / ( E(g_t^2) + \epsilon ) }
|
|
|
|
E(dx_t^2) &= \rho * E(dx_{t-1}^2) + (1-\rho) * (-g*learning\_rate)^2
|
|
|
|
Args:
|
|
learning_rate (float|Tensor|LRScheduler, optional): The learning rate used to update ``Parameter``.
|
|
It can be a float value, a ``Tensor`` with a float type or a LearningRateDecay. The default value is 0.001.
|
|
epsilon (float): a small float number for numeric stability. Default 1.0e-6.
|
|
rho (float): a floating point value indicating the decay rate. Default 0.95.
|
|
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 can be 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_ParamAttr` 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
|
|
( :ref:`api_paddle_nn_ClipGradByGlobalNorm` , :ref:`api_paddle_nn_ClipGradByNorm` ,
|
|
:ref:`api_paddle_nn_ClipGradByValue` ). Default None, meaning there is no gradient clipping.
|
|
name (str|None, optional): The default value is None. Normally there is no need for user
|
|
to set this property. For more information, please refer to
|
|
:ref:`api_guide_Name` .
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
|
|
>>> inp = paddle.uniform([10, 10], dtype="float32", min=-0.1, max=0.1)
|
|
>>> linear = paddle.nn.Linear(10, 10)
|
|
>>> out = linear(inp)
|
|
>>> loss = paddle.mean(out)
|
|
>>> beta1 = paddle.to_tensor([0.9], dtype="float32")
|
|
>>> beta2 = paddle.to_tensor([0.99], dtype="float32")
|
|
>>> adadelta = paddle.optimizer.Adadelta(learning_rate=0.1, parameters=linear.parameters(), weight_decay=0.01)
|
|
>>> back = out.backward()
|
|
>>> adadelta.step()
|
|
>>> adadelta.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)
|
|
>>> adadelta = paddle.optimizer.Adadelta(
|
|
... 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()
|
|
>>> adadelta.step()
|
|
>>> adadelta.clear_grad()
|
|
|
|
"""
|
|
|
|
type: str
|
|
_avg_squared_grad_acc_str = "_avg_squared_grad"
|
|
_avg_squared_update_acc_str = "_avg_squared_update"
|
|
|
|
def __init__(
|
|
self,
|
|
learning_rate: float | Tensor | LRScheduler = 0.001,
|
|
epsilon: float = 1.0e-6,
|
|
rho: float = 0.95,
|
|
parameters: (
|
|
Sequence[Tensor] | Sequence[_AdadeltaParameterConfig] | None
|
|
) = None,
|
|
weight_decay: float | WeightDecayRegularizer | None = None,
|
|
grad_clip: GradientClipBase | None = None,
|
|
name: str | None = None,
|
|
) -> None:
|
|
if learning_rate is None:
|
|
raise ValueError("learning_rate is not set.")
|
|
if epsilon is None:
|
|
raise ValueError("epsilon is not set.")
|
|
if rho is None:
|
|
raise ValueError("rho is not set.")
|
|
super().__init__(
|
|
learning_rate=learning_rate,
|
|
parameters=parameters,
|
|
weight_decay=weight_decay,
|
|
grad_clip=grad_clip,
|
|
name=name,
|
|
)
|
|
self._multi_precision = False
|
|
self._master_weights = {}
|
|
self.type = "adadelta"
|
|
self._epsilon = epsilon
|
|
self._rho = rho
|
|
self._default_dict = {
|
|
'epsilon': epsilon,
|
|
'rho': rho,
|
|
}
|
|
|
|
def _create_accumulators(self, block, parameters):
|
|
if not isinstance(block, (framework.Block, paddle.pir.Block)):
|
|
raise TypeError("block is not instance of framework.Block.")
|
|
if isinstance(parameters, dict):
|
|
parameters = parameters.get('params')
|
|
|
|
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._avg_squared_grad_acc_str, master_p)
|
|
self._add_accumulator(
|
|
self._avg_squared_update_acc_str, master_p
|
|
)
|
|
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 Lars optimizer."
|
|
)
|
|
self._add_accumulator(self._avg_squared_grad_acc_str, p)
|
|
self._add_accumulator(self._avg_squared_update_acc_str, p)
|
|
self._already_create_accumulator.add(p.name)
|
|
|
|
def _append_optimize_op(self, block, param_and_grad):
|
|
if isinstance(param_and_grad, dict):
|
|
param_and_grad = self._update_param_group(param_and_grad)
|
|
|
|
avg_squared_grad_acc = self._get_accumulator_master(
|
|
self._avg_squared_grad_acc_str, param_and_grad[0]
|
|
)
|
|
avg_squared_update_acc = self._get_accumulator_master(
|
|
self._avg_squared_update_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():
|
|
with no_grad():
|
|
_C_ops.adadelta_(
|
|
param_and_grad[0],
|
|
param_and_grad[1],
|
|
avg_squared_grad_acc,
|
|
avg_squared_update_acc,
|
|
self._create_param_lr(param_and_grad),
|
|
master_weight,
|
|
self._rho,
|
|
self._epsilon,
|
|
find_master,
|
|
)
|
|
return None
|
|
else:
|
|
if not isinstance(block, (framework.Block, paddle.pir.Block)):
|
|
raise TypeError("block is not instance of framework.Block.")
|
|
|
|
# Create the adadelta optimizer op
|
|
inputs = {
|
|
"Param": param_and_grad[0],
|
|
"Grad": param_and_grad[1],
|
|
"AvgSquaredGrad": avg_squared_grad_acc,
|
|
"AvgSquaredUpdate": avg_squared_update_acc,
|
|
"LearningRate": self._create_param_lr(param_and_grad),
|
|
}
|
|
outputs = {
|
|
"ParamOut": param_and_grad[0],
|
|
"AvgSquaredGradOut": avg_squared_grad_acc,
|
|
"AvgSquaredUpdateOut": avg_squared_update_acc,
|
|
}
|
|
if find_master:
|
|
inputs["MasterParam"] = master_weight
|
|
outputs["MasterParamOut"] = master_weight
|
|
adadelta_op = block.append_op(
|
|
type=self.type,
|
|
inputs=inputs,
|
|
outputs=outputs,
|
|
attrs={
|
|
"epsilon": self._epsilon,
|
|
"rho": self._rho,
|
|
"multi_precision": find_master,
|
|
},
|
|
stop_gradient=True,
|
|
)
|
|
|
|
return adadelta_op
|
|
|
|
def _update_param_group(self, parameters):
|
|
self._epsilon = parameters.get('epsilon', self._default_dict['epsilon'])
|
|
self._rho = parameters.get('rho', self._default_dict['rho'])
|
|
parameters = parameters.get('params')
|
|
return parameters
|