chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,398 @@
|
||||
# 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 ..base import core, framework
|
||||
from ..base.dygraph import no_grad
|
||||
from ..base.framework import name_scope
|
||||
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 _AdamaxParameterConfig(_ParameterConfig):
|
||||
beta1: NotRequired[float | Tensor]
|
||||
beta2: NotRequired[float | Tensor]
|
||||
epsilon: NotRequired[float | Tensor]
|
||||
|
||||
|
||||
__all__ = []
|
||||
|
||||
|
||||
class Adamax(Optimizer):
|
||||
r"""
|
||||
The Adamax optimizer is implemented based on the Adamax Optimization
|
||||
in Section 7 of `Adam paper <https://arxiv.org/abs/1412.6980>`_.
|
||||
The Adamax algorithm is a variant of the Adam algorithm based on the infinite norm,
|
||||
which makes the learning rate update algorithm more stable and simple.
|
||||
|
||||
The parameter ``param_out`` update rule with gradient ``grad``:
|
||||
|
||||
.. math::
|
||||
|
||||
t & = t + 1
|
||||
|
||||
moment\_out & = {\beta}_1 * moment + (1 - {\beta}_1) * grad
|
||||
|
||||
inf\_norm\_out & = max({\beta}_2 * inf\_norm + \epsilon, |grad|)
|
||||
|
||||
learning\_rate & = \frac{learning\_rate}{1 - {\beta}_1^t}
|
||||
|
||||
param\_out & = param - learning\_rate * \frac{moment\_out}{inf\_norm\_out}
|
||||
|
||||
Related paper: `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_
|
||||
|
||||
The original paper does not have an ``epsilon`` attribute,
|
||||
it is added here for numerical stability to prevent the division by 0 error.
|
||||
|
||||
Args:
|
||||
learning_rate (float|LRScheduler, optional): The learning rate used to update ``Parameter``.
|
||||
It can be a float value or a LRScheduler. The default value is 0.001.
|
||||
beta1 (float|Tensor, optional): The exponential decay rate for the 1st moment estimates.
|
||||
It should be a float number or a 0-D Tensor with shape [] and data type as float32.
|
||||
The default value is 0.9.
|
||||
beta2 (float|Tensor, optional): The exponential decay rate for the 2nd moment estimates.
|
||||
It should be a float number or a 0-D Tensor with shape [] and data type as float32.
|
||||
The default value is 0.999.
|
||||
epsilon (float|Tensor, optional): A small float value for numerical stability.
|
||||
The default value is 1e-08.
|
||||
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): 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.
|
||||
|
||||
**Notes**:
|
||||
**Currently, Adamax doesn't support sparse parameter optimization.**
|
||||
|
||||
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)
|
||||
>>> inp = paddle.to_tensor(inp)
|
||||
>>> out = linear(inp)
|
||||
>>> loss = paddle.mean(out)
|
||||
|
||||
>>> beta1 = paddle.to_tensor([0.9], dtype="float32")
|
||||
>>> beta2 = paddle.to_tensor([0.99], dtype="float32")
|
||||
|
||||
>>> adamax = paddle.optimizer.Adamax(
|
||||
... learning_rate=0.1,
|
||||
... parameters=linear.parameters(),
|
||||
... beta1=beta1,
|
||||
... beta2=beta2,
|
||||
... weight_decay=0.01,
|
||||
... )
|
||||
>>> out.backward()
|
||||
>>> adamax.step()
|
||||
>>> adamax.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)
|
||||
>>> adamax = paddle.optimizer.Adamax(
|
||||
... learning_rate=0.1,
|
||||
... parameters=[ # type: ignore
|
||||
... {
|
||||
... 'params': linear_1.parameters(),
|
||||
... },
|
||||
... {
|
||||
... 'params': linear_2.parameters(),
|
||||
... 'weight_decay': 0.001,
|
||||
... 'learning_rate': 0.1,
|
||||
... 'beta1': 0.8,
|
||||
... },
|
||||
... ],
|
||||
... weight_decay=0.01,
|
||||
... beta1=0.9,
|
||||
... )
|
||||
>>> out.backward()
|
||||
>>> adamax.step()
|
||||
>>> adamax.clear_grad()
|
||||
"""
|
||||
|
||||
type: str
|
||||
_moment_acc_str = "moment"
|
||||
_inf_norm_acc_str = "inf_norm"
|
||||
_beta1_pow_acc_str = "beta1_pow_acc"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
learning_rate: float | LRScheduler = 0.001,
|
||||
beta1: float | Tensor = 0.9,
|
||||
beta2: float | Tensor = 0.999,
|
||||
epsilon: float | Tensor = 1e-8,
|
||||
parameters: (
|
||||
Sequence[Tensor] | Sequence[_AdamaxParameterConfig] | None
|
||||
) = None,
|
||||
weight_decay: float | WeightDecayRegularizer | None = None,
|
||||
grad_clip: GradientClipBase | None = None,
|
||||
name: str | None = None,
|
||||
) -> None:
|
||||
assert learning_rate is not None
|
||||
assert beta1 is not None
|
||||
assert beta2 is not None
|
||||
assert epsilon is not None
|
||||
if not 0 <= beta1 < 1:
|
||||
raise ValueError("Invalid value of beta1, expect beta1 in [0,1).")
|
||||
if not 0 <= beta2 < 1:
|
||||
raise ValueError("Invalid value of beta2, expect beta2 in [0,1).")
|
||||
if not 0 <= epsilon:
|
||||
raise ValueError("Invalid value of epsilon, expect epsilon >= 0.")
|
||||
super().__init__(
|
||||
learning_rate=learning_rate,
|
||||
parameters=parameters,
|
||||
weight_decay=weight_decay,
|
||||
grad_clip=grad_clip,
|
||||
name=name,
|
||||
)
|
||||
self.type = "adamax"
|
||||
self._beta1 = beta1
|
||||
self._beta2 = beta2
|
||||
self._epsilon = epsilon
|
||||
self._multi_precision = False
|
||||
self._master_weights = {}
|
||||
|
||||
self._default_dict = {
|
||||
'beta1': beta1,
|
||||
'beta2': beta2,
|
||||
'epsilon': epsilon,
|
||||
}
|
||||
|
||||
def _add_moments_pows(self, p):
|
||||
acc_dtype = p.dtype
|
||||
if self._is_dtype_fp16_or_bf16(acc_dtype):
|
||||
acc_dtype = core.VarDesc.VarType.FP32
|
||||
|
||||
self._add_accumulator(self._moment_acc_str, p, dtype=acc_dtype)
|
||||
self._add_accumulator(self._inf_norm_acc_str, p, dtype=acc_dtype)
|
||||
self._add_accumulator(
|
||||
name=self._beta1_pow_acc_str,
|
||||
param=p,
|
||||
fill_value=self._beta1,
|
||||
shape=[1],
|
||||
)
|
||||
|
||||
def _create_accumulators(self, block, parameters):
|
||||
if isinstance(parameters, dict):
|
||||
parameters = self._update_param_group(parameters)
|
||||
|
||||
# Create accumulator tensors for first moment and infinity norm
|
||||
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_moments_pows(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 Adam optimizer."
|
||||
)
|
||||
self._add_moments_pows(p)
|
||||
self._already_create_accumulator.add(p.name)
|
||||
|
||||
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)
|
||||
|
||||
moment = self._get_accumulator_master(
|
||||
self._moment_acc_str, param_and_grad[0]
|
||||
)
|
||||
inf_norm = self._get_accumulator_master(
|
||||
self._inf_norm_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
|
||||
)
|
||||
|
||||
beta1_pow_acc = self._get_accumulator_master(
|
||||
self._beta1_pow_acc_str, param_and_grad[0]
|
||||
)
|
||||
if framework.in_dynamic_or_pir_mode():
|
||||
_C_ops.adamax_(
|
||||
param_and_grad[0],
|
||||
param_and_grad[1],
|
||||
self._create_param_lr(param_and_grad),
|
||||
moment,
|
||||
inf_norm,
|
||||
beta1_pow_acc,
|
||||
master_weight,
|
||||
self._beta1,
|
||||
self._beta2,
|
||||
self._epsilon,
|
||||
find_master,
|
||||
)
|
||||
|
||||
else:
|
||||
# create the adamax optimize op
|
||||
inputs = {
|
||||
"Param": param_and_grad[0],
|
||||
"Grad": param_and_grad[1],
|
||||
"LearningRate": self._create_param_lr(param_and_grad),
|
||||
"Moment": moment,
|
||||
"InfNorm": inf_norm,
|
||||
"Beta1Pow": beta1_pow_acc,
|
||||
}
|
||||
outputs = {
|
||||
"ParamOut": param_and_grad[0],
|
||||
"MomentOut": moment,
|
||||
"InfNormOut": inf_norm,
|
||||
}
|
||||
if find_master:
|
||||
inputs["MasterParam"] = master_weight
|
||||
outputs["MasterParamOut"] = master_weight
|
||||
attrs = {
|
||||
"beta1": self._beta1,
|
||||
"beta2": self._beta2,
|
||||
"epsilon": self._epsilon,
|
||||
"multi_precision": find_master,
|
||||
}
|
||||
adamax_op = block.append_op(
|
||||
type=self.type,
|
||||
inputs=inputs,
|
||||
outputs=outputs,
|
||||
attrs=attrs,
|
||||
stop_gradient=True,
|
||||
)
|
||||
|
||||
return adamax_op
|
||||
|
||||
def _finish_update(self, block, parameters_and_grads):
|
||||
"""Update Beta1 Power accumulator"""
|
||||
assert isinstance(block, (framework.Block, pir.Block))
|
||||
if isinstance(parameters_and_grads, list):
|
||||
for param, grad in parameters_and_grads:
|
||||
if grad is None or param.stop_gradient is True:
|
||||
continue
|
||||
if framework.in_dygraph_mode():
|
||||
beta1_pow_acc = self._get_accumulator_master(
|
||||
self._beta1_pow_acc_str, param
|
||||
)
|
||||
with no_grad():
|
||||
tmp = _C_ops.scale(
|
||||
beta1_pow_acc, self._beta1, 0.0, True
|
||||
)
|
||||
beta1_pow_acc.copy_(tmp, False)
|
||||
elif framework.in_pir_mode():
|
||||
with param.block.program._optimized_guard([param, grad]):
|
||||
beta1_pow_acc = self._get_accumulator_master(
|
||||
self._beta1_pow_acc_str, param
|
||||
)
|
||||
_C_ops.scale_(beta1_pow_acc, self._beta1, 0.0, True)
|
||||
else:
|
||||
with (
|
||||
param.block.program._optimized_guard([param, grad]),
|
||||
name_scope('adamax'),
|
||||
):
|
||||
beta1_pow_acc = self._get_accumulator_master(
|
||||
self._beta1_pow_acc_str, param
|
||||
)
|
||||
block.append_op(
|
||||
type="scale",
|
||||
inputs={"X": beta1_pow_acc},
|
||||
outputs={"Out": beta1_pow_acc},
|
||||
attrs={"scale": self._beta1},
|
||||
stop_gradient=True,
|
||||
)
|
||||
else:
|
||||
for param, grad in parameters_and_grads['params']:
|
||||
if grad is None or param.stop_gradient is True:
|
||||
continue
|
||||
if framework.in_dygraph_mode():
|
||||
beta1_pow_acc = self._get_accumulator_master(
|
||||
self._beta1_pow_acc_str, param
|
||||
)
|
||||
self._beta1 = parameters_and_grads.get(
|
||||
'beta1', self._default_dict['beta1']
|
||||
)
|
||||
with no_grad():
|
||||
tmp = _C_ops.scale(
|
||||
beta1_pow_acc, self._beta1, 0.0, True
|
||||
)
|
||||
beta1_pow_acc.copy_(tmp, False)
|
||||
else:
|
||||
with (
|
||||
param.block.program._optimized_guard([param, grad]),
|
||||
name_scope('adamax'),
|
||||
):
|
||||
beta1_pow_acc = self._get_accumulator_master(
|
||||
self._beta1_pow_acc_str, param
|
||||
)
|
||||
self._beta1 = parameters_and_grads.get(
|
||||
'beta1', self._default_dict['beta1']
|
||||
)
|
||||
block.append_op(
|
||||
type="scale",
|
||||
inputs={"X": beta1_pow_acc},
|
||||
outputs={"Out": beta1_pow_acc},
|
||||
attrs={"scale": self._beta1},
|
||||
stop_gradient=True,
|
||||
)
|
||||
|
||||
def _update_param_group(self, parameters):
|
||||
self._beta1 = parameters.get('beta1', self._default_dict['beta1'])
|
||||
self._beta2 = parameters.get('beta2', self._default_dict['beta2'])
|
||||
self._epsilon = parameters.get('epsilon', self._default_dict['epsilon'])
|
||||
parameters = parameters.get('params')
|
||||
return parameters
|
||||
Reference in New Issue
Block a user