# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""asgd"""
from __future__ import absolute_import
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.common import Tensor, Parameter
import mindspore.common.dtype as mstype
from mindspore.experimental.optim.optimizer import Optimizer, check_not_less_than, check_not_less_than_without_equal
from mindspore.common.api import jit
_asgd_opt = C.MultitypeFuncGraph("asgd_opt")
op_cast = P.Cast()
op_pow = P.Pow()
op_maximum = P.Maximum()
op_assign = P.Assign()
op_assignadd = P.AssignAdd()
@_asgd_opt.register("Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor",
"Tensor", "Tensor", "Tensor")
def _run_asgd_opt(lambd, alpha, t0, step, lr, param, grad, eta, mu, ax):
"""Apply asgd optimizer to the weight parameter using dynamic learning rate."""
if step == 1:
op_assign(eta, lr)
next_param = op_cast(param * (1. - lambd * eta) - eta * grad, param.dtype)
F.assign(param, next_param)
if mu != 1:
op_assignadd(ax, op_cast((next_param - ax) * mu, ax.dtype))
else:
op_assign(ax, next_param)
op_assign(eta, lr / (op_pow((1. + lambd * lr * step), alpha)))
op_assign(mu, 1. / op_maximum(1., step - t0))
return True
[文档]class ASGD(Optimizer):
r"""
Implements Averaged Stochastic Gradient Descent algorithm.
.. warning::
This is an experimental optimizer API that is subject to change.
This module must be used with lr scheduler module in `LRScheduler Class
<https://www.mindspore.cn/docs/en/master/api_python/mindspore.experimental.html#lrscheduler-class>`_ .
Args:
params (Union[list(Parameter), list(dict)]): list of parameters to optimize or dicts defining
parameter groups.
lr (Union[int, float, Tensor], optional): learning rate. Default: ``1e-2``.
lambd (float, optional): decay term. Default: ``1e-4``.
alpha (float, optional): power for eta update. Default: ``0.75``.
t0 (float, optional): point at which to start averaging. Default: ``1e6``.
weight_decay (float, optional): weight decay (L2 penalty). Default: ``0.``.
maximize (bool, optional): maximize the params based on the objective, instead of minimizing.
Default: ``False``.
Inputs:
- **gradients** (tuple[Tensor]) - The gradients of `params`.
Raises:
ValueError: If the learning rate is not int, float or Tensor.
ValueError: If the learning rate is less than 0.
ValueError: If the `weight_decay` is less than 0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import nn
>>> from mindspore.experimental import optim
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/master/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> optimizer = optim.ASGD(net.trainable_params(), lr=0.1)
>>> def forward_fn(data, label):
... logits = net(data)
... loss = loss_fn(logits, label)
... return loss, logits
>>> grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
>>> def train_step(data, label):
... (loss, _), grads = grad_fn(data, label)
... optimizer(grads)
... return loss
"""
def __init__(self, params, lr=1e-2, lambd=1e-4, alpha=0.75, t0=1e6, weight_decay=0.0, maximize=False):
check_not_less_than_without_equal(lr, "lr", self.cls_name)
check_not_less_than(weight_decay, "weight_decay", self.cls_name)
if not isinstance(lambd, float):
raise TypeError(f"For 'ASGD', the type of lambd must be float, but got {type(lambd)}.")
if not isinstance(t0, float):
raise TypeError(f"For 'ASGD', the type of t0 must be float, but got {type(t0)}.")
defaults = dict(
lr=lr,
lambd=lambd,
alpha=alpha,
t0=t0,
weight_decay=weight_decay,
maximize=maximize,
)
super(ASGD, self).__init__(params, defaults)
self.mu = [Parameter(Tensor(1.), "mu_" + param.name) for param in self.parameters]
self.eta = [Parameter(Tensor(0.), "eta_" + param.name) for param in self.parameters]
self.ax = self.parameters.clone(prefix="ax", init='zeros')
self.step_t = Parameter(Tensor(0, mstype.int32), "step_t")
self.increase_tensor = Tensor(1, mstype.int32)
self.assignadd = P.AssignAdd()
self.op_cast = P.Cast()
@jit
def implementation(self, lambd, alpha, t0, lr, group_id, maximize, gradients, weight_decay):
"""Extract the common computing part for acceleration"""
start_id = self.group_start_id[group_id]
end_id = self.group_start_id[group_id + 1]
params = self.parameters[start_id: end_id]
grads = tuple([grad if not maximize else F.neg(grad) for grad in gradients[start_id: end_id]])
grads = self._decay_weight(weight_decay, params, grads)
ax = self.ax[start_id: end_id]
eta = self.eta[start_id: end_id]
mu = self.mu[start_id: end_id]
self.hyper_map(F.partial(_asgd_opt, lambd, alpha, t0, self.step_t, lr),
params, grads, eta, mu, ax)
return True
def construct(self, gradients):
self.assignadd(self.step_t, self.increase_tensor)
for group_id, group in enumerate(self.param_groups):
lr = self.lrs[group_id]
if isinstance(group.get("lr"), float):
lr = self.op_cast(group.get("lr"), mstype.float32)
maximize = group.get("maximize")
self.implementation(group["lambd"], group["alpha"], group["t0"], lr, group_id, maximize, gradients,
group["weight_decay"])
return True