# Copyright 2023 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.
# ============================================================================
"""adamax"""
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 import _checkparam as validator
from mindspore.experimental.optim.optimizer import Optimizer, check_not_less_than, check_not_less_than_without_equal
from mindspore import ops
from mindspore import jit
_adamax_opt = C.MultitypeFuncGraph("adamax_opt")
@_adamax_opt.register("Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt(beta1, beta2, eps, clr, param, grad, exp_avg, exp_inf):
"""Apply adamax optimizer to the weight parameter."""
F.assign(exp_avg, exp_avg * beta1 + grad * (1-beta1))
norm_buf = ops.cat([ops.unsqueeze(exp_inf * beta2, 0), ops.unsqueeze(grad.abs().add(eps), 0)], 0)
F.assign(exp_inf, ops.amax(norm_buf, 0))
F.assign(param, param - clr * exp_avg / exp_inf)
return True
[docs]class Adamax(Optimizer):
r"""
Implements Adamax algorithm (a variant of Adam based on infinity norm).
.. math::
\begin{aligned}
&\rule{110mm}{0.4pt} \\
&\textbf{input} : \gamma \text{ (lr)}, \beta_1, \beta_2
\text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)},
\: \lambda \text{ (weight decay)}, \\
&\hspace{13mm} \epsilon \text{ (epsilon)} \\
&\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)},
u_0 \leftarrow 0 \text{ ( infinity norm)} \\[-1.ex]
&\rule{110mm}{0.4pt} \\
&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
&\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
&\hspace{5mm}if \: \lambda \neq 0 \\
&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
&\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
&\hspace{5mm}u_t \leftarrow \mathrm{max}(\beta_2 u_{t-1}, |g_{t}|+\epsilon) \\
&\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \frac{\gamma m_t}{(1-\beta^t_1) u_t} \\
&\rule{110mm}{0.4pt} \\[-1.ex]
&\bf{return} \: \theta_t \\[-1.ex]
&\rule{110mm}{0.4pt} \\[-1.ex]
\end{aligned}
.. 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/r2.3.0rc2/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: ``2e-3``.
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square. Default: ``(0.9, 0.999)``.
eps (float, optional): term added to the denominator to improve
numerical stability. Default: ``1e-8``.
weight_decay (float, optional): weight decay (L2 penalty). Default: ``0.``.
Keyword Args:
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 `eps` is less than 0.0.
ValueError: If the `weight_decay` is less than 0.
ValueError: If elements of the `betas` not in the range of [0,1).
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/r2.3.0rc2/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> optimizer = optim.Adamax(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=2e-3, betas=(0.9, 0.999), eps=1e-8, 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)
check_not_less_than_without_equal(eps, "eps", self.cls_name)
validator.check_float_range(betas[0], 0., 1., validator.INC_LEFT, "betas[0]", self.cls_name)
validator.check_float_range(betas[1], 0., 1., validator.INC_LEFT, "betas[1]", self.cls_name)
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
maximize=maximize,
)
super(Adamax, self).__init__(params, defaults)
self.step_t = Parameter(Tensor(0, mstype.int32), "step_t")
self.exp_avg = self.parameters.clone(prefix="exp_avg", init='zeros')
self.exp_inf = self.parameters.clone(prefix="exp_inf", init='zeros')
self.increase_tensor = Tensor(1, mstype.int32)
self.assignadd = P.AssignAdd()
self.op_cast = P.Cast()
@jit
def implementation(self, group_id, lr, gradients, maximize, weight_decay, beta1, beta2, eps):
"""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)
exp_avg = self.exp_avg[start_id: end_id]
exp_inf = self.exp_inf[start_id: end_id]
bias_correction = 1 - beta1 ** self.step_t
clr = lr / bias_correction
self.hyper_map(F.partial(_adamax_opt, beta1, beta2, eps, clr),
params, grads, exp_avg, exp_inf)
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")
beta1, beta2 = group["betas"]
self.implementation(group_id, lr, gradients, maximize, group["weight_decay"], beta1, beta2, group["eps"])
return True