mindspore.experimental.optim.adamax 源代码

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"""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


[文档]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/r2.3.1/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/r2.3.1/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