Source code for mindspore.experimental.optim.rmsprop

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"""rmsprop"""
from __future__ import absolute_import

from mindspore.ops import functional as F, composite as C, operations as P
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 import ops
from mindspore import jit

_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")

op_mul = P.Mul()
op_sqrt = P.Sqrt()


@_rmsprop_opt.register("Bool", "Number", "Number", "Number", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _run_rmsprop_opt(centered, alpha, eps, momentum, lr, weight, mean_square, mean_grad, mom, grad):
    """Apply rmsprop optimizer to the weight parameter using dynamic learning rate."""
    F.assign(mean_square, ops.addcmul(op_mul(mean_square, alpha), grad, grad, 1 - alpha))

    if centered:
        F.assign(mean_grad, op_mul(mean_grad, alpha) + op_mul(grad, 1 - alpha))
        avg = op_sqrt(ops.addcmul(mean_square, mean_grad, mean_grad, -1.)) + eps
    else:
        avg = op_sqrt(mean_square) + eps

    if momentum > 0:
        F.assign(mom, op_mul(mom, momentum) + grad / avg)
        F.assign(weight, weight - mom * lr)
    else:
        F.assign(weight, weight - lr * grad / avg)
    return True


[docs]class RMSprop(Optimizer): r""" Implements RMSprop 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/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: ``1e-2``. alpha (float, optional): smoothing constant. Default: ``0.99``. 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.``. momentum (float, optional): momentum factor. Default: ``0.``. centered (bool, optional) : if ``True``, compute the centered RMSProp, the gradient is normalized by an estimation of its variance. Default: ``False``. 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 `momentum` is less than 0.0. ValueError: If the `alpha` is less than 0.0. ValueError: If the `eps` is less than 0.0. 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/r2.3.0rc2/docs/mindspore/code/lenet.py >>> net = LeNet5() >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True) >>> optimizer = optim.RMSprop(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, alpha=0.99, eps=1e-8, weight_decay=0.0, momentum=0.0, centered=False, maximize=False): check_not_less_than_without_equal(lr, "lr", self.cls_name) check_not_less_than(alpha, "alpha", self.cls_name) check_not_less_than_without_equal(eps, "eps", self.cls_name) check_not_less_than(momentum, "momentum", self.cls_name) check_not_less_than(weight_decay, "weight_decay", self.cls_name) defaults = dict( lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered, weight_decay=weight_decay, maximize=maximize, ) super(RMSprop, self).__init__(params, defaults) self.mean_grad = self.parameters.clone(prefix="mean_grad", init='zeros') self.mean_square = self.parameters.clone(prefix="mean_square", init='zeros') self.moment = self.parameters.clone(prefix="moment", init='zeros') self.op_cast = P.Cast() @jit def implementation(self, group_id, lr, gradients, maximize, weight_decay, centered, alpha, eps, momentum): """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) mean_grad = self.mean_grad[start_id: end_id] mean_square = self.mean_square[start_id: end_id] moment = self.moment[start_id: end_id] self.hyper_map(F.partial(_rmsprop_opt, centered, alpha, eps, momentum, lr), params, mean_square, mean_grad, moment, grads) return True def construct(self, gradients): 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_id, lr, gradients, maximize, group["weight_decay"], group["centered"], group["alpha"], group["eps"], group["momentum"]) return True