Source code for mindspore.experimental.optim.sgd

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"""sgd"""
from __future__ import absolute_import

from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
from mindspore import _checkparam as Validator
from mindspore.experimental.optim.optimizer import Optimizer
from mindspore import jit

_sgd_opt = C.MultitypeFuncGraph("sgd_opt")


@_sgd_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt_ext(opt, momentum, learning_rate, gradient, weight, accum, stat):
    """Apply sgd optimizer to the weight parameter using Tensor."""
    opt(weight, gradient, learning_rate, accum, momentum, stat)
    return True


[docs]class SGD(Optimizer): r""" Stochastic Gradient Descent optimizer. .. math:: v_{t+1} = u \ast v_{t} + gradient \ast (1-dampening) If nesterov is True: .. math:: p_{t+1} = p_{t} - lr \ast (gradient + u \ast v_{t+1}) If nesterov is False: .. math:: p_{t+1} = p_{t} - lr \ast v_{t+1} To be noticed, for the first step, :math:`v_{t+1} = gradient`. Here : where p, v and u denote the parameters, accum, and momentum respectively. .. 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]): learning rate. momentum (Union[int, float], optional): momentum factor. Default: ``0``. weight_decay (float, optional): weight decay (L2 penalty). Default: ``0.``. dampening (Union[int, float], optional): dampening for momentum. Default: ``0``. nesterov (bool, optional): enables Nesterov momentum. Default: ``False``. 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 `momentum` or `weight_decay` value is less than 0.0. ValueError: If the `momentum`, `dampening` or `weight_decay` value is not int or float. ValueError: If the `nesterov` and `maximize` is not bool. ValueError: If the `nesterov` is true, `momentum` is not positive or `dampening` is not 0.0. 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.SGD(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, momentum=0, dampening=0, weight_decay=0.0, nesterov=False, *, maximize=False): Validator.check_value_type("lr", lr, [float, int, Tensor], self.cls_name) if lr < 0.0: raise ValueError("Invalid learning rate: {}".format(lr)) Validator.check_value_type("momentum", momentum, [int, float], self.cls_name) if momentum < 0.0: raise ValueError("Invalid momentum value: {}".format(momentum)) momentum = float(momentum) Validator.check_value_type("nesterov", nesterov, [bool], self.cls_name) Validator.check_value_type("maximize", maximize, [bool], self.cls_name) defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov, maximize=maximize, grad_centralization=False) super(SGD, self).__init__(params, defaults) for group in self.param_groups: Validator.check_value_type("dampening", group.get("dampening"), [int, float], self.cls_name) group["dampening"] = float(group.get("dampening")) if nesterov and (momentum <= 0.0 or dampening != 0.0): raise ValueError("For 'SGD', if 'nesterov' is true, 'momentum' must be > 0.0 and 'dampening' must " "equal to 0.0, but got 'momentum' {}, 'dampening' {}".format(momentum, dampening)) self.accum = self.parameters.clone(prefix="accum", init='zeros') self.stat = self.parameters.clone(prefix="stat", init='ones') self.op_cast = P.Cast() @jit def implementation(self, momentum, lr, group_id, gradients, maximize, dampening, weight_decay, nesterov): """Extract the common computing part for acceleration""" start_id = self.group_start_id[group_id] end_id = self.group_start_id[group_id + 1] momentum = self.op_cast(momentum, mstype.float32) opt = P.SGD(dampening, weight_decay, nesterov) grads = tuple([grad if not maximize else F.neg(grad) for grad in gradients[start_id: end_id]]) self.hyper_map(F.partial(_sgd_opt, opt, momentum, lr), grads, self.parameters[start_id: end_id], self.accum[start_id: end_id], self.stat[start_id: end_id]) 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) self.implementation(group.get("momentum"), lr, group_id, gradients, group.get("maximize"), group.get("dampening"), group.get("weight_decay"), group.get("nesterov")) return True