Source code for mindspore.experimental.optim.adagrad

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"""adagrad"""
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 import jit

_adagrad_opt = C.MultitypeFuncGraph("adagrad_opt")


@_adagrad_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt(opt, learning_rate, weight, accum, gradient):
    """Apply adagrad optimizer to the weight parameter."""
    success = True
    success = F.depend(success, opt(weight, accum, learning_rate, gradient))
    return success


[docs]class Adagrad(Optimizer): r""" Implements Adagrad algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}, \: \lambda \text{ (weight decay)}, \\ &\hspace{12mm} \tau \text{ (initial accumulator value)}, \: \eta\text{ (lr decay)}\\ &\textbf{initialize} : state\_sum_0 \leftarrow 0 \\[-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} \tilde{\gamma} \leftarrow \gamma / (1 +(t-1) \eta) \\ &\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm}state\_sum_t \leftarrow state\_sum_{t-1} + g^2_t \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \tilde{\gamma} \frac{g_t}{\sqrt{state\_sum_t}+\epsilon} \\ &\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/r2.4.1/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: ``1e-2``. lr_decay (float, optional): learning rate decay. Default: ``0.``. weight_decay (float, optional): weight decay (L2 penalty). Default: ``0.``. initial_accumulator_value (float, optional): the initial accumulator value. Default: ``0.``. eps (float, optional): term added to the denominator to improve numerical stability. Default: ``1e-10``. 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 learning rate decay is less than 0. ValueError: If the `weight_decay` is less than 0. ValueError: If the `initial_accumulator_value` is less than 0.0. ValueError: If the `eps` is less than 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/r2.4.1/docs/mindspore/code/lenet.py >>> net = LeNet5() >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True) >>> optimizer = optim.Adagrad(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, lr_decay=0.0, weight_decay=0.0, initial_accumulator_value=0.0, eps=1e-10, *, maximize=False): check_not_less_than_without_equal(lr, "lr", self.cls_name) check_not_less_than(lr_decay, "lr_decay", self.cls_name) check_not_less_than(weight_decay, "weight_decay", self.cls_name) check_not_less_than(initial_accumulator_value, "initial_accumulator_value", self.cls_name) check_not_less_than_without_equal(eps, "eps", self.cls_name) defaults = dict( lr=lr, lr_decay=lr_decay, eps=eps, weight_decay=weight_decay, initial_accumulator_value=initial_accumulator_value, maximize=maximize, ) super(Adagrad, self).__init__(params, defaults) self.accum = self.parameters.clone(prefix="accum", init=initial_accumulator_value) self.op_cast = P.Cast() self.step_t = Parameter(Tensor(0, mstype.int32), "step_t") self.increase_tensor = Tensor(1, mstype.int32) self.assignadd = P.AssignAdd() self.assign = P.Assign() @jit def implementation(self, eps, lr, lr_decay, maximize, weight_decay, start_id, end_id, gradients): """Extract the common computing part for acceleration""" opt = P.ApplyAdagradV2(epsilon=eps, update_slots=True) decay_lr = lr / (1 + self.step_t * lr_decay) 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) accum = self.accum[start_id: end_id] self.hyper_map(F.partial(_adagrad_opt, opt, decay_lr), params, accum, 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) lr_decay = group["lr_decay"] maximize = group.get("maximize") weight_decay = group["weight_decay"] eps = group["eps"] start_id = self.group_start_id[group_id] end_id = self.group_start_id[group_id + 1] self.implementation(eps, lr, lr_decay, maximize, weight_decay, start_id, end_id, gradients) self.assignadd(self.step_t, self.increase_tensor) return True