mindspore.experimental.optim.radam 源代码

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"""radam"""
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 jit

_radam_opt = C.MultitypeFuncGraph("radam_opt")

op_pow = P.Pow()
op_sqrt = P.Sqrt()
op_cast = P.Cast()


@_radam_opt.register("Number", "Number", "Number", "Tensor", "Number", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
                     "Tensor", "Tensor")
def _tensor_run_opt(beta1, beta2, eps, lr, rho_inf, rho_t, bias_correction1, bias_correction2, param, grad, exp_avg,
                    exp_avg_sq):
    """Apply radam optimizer to the weight parameter."""

    F.assign(exp_avg, exp_avg * beta1 + grad * (1 - beta1))
    F.assign(exp_avg_sq, exp_avg_sq * beta2 + grad * grad * (1 - beta2))
    bias_corrected_exp_avg = exp_avg / bias_correction1

    if rho_t > 5.0:
        rect = op_sqrt((rho_t - 4) * (rho_t - 2) * rho_inf / ((rho_inf - 4) * (rho_inf - 2) * rho_t))
        exp_avg_sq_sqrt = op_sqrt(exp_avg_sq) + eps
        adaptive_lr = op_sqrt(bias_correction2) / exp_avg_sq_sqrt
        F.assign(param, param - bias_corrected_exp_avg * lr * adaptive_lr * rect)
    else:
        F.assign(param, param - bias_corrected_exp_avg * lr)

    return True


[文档]class RAdam(Optimizer): r""" Implements RAdam algorithm. .. 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{ (weightdecay)}, \\ &\hspace{13mm} \epsilon \text{ (epsilon)} \\ &\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)}, v_0 \leftarrow 0 \text{ ( second moment)}, \\ &\hspace{18mm} \rho_{\infty} \leftarrow 2/(1-\beta_2) -1 \\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{6mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{6mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ &\hspace{6mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\ &\hspace{6mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\ &\hspace{6mm}\rho_t \leftarrow \rho_{\infty} - 2 t \beta^t_2 /\big(1-\beta_2^t \big) \\[-1.ex] &\hspace{6mm}\textbf{if} \: \rho_t > 5 \\ &\hspace{12mm} l_t \leftarrow \frac{\sqrt{ (1-\beta^t_2) }}{ \sqrt{v_t} +\epsilon } \\ &\hspace{12mm} r_t \leftarrow \sqrt{\frac{(\rho_t-4)(\rho_t-2)\rho_{\infty}}{(\rho_{\infty}-4)(\rho_{\infty}-2) \rho_t}} \\ &\hspace{12mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_t} r_t l_t \\ &\hspace{6mm}\textbf{else} \\ &\hspace{12mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_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: ``1e-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: ``0.0``. 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 `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.RAdam(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-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.0): 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, ) super(RAdam, 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_avg_sq = self.parameters.clone(prefix="exp_avg_sq", init='zeros') self.increase_tensor = Tensor(1, mstype.int32) self.assignadd = P.AssignAdd() @jit def implementation(self, lr, beta1, beta2, weight_decay, eps, start_id, end_id, gradients): """Extract the common computing part for acceleration""" params = self.parameters[start_id: end_id] grads = gradients[start_id: end_id] grads = self._decay_weight(weight_decay, params, grads) exp_avg = self.exp_avg[start_id: end_id] exp_avg_sq = self.exp_avg_sq[start_id: end_id] bias_correction1 = 1 - op_pow(beta1, self.step_t.value()) bias_correction2 = 1 - op_pow(beta2, self.step_t.value()) rho_inf = 2 / (1 - beta2) - 1 beta2_pow = op_pow(beta2, self.step_t.value()) right = 2 * self.step_t.value() * beta2_pow / bias_correction2 rho_t = rho_inf - right self.hyper_map(F.partial(_radam_opt, beta1, beta2, eps, lr, rho_inf, rho_t, bias_correction1, bias_correction2), params, grads, exp_avg, exp_avg_sq) 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 = op_cast(group.get("lr"), mstype.float32) beta1, beta2 = group["betas"] start_id = self.group_start_id[group_id] end_id = self.group_start_id[group_id + 1] weight_decay = group["weight_decay"] eps = group["eps"] self.implementation(lr, beta1, beta2, weight_decay, eps, start_id, end_id, gradients) return True