Source code for mindspore.experimental.optim.adamw

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"""adamw"""
from __future__ import absolute_import

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

_adamw_opt = C.MultitypeFuncGraph("adamw_opt")

op_mul = P.Mul()
op_pow = P.Pow()
op_sqrt = P.Sqrt()
op_maximum = P.Maximum()
hyper_map = C.HyperMap()


@jit
def prepare_func(lr, weight_decay, state_step, beta1, beta2):
    weight_decay_new = 1 - lr * weight_decay
    bias_correction1 = 1 - op_pow(beta1, state_step)
    bias_correction2 = 1 - op_pow(beta2, state_step)
    step_size = lr / bias_correction1
    bias_correction2_sqrt = op_sqrt(bias_correction2)
    return weight_decay_new, step_size, bias_correction2_sqrt


@_adamw_opt.register("Tensor", "Tensor", "Bool", "Float", "Tensor", "Float", "Float", "Tensor", "Tensor",
                     "Tensor", "Tensor", "Tensor")
def _run_adamw_opt(weight_decay_new, step_size, amsgrad, eps, bias_correction2_sqrt, beta1, beta2, param, grad,
                   exp_avg, exp_avg_sq, max_exp_avg_sq):
    """Apply adamw optimizer to the weight parameter."""
    success = True
    next_param = op_mul(param, weight_decay_new)
    F.assign(exp_avg, op_mul(exp_avg, beta1) + op_mul(grad, 1 - beta1))
    F.assign(exp_avg_sq, ops.addcmul(op_mul(exp_avg_sq, beta2), grad, grad, 1 - beta2))

    if amsgrad:
        next_max_exp_avg = op_maximum(max_exp_avg_sq, exp_avg_sq)
        denom = op_sqrt(next_max_exp_avg) / bias_correction2_sqrt + eps
        F.assign(max_exp_avg_sq, next_max_exp_avg)
    else:
        denom = op_sqrt(exp_avg_sq) / bias_correction2_sqrt + eps

    return_param = next_param - op_mul(exp_avg / denom, step_size)
    F.assign(param, return_param)
    return success


[docs]class AdamW(Optimizer): r""" Implements Adam Weight Decay algorithm. .. math:: \begin{aligned} &\textbf{input} : \gamma \text{(lr)}, \: \beta_1, \beta_2 \text{(betas)}, \: \theta_0 \text{(params)}, \: f(\theta) \text{(objective)}, \: \epsilon \text{ (epsilon)} \\ &\hspace{13mm} \lambda \text{(weight decay)}, \: \textit{amsgrad}, \: \textit{maximize} \\ &\textbf{initialize} : m_0 \leftarrow 0 \text{ (first moment)}, v_0 \leftarrow 0 \text{ ( second moment)}, \: \widehat{v_0}^{max}\leftarrow 0 \\[-1.ex] &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{5mm}\textbf{if} \: \textit{maximize}: \\ &\hspace{10mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm}\textbf{else} \\ &\hspace{10mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1} \\ &\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ &\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\ &\hspace{5mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\ &\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\ &\hspace{5mm}\textbf{if} \: amsgrad \\ &\hspace{10mm}\widehat{v_t}^{max} \leftarrow \mathrm{max}(\widehat{v_t}^{max}, \widehat{v_t}) \\ &\hspace{10mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/ \big(\sqrt{\widehat{v_t}^{max}} + \epsilon \big) \\ &\hspace{5mm}\textbf{else} \\ &\hspace{10mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/ \big(\sqrt{\widehat{v_t}} + \epsilon \big) \\ &\bf{return} \: \theta_t \\[-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/r2.3.0/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): The exponential decay rate for the moment estimations. 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.``. amsgrad (bool, optional): whether to use the AMSGrad algorithm. 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 `eps` is less than 0.0. ValueError: If the `betas` not in the range of [0, 1). 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/r2.3.0/docs/mindspore/code/lenet.py >>> net = LeNet5() >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True) >>> optimizer = optim.AdamW(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=1e-2, amsgrad=False, *, maximize=False): if lr < 0.0: raise ValueError("Invalid learning rate: {}".format(lr)) if eps < 0.0: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= betas[0] < 1.0: raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) if not 0.0 <= betas[1] < 1.0: raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) if weight_decay < 0.0: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad, maximize=maximize) super(AdamW, self).__init__(params, defaults) 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.max_exp_avg_sq = self.parameters.clone(prefix="max_exp_avg_sq", init='zeros') self.state_step = Parameter(Tensor(0, mstype.int32), "state_step") self.increase_tensor = Tensor(1, mstype.int32) self.assignadd = P.AssignAdd() self.op_cast = P.Cast() @jit def implementation(self, lr, weight_decay, beta1, beta2, amsgrad, eps, grads, start_id, end_id): """Extract the common computing part for acceleration""" weight_decay_new, step_size, bias_correction2_sqrt = prepare_func(lr, weight_decay, self.state_step, beta1, beta2) self.hyper_map(F.partial(_adamw_opt, weight_decay_new, step_size, amsgrad, eps, bias_correction2_sqrt, beta1, beta2), self.parameters[start_id: end_id], grads, self.exp_avg[start_id: end_id], self.exp_avg_sq[start_id: end_id], self.max_exp_avg_sq[start_id: end_id]) return True def construct(self, gradients): self.assignadd(self.state_step, self.increase_tensor) for group_id, group in enumerate(self.param_groups): beta1, beta2 = group['betas'] start_id = self.group_start_id[group_id] end_id = self.group_start_id[group_id + 1] lr = self.lrs[group_id] if isinstance(group.get("lr"), float): lr = self.op_cast(group.get("lr"), mstype.float32) grads = tuple([grad if not group.get("maximize") else F.neg(grad) for grad in gradients[start_id: end_id]]) self.implementation(lr, group.get("weight_decay"), beta1, beta2, group.get("amsgrad"), group.get("eps"), grads, start_id, end_id) return True