Source code for mindspore.nn.optim_ex.adam

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"""adam"""
from __future__ import absolute_import

from mindspore.ops import functional as F, operations as P
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
from mindspore.nn.optim_ex.optimizer import Optimizer
from mindspore import ops


[docs]class Adam(Optimizer): r""" Implements Adam algorithm.. The updating formulas are as follows: .. math:: \begin{aligned} &\textbf{input} : \gamma \text{ (lr)}, \beta_1, \beta_2 \text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)} \\ &\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}\textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \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-1} - \gamma \widehat{m_t}/ \big(\sqrt{\widehat{v_t}^{max}} + \epsilon \big) \\ &\hspace{5mm}\textbf{else} \\ &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \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/r2.1/api_python/mindspore.nn.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 `lr` is not int, float or Tensor. ValueError: If the `lr` 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 >>> # Define the network structure of LeNet5. Refer to >>> # https://gitee.com/mindspore/docs/blob/r2.1/docs/mindspore/code/lenet.py >>> net = LeNet5() >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True) >>> optimizer = nn.optim_ex.Adam(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, 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(Adam, 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 = ParameterTuple(Parameter( Tensor(0, mstype.int32), "step_"+str(i)) for i in range(len(self.parameters))) self.increase_tensor = Tensor(1, mstype.int32) self.op_mul = P.Mul() self.assignadd = P.AssignAdd() self.op_pow = P.Pow() self.op_sqrt = P.Sqrt() self.op_maximum = P.Maximum() def construct(self, gradients): for group_id, group in enumerate(self.param_groups): params = [] grads = [] exp_avgs = [] exp_avg_sqs = [] max_exp_avg_sqs = [] state_steps = [] params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps = \ self._init_group(group, gradients, params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, group_id) beta1, beta2 = group['betas'] self.apply_adam(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, group['amsgrad'], beta1, beta2, group['lr'], group['weight_decay'], group['eps'], group["maximize"], group["grad_centralization"]) def apply_adam(self, params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize, grad_centralization): grads = self._decay_weight(weight_decay, params, grads) grads = self._gradients_centralization(grad_centralization, grads) for i, param in enumerate(params): grad = grads[i] if not maximize else -grads[i] exp_avg = exp_avgs[i] exp_avg_sq = exp_avg_sqs[i] step_t = state_steps[i] F.assign(exp_avg, self.op_mul(exp_avg, beta1) + self.op_mul(grad, 1-beta1)) F.assign(exp_avg_sq, ops.addcmul(self.op_mul(exp_avg_sq, beta2), grad, grad.conj(), 1-beta2)) step_t = F.depend(step_t, self.assignadd(step_t, self.increase_tensor)) bias_correction1 = F.tuple_to_array((1.0,)) - self.op_pow(beta1, step_t) bias_correction2 = F.tuple_to_array((1.0,)) - self.op_pow(beta2, step_t) step_size = lr / bias_correction1 bias_correction2_sqrt = self.op_sqrt(bias_correction2) if amsgrad: F.assign(max_exp_avg_sqs[i], self.op_maximum(max_exp_avg_sqs[i], exp_avg_sq)) denom = self.op_sqrt(max_exp_avg_sqs[i]) / bias_correction2_sqrt + eps else: denom = self.op_sqrt(exp_avg_sq) / bias_correction2_sqrt + eps param = F.depend(param, denom) next_param = param - self.op_mul(exp_avg / denom, step_size) F.assign(param, next_param) def _init_group(self, group, gradients, params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, group_id): """ Initialize group params. """ p_id = self.group_start_id[group_id] for i, param in enumerate(group["params"]): params.append(param) grads.append(gradients[p_id+i]) exp_avgs.append(self.exp_avg[p_id+i]) exp_avg_sqs.append(self.exp_avg_sq[p_id+i]) max_exp_avg_sqs.append(self.max_exp_avg_sq[p_id+i]) state_steps.append(self.state_step[p_id+i]) return params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps