Shortcuts

Source code for torch.optim.rmsprop

import torch
from torch import Tensor
from .optimizer import (Optimizer, _default_to_fused_or_foreach, _use_grad_for_differentiable,
                        _differentiable_doc, _foreach_doc, _maximize_doc)
from typing import List, Optional
from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype

__all__ = ["RMSprop", "rmsprop"]


[docs]class RMSprop(Optimizer): def __init__( self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False, foreach: Optional[bool] = None, maximize: bool = False, differentiable: bool = False, ): if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= momentum: raise ValueError("Invalid momentum value: {}".format(momentum)) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) if not 0.0 <= alpha: raise ValueError("Invalid alpha value: {}".format(alpha)) defaults = dict( lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered, weight_decay=weight_decay, foreach=foreach, maximize=maximize, differentiable=differentiable, ) super().__init__(params, defaults) def __setstate__(self, state): super().__setstate__(state) for group in self.param_groups: group.setdefault("momentum", 0) group.setdefault("centered", False) group.setdefault("foreach", None) group.setdefault("maximize", False) group.setdefault("differentiable", False) def _init_group(self, group, params_with_grad, grads, square_avgs, momentum_buffer_list, grad_avgs): for p in group["params"]: if p.grad is None: continue params_with_grad.append(p) if p.grad.is_sparse: raise RuntimeError("RMSprop does not support sparse gradients") grads.append(p.grad) state = self.state[p] # State initialization if len(state) == 0: state["step"] = 0 state["square_avg"] = torch.zeros_like( p, memory_format=torch.preserve_format ) if group["momentum"] > 0: state["momentum_buffer"] = torch.zeros_like( p, memory_format=torch.preserve_format ) if group["centered"]: state["grad_avg"] = torch.zeros_like( p, memory_format=torch.preserve_format ) square_avgs.append(state["square_avg"]) if group["momentum"] > 0: momentum_buffer_list.append(state["momentum_buffer"]) if group["centered"]: grad_avgs.append(state["grad_avg"]) if group["differentiable"] and isinstance(state["step"], Tensor): raise RuntimeError("`step` can't be a tensor") state["step"] += 1 @_use_grad_for_differentiable def step(self, closure=None): """Performs a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: params_with_grad = [] grads = [] square_avgs = [] grad_avgs = [] momentum_buffer_list = [] self._init_group(group, params_with_grad, grads, square_avgs, momentum_buffer_list, grad_avgs) rmsprop( params_with_grad, grads, square_avgs, grad_avgs, momentum_buffer_list, lr=group["lr"], alpha=group["alpha"], eps=group["eps"], weight_decay=group["weight_decay"], momentum=group["momentum"], centered=group["centered"], foreach=group["foreach"], maximize=group["maximize"], differentiable=group["differentiable"], ) return loss
RMSprop.__doc__ = r"""Implements RMSprop algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \alpha \text{ (alpha)},\: \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)} \\ &\hspace{13mm} \lambda \text{ (weight decay)},\: \mu \text{ (momentum)},\: centered\\ &\textbf{initialize} : v_0 \leftarrow 0 \text{ (square average)}, \: \textbf{b}_0 \leftarrow 0 \text{ (buffer)}, \: g^{ave}_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}if \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm}v_t \leftarrow \alpha v_{t-1} + (1 - \alpha) g^2_t \hspace{8mm} \\ &\hspace{5mm} \tilde{v_t} \leftarrow v_t \\ &\hspace{5mm}if \: centered \\ &\hspace{10mm} g^{ave}_t \leftarrow g^{ave}_{t-1} \alpha + (1-\alpha) g_t \\ &\hspace{10mm} \tilde{v_t} \leftarrow \tilde{v_t} - \big(g^{ave}_{t} \big)^2 \\ &\hspace{5mm}if \: \mu > 0 \\ &\hspace{10mm} \textbf{b}_t\leftarrow \mu \textbf{b}_{t-1} + g_t/ \big(\sqrt{\tilde{v_t}} + \epsilon \big) \\ &\hspace{10mm} \theta_t \leftarrow \theta_{t-1} - \gamma \textbf{b}_t \\ &\hspace{5mm} else \\ &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma g_t/ \big(\sqrt{\tilde{v_t}} + \epsilon \big) \hspace{3mm} \\ &\rule{110mm}{0.4pt} \\[-1.ex] &\bf{return} \: \theta_t \\[-1.ex] &\rule{110mm}{0.4pt} \\[-1.ex] \end{aligned} For further details regarding the algorithm we refer to `lecture notes <https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf>`_ by G. Hinton. and centered version `Generating Sequences With Recurrent Neural Networks <https://arxiv.org/pdf/1308.0850v5.pdf>`_. The implementation here takes the square root of the gradient average before adding epsilon (note that TensorFlow interchanges these two operations). The effective learning rate is thus :math:`\gamma/(\sqrt{v} + \epsilon)` where :math:`\gamma` is the scheduled learning rate and :math:`v` is the weighted moving average of the squared gradient. """ + r""" Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-2) momentum (float, optional): momentum factor (default: 0) alpha (float, optional): smoothing constant (default: 0.99) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) centered (bool, optional) : if ``True``, compute the centered RMSProp, the gradient is normalized by an estimation of its variance weight_decay (float, optional): weight decay (L2 penalty) (default: 0) {foreach} {maximize} {differentiable} """.format(foreach=_foreach_doc, maximize=_maximize_doc, differentiable=_differentiable_doc) def rmsprop( params: List[Tensor], grads: List[Tensor], square_avgs: List[Tensor], grad_avgs: List[Tensor], momentum_buffer_list: List[Tensor], # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627 # setting this as kwarg for now as functional API is compiled by torch/distributed/optim foreach: Optional[bool] = None, maximize: bool = False, differentiable: bool = False, *, lr: float, alpha: float, eps: float, weight_decay: float, momentum: float, centered: bool, ): r"""Functional API that performs rmsprop algorithm computation. See :class:`~torch.optim.RMSProp` for details. """ if foreach is None: _, foreach = _default_to_fused_or_foreach(params, differentiable, use_fused=False) if foreach and torch.jit.is_scripting(): raise RuntimeError("torch.jit.script not supported with foreach optimizers") if foreach and not torch.jit.is_scripting(): func = _multi_tensor_rmsprop else: func = _single_tensor_rmsprop func( params, grads, square_avgs, grad_avgs, momentum_buffer_list, lr=lr, alpha=alpha, eps=eps, weight_decay=weight_decay, momentum=momentum, centered=centered, maximize=maximize, differentiable=differentiable, ) def _single_tensor_rmsprop( params: List[Tensor], grads: List[Tensor], square_avgs: List[Tensor], grad_avgs: List[Tensor], momentum_buffer_list: List[Tensor], *, lr: float, alpha: float, eps: float, weight_decay: float, momentum: float, centered: bool, maximize: bool, differentiable: bool, ): for i, param in enumerate(params): grad = grads[i] grad = grad if not maximize else -grad square_avg = square_avgs[i] if weight_decay != 0: grad = grad.add(param, alpha=weight_decay) is_complex_param = torch.is_complex(param) if is_complex_param: param = torch.view_as_real(param) grad = torch.view_as_real(grad) square_avg = torch.view_as_real(square_avg) square_avg.mul_(alpha).addcmul_(grad, grad, value=1 - alpha) if centered: grad_avg = grad_avgs[i] if is_complex_param: grad_avg = torch.view_as_real(grad_avg) grad_avg.mul_(alpha).add_(grad, alpha=1 - alpha) avg = square_avg.addcmul(grad_avg, grad_avg, value=-1).sqrt_() else: avg = square_avg.sqrt() if differentiable: avg = avg.add(eps) else: avg = avg.add_(eps) if momentum > 0: buf = momentum_buffer_list[i] if is_complex_param: buf = torch.view_as_real(buf) buf.mul_(momentum).addcdiv_(grad, avg) param.add_(buf, alpha=-lr) else: param.addcdiv_(grad, avg, value=-lr) def _multi_tensor_rmsprop( params: List[Tensor], grads: List[Tensor], square_avgs: List[Tensor], grad_avgs: List[Tensor], momentum_buffer_list: List[Tensor], *, lr: float, alpha: float, eps: float, weight_decay: float, momentum: float, centered: bool, maximize: bool, differentiable: bool, ): if len(params) == 0: return assert not differentiable, "_foreach ops don't support autograd" grouped_tensors = _group_tensors_by_device_and_dtype([params, grads, square_avgs, grad_avgs, momentum_buffer_list]) for (grouped_params, grouped_grads, grouped_square_avgs, grouped_grad_avgs, grouped_momentum_buffer_list) in grouped_tensors.values(): if maximize: grouped_grads = torch._foreach_neg(grouped_grads) if weight_decay != 0: grouped_grads = torch._foreach_add(grouped_grads, grouped_params, alpha=weight_decay) def _view_complex_as_real(tensor_list): return [ torch.view_as_real(t) if torch.is_complex(t) else t for t in tensor_list ] grouped_grads = _view_complex_as_real(grouped_grads) grouped_params = _view_complex_as_real(grouped_params) grouped_square_avgs = _view_complex_as_real(grouped_square_avgs) torch._foreach_mul_(grouped_square_avgs, alpha) torch._foreach_addcmul_(grouped_square_avgs, grouped_grads, grouped_grads, value=1 - alpha) if centered: grouped_grad_avgs = _view_complex_as_real(grouped_grad_avgs) torch._foreach_mul_(grouped_grad_avgs, alpha) torch._foreach_add_(grouped_grad_avgs, grouped_grads, alpha=1 - alpha) avg = torch._foreach_addcmul(grouped_square_avgs, grouped_grad_avgs, grouped_grad_avgs, value=-1) torch._foreach_sqrt_(avg) torch._foreach_add_(avg, eps) else: avg = torch._foreach_sqrt(grouped_square_avgs) torch._foreach_add_(avg, eps) if momentum > 0: grouped_momentum_buffer_list = _view_complex_as_real(grouped_momentum_buffer_list) torch._foreach_mul_(grouped_momentum_buffer_list, momentum) torch._foreach_addcdiv_(grouped_momentum_buffer_list, grouped_grads, avg) torch._foreach_add_(grouped_params, grouped_momentum_buffer_list, alpha=-lr) else: torch._foreach_addcdiv_(grouped_params, grouped_grads, avg, value=-lr)

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources