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Source code for torch.optim.rmsprop

# mypy: allow-untyped-defs
from typing import List, Optional

import torch
from torch import Tensor
from .optimizer import (
    _capturable_doc,
    _default_to_fused_or_foreach,
    _differentiable_doc,
    _disable_dynamo_if_unsupported,
    _foreach_doc,
    _get_capturable_supported_devices,
    _get_scalar_dtype,
    _maximize_doc,
    _use_grad_for_differentiable,
    _view_as_real,
    Optimizer,
    ParamsT,
)

__all__ = ["RMSprop", "rmsprop"]


[docs]class RMSprop(Optimizer): def __init__( self, params: ParamsT, lr: float = 1e-2, alpha: float = 0.99, eps: float = 1e-8, weight_decay: float = 0, momentum: float = 0, centered=False, capturable=False, foreach: Optional[bool] = None, maximize: bool = False, differentiable: bool = False, ): if not 0.0 <= lr: raise ValueError(f"Invalid learning rate: {lr}") if not 0.0 <= eps: raise ValueError(f"Invalid epsilon value: {eps}") if not 0.0 <= momentum: raise ValueError(f"Invalid momentum value: {momentum}") if not 0.0 <= weight_decay: raise ValueError(f"Invalid weight_decay value: {weight_decay}") if not 0.0 <= alpha: raise ValueError(f"Invalid alpha value: {alpha}") defaults = dict( lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered, weight_decay=weight_decay, capturable=capturable, 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) group.setdefault("capturable", False) for p in group["params"]: p_state = self.state.get(p, []) if len(p_state) != 0 and not torch.is_tensor(p_state["step"]): step_val = float(p_state["step"]) p_state["step"] = ( torch.tensor( step_val, dtype=_get_scalar_dtype(), device=p.device ) if group["capturable"] else torch.tensor(step_val, dtype=_get_scalar_dtype()) ) def _init_group( self, group, params_with_grad, grads, square_avgs, momentum_buffer_list, grad_avgs, state_steps, ): has_complex = False for p in group["params"]: if p.grad is None: continue has_complex |= torch.is_complex(p) 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"] = ( torch.zeros((), dtype=_get_scalar_dtype(), device=p.device) if group["capturable"] else torch.zeros((), dtype=_get_scalar_dtype()) ) 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"]) state_steps.append(state["step"]) if group["momentum"] > 0: momentum_buffer_list.append(state["momentum_buffer"]) if group["centered"]: grad_avgs.append(state["grad_avg"]) return has_complex
[docs] @_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. """ self._cuda_graph_capture_health_check() loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: params_with_grad: List[Tensor] = [] grads: List[Tensor] = [] square_avgs: List[Tensor] = [] grad_avgs: List[Tensor] = [] momentum_buffer_list: List[Tensor] = [] state_steps: List[Tensor] = [] has_complex = self._init_group( group, params_with_grad, grads, square_avgs, momentum_buffer_list, grad_avgs, state_steps, ) rmsprop( params_with_grad, grads, square_avgs, grad_avgs, momentum_buffer_list, state_steps, 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"], capturable=group["capturable"], has_complex=has_complex, ) 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. """ + rf""" 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_doc} {_maximize_doc} {_capturable_doc} {_differentiable_doc} """ ) def _single_tensor_rmsprop( params: List[Tensor], grads: List[Tensor], square_avgs: List[Tensor], grad_avgs: List[Tensor], momentum_buffer_list: List[Tensor], state_steps: List[Tensor], *, lr: float, alpha: float, eps: float, weight_decay: float, momentum: float, centered: bool, maximize: bool, differentiable: bool, capturable: bool, has_complex: bool, ): for i, param in enumerate(params): step = state_steps[i] # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable] if not torch._utils.is_compiling() and capturable: capturable_supported_devices = _get_capturable_supported_devices() assert ( param.device.type == step.device.type and param.device.type in capturable_supported_devices ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}." grad = grads[i] grad = grad if not maximize else -grad square_avg = square_avgs[i] step += 1 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.lerp_(grad, 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], state_steps: List[Tensor], *, lr: float, alpha: float, eps: float, weight_decay: float, momentum: float, centered: bool, maximize: bool, differentiable: bool, capturable: bool, has_complex: bool, ): if len(params) == 0: return assert not differentiable, "_foreach ops don't support autograd" # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable] if not torch._utils.is_compiling() and capturable: capturable_supported_devices = _get_capturable_supported_devices() assert all( p.device.type == step.device.type and p.device.type in capturable_supported_devices for p, step in zip(params, state_steps) ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}." grouped_tensors = Optimizer._group_tensors_by_device_and_dtype( [params, grads, square_avgs, grad_avgs, momentum_buffer_list, state_steps] ) for ( ( grouped_params, grouped_grads, grouped_square_avgs, grouped_grad_avgs, grouped_momentum_buffer_list, grouped_state_steps, ) ), _ in grouped_tensors.values(): if has_complex: state_and_grads = [grouped_grads, grouped_square_avgs] if momentum > 0: state_and_grads.append(grouped_momentum_buffer_list) if centered: state_and_grads.append(grouped_grad_avgs) _view_as_real(grouped_params, *state_and_grads) if maximize: grouped_grads = torch._foreach_neg(grouped_grads) # type: ignore[assignment] # Update steps # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just # wrapped it once now. The alpha is required to assure we go to the right overload. if grouped_state_steps[0].is_cpu: torch._foreach_add_( grouped_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0 ) else: torch._foreach_add_(grouped_state_steps, 1) if weight_decay != 0: # Re-use the intermediate memory (grouped_grads) already allocated for maximize if maximize: torch._foreach_add_(grouped_grads, grouped_params, alpha=weight_decay) else: grouped_grads = torch._foreach_add( # type: ignore[assignment] grouped_grads, grouped_params, alpha=weight_decay ) torch._foreach_mul_(grouped_square_avgs, alpha) torch._foreach_addcmul_( grouped_square_avgs, grouped_grads, grouped_grads, value=1 - alpha ) if centered: torch._foreach_lerp_(grouped_grad_avgs, grouped_grads, 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: torch._foreach_mul_(grouped_momentum_buffer_list, momentum) torch._foreach_addcdiv_(grouped_momentum_buffer_list, grouped_grads, avg) # If LR is a tensor, the else branch will internally call item() # which will cause silent incorrectness if we are capturing if capturable and isinstance(lr, torch.Tensor): momentum_lr = torch._foreach_mul(grouped_momentum_buffer_list, -lr) torch._foreach_add_(grouped_params, momentum_lr) else: torch._foreach_add_( grouped_params, grouped_momentum_buffer_list, alpha=-lr ) else: # If LR is a tensor, the else branch will internally call item() # which will cause silent incorrectness if we are capturing if capturable and isinstance(lr, torch.Tensor): torch._foreach_div_(avg, -lr) torch._foreach_addcdiv_(grouped_params, grouped_grads, avg) else: torch._foreach_addcdiv_(grouped_params, grouped_grads, avg, value=-lr) @_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_rmsprop) def rmsprop( params: List[Tensor], grads: List[Tensor], square_avgs: List[Tensor], grad_avgs: List[Tensor], momentum_buffer_list: List[Tensor], state_steps: 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, capturable: bool = False, has_complex: 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. """ # this check is slow during compilation, so we skip it # if it's strictly needed we can add this check back in dynamo if not torch._utils.is_compiling() and not all( isinstance(t, torch.Tensor) for t in state_steps ): raise RuntimeError( "API has changed, `state_steps` argument must contain a list of singleton tensors" ) 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, state_steps, lr=lr, alpha=alpha, eps=eps, weight_decay=weight_decay, momentum=momentum, centered=centered, maximize=maximize, capturable=capturable, differentiable=differentiable, has_complex=has_complex, )

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