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, _view_as_real)
from typing import List, Optional
__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(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,
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):
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"] = 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
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.
"""
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 = []
has_complex = 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"],
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.
""" + fr"""
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}
{_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,
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.
"""
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,
has_complex=has_complex,
)
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,
has_complex: 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.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],
*,
lr: float,
alpha: float,
eps: float,
weight_decay: float,
momentum: float,
centered: bool,
maximize: bool,
differentiable: bool,
has_complex: bool,
):
if len(params) == 0:
return
assert not differentiable, "_foreach ops don't support autograd"
grouped_tensors = Optimizer._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:
# 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(grouped_grads, grouped_params, alpha=weight_decay)
grouped_grads = list(grouped_grads)
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)
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)
torch._foreach_add_(grouped_params, grouped_momentum_buffer_list, alpha=-lr)
else:
torch._foreach_addcdiv_(grouped_params, grouped_grads, avg, value=-lr)
