529 lines
20 KiB
Python
529 lines
20 KiB
Python
# mypy: allow-untyped-decorators
|
|
# mypy: allow-untyped-defs
|
|
r"""Implementation for the RMSprop algorithm."""
|
|
from typing import cast, List, Optional, Union
|
|
|
|
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"]
|
|
|
|
|
|
class RMSprop(Optimizer): # noqa: D101
|
|
def __init__(
|
|
self,
|
|
params: ParamsT,
|
|
lr: Union[float, Tensor] = 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,
|
|
): # noqa: D107
|
|
if isinstance(lr, Tensor) and lr.numel() != 1:
|
|
raise ValueError("Tensor lr must be 1-element")
|
|
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): # noqa: D105
|
|
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
|
|
|
|
@_use_grad_for_differentiable
|
|
def step(self, closure=None):
|
|
"""Perform 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, Tensor, 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] # type: ignore[list-item]
|
|
)
|
|
for (
|
|
(
|
|
grouped_params_,
|
|
grouped_grads_,
|
|
grouped_square_avgs_,
|
|
grouped_grad_avgs_,
|
|
grouped_momentum_buffer_list_,
|
|
grouped_state_steps_,
|
|
)
|
|
), _ in grouped_tensors.values():
|
|
grouped_params = cast(List[Tensor], grouped_params_)
|
|
grouped_grads = cast(List[Tensor], grouped_grads_)
|
|
grouped_square_avgs = cast(List[Tensor], grouped_square_avgs_)
|
|
grouped_state_steps = cast(List[Tensor], grouped_state_steps_)
|
|
|
|
if has_complex:
|
|
state_and_grads = [grouped_grads, grouped_square_avgs]
|
|
if momentum > 0:
|
|
grouped_momentum_buffer_list = cast(
|
|
List[Tensor], grouped_momentum_buffer_list_
|
|
)
|
|
state_and_grads.append(grouped_momentum_buffer_list)
|
|
if centered:
|
|
grouped_grad_avgs = cast(List[Tensor], grouped_grad_avgs_)
|
|
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 not torch._utils.is_compiling() and 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:
|
|
grouped_grad_avgs = cast(List[Tensor], grouped_grad_avgs_)
|
|
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:
|
|
grouped_momentum_buffer_list = cast(
|
|
List[Tensor], grouped_momentum_buffer_list_
|
|
)
|
|
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,
|
|
)
|