Source code for torch.optim.rprop
# mypy: allow-untyped-defs
from typing import List, Optional, Tuple
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__ = ["Rprop", "rprop"]
[docs]class Rprop(Optimizer):
def __init__(
self,
params: ParamsT,
lr: float = 1e-2,
etas: Tuple[float, float] = (0.5, 1.2),
step_sizes: Tuple[float, float] = (1e-6, 50),
*,
capturable: bool = 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 < etas[0] < 1.0 < etas[1]:
raise ValueError(f"Invalid eta values: {etas[0]}, {etas[1]}")
defaults = dict(
lr=lr,
etas=etas,
step_sizes=step_sizes,
foreach=foreach,
maximize=maximize,
differentiable=differentiable,
capturable=capturable,
)
super().__init__(params, defaults)
def __setstate__(self, state):
super().__setstate__(state)
for group in self.param_groups:
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, grads, prevs, step_sizes, state_steps):
has_complex = False
for p in group["params"]:
if p.grad is None:
continue
has_complex |= torch.is_complex(p)
params.append(p)
grad = p.grad
if grad.is_sparse:
raise RuntimeError("Rprop does not support sparse gradients")
grads.append(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["prev"] = torch.zeros_like(p, memory_format=torch.preserve_format)
if p.dtype.is_complex:
# Complex Number should be as if they are two independent real numbers.
# Hence the step_size shouldn't be zero for imaginary part.
state["step_size"] = torch.full_like(
grad, complex(group["lr"], group["lr"])
)
else:
state["step_size"] = torch.full_like(grad, group["lr"])
prevs.append(state["prev"])
step_sizes.append(state["step_size"])
state_steps.append(state["step"])
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: List[Tensor] = []
grads: List[Tensor] = []
prevs: List[Tensor] = []
step_sizes: List[Tensor] = []
state_steps: List[Tensor] = []
etaminus, etaplus = group["etas"]
step_size_min, step_size_max = group["step_sizes"]
foreach = group["foreach"]
maximize = group["maximize"]
has_complex = self._init_group(
group, params, grads, prevs, step_sizes, state_steps
)
rprop(
params,
grads,
prevs,
step_sizes,
state_steps,
step_size_min=step_size_min,
step_size_max=step_size_max,
etaminus=etaminus,
etaplus=etaplus,
foreach=foreach,
maximize=maximize,
differentiable=group["differentiable"],
capturable=group["capturable"],
has_complex=has_complex,
)
return loss
Rprop.__doc__ = (
r"""Implements the resilient backpropagation algorithm.
.. math::
\begin{aligned}
&\rule{110mm}{0.4pt} \\
&\textbf{input} : \theta_0 \in \mathbf{R}^d \text{ (params)},f(\theta)
\text{ (objective)}, \\
&\hspace{13mm} \eta_{+/-} \text{ (etaplus, etaminus)}, \Gamma_{max/min}
\text{ (step sizes)} \\
&\textbf{initialize} : g^0_{prev} \leftarrow 0,
\: \eta_0 \leftarrow \text{lr (learning rate)} \\
&\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} \textbf{for} \text{ } i = 0, 1, \ldots, d-1 \: \mathbf{do} \\
&\hspace{10mm} \textbf{if} \: g^i_{prev} g^i_t > 0 \\
&\hspace{15mm} \eta^i_t \leftarrow \mathrm{min}(\eta^i_{t-1} \eta_{+},
\Gamma_{max}) \\
&\hspace{10mm} \textbf{else if} \: g^i_{prev} g^i_t < 0 \\
&\hspace{15mm} \eta^i_t \leftarrow \mathrm{max}(\eta^i_{t-1} \eta_{-},
\Gamma_{min}) \\
&\hspace{15mm} g^i_t \leftarrow 0 \\
&\hspace{10mm} \textbf{else} \: \\
&\hspace{15mm} \eta^i_t \leftarrow \eta^i_{t-1} \\
&\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \eta_t \mathrm{sign}(g_t) \\
&\hspace{5mm}g_{prev} \leftarrow g_t \\
&\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 the paper
`A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417>`_.
"""
+ rf"""
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-2)
etas (Tuple[float, float], optional): pair of (etaminus, etaplus), that
are multiplicative increase and decrease factors
(default: (0.5, 1.2))
step_sizes (Tuple[float, float], optional): a pair of minimal and
maximal allowed step sizes (default: (1e-6, 50))
{_foreach_doc}
{_capturable_doc}
{_maximize_doc}
{_differentiable_doc}
"""
)
def _single_tensor_rprop(
params: List[Tensor],
grads: List[Tensor],
prevs: List[Tensor],
step_sizes: List[Tensor],
state_steps: List[Tensor],
*,
step_size_min: float,
step_size_max: float,
etaminus: float,
etaplus: float,
maximize: bool,
capturable: bool,
differentiable: bool,
has_complex: bool,
):
for i, param in enumerate(params):
grad = grads[i]
grad = grad if not maximize else -grad
prev = prevs[i]
step_size = step_sizes[i]
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}."
step += 1
if torch.is_complex(param):
grad = torch.view_as_real(grad)
prev = torch.view_as_real(prev)
param = torch.view_as_real(param)
step_size = torch.view_as_real(step_size)
if differentiable:
sign = grad.mul(prev.clone()).sign()
else:
sign = grad.mul(prev).sign()
if capturable:
sign.copy_(torch.where(sign.gt(0), etaplus, sign))
sign.copy_(torch.where(sign.lt(0), etaminus, sign))
sign.copy_(torch.where(sign.eq(0), 1, sign))
else:
sign[sign.gt(0)] = etaplus
sign[sign.lt(0)] = etaminus
sign[sign.eq(0)] = 1
# update stepsizes with step size updates
step_size.mul_(sign).clamp_(step_size_min, step_size_max)
# for dir<0, dfdx=0
# for dir>=0 dfdx=dfdx
grad = grad.clone(memory_format=torch.preserve_format)
if capturable:
grad.copy_(torch.where(sign.eq(etaminus), 0, grad))
else:
grad[sign.eq(etaminus)] = 0
# update parameters
param.addcmul_(grad.sign(), step_size, value=-1)
prev.copy_(grad)
def _multi_tensor_rprop(
params: List[Tensor],
grads: List[Tensor],
prevs: List[Tensor],
step_sizes: List[Tensor],
state_steps: List[Tensor],
*,
step_size_min: float,
step_size_max: float,
etaminus: float,
etaplus: float,
maximize: bool,
capturable: bool,
differentiable: 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, prevs, step_sizes, state_steps]
)
for (
grouped_params,
grouped_grads,
grouped_prevs,
grouped_step_sizes,
grouped_state_steps,
), _ in grouped_tensors.values():
# 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)
# Handle complex params
if has_complex:
_view_as_real(
grouped_params, grouped_grads, grouped_prevs, grouped_step_sizes
)
signs = torch._foreach_mul(grouped_grads, grouped_prevs)
if maximize:
torch._foreach_neg_(signs)
# At the end of the step, grouped_prevs will contain the current grads, so we reuse
# grouped_prevs memory instead of creating a new buffer, but, for clarity, we reassign
# to keep referring to the buffer as grouped_grads.
torch._foreach_copy_(grouped_prevs, grouped_grads)
if maximize:
torch._foreach_neg_(grouped_prevs)
grouped_grads = grouped_prevs
torch._foreach_sign_(signs)
if capturable:
for sign in signs:
sign.copy_(torch.where(sign.gt(0), etaplus, sign))
sign.copy_(torch.where(sign.lt(0), etaminus, sign))
sign.copy_(torch.where(sign.eq(0), 1, sign))
else:
for sign in signs:
sign[sign.gt(0)] = etaplus
sign[sign.lt(0)] = etaminus
sign[sign.eq(0)] = 1
# update stepsizes with step size updates
torch._foreach_mul_(grouped_step_sizes, signs)
for step_size in grouped_step_sizes:
step_size.clamp_(step_size_min, step_size_max)
# for dir<0, dfdx=0
# for dir>=0 dfdx=dfdx
grouped_grads = list(grouped_grads)
for i in range(len(grouped_grads)):
grouped_grads[i].copy_(
torch.where(signs[i].eq(etaminus), 0, grouped_grads[i])
)
# explicitly del signs as it's not used after here to save memory
del signs
# update parameters
grad_signs = [grad.sign() for grad in grouped_grads]
torch._foreach_addcmul_(
grouped_params, grad_signs, grouped_step_sizes, value=-1
)
# Logically, you may expect grouped_prevs to get updated to grouped_grads, but that's
# basically already happened since we've been using grouped_prevs' memory to store
# updated grouped_grads!
@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_rprop)
def rprop(
params: List[Tensor],
grads: List[Tensor],
prevs: List[Tensor],
step_sizes: 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,
capturable: bool = False,
maximize: bool = False,
differentiable: bool = False,
has_complex: bool = False,
*,
step_size_min: float,
step_size_max: float,
etaminus: float,
etaplus: float,
):
r"""Functional API that performs rprop algorithm computation.
See :class:`~torch.optim.Rprop` 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_rprop
else:
func = _single_tensor_rprop
func(
params,
grads,
prevs,
step_sizes,
state_steps,
step_size_min=step_size_min,
step_size_max=step_size_max,
etaminus=etaminus,
etaplus=etaplus,
capturable=capturable,
maximize=maximize,
differentiable=differentiable,
has_complex=has_complex,
)