332 lines
12 KiB
Python
332 lines
12 KiB
Python
import torch
|
|
from torch import Tensor
|
|
from .optimizer import (Optimizer, _use_grad_for_differentiable, _default_to_fused_or_foreach,
|
|
_differentiable_doc, _foreach_doc, _maximize_doc, _view_as_real)
|
|
from typing import List, Optional
|
|
|
|
__all__ = ["Rprop", "rprop"]
|
|
|
|
|
|
class Rprop(Optimizer):
|
|
def __init__(
|
|
self,
|
|
params,
|
|
lr=1e-2,
|
|
etas=(0.5, 1.2),
|
|
step_sizes=(1e-6, 50),
|
|
*,
|
|
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,
|
|
)
|
|
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)
|
|
|
|
def _init_group(self, group, params, grads, prevs, step_sizes):
|
|
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"] = 0
|
|
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["step"] += 1
|
|
return has_complex
|
|
|
|
@_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 = []
|
|
grads = []
|
|
prevs = []
|
|
step_sizes = []
|
|
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)
|
|
|
|
rprop(
|
|
params,
|
|
grads,
|
|
prevs,
|
|
step_sizes,
|
|
step_size_min=step_size_min,
|
|
step_size_max=step_size_max,
|
|
etaminus=etaminus,
|
|
etaplus=etaplus,
|
|
foreach=foreach,
|
|
maximize=maximize,
|
|
differentiable=group["differentiable"],
|
|
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>`_.
|
|
""" + fr"""
|
|
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}
|
|
{_maximize_doc}
|
|
{_differentiable_doc}
|
|
|
|
"""
|
|
|
|
def rprop(
|
|
params: List[Tensor],
|
|
grads: List[Tensor],
|
|
prevs: List[Tensor],
|
|
step_sizes: 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,
|
|
*,
|
|
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.
|
|
"""
|
|
|
|
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,
|
|
step_size_min=step_size_min,
|
|
step_size_max=step_size_max,
|
|
etaminus=etaminus,
|
|
etaplus=etaplus,
|
|
maximize=maximize,
|
|
differentiable=differentiable,
|
|
has_complex=has_complex,
|
|
)
|
|
|
|
|
|
def _single_tensor_rprop(
|
|
params: List[Tensor],
|
|
grads: List[Tensor],
|
|
prevs: List[Tensor],
|
|
step_sizes: List[Tensor],
|
|
*,
|
|
step_size_min: float,
|
|
step_size_max: float,
|
|
etaminus: float,
|
|
etaplus: float,
|
|
maximize: 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]
|
|
|
|
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()
|
|
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)
|
|
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],
|
|
*,
|
|
step_size_min: float,
|
|
step_size_max: float,
|
|
etaminus: float,
|
|
etaplus: float,
|
|
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, prevs, step_sizes])
|
|
for ((grouped_params, grouped_grads, grouped_prevs, grouped_step_sizes), _) in grouped_tensors.values():
|
|
# 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)
|
|
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][signs[i].eq(etaminus)] = 0
|
|
|
|
# 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!
|