Spaces:
Running
Running
File size: 14,753 Bytes
c61ccee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 |
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"]
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
@_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 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)
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)
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)
|