forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
rprop.py
282 lines (238 loc) · 11.3 KB
/
rprop.py
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
import torch
from torch import Tensor
from .optimizer import Optimizer, _use_grad_for_differentiable
from typing import List, Optional
__all__ = ['Rprop', 'rprop']
class Rprop(Optimizer):
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>`_.
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 (bool, optional): whether foreach implementation of optimizer
is used (default: None)
maximize (bool, optional): maximize the params based on the objective, instead of
minimizing (default: False)
"""
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("Invalid learning rate: {}".format(lr))
if not 0.0 < etas[0] < 1.0 < etas[1]:
raise ValueError("Invalid eta values: {}, {}".format(etas[0], etas[1]))
defaults = dict(lr=lr, etas=etas, step_sizes=step_sizes, foreach=foreach, maximize=maximize, differentiable=differentiable)
super(Rprop, self).__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)
@_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']
for p in group['params']:
if p.grad is None:
continue
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'] = grad.new().resize_as_(grad).fill_(complex(group['lr'], group['lr']))
else:
state['step_size'] = grad.new().resize_as_(grad).fill_(group['lr'])
prevs.append(state['prev'])
step_sizes.append(state['step_size'])
state['step'] += 1
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'])
return loss
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: bool = None,
maximize: bool = False,
differentiable: 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:
# Placeholder for more complex foreach logic to be added when value is not set
foreach = 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)
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):
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):
if len(params) == 0:
return
assert not differentiable, "_foreach ops don't support autograd"
# Handle complex params
def _view_complex_as_real(tensor_list):
return [torch.view_as_real(t) if torch.is_complex(t) else t for t in tensor_list]
grads = _view_complex_as_real(grads)
prevs = _view_complex_as_real(prevs)
params = _view_complex_as_real(params)
step_sizes = _view_complex_as_real(step_sizes)
if maximize:
grads = torch._foreach_neg(grads)
signs = torch._foreach_mul(grads, prevs)
signs = [s.sign() for s in 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_(step_sizes, signs)
for step_size in step_sizes:
step_size.clamp_(step_size_min, step_size_max)
# for dir<0, dfdx=0
# for dir>=0 dfdx=dfdx
grads = list(grads)
for i in range(len(grads)):
grads[i] = grads[i].clone(memory_format=torch.preserve_format)
grads[i][signs[i].eq(etaminus)] = 0
# update parameters
grad_signs = [grad.sign() for grad in grads]
torch._foreach_addcmul_(params, grad_signs, step_sizes, value=-1)
for i in range(len(prevs)):
prevs[i].copy_(grads[i])