-
Notifications
You must be signed in to change notification settings - Fork 5.5k
/
lbfgs.py
780 lines (657 loc) · 28.3 KB
/
lbfgs.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
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
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import defaultdict
from functools import reduce
import paddle
from ..fluid import framework
from .optimizer import Optimizer
__all__ = []
def _cubic_interpolate(x1, f1, g1, x2, f2, g2, bounds=None):
r"""Cubic interpolation between (x1, f1, g1) and (x2, f2, g2).
Use two points and their gradient to determine a cubic function and get the minimun point
between them in the cubic curve.
Reference:
Jorge Nocedal, Stephen J. Wright, Numerical Optimization, Second Edition, 2006.
pp59: formula 3.59
Args:
x1, f1, g1: point1's position, value and gradient.
x2, f2, g2: point2's position, value and gradient.
bounds: bounds of interpolation area
Returns:
min_pos: the minimun point between the specified points in the cubic curve.
"""
# Compute bounds of interpolation area
if bounds is not None:
xmin_bound, xmax_bound = bounds
else:
xmin_bound, xmax_bound = (x1, x2) if x1 <= x2 else (x2, x1)
d1 = g1 + g2 - 3 * (f1 - f2) / (x1 - x2)
d2_square = d1**2 - g1 * g2
if d2_square >= 0:
d2 = d2_square.sqrt()
if x1 <= x2:
min_pos = x2 - (x2 - x1) * ((g2 + d2 - d1) / (g2 - g1 + 2 * d2))
else:
min_pos = x1 - (x1 - x2) * ((g1 + d2 - d1) / (g1 - g2 + 2 * d2))
return min(max(min_pos, xmin_bound), xmax_bound)
else:
return (xmin_bound + xmax_bound) / 2.0
def _strong_wolfe(
obj_func,
xk,
alpha,
d,
loss,
grad,
gtd,
c1=1e-4,
c2=0.9,
tolerance_change=1e-9,
max_ls=25,
):
r"""Implements of line search algorithm that satisfies the strong Wolfe conditions using double zoom.
Reference:
Jorge Nocedal, Stephen J. Wright, Numerical Optimization, Second Edition, 2006.
pp60: Algorithm 3.5 (Line Search Algorithm).
Args:
obj_func: the objective function to minimize. ```` accepts a multivariate input and returns a scalar.
xk (Tensor): the starting point of the iterates.
alpha (Scalar): the initial step size.
d (Tensor): search direction.
loss (scalar): the initial loss
grad (Tensor): the initial grad
c1 (Scalar): parameter for sufficient decrease condition.
c2 (Scalar): parameter for curvature condition.
tolerance_change (Scalar): terminates if the change of function value/position/parameter between
two iterations is smaller than this value.
max_ls(int): max iteration of line search.
alpha_max (float): max step length.
Returns:
loss_new (Scaler): loss of obj_func at final alpha.
grad_new, (Tensor): derivative of obj_func at final alpha.
alpha(Tensor): optimal step length, or 0. if the line search algorithm did not converge.
ls_func_evals (Scaler): number of objective function called in line search process.
Following summarizes the essentials of the strong Wolfe line search algorithm.
Some notations used in the description:
- `func` denotes the objective function.
- `obi_func` is a function of step size alpha, restricting `obj_func` on a line.
obi_func = func(xk + alpha * d),
where xk is the position of k'th iterate, d is the line search direction(decent direction),
and a is the step size.
- alpha : substitute of alpha
- a1 is alpha of last iteration, which is alpha_(i-1).
- a2 is alpha of current iteration, which is alpha_i.
- a_lo is alpha in left position when calls zoom, which is alpha_low.
- a_hi is alpha in right position when calls zoom, which is alpha_high.
Line Search Algorithm:
repeat
Compute obi_func(a2) and derphi(a2).
1. If obi_func(a2) > obi_func(0) + c_1 * a2 * obi_func'(0) or [obi_func(a2) >= obi_func(a1) and i > 1],
alpha= zoom(a1, a2) and stop;
2. If |obi_func'(a2)| <= -c_2 * obi_func'(0),
alpha= a2 and stop;
3. If obi_func'(a2) >= 0,
alpha= zoom(a2, a1) and stop;
a1 = a2
a2 = min(2 * a2, a2)
i = i + 1
end(repeat)
zoom(a_lo, a_hi) Algorithm:
repeat
aj = cubic_interpolation(a_lo, a_hi)
Compute obi_func(aj) and derphi(aj).
1. If obi_func(aj) > obi_func(0) + c_1 * aj * obi_func'(0) or obi_func(aj) >= obi_func(a_lo),
then a_hi <- aj;
2.
2.1. If |obi_func'(aj)| <= -c_2 * obi_func'(0), then alpha= a2 and stop;
2.2. If obi_func'(aj) * (a2 - a1) >= 0, then a_hi = a_lo
a_lo = aj;
end(repeat)
"""
d_norm = d.abs().max()
grad = grad.clone()
# evaluate objective and gradient using initial step
loss_new, grad_new = obj_func(xk, alpha, d)
ls_func_evals = 1
gtd_new = paddle.dot(grad_new, d)
# bracket an interval containing a point satisfying the Wolfe criteria
t_prev, f_prev, g_prev, gtd_prev = (
paddle.to_tensor(0, dtype=grad.dtype),
loss,
grad,
gtd,
)
done = False
ls_iter = 0
while ls_iter < max_ls:
# check conditions
if loss_new > (loss + c1 * alpha * gtd) or (
ls_iter > 1 and loss_new >= f_prev
):
bracket = [t_prev, alpha]
bracket_f = [f_prev, loss_new]
bracket_g = [g_prev, grad_new.clone()]
bracket_gtd = [gtd_prev, gtd_new]
break
if paddle.abs(gtd_new) <= -c2 * gtd:
bracket = [alpha]
bracket_f = [loss_new]
bracket_g = [grad_new]
done = True
break
if gtd_new >= 0:
bracket = [t_prev, alpha]
bracket_f = [f_prev, loss_new]
bracket_g = [g_prev, grad_new.clone()]
bracket_gtd = [gtd_prev, gtd_new]
break
# interpolate
min_step = alpha + 0.01 * (alpha - t_prev)
max_step = alpha * 10
tmp = alpha
alpha = _cubic_interpolate(
t_prev,
f_prev,
gtd_prev,
alpha,
loss_new,
gtd_new,
bounds=(min_step, max_step),
)
# next step
t_prev = tmp
f_prev = loss_new
g_prev = grad_new.clone()
gtd_prev = gtd_new
loss_new, grad_new = obj_func(xk, alpha, d)
ls_func_evals += 1
gtd_new = grad_new.dot(d)
ls_iter += 1
# reached max number of iterations?
if ls_iter == max_ls:
bracket = [0, alpha]
bracket_f = [loss, loss_new]
bracket_g = [grad, grad_new]
# zoom phase: we now have a point satisfying the criteria, or
# a bracket around it. We refine the bracket until we find the
# exact point satisfying the criteria
insuf_progress = False
# find high and low points in bracket
low_pos, high_pos = (0, 1) if bracket_f[0] <= bracket_f[-1] else (1, 0)
while not done and ls_iter < max_ls:
# line-search bracket is so small
if paddle.abs(bracket[1] - bracket[0]) * d_norm < tolerance_change:
break
# compute new trial value
alpha = _cubic_interpolate(
bracket[0],
bracket_f[0],
bracket_gtd[0],
bracket[1],
bracket_f[1],
bracket_gtd[1],
)
# test that we are making sufficient progress:
# in case `alpha` is so close to boundary, we mark that we are making
# insufficient progress, and if
# + we have made insufficient progress in the last step, or
# + `alpha` is at one of the boundary,
# we will move `alpha` to a position which is `0.1 * len(bracket)`
# away from the nearest boundary point.
eps = 0.1 * (max(bracket) - min(bracket))
if min(max(bracket) - alpha, alpha - min(bracket)) < eps:
# interpolation close to boundary
if insuf_progress or alpha >= max(bracket) or alpha <= min(bracket):
# evaluate at 0.1 away from boundary
if paddle.abs(alpha - max(bracket)) < paddle.abs(
alpha - min(bracket)
):
alpha = max(bracket) - eps
else:
alpha = min(bracket) + eps
insuf_progress = False
else:
insuf_progress = True
else:
insuf_progress = False
# Evaluate new point
loss_new, grad_new = obj_func(xk, alpha, d)
ls_func_evals += 1
gtd_new = grad_new.dot(d)
ls_iter += 1
if (
loss_new > (loss + c1 * alpha * gtd)
or loss_new >= bracket_f[low_pos]
):
# Armijo condition not satisfied or not lower than lowest point
bracket[high_pos] = alpha
bracket_f[high_pos] = loss_new
# bracket_g[high_pos] = grad_new.clone(memory_format=torch.contiguous_format)
bracket_g[high_pos] = grad_new.clone()
bracket_gtd[high_pos] = gtd_new
low_pos, high_pos = (
(0, 1) if bracket_f[0] <= bracket_f[1] else (1, 0)
)
else:
if paddle.abs(gtd_new) <= -c2 * gtd:
# Wolfe conditions satisfied
done = True
elif gtd_new * (bracket[high_pos] - bracket[low_pos]) >= 0:
# old high becomes new low
bracket[high_pos] = bracket[low_pos]
bracket_f[high_pos] = bracket_f[low_pos]
bracket_g[high_pos] = bracket_g[low_pos]
bracket_gtd[high_pos] = bracket_gtd[low_pos]
# new point becomes new low
bracket[low_pos] = alpha
bracket_f[low_pos] = loss_new
bracket_g[low_pos] = grad_new.clone()
bracket_gtd[low_pos] = gtd_new
# return stuff
alpha = bracket[low_pos]
loss_new = bracket_f[low_pos]
grad_new = bracket_g[low_pos]
return loss_new, grad_new, alpha, ls_func_evals
class LBFGS(Optimizer):
r"""
The L-BFGS is a quasi-Newton method for solving an unconstrained optimization problem over a differentiable function.
Closely related is the Newton method for minimization. Consider the iterate update formula:
.. math::
x_{k+1} = x_{k} + H_k \nabla{f_k}
If :math:`H_k` is the inverse Hessian of :math:`f` at :math:`x_k`, then it's the Newton method.
If :math:`H_k` is symmetric and positive definite, used as an approximation of the inverse Hessian, then
it's a quasi-Newton. In practice, the approximated Hessians are obtained
by only using the gradients, over either whole or part of the search
history, the former is BFGS, the latter is L-BFGS.
Reference:
Jorge Nocedal, Stephen J. Wright, Numerical Optimization, Second Edition, 2006. pp179: Algorithm 7.5 (L-BFGS).
Args:
learning_rate (float, optional): learning rate .The default value is 1.
max_iter (int, optional): maximal number of iterations per optimization step.
The default value is 20.
max_eval (int, optional): maximal number of function evaluations per optimization
step. The default value is max_iter * 1.25.
tolerance_grad (float, optional): termination tolerance on first order optimality
The default value is 1e-5.
tolerance_change (float, optional): termination tolerance on function
value/parameter changes. The default value is 1e-9.
history_size (int, optional): update history size. The default value is 100.
line_search_fn (string, optional): either 'strong_wolfe' or None. The default value is strong_wolfe.
parameters (list|tuple, optional): List/Tuple of ``Tensor`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. The default value is None.
weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \
It canbe a float value as coeff of L2 regularization or \
:ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`.
If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \
the regularization setting here in optimizer will be ignored for this parameter. \
Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of \
some derived class of ``GradientClipBase`` . There are three cliping strategies \
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` , \
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.
The default value is None.
Return:
loss (Tensor): the final loss of closure.
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
np.random.seed(0)
np_w = np.random.rand(1).astype(np.float32)
np_x = np.random.rand(1).astype(np.float32)
inputs = [np.random.rand(1).astype(np.float32) for i in range(10)]
# y = 2x
targets = [2 * x for x in inputs]
class Net(paddle.nn.Layer):
def __init__(self):
super().__init__()
w = paddle.to_tensor(np_w)
self.w = paddle.create_parameter(shape=w.shape, dtype=w.dtype, default_initializer=paddle.nn.initializer.Assign(w))
def forward(self, x):
return self.w * x
net = Net()
opt = paddle.optimizer.LBFGS(learning_rate=1, max_iter=1, max_eval=None, tolerance_grad=1e-07, tolerance_change=1e-09, history_size=100, line_search_fn='strong_wolfe', parameters=net.parameters())
def train_step(inputs, targets):
def closure():
outputs = net(inputs)
loss = paddle.nn.functional.mse_loss(outputs, targets)
print('loss: ', loss.item())
opt.clear_grad()
loss.backward()
return loss
opt.step(closure)
for input, target in zip(inputs, targets):
input = paddle.to_tensor(input)
target = paddle.to_tensor(target)
train_step(input, target)
"""
def __init__(
self,
learning_rate=1.0,
max_iter=20,
max_eval=None,
tolerance_grad=1e-7,
tolerance_change=1e-9,
history_size=100,
line_search_fn=None,
parameters=None,
weight_decay=None,
grad_clip=None,
name=None,
):
if max_eval is None:
max_eval = max_iter * 5 // 4
self.learning_rate = learning_rate
self.max_iter = max_iter
self.max_eval = max_eval
self.tolerance_grad = tolerance_grad
self.tolerance_change = tolerance_change
self.history_size = history_size
self.line_search_fn = line_search_fn
if isinstance(parameters, paddle.Tensor):
raise TypeError(
"parameters argument given to the optimizer should be "
"an iterable of Tensors or dicts, but got " + type(parameters)
)
self.state = defaultdict(dict)
super().__init__(
learning_rate=1.0,
parameters=parameters,
weight_decay=weight_decay,
grad_clip=grad_clip,
name=name,
)
if not isinstance(self._parameter_list[0], dict):
self._params = self._parameter_list
else:
for idx, param_group in enumerate(self._param_groups):
self._params = param_group['params']
self._numel_cache = None
def state_dict(self):
r"""Returns the state of the optimizer as a :class:`dict`.
Return:
state, a dict holding current optimization state. Its content
differs between optimizer classes.
Examples:
.. code-block:: python
import paddle
paddle.disable_static()
net = paddle.nn.Linear(10, 10)
opt = paddle.optimizer.LBFGS(
learning_rate=1,
max_iter=1,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=100,
line_search_fn='strong_wolfe',
parameters=net.parameters(),
)
def train_step(inputs, targets):
def closure():
outputs = net(inputs)
loss = paddle.nn.functional.mse_loss(outputs, targets)
opt.clear_grad()
loss.backward()
return loss
opt.step(closure)
inputs = paddle.rand([10, 10], dtype="float32")
targets = paddle.to_tensor([2 * x for x in inputs])
n_iter = 0
while n_iter < 20:
loss = train_step(inputs, targets)
n_iter = opt.state_dict()["state"]["func_evals"]
print("n_iter:", n_iter)
"""
packed_state = {}
for k, v in self.state.items():
packed_state.update({k: v})
return {'state': packed_state}
def _numel(self):
# compute the number of all parameters
if self._numel_cache is None:
self._numel_cache = reduce(
lambda total, p: total + p.numel(), self._params, 0
)
return self._numel_cache
# flatten grad of all parameters
def _gather_flat_grad(self):
views = []
for p in self._params:
if p.grad is None:
view = paddle.zeros_like(p).reshape([-1])
else:
view = p.grad.reshape([-1])
views.append(view)
return paddle.concat(views, axis=0)
# compute xk = xk + alpha * direction
def _add_grad(self, alpha, direction):
offset = 0
for p in self._params:
numel = reduce(lambda x, y: x * y, p.shape)
p = paddle.assign(
p.add(
direction[offset : offset + numel].reshape(p.shape) * alpha
),
p,
)
offset += numel
assert offset == self._numel()
def _clone_param(self):
return [p.clone() for p in self._params]
def _set_param(self, params_data):
for p, pdata in zip(self._params, params_data):
paddle.assign(pdata, p)
def _directional_evaluate(self, closure, x, alpha, d):
self._add_grad(alpha, d)
loss = float(closure())
flat_grad = self._gather_flat_grad()
self._set_param(x)
return loss, flat_grad
@framework.non_static_only
def step(self, closure):
"""Performs a single optimization step.
Args:
closure (callable): A closure that reevaluates the model
and returns the loss.
Examples:
.. code-block:: python
import paddle
paddle.disable_static()
inputs = paddle.rand([10, 10], dtype="float32")
targets = paddle.to_tensor([2 * x for x in inputs])
net = paddle.nn.Linear(10, 10)
opt = paddle.optimizer.LBFGS(
learning_rate=1,
max_iter=1,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=100,
line_search_fn='strong_wolfe',
parameters=net.parameters(),
)
def closure():
outputs = net(inputs)
loss = paddle.nn.functional.mse_loss(outputs, targets)
print("loss:", loss.item())
opt.clear_grad()
loss.backward()
return loss
opt.step(closure)
"""
with paddle.no_grad():
# Make sure the closure is always called with grad enabled
closure = paddle.enable_grad()(closure)
learning_rate = self.learning_rate
max_iter = self.max_iter
max_eval = self.max_eval
tolerance_grad = self.tolerance_grad
tolerance_change = self.tolerance_change
line_search_fn = self.line_search_fn
history_size = self.history_size
state = self.state
state.setdefault('func_evals', 0)
state.setdefault('n_iter', 0)
# evaluate initial f(x) and df/dx
orig_loss = closure()
loss = float(orig_loss)
current_evals = 1
state['func_evals'] += 1
flat_grad = self._gather_flat_grad()
opt_cond = flat_grad.abs().max() <= tolerance_grad
# optimal condition
if opt_cond:
return orig_loss
# tensors cached in state (for tracing)
d = state.get('d')
alpha = state.get('alpha')
old_yk = state.get('old_yk')
old_sk = state.get('old_sk')
ro = state.get('ro')
H_diag = state.get('H_diag')
prev_flat_grad = state.get('prev_flat_grad')
prev_loss = state.get('prev_loss')
n_iter = 0
# optimize for a max of max_iter iterations
while n_iter < max_iter:
# keep track of nb of iterations
n_iter += 1
state['n_iter'] += 1
############################################################
# compute gradient descent direction
############################################################
if state['n_iter'] == 1:
d = flat_grad.neg()
old_yk = []
old_sk = []
ro = []
H_diag = paddle.to_tensor(1.0, dtype=orig_loss.dtype)
else:
# do lbfgs update (update memory)
y = flat_grad.subtract(prev_flat_grad)
s = d.multiply(paddle.to_tensor(alpha, dtype=d.dtype))
ys = y.dot(s)
if ys > 1e-10:
# updating memory
if len(old_yk) == history_size:
# shift history by one (limited-memory)
old_yk.pop(0)
old_sk.pop(0)
ro.pop(0)
# store new direction/step
old_yk.append(y)
old_sk.append(s)
ro.append(1.0 / ys)
# update scale of initial Hessian approximation
H_diag = ys / y.dot(y) # (y*y)
# compute the approximate (L-BFGS) inverse Hessian
# multiplied by the gradient
num_old = len(old_yk)
if 'al' not in state:
state['al'] = [None] * history_size
al = state['al']
# iteration in L-BFGS loop collapsed to use just one buffer
q = flat_grad.neg()
for i in range(num_old - 1, -1, -1):
al[i] = old_sk[i].dot(q) * ro[i]
paddle.assign(q.add(old_yk[i] * (-al[i])), q)
# multiply by initial Hessian
# r/d is the final direction
d = r = paddle.multiply(q, H_diag)
for i in range(num_old):
be_i = old_yk[i].dot(r) * ro[i]
paddle.assign(r.add(old_sk[i] * (al[i] - be_i)), r)
if prev_flat_grad is None:
prev_flat_grad = flat_grad.clone()
else:
paddle.assign(flat_grad, prev_flat_grad)
prev_loss = loss
############################################################
# compute step length
############################################################
# reset initial guess for step size
if state['n_iter'] == 1:
alpha = (
min(1.0, 1.0 / flat_grad.abs().sum()) * learning_rate
)
else:
alpha = learning_rate
# directional derivative
gtd = flat_grad.dot(d)
# directional derivative is below tolerance
if gtd > -tolerance_change:
break
# optional line search: user function
ls_func_evals = 0
if line_search_fn is not None:
# perform line search, using user function
if line_search_fn != "strong_wolfe":
raise RuntimeError("only 'strong_wolfe' is supported")
else:
x_init = self._clone_param()
def obj_func(x, alpha, d):
return self._directional_evaluate(
closure, x, alpha, d
)
loss, flat_grad, alpha, ls_func_evals = _strong_wolfe(
obj_func, x_init, alpha, d, loss, flat_grad, gtd
)
self._add_grad(alpha, d)
opt_cond = flat_grad.abs().max() <= tolerance_grad
else:
# no line search, simply move with fixed-step
self._add_grad(alpha, d)
if n_iter != max_iter:
with paddle.enable_grad():
loss = float(closure())
flat_grad = self._gather_flat_grad()
opt_cond = flat_grad.abs().max() <= tolerance_grad
ls_func_evals = 1
# update func eval
current_evals += ls_func_evals
state['func_evals'] += ls_func_evals
# optimal condition
if opt_cond:
break
# lack of progress
if (d * alpha).abs().max() <= tolerance_change:
break
if abs(loss - prev_loss) < tolerance_change:
break
# check conditions
if current_evals >= max_eval:
break
if n_iter == max_iter:
break
state['d'] = d
state['alpha'] = alpha
state['old_yk'] = old_yk
state['old_sk'] = old_sk
state['ro'] = ro
state['H_diag'] = H_diag
state['prev_flat_grad'] = prev_flat_grad
state['prev_loss'] = prev_loss
return orig_loss
def minimize(
self, loss, startup_program=None, parameters=None, no_grad_set=None
):
"""Empty method. LBFGS optimizer does not use this way to minimize ``loss``. Please refer 'Examples' of LBFGS() above for usage."""
raise NotImplementedError(
"LBFGS optimizer does not use this way to minimize loss. Please refer 'Examples' of LBFGS() for usage."
)