-
Notifications
You must be signed in to change notification settings - Fork 7
/
rdp_utils.py
712 lines (569 loc) · 26.2 KB
/
rdp_utils.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
import numpy as np
import math
import sys
import torch
from sklearn.preprocessing import normalize
from pate_core import *
from numpy import linalg as LA
EPS = sys.float_info.epsilon
# Algorithm 1 in 'Scalable Private Learning with PATE'
def gnmax_thresh_aggregator(counts, thresh_cnt, sigma_thresh, sigma, orders):
log_pr_answered = compute_logpr_answered(thresh_cnt, sigma_thresh, counts)
rdp_budget = compute_rdp_threshold(log_pr_answered, sigma_thresh, orders)
# print("Threshold budget:" + str(rdp_budget))
if np.random.normal(np.max(counts), sigma_thresh) >= thresh_cnt:
logq = compute_logq_gaussian(counts, sigma)
res = np.argmax(np.random.normal(counts, sigma))
g_rdp_budget = rdp_gaussian(logq, sigma, orders)
rdp_budget += g_rdp_budget
else:
# do not return result if teacher models do not agree
res = -1
return res, rdp_budget
def gnmax_aggregator(counts, sigma, orders):
logq = compute_logq_gaussian(counts, sigma)
dir_index = np.argmax(np.random.normal(counts, sigma))
rdp_budget = rdp_gaussian(logq, sigma, orders)
return dir_index, rdp_budget
def rdp_percentile(arr_list, q, orders, vmin, vmax, lmbd, axis=0):
arr_length = len(arr_list)
arr_size = arr_list[0].size
input_shape = arr_list[0].shape
arr_reshaped = np.vstack([arr.reshape([1, arr_size]) for arr in arr_list])
arr_ordered = np.sort(arr_reshaped, axis=0)
arr_ordered = arr_ordered.clip(min=vmin, max=vmax)
arr_ordered_new = np.vstack([np.ones([1, arr_size]) * vmin, arr_ordered, np.ones([1, arr_size]) * vmax])
arr_ordered_new[np.abs(arr_ordered_new) < sys.float_info.epsilon] = 0
n_teachers, n_feature = arr_reshaped.shape
arr_prob = np.zeros([n_teachers + 1, n_feature])
for i in range(arr_length + 1):
diff = arr_ordered_new[i + 1, :] - arr_ordered_new[i, :]
diff = diff.clip(min=0)
arr_prob[i] = diff * np.exp(-0.5 / lmbd * abs(i - q / 100 * arr_length))
# arr_prob[i] = np.exp(np.log(diff) - 0.5/lmbd * abs(i - q/100 * arr_length))
# arr_prob = normalize(arr_prob, norm='l1', axis=0)
if np.min(arr_prob) < 0:
print(arr_prob)
exit()
low = np.zeros([1, arr_size])
high = np.zeros([1, arr_size])
for i in range(arr_size):
prob = arr_prob[:, i] / np.sum(arr_prob[:, i])
rindex = np.random.choice(arr_length + 1, p=prob)
# print(rindex)
low[0, i] = arr_ordered_new[rindex, i]
high[0, i] = arr_ordered_new[rindex + 1, i]
output_q = np.random.uniform(low=low, high=high, size=[1, arr_size])
output_q = output_q.reshape(input_shape)
rdp_budget = arr_size * np.multiply(
1 / (orders - 1),
np.log(
np.multiply(np.divide(orders, 2 * orders - 1), np.exp((orders - 1) / lmbd)) \
+ np.multiply(np.divide(orders - 1, 2 * orders - 1), np.exp(-orders / lmbd))
)
)
return output_q, rdp_budget
def rdp_winsorized_mean(arr_list, step_size, sigma_mean, sigma_percentile, orders, pca_mat=None):
vmin = -step_size
vmax = step_size
flatten_arr = np.asarray([arr.flatten() for arr in arr_list])
n_teachers, n_features = flatten_arr.shape
if pca_mat is not None:
# project to principal components
flatten_arr = np.matmul(flatten_arr, pca_mat)
n_features = flatten_arr.shape[1]
q25, q25_budget = rdp_percentile(flatten_arr, 25, orders, vmin=vmin, vmax=vmax, lmbd=sigma_percentile)
q75, q75_budget = rdp_percentile(flatten_arr, 75, orders, vmin=vmin, vmax=vmax, lmbd=sigma_percentile)
arr_mean = np.mean(flatten_arr.clip(min=q25, max=q75), axis=0)
arr_mean[np.sign(q75) != np.sign(q25)] = 0
# when 75 percentile is smaller, update the model with the average of 75 and 25 percentile
# quantile_mean = (q75 + q25) / 2
arr_mean[q75 < q25] = 0
update_index = np.nonzero(np.logical_and(np.sign(q75) == np.sign(q25), q75 > q25))
q_range = q75 - q25
sensitivity = LA.norm(q_range[update_index] / len(arr_list))
gaussian_noise, mean_budget = gaussian_rdp(arr_mean[update_index], sensitivity, orders, sigma_mean)
arr_mean[update_index] += gaussian_noise
arr_mean[update_index] = arr_mean[update_index].clip(min=q25[update_index], max=q75[update_index])
# for testing only
# update_ratio = gaussian_noise.size / arr_mean.size
# print("Update ratio: %.8f, norm: %.8f" % (update_ratio, sensitivity))
rdp_budget = q25_budget + q75_budget + mean_budget
if pca_mat is not None:
# project res direction back to original axis
arr_mean = np.matmul(arr_mean, np.transpose(pca_mat))
return arr_mean.reshape(arr_list[0].shape), rdp_budget
def gradient_voting_nonprivate(output_list, step_size, nbins=10):
n = len(output_list)
flatten_arr = np.asarray([arr.flatten() for arr in output_list])
n_teachers, n_features = flatten_arr.shape
flatten_arr = flatten_arr.clip(min=-step_size, max=step_size)
bins = np.arange(-step_size, step_size, (step_size * 2 / nbins))
bins = np.hstack([bins, step_size])
result = np.zeros([1, n_features])
for i in range(n_features):
votes_arr, _ = np.histogram(flatten_arr[:, i], bins)
res_idx = np.argmax(votes_arr)
result[:, i] = (bins[res_idx] + bins[res_idx + 1]) / 2
return result.reshape(output_list[0].shape)
def gradient_voting_rdp(output_list, step_size, sigma, sigma_thresh, orders, pca_mat=None, nbins=10, thresh=0.9):
import time
st = time.time()
n = len(output_list)
use_gpu = False # turn it on if you are running a huge matrix and the bottleneck lies on CPU matmul
if use_gpu:
# have to use torch==1.2.0 and torchvision==0.4.0 to run tensorflow-gpu==1.4.0
import torch
flatten_arr = torch.tensor([arr.flatten() for arr in output_list], device='cuda:0')
else:
flatten_arr = np.asarray([arr.flatten() for arr in output_list])
n_teachers, n_features = flatten_arr.shape
if pca_mat is not None:
# project to principal components
if use_gpu:
pca_mat_tensor = torch.from_numpy(pca_mat).float().to('cuda:0')
flatten_arr = torch.matmul(flatten_arr, pca_mat_tensor)
flatten_arr = flatten_arr.cpu().numpy()
else:
flatten_arr = np.matmul(flatten_arr, pca_mat)
n_features = flatten_arr.shape[1]
flatten_arr = flatten_arr.clip(min=-step_size, max=step_size)
bins = np.arange(-step_size, step_size, (step_size * 2 / nbins))
bins = np.hstack([bins, step_size])
result = np.zeros([1, n_features])
rdp_budget = 0
skipped_cnt = 0
for i in range(n_features):
votes_arr, _ = np.histogram(flatten_arr[:, i], bins)
print(votes_arr)
res_idx, cur_budget = gnmax_thresh_aggregator(votes_arr, thresh * n_teachers, sigma_thresh, sigma, orders)
rdp_budget += cur_budget
if res_idx < 0:
skipped_cnt += 1
else:
result[:, i] = (bins[res_idx] + bins[res_idx + 1]) / 2
print("Skipped %d feaatures out of %d" % (skipped_cnt, n_features))
if pca_mat is not None:
# project res direction back to original axis
result = np.matmul(result, np.transpose(pca_mat))
return result.reshape(output_list[0].shape), rdp_budget
def convert2topk(grad, topk):
"""
:param grad: original gradient
:param topk: topk we want to choose
:return: voting array (+1/0/-1)
"""
topk_ind = np.argpartition(np.abs(grad), -topk)[:, -topk:]
votes = np.zeros_like(grad)
sign_grad = np.sign(grad)
for i in range(len(grad)):
votes[i, topk_ind[i]] = 1
votes[i] = sign_grad[i] * votes[i]
return votes
def convert2topk_gpu(grad, topk):
"""
:param grad: sign grad (torch.tensor.cuda())
:param topk: topk value (int)
:return: voted sign grad (torch.tensor.cuda())
"""
topk_ind = torch.topk(torch.abs(grad), k=topk)[1]
sign_grad = torch.sign(grad)
votes = torch.zeros_like(grad).cuda()
votes.scatter_(1, topk_ind, 1)
votes = sign_grad * votes
print(votes.type())
return votes
def stachastic_convert2topk(grad, topk, b=None):
abs_grad = np.abs(grad)
topk_ind = np.argpartition(abs_grad, -topk)[:, -topk:]
if b is None:
# b = np.max(abs_grad, axis=0) ## DP proof assumes all the teachers to be independent
b = np.max(abs_grad, axis=1)
else:
b = np.max(abs_grad, axis=1).clip(max=b)
prob = 1/2 + (grad.T / b).T / 2 # prob of positive sign
rand = np.random.rand(*prob.shape)
sign_grad = np.ones_like(grad)
sign_grad[rand > prob] = -1
votes = np.zeros_like(grad)
for i in range(len(votes)):
votes[i, topk_ind[i]] = 1
votes[i] = sign_grad[i] * votes[i]
return votes
def stochastic_klevel(grad, b, k_level):
from scipy.stats import special_ortho_group
from tqdm import tqdm
# for i in tqdm(range(len(grad))): ## extremely slow (1h to finish on CPU)
# clipped_grad = grad[i] # clipping factor = 1
# rotation_matrix= special_ortho_group.rvs(len(grad[i]))
# grad[i] = rotation_matrix @ clipped_grad # gradient rotation
interval = 2 * b / (k_level - 1)
lower = -b
lower_grad = - k_level / 2
rand = np.random.rand(*grad.shape)
votes = np.zeros_like(grad)
for i in range(1, k_level):
upper = lower + interval
upper_grad = lower_grad + 1
if i == 1:
mask = (grad <= upper)
elif i == k_level - 1:
mask = (grad >= lower)
else:
mask = (grad <= upper) & (grad >= lower)
print(f"level {i}: {np.sum(mask)}")
print(f"lower_grad : {lower_grad}")
print(f"interval: {interval}")
prob = (grad[mask] - lower) / (upper - lower)
prob_grad = np.full_like(prob, lower_grad)
prob_grad[rand[mask] <= prob] = upper_grad
votes[mask] = prob_grad
lower = upper
lower_grad = upper_grad
return votes
def ablation_test_on_alpha_k(grad, topk):
abs_grad = torch.abs(grad)
topk_ind = torch.topk(abs_grad, k=topk)[1]
votes = torch.zeros_like(grad).cuda()
votes.scatter_(1, topk_ind, 1)
masked_topk_grad = grad * votes
residual_grad_norm = torch.norm(grad - masked_topk_grad, dim=1)
grad_norm = torch.norm(grad, dim=1)
alpha_k = 1 - residual_grad_norm / grad_norm
return alpha_k.cpu().numpy()
def ablation_test_on_different_k(output_list, ckpt_dir='', epoch=0):
grad = torch.tensor([arr.flatten() for arr in output_list]).cuda()
dim = grad.shape[-1]
alpha_k = []
for i in range(5):
topk = int(dim / 2**(i))
alpha_k.append(ablation_test_on_alpha_k(grad, topk))
alpha_k = np.vstack(alpha_k)
import joblib
import os
save_dir = os.path.join(ckpt_dir, 'alpha_k_epoch_' + str(epoch) + '.pkl')
joblib.dump(alpha_k, save_dir)
def stochastic_klevel_gpu(grad, b, k_level):
# from scipy.stats import special_ortho_group
# rotation_matrix = torch.tensor(special_ortho_group.rvs(len(grad[0])), dtype=torch.float32).cuda()
# grad = torch.matmul(rotation_matrix, grad.T).T # gradient rotation
interval = 2 * b / (k_level - 1)
lower = -b
lower_grad = - k_level / 2
rand = torch.rand_like(grad).cuda()
votes = torch.zeros_like(grad).cuda()
for i in range(1, k_level):
upper = lower + interval
upper_grad = lower_grad + 1
if i == 1:
mask = (grad <= upper)
elif i == k_level - 1:
mask = (grad >= lower)
else:
mask = (grad <= upper) & (grad >= lower)
print(f"level {i}: {torch.sum(mask)}")
print(f"lower_grad : {lower_grad}")
print(f"interval: {interval}")
prob = (grad[mask] - lower) / (upper - lower)
prob_grad = torch.full_like(prob, lower_grad)
prob_grad[rand[mask] <= prob] = upper_grad
votes[mask] = prob_grad
lower = upper
lower_grad = upper_grad
return votes
def stachastic_convert2topk_gpu(grad, topk, b=None):
"""
:param grad: sign grad (torch.tensor.cuda())
:param topk: topk value (int)
:return: voted sign grad (torch.tensor.cuda())
"""
abs_grad = torch.abs(grad)
topk_ind = torch.topk(abs_grad, k=topk)[1]
if b is None:
b = torch.max(abs_grad, dim=1)[0]
else:
b = torch.max(abs_grad, dim=1)[0].clamp(max=b)
prob = 1/2 + (grad.T / b).T / 2 # prob of positive sign
rand = torch.rand_like(prob).cuda()
sign_grad = torch.ones_like(grad).cuda()
sign_grad[rand > prob] = -1
votes = torch.zeros_like(grad).cuda()
votes.scatter_(1, topk_ind, 1)
votes = sign_grad * votes
sign_sgd = torch.sign(grad)
biased_votes = torch.zeros_like(grad).cuda()
biased_votes.scatter_(1, topk_ind, 1)
biased_votes = sign_sgd * biased_votes
print("prob topk", prob[0][topk_ind[0]])
print("grad topk", grad[0][topk_ind[0]])
print("sto sign topk", votes[0][topk_ind[0]])
print("biased sign topk", biased_votes[0][topk_ind[0]])
print("not agreed all sign:", torch.sum(votes != biased_votes))
print("not agreed one sign:", torch.sum(votes[0][topk_ind[0]] != biased_votes[0][topk_ind[0]]))
return votes
def stochastic_sketch_topk_gpu(grad, topk, b=None):
"""
:param grad: sign grad (torch.tensor.cuda())
:param topk: topk value (int)
:return: voted sign grad (torch.tensor.cuda())
"""
abs_grad = torch.abs(grad)
if b is None:
b = torch.max(abs_grad, dim=1)[0]
else:
b = torch.max(abs_grad, dim=1)[0].clamp(max=b)
prob = 1/2 + (grad.T / b).T / 2 # prob of positive sign
rand = torch.rand_like(prob).cuda()
sign_grad = torch.ones_like(grad).cuda()
sign_grad[rand > prob] = -1
d = sign_grad.shape[1]
c = 10000
r = 20
from csvec import CSVec
sketch = CSVec(d, c, r)
for grad in sign_grad:
sketch.accumulateVec(grad)
votes = sketch.unSketch(topk)
print(votes.shape)
return votes
def signsgd_aggregate(output_list, sigma, orders, topk, beta=0.1, alpha=1e-3, stochastic=False, b=None):
use_gpu = True
if not use_gpu:
nteacher = len(output_list)
flatten_grad = np.asarray([arr.flatten() for arr in output_list])
if stochastic:
voted_arr = np.sum(stachastic_convert2topk(flatten_grad, topk, b=b), axis=0)
else:
voted_arr = np.sum(convert2topk(flatten_grad, topk), axis=0)
else:
nteacher = len(output_list)
flatten_grad = torch.tensor([arr.flatten() for arr in output_list]).cuda()
if stochastic:
voted_arr = torch.sum(stachastic_convert2topk_gpu(flatten_grad, topk, b=b), dim=0).cpu().numpy()
else:
voted_arr = torch.sum(convert2topk_gpu(flatten_grad, topk), dim=0).cpu().numpy()
voted_arr = np.random.normal(voted_arr, sigma)
logq = compute_logq_gaussian(voted_arr, sigma)
rdp_budget = rdp_gaussian(logq, sigma / ((2*topk) ** 0.5), orders)
sign_grad = np.zeros_like(voted_arr)
sign_grad[voted_arr > beta * nteacher] = 1
sign_grad[voted_arr < -beta * nteacher] = -1
print("Agreed Dimension: " + str(np.sum(abs(sign_grad))))
return alpha * sign_grad.reshape(output_list[0].shape), rdp_budget
def signsgd_aggregate_no_thresh(output_list, sigma, orders, topk, beta=0.1, alpha=1e-3, stochastic=False, b=None):
use_gpu = True
if not use_gpu:
nteacher = len(output_list)
flatten_grad = np.asarray([arr.flatten() for arr in output_list])
if stochastic:
voted_arr = np.sum(stachastic_convert2topk(flatten_grad, topk, b=b), axis=0)
else:
voted_arr = np.sum(convert2topk(flatten_grad, topk), axis=0)
else:
nteacher = len(output_list)
flatten_grad = torch.tensor([arr.flatten() for arr in output_list]).cuda()
if stochastic:
voted_arr = torch.sum(stachastic_convert2topk_gpu(flatten_grad, topk, b=b), dim=0).cpu().numpy()
else:
voted_arr = torch.sum(convert2topk_gpu(flatten_grad, topk), dim=0).cpu().numpy()
# noise, rdp_budget2 = gaussian_rdp(voted_arr, (4 * topk) **0.5, orders, sigma)
# print("before adding noise:", voted_arr)
voted_arr = np.random.normal(voted_arr, sigma)
# print("after adding noise:", voted_arr)
# voted_arr += noise
logq = compute_logq_gaussian(voted_arr, sigma)
## l2-sensitivity is (4k)**0.5, while GNMax sensitivity is 2**0.5. Hence the factor is 2k**0.5
rdp_budget = rdp_gaussian(logq, sigma / ((2*topk) ** 0.5), orders)
# print(rdp_budget)
# sign_grad = np.zeros_like(voted_arr)
# sign_grad[voted_arr > beta * nteacher] = 1
# sign_grad[voted_arr < -beta * nteacher] = -1
# print("Agreed Dimension: " + str(np.sum(abs(sign_grad))))
return alpha * voted_arr.reshape(output_list[0].shape) / topk, rdp_budget
def sketchtopk_aggregate(output_list, sigma, orders, topk, beta=0.1, alpha=1e-3, stochastic=False, b=None):
nteacher = len(output_list)
flatten_grad = torch.tensor([arr.flatten() for arr in output_list]).cuda()
voted_arr = stochastic_sketch_topk_gpu(flatten_grad, topk, b=b).cpu().numpy()
# noise, rdp_budget2 = gaussian_rdp(voted_arr, (4 * topk) **0.5, orders, sigma)
# print("before adding noise:", voted_arr)
voted_arr = np.random.normal(voted_arr, sigma)
# print("after adding noise:", voted_arr)
# voted_arr += noise
logq = compute_logq_gaussian(voted_arr, sigma)
## l2-sensitivity is (4k)**0.5, while GNMax sensitivity is 2**0.5. Hence the factor is 2k**0.5
rdp_budget = rdp_gaussian(logq, sigma / ((2*topk) ** 0.5), orders)
# print(rdp_budget)
sign_grad = np.zeros_like(voted_arr)
sign_grad[voted_arr > beta * nteacher] = 1
sign_grad[voted_arr < -beta * nteacher] = -1
print("Agreed Dimension: " + str(np.sum(abs(sign_grad))))
return alpha * sign_grad.reshape(output_list[0].shape), rdp_budget
def k_level_sgd_aggregate(output_list, sigma, orders, k_level, beta=0.1, alpha=1e-3, b=None):
use_gpu = False
if not use_gpu:
nteacher = len(output_list)
flatten_grad = np.asarray([arr.flatten() for arr in output_list])
voted_arr = np.sum(stochastic_klevel(flatten_grad, k_level=k_level, b=b), axis=0)
else:
nteacher = len(output_list)
flatten_grad = torch.tensor([arr.flatten() for arr in output_list]).cuda()
voted_arr = torch.sum(stochastic_klevel_gpu(flatten_grad, k_level=k_level, b=b), dim=0).cpu().numpy()
# noise, rdp_budget2 = gaussian_rdp(voted_arr, (4 * topk) **0.5, orders, sigma)
# print("before adding noise:", voted_arr)
voted_arr = np.random.normal(voted_arr, sigma)
# print("after adding noise:", voted_arr)
# voted_arr += noise
logq = compute_logq_gaussian(voted_arr, sigma)
dim = voted_arr.shape[0]
## l2-sensitivity is (k_level^2 * dim)**0.5, while GNMax sensitivity is 2**0.5. Hence the factor is 2k**0.5
rdp_budget = rdp_gaussian(logq, sigma / ((k_level**2 * dim) ** 0.5), orders)
# print(rdp_budget)
sign_grad = np.zeros_like(voted_arr)
sign_grad[voted_arr > beta * nteacher] = 1
sign_grad[voted_arr < -beta * nteacher] = -1
print("Agreed Dimension: " + str(np.sum(abs(sign_grad))))
return alpha * sign_grad.reshape(output_list[0].shape), rdp_budget
def signsgd_aggregate_dept(output_list, sigma, orders, topk, beta=0.1, alpha=1e-3, stochastic=False, b=None):
use_gpu = True
if not use_gpu:
nteacher = len(output_list)
flatten_grad = np.asarray([arr.flatten() for arr in output_list])
if stochastic:
voted_arr = np.sum(stachastic_convert2topk(flatten_grad, topk, b=b), axis=0)
else:
voted_arr = np.sum(convert2topk(flatten_grad, topk), axis=0)
else:
nteacher = len(output_list)
flatten_grad = torch.tensor([arr.flatten() for arr in output_list]).cuda()
if stochastic:
voted_arr = torch.sum(stachastic_convert2topk_gpu(flatten_grad, topk, b=b), dim=0).cpu().numpy()
else:
voted_arr = torch.sum(convert2topk_gpu(flatten_grad, topk), dim=0).cpu().numpy()
# noise, rdp_budget2 = gaussian_rdp(voted_arr, (4 * topk) **0.5, orders, sigma)
# print("before adding noise:", voted_arr)
voted_arr = np.random.normal(voted_arr, sigma)
# print("after adding noise:", voted_arr)
# voted_arr += noise
logq = compute_logq_gaussian(voted_arr, sigma)
## l2-sensitivity is (4k)**0.5, while GNMax sensitivity is 2**0.5. Hence the factor is 2k**0.5
# rdp_budget = rdp_gaussian(logq, sigma / ((2*topk) ** 0.5), orders)
rdp_budget, dept_rdp_budget = double_rdp_gaussian(logq, sigma / ((2*topk) ** 0.5), orders)
# print(rdp_budget)
sign_grad = np.zeros_like(voted_arr)
sign_grad[voted_arr > beta * nteacher] = 1
sign_grad[voted_arr < -beta * nteacher] = -1
print("Agreed Dimension: " + str(np.sum(abs(sign_grad))))
return alpha * sign_grad.reshape(output_list[0].shape), rdp_budget, dept_rdp_budget
def gradient_voting_rdp_multiproj(output_list, step_size, sigma, sigma_thresh, orders, pca_mats=None, nbins=10, thresh=0.9):
n = len(output_list)
flatten_arr = np.asarray([arr.flatten() for arr in output_list])
n_teachers, n_features = flatten_arr.shape
print("flatten arr shape", flatten_arr.shape)
if pca_mats is not None:
# project to principal components
split_flatten_arr = np.split(flatten_arr, len(pca_mats), axis=1)
reduced_flatten_arr = []
for pca_mat, arr in zip(pca_mats, split_flatten_arr):
print("arr shape", arr.shape)
print("pca shape", pca_mat.shape)
arr = np.matmul(arr, pca_mat)
reduced_flatten_arr.append(arr)
flatten_arr = np.concatenate(reduced_flatten_arr, axis=1)
n_features = flatten_arr.shape[1]
flatten_arr = flatten_arr.clip(min=-step_size, max=step_size)
bins = np.arange(-step_size, step_size, (step_size * 2 / nbins))
bins = np.hstack([bins, step_size])
result = np.zeros([1, n_features])
rdp_budget = 0
skipped_cnt = 0
for i in range(n_features):
votes_arr, _ = np.histogram(flatten_arr[:, i], bins)
print(votes_arr)
res_idx, cur_budget = gnmax_thresh_aggregator(votes_arr, thresh * n_teachers, sigma_thresh, sigma, orders)
rdp_budget += cur_budget
if res_idx < 0:
skipped_cnt += 1
else:
result[:, i] = (bins[res_idx] + bins[res_idx + 1]) / 2
print("Skipped %d feaatures out of %d" % (skipped_cnt, n_features))
if pca_mat is not None:
# project res direction back to original axis
split_results = np.split(result, len(pca_mats), axis=1)
final_results = []
for split_result, pca_mat in zip(split_results, pca_mats):
final_results.append(np.matmul(split_result, np.transpose(pca_mat)))
final_results = np.concatenate(final_results, axis=1)
return final_results.reshape(output_list[0].shape), rdp_budget
def gradient_sign_rdp(output_list, step_size, sigma, sigma_thresh, orders, pca_mat=None, thresh=0.9):
n = len(output_list)
flatten_arr = np.asarray([arr.flatten() for arr in output_list])
n_teachers, n_features = flatten_arr.shape
if pca_mat is not None:
# project to principal components
flatten_arr = np.matmul(flatten_arr, pca_mat)
n_features = flatten_arr.shape[1]
# first line for positive votes, second line for negative votes
votes_arr = np.zeros([2, n_features])
votes_sign = np.sign(flatten_arr)
# counts for positive votes
votes_arr[0, :] = np.sum(votes_sign[votes_sign > 0], axis=0)
# counts for negative votes
votes_arr[1, :] = -np.sum(votes_sign[votes_sign < 0], axis=0)
res_dir = np.zeros([1, n_features])
rdp_budget = 0
skipped_cnt = 0
for i in range(n_features):
dir_index, cur_budget = gnmax_thresh_aggregator(votes_arr[:, i], thresh * n_teachers, sigma_thresh, sigma,
orders)
if dir_index == 0:
res_dir[0, i] = step_size
elif dir_index == 1:
res_dir[0, i] = -step_size
else:
skipped_cnt += 1
rdp_budget += cur_budget
print("Skipped %d feaatures out of %d" % (skipped_cnt, n_features))
if pca_mat is not None:
# project res direction back to original axis
res_dir = np.matmul(res_dir, np.transpose(pca_mat))
return res_dir.reshape(output_list[0].shape), rdp_budget
def gradient_rdp(output_list, step_size, sigma, orders, pca_mat=None, thresh=None, sigma_thresh=1):
n = len(output_list)
flatten_arr = np.asarray([arr.flatten() for arr in output_list])
n_teachers, n_features = flatten_arr.shape
if pca_mat is not None:
# project to principal components
flatten_arr = np.matmul(flatten_arr, pca_mat)
n_features = flatten_arr.shape[1]
# first half votes for positive direction, second half votes for negative direction
votes_arr = np.zeros([n_teachers, n_features * 2])
max_index = np.argmax(np.abs(flatten_arr), axis=1)
for i in range(n_teachers):
if flatten_arr[i, max_index[i]] > 0:
votes_arr[i, max_index[i]] = 1
else:
votes_arr[i, max_index[i] + n_features] = 1
votes_count = np.sum(votes_arr, axis=0)
if thresh is None:
dir_index, rdp_budget = gnmax_aggregator(votes_count, sigma, orders)
else:
dir_index, rdp_budget = gnmax_thresh_aggregator(votes_count, thresh * n_teachers, sigma_thresh, sigma, orders)
max_votes = np.max(votes_count)
selected_votes = votes_count[dir_index]
# print("Max cnt: %d, selected cnt: %d" % (max_votes, selected_votes))
res_dir = np.zeros([1, n_features])
if dir_index < n_features and dir_index >= 0:
res_dir[0, dir_index] = step_size
elif dir_index >= n_features:
res_dir[0, dir_index - n_features] = -step_size
else:
print("Teachers don't agree. Skip...")
if pca_mat is not None:
# project res direction back to original axis
res_dir = np.matmul(res_dir, np.transpose(pca_mat))
return res_dir.reshape(output_list[0].shape), rdp_budget
def gaussian_rdp(arr, sensitivity, orders, sigma):
gaussian_noise = np.random.normal(loc=np.zeros(arr.shape), scale=sigma * sensitivity, size=arr.shape)
# Table 2 @ https://arxiv.org/pdf/1702.07476.pdf
rdp_budget = [o / ((2 * sigma) ** 2) for o in orders]
return gaussian_noise, rdp_budget