Repository navigation
Expand file tree
/
Copy pathTuner.cpp
More file actions
730 lines (630 loc) · 22.9 KB
/
Copy pathTuner.cpp
File metadata and controls
730 lines (630 loc) · 22.9 KB
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
/*
This file is part of Leela Zero.
Copyright (C) 2017-2019 Gian-Carlo Pascutto and contributors
Leela Zero is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Leela Zero is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with Leela Zero. If not, see <http://www.gnu.org/licenses/>.
Additional permission under GNU GPL version 3 section 7
If you modify this Program, or any covered work, by linking or
combining it with NVIDIA Corporation's libraries from the
NVIDIA CUDA Toolkit and/or the NVIDIA CUDA Deep Neural
Network library and/or the NVIDIA TensorRT inference library
(or a modified version of those libraries), containing parts covered
by the terms of the respective license agreement, the licensors of
this Program grant you additional permission to convey the resulting
work.
*/
#include "config.h"
#ifdef USE_OPENCL
#include <array>
#include <cassert>
#include <iostream>
#include <sstream>
#include <string>
#include <map>
#include <random>
#include <cmath>
#include <fstream>
#ifndef USE_BLAS
#include <Eigen/Dense>
#endif
#include "GTP.h"
#include "OpenCL.h"
#include "Tuner.h"
#include "Utils.h"
#include "Random.h"
const auto TUNER_FILE_LOCAL = std::string("leelaz_opencl_tuning");
template <typename net_t>
std::vector<std::string> Tuner<net_t>::tuned_devices;
#ifndef USE_BLAS
// Eigen helpers
template <typename T>
using EigenMatrixMap =
Eigen::Map<Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using ConstEigenMatrixMap =
Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>;
#endif
template <typename net_t> static std::string getTunerKernel();
template <typename net_t> static float getTunerMaxError();
template <> std::string getTunerKernel<float>() {
return std::string("XgemmBatched");
}
template <> float getTunerMaxError<float>() {
return 1e-4f;
}
#ifdef USE_HALF
template <> std::string getTunerKernel<half_float::half>() {
return std::string("XgemmBatchedHalf");
}
template <> float getTunerMaxError<half_float::half>() {
return 1e-1f;
}
#endif
using namespace Utils;
template <typename net_t>
static void sgemmBatched_ref(const std::vector<net_t>& a,
const std::vector<net_t>& b,
std::vector<net_t>& c,
const int m, const int n, const int k,
const int batch_size) {
std::vector<float> ar(a.size());
std::vector<float> br(b.size());
std::vector<float> cr(c.size());
std::copy(begin(a), end(a), begin(ar));
std::copy(begin(b), end(b), begin(br));
for (auto batch = 0; batch < batch_size; batch++) {
auto offset_u = batch * m * k;
auto offset_v = batch * n * k;
auto offset_m = batch * m * n;
#ifdef USE_BLAS
// Calculates C = transpose(tranpose(A) * B) in row major, or
// C = A * transpose(B) in column major.
for (auto i = 0; i < m; i++) {
for (auto j = 0; j < n; j++) {
auto acc = 0.0f;
for (auto l = 0; l < k; l++) {
acc += ar[l * m + i + offset_u] * br[l * n + j + offset_v];
}
cr[j * m + i + offset_m] = acc;
}
}
#else
auto C = EigenMatrixMap<float>(cr.data() + offset_m, m, n);
8000
auto A = ConstEigenMatrixMap<float>(ar.data() + offset_u, m, k);
auto B = ConstEigenMatrixMap<float>(br.data() + offset_v, n, k);
C.noalias() = (A * B.transpose());
#endif
}
std::copy(begin(cr), end(cr), begin(c));
}
static bool IsMultiple(const size_t a, const size_t b) {
return (a % b == 0);
}
template <typename net_t>
bool Tuner<net_t>::valid_config_sgemm(Parameters p, bool exhaustive) {
if (p["TCE"] == 0) {
if (!IsMultiple(p["MWG"], p["MDIMC"]*p["VWM"])) {
return false;
}
if (!IsMultiple(p["NWG"], p["NDIMC"]*p["VWN"])) {
return false;
}
if (!IsMultiple(p["MWG"], p["MDIMA"]*p["VWM"])) {
return false;
}
if (!IsMultiple(p["NWG"], p["NDIMB"]*p["VWN"])) {
return false;
}
if (!IsMultiple(p["KWG"], p["MDIMC"]*p["NDIMC"]/p["MDIMA"])) {
return false;
}
if (!IsMultiple(p["KWG"], p["MDIMC"]*p["NDIMC"]/p["NDIMB"])) {
return false;
}
// Extra restrictions for a fast tuning run
if (!exhaustive) {
if (p["MDIMC"] != p["MDIMA"]) {
return false;
}
if (p["NDIMC"] != p["NDIMB"]) {
return false;
}
if (p["SA"] != p["SB"]) {
return false;
}
}
} else {
if (!m_use_tensorcore) {
return false;
}
// In Tensor Core implementations, MDIMA and NDIMB represents the
// wmmv multiplication dimensions, that is,
// m16n16k16 / m32n8k16 / m8n32k16. Thus m * n is fixed to 256.
if (p["MDIMA"] * p["NDIMB"] != 256) {
return false;
}
if (p["MWG"] < p["MDIMC"]) {
return false;
}
if (p["NWG"] < p["NDIMC"]) {
return false;
}
if (p["MDIMC"] < p["MDIMA"]) {
return false;
}
if (p["NDIMC"] < p["NDIMB"]) {
return false;
}
// VWM / VWN has no meaning if we don't do SA / SB.
// Only test VWM / VWN == 2
if (p["SA"] == 0 && p["VWM"] != 2) {
return false;
}
if (p["SB"] == 0 && p["VWN"] != 2) {
return false;
}
}
return true;
}
template <typename net_t>
Parameters Tuner<net_t>::get_parameters_by_int(
const std::vector<Configurations>& opts, const int n) {
Parameters param;
std::vector<size_t> choices(opts.size());
auto cfgs = 1;
for (auto c = size_t{0}; c < opts.size(); c++) {
choices[c] = opts[c].second.size();
cfgs *= choices[c];
}
auto j = n;
for (auto c = size_t{0}; c < opts.size(); c++) {
auto o = opts[c];
auto s = o.first;
auto v = o.second[j % choices[c]];
j /= choices[c];
param[s] = v;
}
return param;
}
template <typename net_t>
std::string Tuner<net_t>::parameters_to_defines(const Parameters& p) {
std::string s;
for (auto const& x : p) {
s += " -D" + x.first + "=" + std::to_string(x.second);
}
return s;
}
template <typename net_t>
std::string Tuner<net_t>::parameters_to_string(const Parameters& p) {
std::string s;
for (auto const& x : p) {
s += x.first + "=" + std::to_string(x.second) + " ";
}
if (s.size() > 0) {
s.resize(s.size() - 1);
}
return s;
}
static size_t next_power_of_two(const size_t x) {
return 2 << size_t(s
10BC8
td::ceil(std::log2(x)) - 1); // why minus one?
}
template <typename net_t>
static void sgemm_generate_data(std::vector<net_t> &x,
const int m, const int n,
const int batch_size,
const int m_ceil, const int n_ceil) {
for (auto batch = 0; batch < batch_size; batch++) {
for (auto i = 0; i < n_ceil; i++) {
if (i < n) {
for (auto j = 0; j < m; j++) {
x[batch*n_ceil*m_ceil + i*m_ceil + j] =
(( (i ^ j) + batch - 128) % 256) / 256.0f;
}
for (auto j = m; j < m_ceil; j++) {
x[batch*n_ceil*m_ceil + i*m_ceil + j] = 0.0f;
}
} else {
for (auto j = 0; j < m_ceil; j++) {
x[batch*n_ceil*m_ceil + i*m_ceil + j] = 0.0f;
}
}
}
}
}
template <typename net_t>
static float compare_ref(std::vector<net_t> &x, std::vector<net_t> &ref,
const int m, const int n, const int batch_size,
const int m_ceil, const int n_ceil) {
auto sum = 0.0f;
for (auto batch = 0; batch < batch_size; batch++) {
for (auto j = 0; j < m; j++) {
for (auto i = 0; i < n; i++) {
auto r = ref[batch*n*m + j*n + i];
auto y = x[batch*n_ceil*m_ceil + j*n_ceil + i];
sum += (r - y) * (r - y);
}
}
}
return sum / (m * n * batch_size);
}
template <typename net_t>
std::vector<Parameters> Tuner<net_t>::build_valid_params() {
auto opts = std::vector<Configurations>();
if (cfg_sgemm_exhaustive) {
opts = {
{"MWG", {16, 32, 64}},
{"NWG", {16, 32, 64}},
{"KWG", {16, 32}},
{"MDIMC", {8, 16, 32}},
{"NDIMC", {8, 16, 32}},
{"MDIMA", {8, 16, 32}},
{"NDIMB", {8, 16, 32}},
{"KWI", {2, 8}},
{"VWM", {1, 2, 4, 8}},
{"VWN", {1, 2, 4, 8}},
{"STRM", {0, 1}},
{"STRN", {0, 1}},
{"SA", {0, 1}},
{"SB", {0, 1}},
};
} else {
opts = {
{"MWG", {16, 32, 64}},
{"NWG", {16, 32, 64}},
{"KWG", {16, 32}},
{"MDIMC", {8, 16, 32}},
{"NDIMC", {8, 16, 32}},
{"MDIMA", {8, 16, 32}},
{"NDIMB", {8, 16, 32}},
{"KWI", {2, 8}},
{"VWM", {2, 4}},
{"VWN", {2, 4}},
{"STRM", {0}},
{"STRN", {0}},
{"SA", {1}},
{"SB", {1}},
};
}
// Tensor Core options
auto topts = std::vector<Configurations>();
if (cfg_sgemm_exhaustive) {
topts = {
{"MWG", {32, 64, 128, 256}},
{"NWG", {8, 16, 32, 64}},
{"KWG", {16, 32, 64}},
{"MDIMC", {8, 16, 32, 64}},
{"NDIMC", {8, 16, 32, 64}},
{"MDIMA", {8, 16, 32}},
{"NDIMB", {8, 16, 32}},
{"KWI", {2}},
{"VWM", {2, 4, 8}},
{"VWN", {2, 4, 8}},
{"STRM", {0}},
{"STRN", {0}},
{"SA", {0, 1}},
{"SB", {0, 1}},
};
} else {
topts = {
{"MWG", {32, 64, 128}},
{"NWG", {8, 16, 32}},
{"KWG", {16, 32}},
{"MDIMC", {8, 16, 32}},
{"NDIMC", {8, 16, 32}},
{"MDIMA", {8, 16, 32}},
{"NDIMB", {8, 16, 32}},
{"KWI", {2}},
{"VWM", {2}},
{"VWN", {2}},
{"STRM", {0}},
{"STRN", {0}},
{"SA", {0}},
{"SB", {0}},
};
}
auto valid_params = std::vector<Parameters>{};
auto build_from = [this, &valid_params](std::vector<Configurations> & opts, int tce) {
auto cfgs = 1;
for (auto c = size_t{0}; c < opts.size(); c++) {
cfgs *= opts[c].second.size();
}
for (auto i = 0; i < cfgs; i++) {
Parameters param = get_parameters_by_int(opts, i);
param["TCE"] = tce;
if (valid_config_sgemm(param, cfg_sgemm_exhaustive)) {
valid_params.push_back(param);
}
}
};
build_from(opts, 0);
build_from(topts, 1);
// Don't use thread RNG or determinism will depend on whether tuner ran.
auto rng = Random{0};
std::shuffle(begin(valid_params), end(valid_params), rng);
if (cfg_sgemm_exhaustive) {
// Likely too many valid params, cut out some of them
valid_params.resize(valid_params.size() / 16);
}
return valid_params;
}
template <typename net_t>
std::string Tuner<net_t>::tune_sgemm(const int m, const int n, const int k,
const int batch_size, const int runs) {
// This needs to be at minimum the maximum (MNK/WG) values above.
auto m_max = std::max(64, m);
auto n_max = std::max(64, n);
auto k_max = std::max(32, k);
auto at_size = batch_size
* next_power_of_two(k_max) * next_power_of_two(m_max);
auto b_size = batch_size
* next_power_of_two(k_max) * next_power_of_two(n_max);
auto c_size = batch_size
* next_power_of_two(m_max) * next_power_of_two(n_max);
auto total_flops = batch_size * 2.0 * m * n * k;
auto at = std::vector<net_t>(at_size);
auto b = std::vector<net_t>(b_size);
auto c = std::vector<net_t>(c_size);
auto c_ref = std::vector<net_t>(c_size);
sgemm_generate_data(at, k, m, batch_size, k, m);
sgemm_generate_data(b, n, k, batch_size, n, k);
sgemmBatched_ref(at, b, c_ref, m, n, k, batch_size);
auto aBuffer = cl::Buffer(
m_context,
CL_MEM_READ_WRITE, sizeof(net_t) * at_size, nullptr, nullptr);
auto bBuffer = cl::Buffer(
m_context,
CL_MEM_READ_WRITE, sizeof(net_t) * b_size, nullptr, nullptr);
auto cBuffer = cl::Buffer(
m_context,
CL_MEM_READ_WRITE, sizeof(net_t) * c_size, nullptr, nullptr);
myprintf("\nStarted OpenCL SGEMM tuner.\n");
auto valid_params = build_valid_params();
myprintf("Will try %zu valid configurations.\n", valid_params.size());
std::string best_params;
auto best_time = unsigned{0};
auto queue = cl::CommandQueue(m_context,
m_device,
CL_QUEUE_PROFILING_ENABLE);
auto event = cl::Event();
auto program = cl::Program(m_context, sourceCode_common + sourceCode_sgemm);
auto m_ceil_prev = 0;
auto n_ceil_prev = 0;
auto k_ceil_prev = 0;
auto param_counter = size_t{0};
auto min_error = 100.0f;
auto failed_compile = 0;
auto failed_enqueue = 0;
auto failed_error = 0;
for (auto & p : valid_params) {
param_counter++;
auto defines = parameters_to_defines(p);
try {
auto args = m_opencl.m_cl_args + " " + defines;
program.build(args.c_str());
} catch (const cl::Error&) {
// Failed to compile, get next parameter
failed_compile++;
continue;
}
auto sgemm_kernel = cl::Kernel(program, "XgemmBatched");
auto m_ceil = int(ceilMultiple(ceilMultiple(m, p["MWG"]), p["VWM"]));
auto n_ceil = int(ceilMultiple(ceilMultiple(n, p["NWG"]), p["VWN"]));
auto k_ceil = int(ceilMultiple(ceilMultiple(k, p["KWG"]), p["VWM"]));
if (m_ceil != m_ceil_prev
|| n_ceil != n_ceil_prev
|| k_ceil != k_ceil_prev) {
m_ceil_prev = m_ceil;
n_ceil_prev = n_ceil;
k_ceil_prev = k_ceil;
sgemm_generate_data(at, k, m, batch_size, k_ceil, m_ceil);
sgemm_generate_data(b, n, k, batch_size, n_ceil, k_ceil);
queue.enqueueWriteBuffer(aBuffer, CL_FALSE, 0,
at_size * sizeof(net_t), at.data());
queue.enqueueWriteBuffer(bBuffer, CL_FALSE, 0,
b_size * sizeof(net_t), b.data());
queue.finish();
}
sgemm_kernel.setArg(0, m_ceil);
sgemm_kernel.setArg(1, n_ceil);
sgemm_kernel.setArg(2, k_ceil);
sgemm_kernel.setArg(3, aBuffer);
sgemm_kernel.setArg(4, bBuffer);
sgemm_kernel.setArg(5, cBuffer);
cl::NDRange local_sgemm = {p["MDIMC"], p["NDIMC"], 1};
cl::NDRange size_sgemm = {(m_ceil * p["MDIMC"]) / p["MWG"],
(n_ceil * p["NDIMC"]) / p["NWG"],
size_t(batch_size)};
// Tensor Core implementation uses a different dimension.
if (p["TCE"]) {
local_sgemm = {32 * p["MDIMC"] / p["MDIMA"], p["NDIMC"] / p["NDIMB"], 1};
size_sgemm = {32 * m_ceil / p["MDIMA"] * p["MDIMC"]
8
/ p[&qu
10760
ot;MWG"],
n_ceil / p["NDIMB"] * p["NDIMC"] / p["NWG"],
size_t(batch_size)};
}
auto sum = 0.0f;
auto error = 0.0f;
for (auto r = 0; r < runs; r++) {
try {
queue.enqueueNDRangeKernel(sgemm_kernel, cl::NullRange,
size_sgemm, local_sgemm,
nullptr, &event);
queue.finish();
event.wait();
queue.enqueueReadBuffer(cBuffer, CL_FALSE, 0,
c_size * sizeof(net_t), c.data());
queue.finish();
auto this_error = compare_ref(c, c_ref, n, m, batch_size,
n_ceil, m_ceil);
error = std::max(error, this_error);
auto elapsed =
event.getProfilingInfo<CL_PROFILING_COMMAND_END>() -
event.getProfilingInfo<CL_PROFILING_COMMAND_START>();
sum += elapsed;
} catch (const cl::Error&) {
// Failed to enqueue kernel. Set error to some big number.
failed_enqueue++;
error = std::numeric_limits<float>::max();
// This failure will be counted to be failed due to error,
// so preemptively subtract one from that count.
failed_error--;
break;
}
}
min_error = std::min(min_error, error);
if (error >= getTunerMaxError<net_t>()) {
failed_error++;
}
if (error < getTunerMaxError<net_t>() && (best_time == 0 || sum < best_time)) {
auto param_str = parameters_to_string(p);
auto kernel_ms = 1e-6f * (sum / runs);
// Timing is in nanoseconds (10^-9), Giga = 10^9, so this works out
auto kernel_gflops = total_flops / (sum / runs);
myprintf("(%u/%u) %s %.4f ms (%.1f GFLOPS)\n",
param_counter, valid_params.size(), param_str.c_str(),
kernel_ms, kernel_gflops);
best_time = sum;
best_params = defines;
}
}
if (best_time == 0) {
if (failed_compile > 0) {
myprintf_error("Failed to compile: %d kernels.\n", failed_compile);
}
if (failed_enqueue > 0) {
myprintf_error("Failed to enqueue: %d kernels\n", failed_enqueue);
}
if (failed_error > 0) {
myprintf_error("Too high error: %d kernels\n", failed_error);
}
myprintf_error("Failed to find a working configuration.\nCheck your OpenCL drivers.\n");
myprintf_error("Minimum error: %f. Error bound: %f\n", min_error, getTunerMaxError<net_t>());
throw std::runtime_error("Tuner failed to find working configuration.");
}
return best_params;
}
template <typename net_t>
void Tuner<net_t>::store_sgemm_tuners(const int m, const int n, const int k,
const int batch_size, std::string tuners) {
auto tuner_file = leelaz_file(TUNER_FILE_LOCAL);
auto file_contents = std::vector<std::string>();
{
// Read the previous contents to string
auto file = std::ifstream{tuner_file};
if (file.good()) {
auto line = std::string{};
while (std::getline(file, line)) {
file_contents.emplace_back(line);
}
}
}
auto file = std::ofstream{tuner_file};
auto device_name = m_opencl.get_device_name();
auto tuning_params = std::stringstream{};
tuning_params << m << ";" << n << ";" << k << ";" << batch_size;
auto tuning_line_prefix = std::to_string(TUNER_VERSION) + ";"
+ getTunerKernel<net_t>() + ";" + tuning_params.str() + ";";
auto tuning_line = tuning_line_prefix + tuners + ";" + device_name;
// Write back previous data as long as it's not the device and
// tuning we just tuned
for (const auto& line : file_contents) {
if (line.find(tuning_line_prefix) == std::string::npos
|| line.find(device_name) == std::string::npos) {
file << line << std::endl;
}
}
// Write new tuning
file << tuning_line << std::endl;
if (file.fail()) {
myprintf("Could not save the tuning result.\n");
myprintf("Do I have write permissions on %s?\n",
tuner_file.c_str());
}
}
template <typename net_t>
std::string Tuner<net_t>::sgemm_tuners_from_line(std::string line,
const int m, const int n, const int k,
const int batch_size) {
auto s = std::vector<std::string>{};
auto ss = std::stringstream{line};
auto item = std::string{};
while (std::getline(ss, item, ';')) {
s.emplace_back(item);
}
if (s.size() != 8) {
return "";
}
if (s[0] != std::to_string(TUNER_VERSION)) {
return "";
}
if (s[1] != getTunerKernel<net_t>()) {
return "";
}
if (s[2] != std::to_string(m)) {
return "";
}
if (s[3] != std::to_string(n)) {
return "";
}
if (s[4] != std::to_string(k)) {
return "";
}
if (s[5] != std::to_string(batch_size)) {
return "";
}
if (s[7] != m_opencl.get_device_name()) {
return "";
}
return s[6];
}
template <typename net_t>
std::string Tuner<net_t>::load_sgemm_tuners(const int m, const int n, const int k,
const int batch_size) {
auto tuner_file = leelaz_file(TUNER_FILE_LOCAL);
auto file = std::ifstream{tuner_file};
auto try_prior_tuning = file.good();
// If we want full tuning, don't reuse previously tuned results
// except if the tuning was created from this run from a different
// GPU instance with the same name. This prevents the tuner running
// for multiple times if the system has multiple same GPUs.
if (try_prior_tuning && cfg_sgemm_exhaustive) {
auto dev = m_opencl.get_device_name();
try_prior_tuning = std::any_of(
begin(tuned_devices),
end(tuned_devices),
[&dev](const std::string & x) { return dev == x; }
);
}
tuned_devices.emplace_back(m_opencl.get_device_name());
if (try_prior_tuning) {
auto line = std::string{};
while (std::getline(file, line)) {
auto tuners = sgemm_tuners_from_line(line, m, n, k, batch_size);
if (tuners.size() != 0) {
myprintf("Loaded existing SGEMM tuning.\n");
return tuners;
}
}
}
auto tuners = tune_sgemm(m, n, k, batch_size);
store_sgemm_tuners(m, n, k, batch_size, tuners);
return tuners;
}
template <typename net_t>
void Tuner<net_t>::enable_tensorcore() {}
template <>
void Tuner<half_float::half>::enable_tensorcore() {
m_use_tensorcore = true;
}
template class Tuner<float>;
#ifdef USE_HALF
template class Tuner<half_float::half>;
#endif
#endif