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
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
|
/*
* Copyright (c) 2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "activation_float_helpers.h"
#include "helpers.h"
#include "tile_helpers.h"
#ifdef BIAS
// This function performs in-place bias addition for integer datatype when bias is enabled.
// Note The tile's dimensions used for the LHS and RHS matrices (M0, N0) must be passed at compile time using -DN0, -DM0 (e.g. -DN0=8, -DM0=4).
inline void perform_bias_addition(uchar *bias_ptr, uint bias_offset_first_element_in_bytes, TILE(int, M0, N0, acc), uint x)
{
TILE(int, 1, N0, bias_tile);
// below expands to use bias_ptr and bias_offset_first_element_in_bytes
T_LOAD(int, 1, N0, BUFFER, bias, x, 0, 1, 0, bias_tile);
// c = c + bias[broadcasted]
T_ELTWISE_BROADCAST_ADD_X(int, M0, N0, acc, bias_tile, acc);
}
#endif // defined(BIAS)
#if defined(MAT_MUL_NATIVE_QUANTIZED_NT_NT)
/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS non-transposed - buffer only
*
* @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it
* should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension
* @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=uchar)
* @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
* @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
* @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output with the relu and bounded relu operations.
* @note The value of 0 in quantized format is equivalent to the quantization offset of the output data. This should be passed with -DZERO_POINT
* @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
* @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_QUANTIZED_NT_NT)
* @note Only the following configurations of M0, N0 and K0 are currently supported:
* - M0 > 0
* - N0 = 1, 2, 3, 4, 8, 16
* - K0 = 1, 2, 3, 4, 8, 16
* @note Values > 8 for M0 are not expected to be efficient
*
* @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: QASYMM8_SIGNED/QASYMM8
* @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
* @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
* @param[in] lhs_w The width of the lhs tensor
* @param[in] lhs_h The height of the lhs tensor
* @param[in] lhs_n Number of the matrices (buffers) in the batch
* @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
* @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
* @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
* @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
* @param[in] rhs_w The width of the rhs tensor
* @param[in] rhs_h The height of the rhs tensor
* @param[in] rhs_n Number of the matrices (buffers) in the batch
* @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
* @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
* @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
* @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
* @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
* @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
* @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
* @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
* @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
* @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
* @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
* @param[in] dst_w The width of the dst tensor
* @param[in] dst_h The height of the dst tensor
* @param[in] dst_n Number of the matrices (buffers) in the batch
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
*/
__kernel void mat_mul_native_quantized_nt_nt(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
#ifdef BIAS
TENSOR3D_T(bias, BUFFER),
#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER))
{
const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0);
const uint z = GET_SPATIAL_IDX(2, 1, 0);
// Compute LHS/RHS/DST matrix address
lhs_offset_first_element_in_bytes += y * lhs_stride_y + z * lhs_stride_z;
rhs_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + z * rhs_stride_z;
dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z;
// Initialize the accumulators
TILE(int, M0, N0, acc);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
acc[i].v = K * ((int)LHS_OFFSET) * ((int)RHS_OFFSET);
})
TILE(int, 1, N0, b_sum);
b_sum[0].v = 0;
TILE(int, 1, M0, a_sum);
a_sum[0].v = 0;
int k;
for(k = 0; k <= K - K0; k += K0)
{
TILE(DATA_TYPE, M0, K0, a);
TILE(DATA_TYPE, N0, K0, b);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
a[i].v = 0;
})
LOOP_UNROLLING(int, i, 0, 1, N0,
{
b[i].v = 0;
})
// Load tile from the lhs tensor
T_LOAD(DATA_TYPE, M0, K0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
// Load tile from the rhs tensor in a transposed fashion
// in order to use T_MMUL_NT_T macro because only this macro
// can utilize dot product instruction for Int8/UInt8 by
// directly multiplying the rows of Lhs and Rhs tensors.
T_LOAD_TRANSPOSED(DATA_TYPE, K0, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, K0, NT, T, a, b, acc);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
LOOP_UNROLLING(int, j, 0, 1, K0,
{
a_sum[0].s[i] += (int)a[i].s[j];
})
})
LOOP_UNROLLING(int, i, 0, 1, K0,
{
LOOP_UNROLLING(int, j, 0, 1, N0,
{
b_sum[0].s[j] += (int)b[j].s[i];
})
})
lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE);
rhs_offset_first_element_in_bytes += K0 * rhs_stride_y;
}
#if((K % K0) != 0)
/* Leftover Loop */
for(; k < K; ++k)
{
TILE(DATA_TYPE, M0, 1, a);
TILE(DATA_TYPE, N0, 1, b);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
a[i].v = 0;
})
LOOP_UNROLLING(int, i, 0, 1, N0,
{
b[i].v = 0;
})
// Load tile from the lhs tensor
T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
// Load tile from the rhs tensor in a transposed fashion.
// See the main loop for more explanation
T_LOAD_TRANSPOSED(DATA_TYPE, 1, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, 1, NT, T, a, b, acc);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
LOOP_UNROLLING(int, j, 0, 1, 1,
{
a_sum[0].s[i] += (int)a[i].s[j];
})
})
LOOP_UNROLLING(int, i, 0, 1, 1,
{
LOOP_UNROLLING(int, j, 0, 1, N0,
{
b_sum[0].s[j] += (int)b[j].s[i];
})
})
lhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE);
rhs_offset_first_element_in_bytes += 1 * rhs_stride_y;
}
#endif // ((K % K0) != 0)
LOOP_UNROLLING(int, i, 0, 1, M0,
{
LOOP_UNROLLING(int, j, 0, 1, N0,
{
acc[i].s[j] -= ((int)RHS_OFFSET) * a_sum[0].s[i] + ((int)(LHS_OFFSET)) * b_sum[0].s[j];
})
})
const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
#ifdef BIAS
perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x);
#endif // defined(BIAS)
// Quantize the tile
TILE(DATA_TYPE, M0, N0, accq);
T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, accq, accq);
TILE(int, M0, 1, indirect_buffer);
LOOP_UNROLLING(int, _i, 0, 1, M0,
{
indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
});
T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, accq, indirect_buffer);
}
#endif // defined(MAT_MUL_NATIVE_QUANTIZED_NT_NT)
#if defined(MAT_MUL_NATIVE_QUANTIZED_NT_T)
/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS transposed - buffer only
*
* @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it
* should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension
* @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=uchar)
* @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
* @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
* @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output bounded activation functions.
* @note The value of 0 in quantized format is equivalent to the quantization offset of the output data. This should be passed with -DZERO_POINT
* @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
* @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_QUANTIZED_NT_T)
* @note Only the following configurations of M0, N0 and K0 are currently supported:
* - M0 > 0
* - N0 = 1, 2, 3, 4, 8, 16
* - K0 = 1, 2, 3, 4, 8, 16
* @note Values > 8 for M0, N0, K0 are not expected to be efficient
*
* @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: QASYMM8/QASYMM8_SIGNED
* @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
* @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
* @param[in] lhs_w The width of the lhs tensor
* @param[in] lhs_h The height of the lhs tensor
* @param[in] lhs_n Number of the matrices (buffers) in the batch
* @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
* @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
* @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
* @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
* @param[in] rhs_w The width of the rhs tensor
* @param[in] rhs_h The height of the rhs tensor
* @param[in] rhs_n Number of the matrices (buffers) in the batch
* @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
* @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
* @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
* @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
* @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
* @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
* @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
* @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
* @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
* @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
* @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
* @param[in] dst_w The width of the dst tensor
* @param[in] dst_h The height of the dst tensor
* @param[in] dst_n Number of the matrices (buffers) in the batch
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
*/
__kernel void mat_mul_native_quantized_nt_t(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
#ifdef BIAS
TENSOR3D_T(bias, BUFFER),
#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER))
{
const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0);
const uint z = GET_SPATIAL_IDX(2, 1, 0);
// Compute LHS/RHS/DST matrix address
lhs_offset_first_element_in_bytes += y * lhs_stride_y + z * lhs_stride_z;
rhs_offset_first_element_in_bytes += x * rhs_stride_y + z * rhs_stride_z;
dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z;
// Initialize the accumulators
TILE(int, M0, N0, acc);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
acc[i].v = K * ((int)LHS_OFFSET) * ((int)RHS_OFFSET);
})
TILE(int, 1, M0, a_sum);
a_sum[0].v = 0;
TILE(int, 1, N0, b_sum);
b_sum[0].v = 0;
int k;
for(k = 0; k <= K - K0; k += K0)
{
TILE(DATA_TYPE, M0, K0, a);
TILE(DATA_TYPE, N0, K0, b);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
a[i].v = 0;
})
LOOP_UNROLLING(int, i, 0, 1, N0,
{
b[i].v = 0;
})
// Load tile from lhs/rhs tensors
T_LOAD(DATA_TYPE, M0, K0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
T_LOAD(DATA_TYPE, N0, K0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, K0, NT, T, a, b, acc);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
LOOP_UNROLLING(int, j, 0, 1, K0,
{
a_sum[0].s[i] += (int)a[i].s[j];
})
})
LOOP_UNROLLING(int, i, 0, 1, N0,
{
LOOP_UNROLLING(int, j, 0, 1, K0,
{
b_sum[0].s[i] += (int)b[i].s[j];
})
})
lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE);
rhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE);
}
#if((K % K0) != 0)
// Leftover loop
for(; k < K; ++k)
{
TILE(DATA_TYPE, M0, 1, a);
TILE(DATA_TYPE, N0, 1, b);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
a[i].v = 0;
})
LOOP_UNROLLING(int, i, 0, 1, N0,
{
b[i].v = 0;
})
// Load tile from lhs/rhs tensors
T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
T_LOAD(DATA_TYPE, N0, 1, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, 1, NT, T, a, b, acc);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
LOOP_UNROLLING(int, j, 0, 1, 1,
{
a_sum[0].s[i] += (int)a[i].s[j];
})
})
LOOP_UNROLLING(int, i, 0, 1, N0,
{
LOOP_UNROLLING(int, j, 0, 1, 1,
{
b_sum[0].s[i] += (int)b[i].s[j];
})
})
lhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE);
rhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE);
}
#endif // ((K % K0) != 0)
LOOP_UNROLLING(int, i, 0, 1, M0,
{
LOOP_UNROLLING(int, j, 0, 1, N0,
{
acc[i].s[j] -= ((int)(RHS_OFFSET)) * a_sum[0].s[i] + ((int)(LHS_OFFSET)) * b_sum[0].s[j];
})
})
const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
#ifdef BIAS
perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x);
#endif // defined(BIAS)
// Quantize the tile
TILE(DATA_TYPE, M0, N0, accq);
T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, accq, accq);
TILE(int, M0, 1, indirect_buffer);
LOOP_UNROLLING(int, _i, 0, 1, M0,
{
indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
});
T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, accq, indirect_buffer);
}
#endif // defined(MAT_MUL_NATIVE_QUANTIZED_NT_T)
#if defined(MAT_MUL_NATIVE_QUANTIZED_T_NT)
/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS transposed, RHS non-transposed
*
* @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it
* should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension
* @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=uchar)
* @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
* @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
* @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output with the relu and bounded relu operations.
* @note The value of 0 in quantized format is equivalent to the quantization offset of the output data. This should be passed with -DZERO_POINT
* @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
* @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_QUANTIZED_T_NT)
* @note Only the following configurations of M0, N0 and K0 are currently supported:
* - M0 > 0
* - N0 = 1, 2, 3, 4, 8, 16
* - K0 = 1, 2, 3, 4, 8, 16
* @note Values > 8 for M0, N0 and K0 are not expected to be efficient
*
* @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: QASYMM8/QASYMM8_SIGNED
* @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
* @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
* @param[in] lhs_w The width of the lhs tensor
* @param[in] lhs_h The height of the lhs tensor
* @param[in] lhs_n Number of the matrices (buffers) in the batch
* @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
* @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
* @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
* @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
* @param[in] rhs_w The width of the rhs tensor
* @param[in] rhs_h The height of the rhs tensor
* @param[in] rhs_n Number of the matrices (buffers) in the batch
* @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
* @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
* @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
* @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
* @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
* @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
* @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
* @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
* @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
* @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
* @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
* @param[in] dst_w The width of the dst tensor
* @param[in] dst_h The height of the dst tensor
* @param[in] dst_n Number of the matrices (buffers) in the batch
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
*/
__kernel void mat_mul_native_quantized_t_nt(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
#ifdef BIAS
TENSOR3D_T(bias, BUFFER),
#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER))
{
const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0);
const uint z = GET_SPATIAL_IDX(2, 1, 0);
// Compute LHS/RHS/DST matrix address
lhs_offset_first_element_in_bytes += y * sizeof(DATA_TYPE) + z * lhs_stride_z;
rhs_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + z * rhs_stride_z;
dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z;
// Initialize the accumulators
TILE(int, M0, N0, acc);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
acc[i].v = K * ((int)LHS_OFFSET) * ((int)RHS_OFFSET);
})
TILE(int, 1, N0, b_sum);
b_sum[0].v = 0;
TILE(int, 1, M0, a_sum);
a_sum[0].v = 0;
int k;
for(k = 0; k <= K - K0; k += K0)
{
TILE(DATA_TYPE, M0, K0, a);
TILE(DATA_TYPE, N0, K0, b);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
a[i].v = 0;
})
LOOP_UNROLLING(int, i, 0, 1, N0,
{
b[i].v = 0;
})
// Load tile from the lhs/rhs tensors in a transposed fashion
// see mat_mul_native_quantized_nt_nt main loop for more explanation
T_LOAD_TRANSPOSED(DATA_TYPE, K0, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
T_LOAD_TRANSPOSED(DATA_TYPE, K0, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, K0, NT, T, a, b, acc);
LOOP_UNROLLING(int, i, 0, 1, K0,
{
LOOP_UNROLLING(int, j, 0, 1, M0,
{
a_sum[0].s[j] += (int)a[j].s[i];
})
})
LOOP_UNROLLING(int, i, 0, 1, K0,
{
LOOP_UNROLLING(int, j, 0, 1, N0,
{
b_sum[0].s[j] += (int)b[j].s[i];
})
})
lhs_offset_first_element_in_bytes += K0 * lhs_stride_y;
rhs_offset_first_element_in_bytes += K0 * rhs_stride_y;
}
#if((K % K0) != 0)
/* Leftover Loop */
for(; k < K; ++k)
{
TILE(DATA_TYPE, M0, 1, a);
TILE(DATA_TYPE, N0, 1, b);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
a[i].v = 0;
})
LOOP_UNROLLING(int, i, 0, 1, N0,
{
b[i].v = 0;
})
// Load tile from the lhs/rhs tensors in a transposed fashion
// see mat_mul_native_quantized_nt_nt main loop for more explanation
T_LOAD_TRANSPOSED(DATA_TYPE, 1, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
T_LOAD_TRANSPOSED(DATA_TYPE, 1, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, 1, NT, T, a, b, acc);
LOOP_UNROLLING(int, i, 0, 1, 1,
{
LOOP_UNROLLING(int, j, 0, 1, M0,
{
a_sum[0].s[j] += (int)a[j].s[i];
})
})
LOOP_UNROLLING(int, i, 0, 1, 1,
{
LOOP_UNROLLING(int, j, 0, 1, N0,
{
b_sum[0].s[j] += (int)b[j].s[i];
})
})
lhs_offset_first_element_in_bytes += 1 * lhs_stride_y;
rhs_offset_first_element_in_bytes += 1 * rhs_stride_y;
}
#endif // ((K % K0) != 0)
LOOP_UNROLLING(int, i, 0, 1, M0,
{
LOOP_UNROLLING(int, j, 0, 1, N0,
{
acc[i].s[j] -= ((int)(RHS_OFFSET)) * a_sum[0].s[i] + ((int)(LHS_OFFSET)) * b_sum[0].s[j];
})
})
const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
#ifdef BIAS
perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x);
#endif // defined(BIAS)
// Quantize the tile
TILE(DATA_TYPE, M0, N0, accq);
T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, accq, accq);
TILE(int, M0, 1, indirect_buffer);
LOOP_UNROLLING(int, _i, 0, 1, M0,
{
indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
});
T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, accq, indirect_buffer);
}
#endif // defined(MAT_MUL_NATIVE_QUANTIZED_T_NT)
#if defined(MAT_MUL_NATIVE_QUANTIZED_T_T)
/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS transposed, RHS transposed
*
* @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it
* should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension
* @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=uchar)
* @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
* @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
* @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output with the relu and bounded relu operations.
* @note The value of 0 in quantized format is equivalent to the quantization offset of the output data. This should be passed with -DZERO_POINT
* @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
* @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_QUANTIZED_T_T)
* @note Only the following configurations of M0, N0 and K0 are currently supported:
* - M0 = 1, 2, 3, 4, 8, 16
* - N0 = 1, 2, 3, 4, 8, 16
* - K0 = 1, 2, 3, 4, 8, 16
* @note Values > 8 for M0, N0 and K0 are not expected to be efficient
*
* @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: QASYMM8/QASYMM8_SIGNED
* @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
* @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
* @param[in] lhs_w The width of the lhs tensor
* @param[in] lhs_h The height of the lhs tensor
* @param[in] lhs_n Number of the matrices (buffers) in the batch
* @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
* @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
* @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
* @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
* @param[in] rhs_w The width of the rhs tensor
* @param[in] rhs_h The height of the rhs tensor
* @param[in] rhs_n Number of the matrices (buffers) in the batch
* @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
* @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
* @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
* @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
* @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
* @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
* @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
* @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
* @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
* @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
* @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
* @param[in] dst_w The width of the dst tensor
* @param[in] dst_h The height of the dst tensor
* @param[in] dst_n Number of the matrices (buffers) in the batch
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
*/
__kernel void mat_mul_native_quantized_t_t(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
#ifdef BIAS
TENSOR3D_T(bias, BUFFER),
#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER))
{
const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0);
const uint z = GET_SPATIAL_IDX(2, 1, 0);
// Compute LHS/RHS/DST matrix address
lhs_offset_first_element_in_bytes += y * sizeof(DATA_TYPE) + z * lhs_stride_z;
rhs_offset_first_element_in_bytes += x * rhs_stride_y + z * rhs_stride_z;
dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z;
// Initialize the accumulators
TILE(int, M0, N0, acc);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
acc[i].v = K * ((int)LHS_OFFSET) * ((int)RHS_OFFSET);
})
TILE(int, 1, M0, a_sum);
a_sum[0].v = 0;
TILE(int, 1, N0, b_sum);
b_sum[0].v = 0;
int k;
for(k = 0; k <= K - K0; k += K0)
{
TILE(DATA_TYPE, M0, K0, a);
TILE(DATA_TYPE, N0, K0, b);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
a[i].v = 0;
})
LOOP_UNROLLING(int, i, 0, 1, N0,
{
b[i].v = 0;
})
// Load tile from the lhs tensor in a transposed fashion
// see mat_mul_native_quantized_nt_nt main loop for more explanation
T_LOAD_TRANSPOSED(DATA_TYPE, K0, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
// Load tile from the rhs tensor
T_LOAD(DATA_TYPE, N0, K0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, K0, NT, T, a, b, acc);
LOOP_UNROLLING(int, i, 0, 1, K0,
{
LOOP_UNROLLING(int, j, 0, 1, M0,
{
a_sum[0].s[j] += (int)a[j].s[i];
})
})
LOOP_UNROLLING(int, i, 0, 1, N0,
{
LOOP_UNROLLING(int, j, 0, 1, K0,
{
b_sum[0].s[i] += (int)b[i].s[j];
})
})
lhs_offset_first_element_in_bytes += K0 * lhs_stride_y;
rhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE);
}
#if((K % K0) != 0)
/* Leftover Loop */
for(; k < K; ++k)
{
TILE(DATA_TYPE, M0, 1, a);
TILE(DATA_TYPE, N0, 1, b);
LOOP_UNROLLING(int, i, 0, 1, M0,
{
a[i].v = 0;
})
LOOP_UNROLLING(int, i, 0, 1, N0,
{
b[i].v = 0;
})
// Load tile from the lhs tensor in a transposed fashion
// see mat_mul_native_quantized_nt_nt main loop for more explanation
T_LOAD_TRANSPOSED(DATA_TYPE, 1, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
// Load tile from the rhs tensor
T_LOAD(DATA_TYPE, N0, 1, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, 1, NT, T, a, b, acc);
LOOP_UNROLLING(int, i, 0, 1, 1,
{
LOOP_UNROLLING(int, j, 0, 1, M0,
{
a_sum[0].s[j] += (int)a[j].s[i];
})
})
LOOP_UNROLLING(int, i, 0, 1, N0,
{
LOOP_UNROLLING(int, j, 0, 1, 1,
{
b_sum[0].s[i] += (int)b[i].s[j];
})
})
lhs_offset_first_element_in_bytes += 1 * lhs_stride_y;
rhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE);
}
#endif // ((K % K0) != 0)
LOOP_UNROLLING(int, i, 0, 1, M0,
{
LOOP_UNROLLING(int, j, 0, 1, N0,
{
acc[i].s[j] -= ((int)RHS_OFFSET) * a_sum[0].s[i] + ((int)(LHS_OFFSET)) * b_sum[0].s[j];
})
})
const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
#ifdef BIAS
perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x);
#endif // defined(BIAS)
// Quantize the tile
TILE(DATA_TYPE, M0, N0, accq);
T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, accq, accq);
TILE(int, M0, 1, indirect_buffer);
LOOP_UNROLLING(int, _i, 0, 1, M0,
{
indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
});
T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, accq, indirect_buffer);
}
#endif // defined(MAT_MUL_NATIVE_QUANTIZED_T_T)
|
