forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
UpSampleKernelAVXAntialias.h
1376 lines (1221 loc) · 56.8 KB
/
UpSampleKernelAVXAntialias.h
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
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
/*
The Python Imaging Library (PIL) is
Copyright © 1997-2011 by Secret Labs AB
Copyright © 1995-2011 by Fredrik Lundh
Pillow is the friendly PIL fork. It is
Copyright © 2010-2022 by Alex Clark and contributors
Like PIL, Pillow is licensed under the open source HPND License
*/
// This code is heavily inspired from PILLOW-SIMD's implementation:
// https://github.com/uploadcare/pillow-simd/blob/simd/master/src/libImaging/Resample.c
#pragma once
#ifdef CPU_CAPABILITY_AVX2
// TODO: This file only supports AVX2. We could split the AVX kernels into
// smaller logical blocks in order to port them into the Vec.h logic. This would
// allow to support other vectorization architectures and perhaps also support
// the non-vectorized fallback (we'd need to make sure it's not slower than the
// current fallback).
#include <ATen/core/Tensor.h>
#include <ATen/cpu/vec/intrinsics.h>
#include <c10/util/irange.h>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#else
#include <ATen/ops/empty.h>
#endif
namespace {
static inline __m128i mm_cvtsi32_si128(const uint8_t* C10_RESTRICT ptr, bool i32_aligned) {
int32_t v;
if (i32_aligned) {
v = *(const int32_t*)ptr;
} else {
std::memcpy(&v, ptr, 4);
}
return _mm_cvtsi32_si128(v);
}
static inline __m128i mm_cvtepu8_epi32(const uint8_t* C10_RESTRICT ptr, bool i32_aligned) {
return _mm_cvtepu8_epi32(mm_cvtsi32_si128(ptr, i32_aligned));
}
static inline void _write_endline_rgb_as_uint32(
uint8_t* C10_RESTRICT output,
uint32_t data
) {
// data is (R G B X), output is (X1 X2 X3 | R1 B1 G1 R2 ...)
// Here we explicitly set X as R1
uint8_t* data_ptr = reinterpret_cast<uint8_t*>(&data);
data_ptr[3] = output[3];
std::memcpy(output, data_ptr, 4);
}
at::Tensor unpack_rgb(const at::Tensor& packed_tensor) {
// Convert a "packed" tensor (typically RGBRGBRGB if channels_last) into
// RGBARGBARGBA format where A is hard-coded to 0. Each pixel is encoded
// into as 32 bits. This generalizes to num_channels <= 4 and also works for
// non-channels_last tensors.
const uint8_t* packed = (const uint8_t*)packed_tensor.data_ptr<uint8_t>();
auto num_pixels = packed_tensor.size(1) * packed_tensor.size(2);
auto num_channels = packed_tensor.size(0);
constexpr int rgba_size = 4;
auto unpacked_tensor = at::empty({rgba_size, packed_tensor.size(1), packed_tensor.size(2)}, at::CPU(at::kByte));
uint8_t* unpacked = (uint8_t*) unpacked_tensor.data_ptr<uint8_t>();
auto stride_i = packed_tensor.stride(2);
auto stride_j = packed_tensor.stride(0);
for (const auto i : c10::irange(num_pixels)) {
for (const auto j : c10::irange(rgba_size)) {
unpacked[rgba_size * i + j] = (j < num_channels) ? packed[stride_i * i + stride_j * j] : 0;
}
}
return unpacked_tensor;
}
void pack_rgb(
const at::Tensor& unpacked_tensor, // IN
const at::Tensor& packed_tensor // OUT
) {
// Convert from unpacked channels last 3-channels or 4-channels tensor into original data layout.
uint8_t* unpacked = (uint8_t*)unpacked_tensor.data_ptr<uint8_t>();
uint8_t* packed = (uint8_t*)packed_tensor.data_ptr<uint8_t>();
auto num_pixels = packed_tensor.size(1) * packed_tensor.size(2);
auto num_channels = packed_tensor.size(0);
auto unpacked_increment = unpacked_tensor.size(0);
auto packed_increment = packed_tensor.stride(2);
auto packed_stride = packed_tensor.stride(0);
TORCH_INTERNAL_ASSERT(unpacked_increment == 3 || unpacked_increment == 4);
for (const auto i C10_UNUSED : c10::irange(num_pixels)) {
for (const auto j : c10::irange(num_channels)) {
packed[j * packed_stride] = unpacked[j];
}
unpacked += unpacked_increment;
packed += packed_increment;
}
}
void ImagingResampleHorizontalConvolution8u4x(
uint8_t* C10_RESTRICT lineOut0,
uint8_t* C10_RESTRICT lineOut1,
uint8_t* C10_RESTRICT lineOut2,
uint8_t* C10_RESTRICT lineOut3,
int64_t out_xsize,
const uint8_t* C10_RESTRICT lineIn0,
const uint8_t* C10_RESTRICT lineIn1,
const uint8_t* C10_RESTRICT lineIn2,
const uint8_t* C10_RESTRICT lineIn3,
int64_t in_xsize,
const int64_t* idx_ptr_xmin,
const int64_t* idx_ptr_size,
const int16_t* kk,
int kmax,
unsigned int coefs_precision,
int64_t num_channels,
bool is_last_line);
void ImagingResampleHorizontalConvolution8u(
uint8_t* C10_RESTRICT lineOut,
int64_t out_xsize,
const uint8_t* C10_RESTRICT lineIn,
int64_t in_xsize,
const int64_t* idx_ptr_xmin,
const int64_t* idx_ptr_size,
const int16_t* kk,
int kmax,
unsigned int coefs_precision,
int64_t num_channels,
bool is_last_line);
void ImagingResampleVerticalConvolution8u(
uint8_t* C10_RESTRICT lineOut,
const uint8_t* C10_RESTRICT lineIn,
int64_t xsize,
int64_t ids_min,
int64_t ids_size,
const int16_t* k,
unsigned int coefs_precision,
int64_t num_channels);
template<int num_channels>
void ImagingResampleHorizontal(
const at::Tensor & unpacked_output,
const at::Tensor & unpacked_input,
int ksize,
const std::vector<at::Tensor>& horiz_indices_weights,
unsigned int horiz_weights_precision) {
// Interpolation horizontal pass: we compute x-axis (image width) interpolation outputs.
// Input data is stored as
// input = [r[0], g[0], b[0], a[0], r[1], g[1], b[1], a[1], r[2], g[2], b[2], a[2], ...]
// Weights are float values computed for each output pixel and rescaled to uint16:
// weights[i] = [w[i, 0], w[i, 1], ..., w[i, K-1]]
// We want to compute the output as following:
// output = [oR[0], oG[0], oB[0], oA[0], oR[1], oG[1], oB[1], oA[1], ...]
// where
// oR[yoffset + i] = r[yoffset + xmin[i]] * w[i, 0] + ... + r[yoffset + xmin[i] + K-1] * w[i, K-1]
// oG[yoffset + i] = g[yoffset + xmin[i]] * w[i, 0] + ... + g[yoffset + xmin[i] + K-1] * w[i, K-1]
// oB[yoffset + i] = b[yoffset + xmin[i]] * w[i, 0] + ... + b[yoffset + xmin[i] + K-1] * w[i, K-1]
//
// TODO: we may want to merge that into the fallback code (currently called
// basic_loop_aa_horizontal<uint8_t>)
// Although this may not be needed if / when we port all this code to use
// Vec.h since this would potentially give us another fall-back implem
const int16_t* kk = (int16_t*)(horiz_indices_weights[3].data_ptr<double>());
auto xout = unpacked_output.size(2);
auto yout = unpacked_output.size(1);
auto xin = unpacked_input.size(2);
TORCH_INTERNAL_ASSERT(num_channels == unpacked_input.size(0));
const int64_t* idx_ptr_xmin = horiz_indices_weights[0].data_ptr<int64_t>();
const int64_t* idx_ptr_size = horiz_indices_weights[1].data_ptr<int64_t>();
uint8_t* unpacked_output_p = unpacked_output.data_ptr<uint8_t>();
const uint8_t* unpacked_input_p = unpacked_input.data_ptr<uint8_t>();
int64_t yy = 0;
auto xout_stride = xout * num_channels;
auto xin_stride = xin * num_channels;
for (; yy < yout - 3; yy += 4) {
ImagingResampleHorizontalConvolution8u4x(
unpacked_output_p + yy * xout_stride,
unpacked_output_p + (yy + 1) * xout_stride,
unpacked_output_p + (yy + 2) * xout_stride,
unpacked_output_p + (yy + 3) * xout_stride,
xout,
unpacked_input_p + yy * xin_stride,
unpacked_input_p + (yy + 1) * xin_stride,
unpacked_input_p + (yy + 2) * xin_stride,
unpacked_input_p + (yy + 3) * xin_stride,
xin,
idx_ptr_xmin,
idx_ptr_size,
kk,
ksize,
horiz_weights_precision,
num_channels,
yy + 3 == yout - 1);
}
for (; yy < yout; yy++) {
ImagingResampleHorizontalConvolution8u(
unpacked_output_p + yy * xout_stride,
xout,
unpacked_input_p + yy * xin_stride,
xin,
idx_ptr_xmin,
idx_ptr_size,
kk,
ksize,
horiz_weights_precision,
num_channels,
yy == yout - 1);
}
}
void ImagingResampleVertical(
const at::Tensor & unpacked_output,
const at::Tensor & unpacked_input,
int ksize,
const std::vector<at::Tensor>& vert_indices_weights,
unsigned int vert_weights_precision) {
// Interpolation vertical pass: we compute y-axis interpolation outputs.
// Input data is stored as
// input = [r[0], g[0], b[0], a[0], r[1], g[1], b[1], a[1], r[2], g[2], b[2], a[2], ...]
// Weights are float values computed for each output pixel and rescaled to uint16:
// weights[i] = [w[i, 0], w[i, 1], ..., w[i, K-1]]
// We want to compute the output as following:
// output = [oR[0], oG[0], oB[0], oA[0], oR[1], oG[1], oB[1], oA[1], ...]
// where
// oR[xoffset + i] = r[xoffset + ymin[i]] * w[i, 0] + ... + r[xoffset + ymin[i] + (K-1) * xsize] * w[i, K-1]
// oG[xoffset + i] = g[xoffset + ymin[i]] * w[i, 0] + ... + g[xoffset + ymin[i] + (K-1) * xsize] * w[i, K-1]
// oB[xoffset + i] = b[xoffset + ymin[i]] * w[i, 0] + ... + b[xoffset + ymin[i] + (K-1) * xsize] * w[i, K-1]
// TODO: we may want to merge that into the fallback code (currently called
// basic_loop_aa_vertical<uint8_t>)
// Although this may not be needed if / when we port all this code to use
// Vec.h since this would potentially give us another fall-back implem
const int16_t* kk = (int16_t*)(vert_indices_weights[3].data_ptr<double>());
const int64_t* idx_ptr_xmin = vert_indices_weights[0].data_ptr<int64_t>();
const int64_t* idx_ptr_size = vert_indices_weights[1].data_ptr<int64_t>();
uint8_t* unpacked_output_p = unpacked_output.data_ptr<uint8_t>();
const uint8_t* unpacked_input_p = unpacked_input.data_ptr<uint8_t>();
auto xout = unpacked_output.size(2);
auto yout = unpacked_output.size(1);
const auto num_channels = unpacked_input.size(0);
TORCH_INTERNAL_ASSERT(num_channels == unpacked_output.size(0));
auto xout_stride = xout * num_channels;
for (const auto yy : c10::irange(yout)) {
const auto* k = &kk[yy * ksize];
auto ids_min = idx_ptr_xmin[yy];
auto ids_size = idx_ptr_size[yy];
ImagingResampleVerticalConvolution8u(
unpacked_output_p + yy * xout_stride,
unpacked_input_p,
xout,
ids_min,
ids_size,
k,
vert_weights_precision,
num_channels);
}
}
// This is the only public entry point in this file. It supports bilinear or bicubic
// mode for uint8 dtype when C <= 4, with or without antialias. The
// implem is based on PIL-SIMD.
// Its equivalent implementation (fallback) for when AVX isn't supported or when
// C > 4 is separable_upsample_generic_Nd_kernel_impl() There are a bunch of
// future improvement that can be done: look for the TODOs in this file.
// For details on how the weights are computed and how the multiplications are
// run on int (instead of float weights), see
// [ Weights computation for uint8_t and multiplication trick ]
// For details on how the AVX kernels are implemented, see
// https://gist.github.com/NicolasHug/47c97d731f05eaad5694c173849b86f5
// See also [ Support for antialias=False as a subcase of antilias=True ] to
// learn more about how the antialias=False case is computed. The same holds
// here: all these kernels are general enough to handle an arbitrary number of
// weights, but when aa=False they could be optimized further.
template <typename scale_type, class F>
void upsample_avx_bilinear_bicubic_uint8(
const at::Tensor& input_,
const at::Tensor& output,
bool align_corners,
const scale_type& scales,
bool antialias) {
auto batch_size = input_.size(0);
auto num_channels = input_.size(1);
auto xin = input_.size(3);
auto yin = input_.size(2);
auto xout = output.size(3);
auto yout = output.size(2);
if (xin == xout && yin == yout) {
output.copy_(input_);
return;
}
at::Tensor input = input_;
if (!(input.is_contiguous() || input.is_contiguous(at::MemoryFormat::ChannelsLast))) {
// If input is not contiguous with memory format channels first or channels last,
// we explicitly convert the input to contiguous channels last memory format.
// This simplifies the rest of the code and let us assume that the format is only contiguous channels first or channels last,
// Most tensors going through this `if` block won't need to go through unpacking, but those having C < 3 may
// have to (this means 2 copies are made). We could avoid the extra copy by handling non-contiguous input
// directly within unpack_rgb() and pack_rgb(), but initial attempts showed that this is fairly complex.
input = input.contiguous(at::MemoryFormat::ChannelsLast);
}
auto need_horizontal = xout != xin;
auto need_vertical = yout != yin;
int ksize_horiz, ksize_vert;
std::vector<at::Tensor> horiz_indices_weights, vert_indices_weights;
unsigned int horiz_weights_precision, vert_weights_precision;
bool skip_unpacking = (num_channels == 3 || num_channels == 4) && input.is_contiguous(at::MemoryFormat::ChannelsLast);
bool skip_packing = (num_channels == 3 || num_channels == 4) && output.is_contiguous(at::MemoryFormat::ChannelsLast);
if (need_horizontal) {
int interp_dim = 3;
auto stride = (skip_unpacking) ? num_channels : 4;
std::tie(horiz_indices_weights, ksize_horiz, horiz_weights_precision) =
F::compute_indices_int16_weights_aa(
/*input_size=*/xin,
/*output_size=*/xout,
/*stride=*/stride,
/*ndims=*/4,
/*reshape_dim=*/interp_dim,
/*align_corners=*/align_corners,
/*opt_scale=*/scales[interp_dim - 2],
/*antialias=*/antialias,
/*align_i32=*/true);
}
if (need_vertical) {
int interp_dim = 2;
auto stride = (skip_unpacking) ? num_channels * xout : 4 * xout;
std::tie(vert_indices_weights, ksize_vert, vert_weights_precision) =
F::compute_indices_int16_weights_aa(
/*input_size=*/yin,
/*output_size=*/yout,
/*stride=*/stride,
/*ndims=*/4,
/*reshape_dim=*/interp_dim,
/*align_corners=*/align_corners,
/*opt_scale=*/scales[interp_dim - 2],
/*antialias=*/antialias,
/*align_i32=*/true);
}
at::Tensor buffer_horiz, buffer_vert;
// Minor optimization: we can avoid allocating an extra buffer if we're performing
// horizontal-only or vertical-only interpolation, and if the tensor doesn't
// need repacking
if (need_horizontal && (need_vertical || !skip_packing)) {
auto c = (skip_unpacking) ? num_channels : 4;
buffer_horiz = at::empty({c, yin, xout}, input.options());
}
if (need_vertical && !skip_packing) {
auto c = (skip_unpacking) ? num_channels : 4;
buffer_vert = at::empty({c, yout, xout}, input.options());
}
for (const auto i : c10::irange(batch_size)) {
at::Tensor unpacked_input = (skip_unpacking) ? input[i] : unpack_rgb(input[i]);
at::Tensor unpacked_output;
if (need_horizontal) {
at::Tensor unpacked_output_temp = (need_vertical || !skip_packing) ? buffer_horiz : output[i];
if (skip_unpacking && num_channels == 3) {
ImagingResampleHorizontal<3>(
unpacked_output_temp,
unpacked_input,
ksize_horiz,
horiz_indices_weights,
horiz_weights_precision);
} else {
ImagingResampleHorizontal<4>(
unpacked_output_temp,
unpacked_input,
ksize_horiz,
horiz_indices_weights,
horiz_weights_precision);
}
unpacked_output = unpacked_input = unpacked_output_temp;
}
if (need_vertical) {
unpacked_output = (skip_packing) ? output[i] : buffer_vert;
ImagingResampleVertical(
unpacked_output,
unpacked_input,
ksize_vert,
vert_indices_weights,
vert_weights_precision
);
}
TORCH_INTERNAL_ASSERT(unpacked_output.defined());
if (!skip_packing) {
pack_rgb(unpacked_output, output[i]);
}
}
}
void ImagingResampleHorizontalConvolution8u4x(
uint8_t* C10_RESTRICT lineOut0,
uint8_t* C10_RESTRICT lineOut1,
uint8_t* C10_RESTRICT lineOut2,
uint8_t* C10_RESTRICT lineOut3,
int64_t out_xsize,
const uint8_t* C10_RESTRICT lineIn0,
const uint8_t* C10_RESTRICT lineIn1,
const uint8_t* C10_RESTRICT lineIn2,
const uint8_t* C10_RESTRICT lineIn3,
int64_t in_xsize,
const int64_t* idx_ptr_xmin,
const int64_t* idx_ptr_size,
const int16_t* kk,
int kmax,
unsigned int coefs_precision,
int64_t num_channels,
bool is_last_line) {
// Interpolation horizontal pass processing together 4 vertical lines.
// - Input data format is RGBA or RGB with R,G,B,A being uint8. In case of RGBA
// we can encode 4 values as a single uint32 value.
// - We split the size of weight vector for a given output index as a sum:
// ids_size = num_blocks_4 * 4 + num_blocks_2 * 2 + num_blocks_1.
// - We load and process 4 weights values in a loop ("block 4") then we process 2 weights values
// in another loop ("block 2") and finally we process 1 weights value in the final loop ("block 1").
// Define shuffling masks (low/high) for num_channels 4 and 3
// Mask low casts lower half of each lane to epi16 and reorder RGBARGBA -> RRGGBBAA:
// [r1 g1 b1 a1 r2 g2 b2 a2 ... | R1 G1 B1 A1 R2 G2 B2 A2 ... ] ->
// [r1 0 r2 0 g1 0 g2 0 b1 0 b2 0 a1 0 a2 0 | R1 0 R2 0 G1 0 G2 0 B1 0 B2 0 A1 0 A2 0]
// Mask high casts upper half of each lane to epi16 and reorder RGBARGBA -> RRGGBBAA::
// [ ... r3 g3 b3 a3 r4 g4 b4 a4 | ... R3 G3 B3 A3 R4 G4 B4 A4 ] ->
// [r3 0 r4 0 g3 0 g4 0 b3 0 b4 0 a3 0 a4 0 | R3 0 R4 0 G3 0 G4 0 B3 0 B4 0 A3 0 A4 0]
const auto mask_low_c4 = _mm256_set_epi8(
-1, 7, -1, 3, -1, 6, -1, 2, -1, 5, -1, 1, -1, 4, -1, 0,
-1, 7, -1, 3, -1, 6, -1, 2, -1, 5, -1, 1, -1, 4, -1, 0);
const auto mask_high_c4 = _mm256_set_epi8(
-1, 15, -1, 11, -1, 14, -1, 10, -1, 13, -1, 9, -1, 12, -1, 8,
-1, 15, -1, 11, -1, 14, -1, 10, -1, 13, -1, 9, -1, 12, -1, 8);
const auto mask_low_c3 = _mm256_set_epi8(
-1, -1, -1, -1, -1, 5, -1, 2, -1, 4, -1, 1, -1, 3, -1, 0,
-1, -1, -1, -1, -1, 5, -1, 2, -1, 4, -1, 1, -1, 3, -1, 0);
const auto mask_high_c3 = _mm256_set_epi8(
-1, -1, -1, -1, -1, 11, -1, 8, -1, 10, -1, 7, -1, 9, -1, 6,
-1, -1, -1, -1, -1, 11, -1, 8, -1, 10, -1, 7, -1, 9, -1, 6);
const auto mask_low = (num_channels == 3) ? mask_low_c3 : mask_low_c4;
const auto mask_high = (num_channels == 3) ? mask_high_c3 : mask_high_c4;
const auto stride = num_channels * sizeof(uint8_t);
TORCH_INTERNAL_ASSERT(stride == 3 || stride == 4);
// out_xsize = output width, out_x = output x index
// ids_min is the input offset index corresponding to out_x
// ids_size is the interpolation size for out_x
// Let's precompute ids_size limits for block 4 and block 2.
//
// In block 4 (4 means we process 4 weight values together), we read input data
// with _mm_loadu_si128, i.e. 16 bytes, per one line:
// lineIn0 + stride * (i + ids_min) + 16 <= lineIn0 + stride * (ids_size + ids_min)
// --> i <= ids_size - 16.0 / stride
// Strict boundary:
// --> i < ids_size + 1 - int(ceil(16.0 / stride)) = ids_size - b4_delta
// Soft boundary for reading inside the buffer except its boundaries:
// --> i < ids_size + 1 - int(16.0 / stride) = ids_size - b4_delta_soft
// RGBA: b4_delta = b4_delta_soft = 3
// RGB : b4_delta = 5
// RGB : b4_delta_soft = 4
const auto b4_delta = (stride == 4) ? 3 : ((is_last_line) ? 5 : 4);
// In block 2 (2 means we process 2 weights values together), we read input data
// with _mm_loadl_epi64, i.e. 8 bytes, per one line:
// lineIn0 + stride * (i + ids_min) + 8 <= lineIn0 + stride * (ids_size + ids_min)
// --> i <= ids_size - 8.0 / stride
// Strict boundary:
// --> i < ids_size + 1 - int(ceil(8.0 / stride)) = ids_size - b2_delta
// Soft boundary for reading inside the buffer except its boundaries:
// --> i < ids_size + 1 - int(8.0 / stride) = ids_size - b2_delta_soft
// RGBA: b2_delta = b2_delta_soft = 1
// RGB : b2_delta = 2
// RGB : b2_delta_soft = 1
const auto b2_delta = (stride == 4) ? 1 : ((is_last_line) ? 2 : 1);
const auto max_out_x_strided = out_xsize * stride;
const auto max_in_x_strided = in_xsize * stride;
const auto zero = _mm256_setzero_si256();
const auto initial = _mm256_set1_epi32(1 << (coefs_precision - 1));
for (const auto out_x : c10::irange(out_xsize)) {
const auto ids_min = idx_ptr_xmin[out_x];
const auto ids_size = idx_ptr_size[out_x];
const auto * k = &kk[out_x * kmax];
int64_t i = 0;
auto sss0 = initial;
auto sss1 = initial;
const auto * lineIn0_min = lineIn0 + ids_min;
const auto * lineIn1_min = lineIn1 + ids_min;
const auto * lineIn2_min = lineIn2 + ids_min;
const auto * lineIn3_min = lineIn3 + ids_min;
// block 4
for (; i < ids_size - b4_delta; i += 4) {
// Load 4 values from weight vector
// mmk0 = [wl_0 wh_0 wl_1 wh_1 wl_0 wh_0 wl_1 wh_1 ...]
// mmk1 = [wl_2 wh_2 wl_3 wh_3 wl_2 wh_2 wl_3 wh_3 ...]
const auto mmk0 = _mm256_set1_epi32(*(int32_t*)&k[i]);
const auto mmk1 = _mm256_set1_epi32(*(int32_t*)&k[i + 2]);
// RGBA: Load 8 pixels (4 per line) from input lines 0 and 1:
// source = [
// r0 g0 b0 a0 r1 g1 b1 a1 r2 g2 b2 a2 r3 g3 b3 a3
// R0 G0 B0 A0 R1 G1 B1 A1 R2 G2 B2 A2 R3 G3 B3 A3
// ]
// RGB: Load 10 pixels (5 per line)
// source = [
// r0 g0 b0 r1 g1 b1 r2 g2 b2 r3 g3 b3 r4 g4 b4 r5
// R0 G0 B0 R1 G1 B1 R2 G2 B2 R3 G3 B3 R4 G4 B4 R5
// ]
auto source = _mm256_inserti128_si256(_mm256_castsi128_si256(
_mm_loadu_si128((__m128i *) (lineIn0_min + stride * i))),
_mm_loadu_si128((__m128i *) (lineIn1_min + stride * i)), 1);
// Apply mask_low:
// RGBA:
// [r0 0 r1 0 g0 0 g1 0 b0 0 b1 0 a0 0 a1 0 | R0 0 R1 0 G0 0 G1 0 B0 0 B1 0 A0 0 A1 0]
// RGB:
// [r0 0 r1 0 g0 0 g1 0 b0 0 b1 0 0 0 0 0 | R0 0 R1 0 G0 0 G1 0 B0 0 B1 0 0 0 0 0]
auto pix1 = _mm256_shuffle_epi8(source, mask_low);
// Compute output value as C += w0 * C0 + w1 * C1 for each channel in 32-bit precision
sss0 = _mm256_add_epi32(sss0, _mm256_madd_epi16(pix1, mmk0));
// Apply mask_high:
// RGBA:
// [r2 0 r3 0 g2 0 g3 0 b2 0 b3 0 a2 0 a3 0 | R2 0 R3 0 G2 0 G3 0 B2 0 B3 0 A2 0 A3 0]
// RGB:
// [r2 0 r3 0 g2 0 g3 0 b2 0 b3 0 0 0 0 0 | R2 0 R3 0 G2 0 G3 0 B2 0 B3 0 0 0 0 0]
auto pix2 = _mm256_shuffle_epi8(source, mask_high);
// Compute output value as C += w2 * C2 + w3 * C3 for each channel in 32-bit precision
sss0 = _mm256_add_epi32(sss0, _mm256_madd_epi16(pix2, mmk1));
// Same as above to next lines 2 and 3:
auto source2 = _mm256_inserti128_si256(_mm256_castsi128_si256(
_mm_loadu_si128((__m128i *) (lineIn2_min + stride * i))),
_mm_loadu_si128((__m128i *) (lineIn3_min + stride * i)), 1);
auto pix3 = _mm256_shuffle_epi8(source2, mask_low);
sss1 = _mm256_add_epi32(sss1, _mm256_madd_epi16(pix3, mmk0));
auto pix4 = _mm256_shuffle_epi8(source2, mask_high);
sss1 = _mm256_add_epi32(sss1, _mm256_madd_epi16(pix4, mmk1));
}
// block 2
for (; i < ids_size - b2_delta; i += 2) {
// Load 2 values from weight vector
// mmk = [wl_0 wh_0 wl_1 wh_1 wl_0 wh_0 wl_1 wh_1 ...]
const auto mmk = _mm256_set1_epi32(*(int32_t*)&k[i]);
// Load 4 pixels (2 per line) from input lines 0 and 1:
// RGBA: source1 = [
// r0 g0 b0 a0 r1 g1 b1 a1 0 0 0 0 0 0 0 0
// R0 G0 B0 A0 R1 G1 B1 A1 0 0 0 0 0 0 0 0
// ]
// RGB: source1 = [
// r0 g0 b0 r1 g1 b1 r2 0 0 0 0 0 0 0 0
// R0 G0 B0 R1 G1 B1 R2 0 0 0 0 0 0 0 0
// ]
auto source1 = _mm256_inserti128_si256(_mm256_castsi128_si256(
_mm_loadl_epi64((__m128i *) (lineIn0_min + stride * i))),
_mm_loadl_epi64((__m128i *) (lineIn1_min + stride * i)), 1);
// Apply mask_low:
// RGBA:
// [r0 0 r1 0 g0 0 g1 0 b0 0 b1 0 a0 0 a1 0 | R0 0 R1 0 G0 0 G1 0 B0 0 B1 0 A0 0 A1 0]
// RGB:
// [r0 0 r1 0 g0 0 g1 0 b0 0 b1 0 0 0 0 0 | R0 0 R1 0 G0 0 G1 0 B0 0 B1 0 0 0 0 0]
auto pix1 = _mm256_shuffle_epi8(source1, mask_low);
// Compute output value as C += w0 * C0 + w1 * C1 for each channel in 32-bit precision
sss0 = _mm256_add_epi32(sss0, _mm256_madd_epi16(pix1, mmk));
// Same as above for lines 2 and 3:
auto source2 = _mm256_inserti128_si256(_mm256_castsi128_si256(
_mm_loadl_epi64((__m128i *) (lineIn2_min + stride * i))),
_mm_loadl_epi64((__m128i *) (lineIn3_min + stride * i)), 1);
auto pix2 = _mm256_shuffle_epi8(source2, mask_low);
sss1 = _mm256_add_epi32(sss1, _mm256_madd_epi16(pix2, mmk));
}
// block 1
const auto i32_aligned = num_channels == 4;
for (; i < ids_size - 1; i++) {
// Load 1 value from weight vector
// mmk = [wl_0 wh_0 0 0 wl_0 wh_0 0 0 ...]
const auto mmk = _mm256_set1_epi32(k[i]);
// Load 2 pixels (one per line) from input lines 0 and 1:
// RGBA: pix1 = [
// r0 0 0 0 g0 0 0 0 b0 0 0 0 a0 0 0 0
// R0 0 0 0 G0 0 0 0 B0 0 0 0 A0 0 0 0
// ]
// RGB: pix1 = [
// r0 0 0 0 g0 0 0 0 b0 0 0 0 r1 0 0 0
// R0 0 0 0 G0 0 0 0 B0 0 0 0 R1 0 0 0
// ]
auto pix1 = _mm256_inserti128_si256(_mm256_castsi128_si256(
mm_cvtepu8_epi32(lineIn0_min + stride * i, i32_aligned)),
mm_cvtepu8_epi32(lineIn1_min + stride * i, i32_aligned), 1);
// Compute output value as C += w0 * C0 for each channel in 32-bit precision
sss0 = _mm256_add_epi32(sss0, _mm256_madd_epi16(pix1, mmk));
// Same as above for lines 2 and 3
auto pix2 = _mm256_inserti128_si256(_mm256_castsi128_si256(
mm_cvtepu8_epi32(lineIn2_min + stride * i, i32_aligned)),
mm_cvtepu8_epi32(lineIn3_min + stride * i, i32_aligned), 1);
sss1 = _mm256_add_epi32(sss1, _mm256_madd_epi16(pix2, mmk));
}
if (i == ids_size - 1) {
// last element
auto mmk = _mm256_set1_epi32(k[i]);
// For num_channels == 3 (3 bytes = one pixel) we tolerate to read 4 bytes
// lines 0, 1 and 2 wont go out of allocated memory bounds
auto pix = _mm256_inserti128_si256(_mm256_castsi128_si256(
mm_cvtepu8_epi32(lineIn0_min + stride * i, i32_aligned)),
mm_cvtepu8_epi32(lineIn1_min + stride * i, i32_aligned), 1);
sss0 = _mm256_add_epi32(sss0, _mm256_madd_epi16(pix, mmk));
auto p0 = mm_cvtepu8_epi32(lineIn2_min + stride * i, i32_aligned);
__m128i p1;
if (num_channels == 3 && C10_UNLIKELY(is_last_line && ids_min + stride * i + 4 >= max_in_x_strided)) {
uint8_t input[4];
std::memcpy(input, lineIn3_min + stride * i, 3);
p1 = mm_cvtepu8_epi32(input, true);
} else {
p1 = mm_cvtepu8_epi32(lineIn3_min + stride * i, i32_aligned);
}
auto pix2 = _mm256_inserti128_si256(_mm256_castsi128_si256(p0), p1, 1);
sss1 = _mm256_add_epi32(sss1, _mm256_madd_epi16(pix2, mmk));
}
// Convert fixed point values back to integers (truncating)
sss0 = _mm256_srai_epi32(sss0, coefs_precision);
sss1 = _mm256_srai_epi32(sss1, coefs_precision);
// Convert packed signed 32-bit integers to packed 16-bit integers using signed saturation
// (a a a a b b b b c c c c d d d d) -> (a a b b c c d d 0 0 0 0 0 0 0 0)
sss0 = _mm256_packs_epi32(sss0, zero);
sss1 = _mm256_packs_epi32(sss1, zero);
// Convert packed signed 16-bit integers to packed 8-bit integers using unsigned saturation
// (a a b b c c d d) -> (a b c d 0 0 0 0)
sss0 = _mm256_packus_epi16(sss0, zero);
sss1 = _mm256_packus_epi16(sss1, zero);
// Write the output into single uint32
// (a b c d) -> x_uint32
auto o0 = _mm_cvtsi128_si32(_mm256_castsi256_si128(sss0));
auto o1 = _mm_cvtsi128_si32(_mm256_extracti128_si256(sss0, 1));
auto o2 = _mm_cvtsi128_si32(_mm256_castsi256_si128(sss1));
auto o3 = _mm_cvtsi128_si32(_mm256_extracti128_si256(sss1, 1));
const auto out_x_strided = stride * out_x;
if (num_channels == 3 && C10_UNLIKELY(out_x_strided + 4 >= max_out_x_strided)) {
// Memcpy 4-bytes is faster than 3-bytes and this is a boundary case when we want to write
// 4 bytes (R G B | X) to the output buffer (X1 X2 X3 | R1).
// The 4th byte in the register (X) has a garbage value and 4th byte in the output buffer (R1) has a correct
// value which was preveiously computed by another line. In other words, it means that we can not overwrite
// it by simply writing 4 bytes from the register to the output. We'll do the following:
// v----------|
// Output = [... X1 X2 X3 | R1 G1 B1 R2 ...]
// First, we write R1 value to the 4th byte of (R G B | X) -> (R G B | R1)
// Second, we write 4 bytes from the register to the output: (X1 X2 X3 | R1) -> (R G B | R1)
// Output = [... R G B | R1 G1 B1 R2 ...]
_write_endline_rgb_as_uint32(lineOut0 + out_x_strided, o0);
_write_endline_rgb_as_uint32(lineOut1 + out_x_strided, o1);
_write_endline_rgb_as_uint32(lineOut2 + out_x_strided, o2);
if (C10_UNLIKELY(is_last_line)) {
// When we handle the last line, we can not access the next 4 bytes
// as they are out of memory bounds.
std::memcpy(lineOut3 + out_x_strided, (uint8_t *) &o3, num_channels);
} else {
_write_endline_rgb_as_uint32(lineOut3 + out_x_strided, o3);
}
} else if (num_channels == 3) {
// Memcpy 4-bytes is faster than 3-bytes and here
// we simply write 4 bytes (... R G B X 0 0 0 0 0 ...) where X is a garbage value
// that we will overwrite on the next iteration: (... R G B R G B X 0 0 ...)
std::memcpy(lineOut0 + out_x_strided, (uint8_t *) &o0, 4);
std::memcpy(lineOut1 + out_x_strided, (uint8_t *) &o1, 4);
std::memcpy(lineOut2 + out_x_strided, (uint8_t *) &o2, 4);
std::memcpy(lineOut3 + out_x_strided, (uint8_t *) &o3, 4);
} else {
// num_channels = 4 -> lineOutX + out_x_strided should be uint32 aligned
*(uint32_t *)(lineOut0 + out_x_strided) = o0;
*(uint32_t *)(lineOut1 + out_x_strided) = o1;
*(uint32_t *)(lineOut2 + out_x_strided) = o2;
*(uint32_t *)(lineOut3 + out_x_strided) = o3;
}
}
}
void ImagingResampleHorizontalConvolution8u(
uint8_t* C10_RESTRICT lineOut,
int64_t out_xsize,
const uint8_t* C10_RESTRICT lineIn,
int64_t in_xsize,
const int64_t* idx_ptr_xmin,
const int64_t* idx_ptr_size,
const int16_t* kk,
int kmax,
unsigned int coefs_precision,
int64_t num_channels,
bool is_last_line) {
// Interpolation horizontal pass processing only one vertical line.
// - Input data format is RGBA or RGB with R,G,B,A being uint8. In case of RGBA
// we can encode 4 values as a single uint32 value.
// - We split the size of weight vector for a given output index as a sum:
// ids_size = num_blocks_8 * 8 + num_blocks_4 * 4 + num_blocks_2 * 2 + num_blocks_1
// - We load and process 8 weights values in a loop ("block 8") then 4 weights and 2 weights values in
// in another loops ("block 4" and "block 2") and finally we process 1 weight value in the final loop ("block 1").
// Define various shuffling masks
const auto kmask_low = _mm256_set_epi8(
11, 10, 9, 8, 11, 10, 9, 8, 11, 10, 9, 8, 11, 10, 9, 8,
3, 2, 1, 0, 3, 2, 1, 0, 3, 2, 1, 0, 3, 2, 1, 0);
const auto kmask_high = _mm256_set_epi8(
15, 14, 13, 12, 15, 14, 13, 12, 15, 14, 13, 12, 15, 14, 13, 12,
7, 6, 5, 4, 7, 6, 5, 4, 7, 6, 5, 4, 7, 6, 5, 4);
const auto kmask_hl = _mm256_set_epi8(
7, 6, 5, 4, 7, 6, 5, 4, 7, 6, 5, 4, 7, 6, 5, 4,
3, 2, 1, 0, 3, 2, 1, 0, 3, 2, 1, 0, 3, 2, 1, 0);
const auto mask_low_c4 = _mm256_set_epi8(
-1, 7, -1, 3, -1, 6, -1, 2, -1, 5, -1, 1, -1, 4, -1, 0,
-1, 7, -1, 3, -1, 6, -1, 2, -1, 5, -1, 1, -1, 4, -1, 0);
const auto mask_high_c4 = _mm256_set_epi8(
-1, 15, -1, 11, -1, 14, -1, 10, -1, 13, -1, 9, -1, 12, -1, 8,
-1, 15, -1, 11, -1, 14, -1, 10, -1, 13, -1, 9, -1, 12, -1, 8);
const auto mask_low_c3 = _mm256_set_epi8(
-1, -1, -1, -1, -1, 5, -1, 2, -1, 4, -1, 1, -1, 3, -1, 0,
-1, -1, -1, -1, -1, 5, -1, 2, -1, 4, -1, 1, -1, 3, -1, 0);
const auto mask_high_c3 = _mm256_set_epi8(
-1, -1, -1, -1, -1, 11, -1, 8, -1, 10, -1, 7, -1, 9, -1, 6,
-1, -1, -1, -1, -1, 11, -1, 8, -1, 10, -1, 7, -1, 9, -1, 6);
const auto mask_hl_c3 = _mm256_set_epi8(
-1, -1, -1, -1, -1, 11, -1, 8, -1, 10, -1, 7, -1, 9, -1, 6,
-1, -1, -1, -1, -1, 5, -1, 2, -1, 4, -1, 1, -1, 3, -1, 0);
const auto mask_hl_c4 = _mm256_set_epi8(
-1, 15, -1, 11, -1, 14, -1, 10, -1, 13, -1, 9, -1, 12, -1, 8,
-1, 7, -1, 3, -1, 6, -1, 2, -1, 5, -1, 1, -1, 4, -1, 0);
const auto mask_low128_c3 = _mm_set_epi8(
-1, -1, -1, -1, -1, 5, -1, 2, -1, 4, -1, 1, -1, 3, -1, 0);
const auto mask_low128_c4 = _mm_set_epi8(
-1, 7, -1, 3, -1, 6, -1, 2, -1, 5, -1, 1, -1, 4, -1, 0);
const auto mask_low = (num_channels == 3) ? mask_low_c3 : mask_low_c4;
const auto mask_high = (num_channels == 3) ? mask_high_c3 : mask_high_c4;
const auto mask_hl = (num_channels == 3) ? mask_hl_c3 : mask_hl_c4;
const auto mask_low128 = (num_channels == 3) ? mask_low128_c3 : mask_low128_c4;
// out_xsize = output width, out_x = output x index
// ids_min is the input offset index corresponding to out_x
// ids_size is the interpolation size for out_x
const auto stride = num_channels * sizeof(uint8_t);
const auto zero = _mm_setzero_si128();
TORCH_INTERNAL_ASSERT(stride == 3 || stride == 4);
// Let's precompute ids_size limits for block 8, block 4 and block 2
//
// In block 8 (8 means we process 8 weight values together), we read at
// most 32 bytes input data (16 + 16 bytes for RGBA and 12 + 16 bytes for RGB)
// lineIn + stride * (i + ids_min) + 32 <= lineIn + stride * (ids_size + ids_min)
// --> i <= ids_size - 32.0 / stride
// Strict boundary:
// --> i < ids_size + 1 - int(ceil(32.0 / stride)) = ids_size - b8_delta
// Soft boundary for reading inside the buffer except its boundaries:
// --> i < ids_size + 1 - int(32.0 / stride) = ids_size - b8_delta_soft
// RGBA: b8_delta = b8_delta_soft = 7
// RGB : b8_delta = 10
// RGB : b8_delta_soft = 9
const auto b8_delta = (stride == 4) ? 7 : ((is_last_line) ? 10 : 9);
// In block 4 (4 means we process 4 weight values together), we read
// 16 bytes of input data.
// lineIn + stride * (i + ids_min) + 16 <= lineIn0 + stride * (ids_size + ids_min)
// --> i <= ids_size - 16.0 / stride
// Strict boundary:
// --> i < ids_size + 1 - int(ceil(16.0 / stride)) = ids_size - b4_delta
// Soft boundary for reading inside the buffer except its boundaries:
// --> i < ids_size + 1 - int(16.0 / stride) = ids_size - b4_delta_soft
// RGBA: b4_delta = b4_delta_soft = 3
// RGB : b4_delta = 5
// RGB : b4_delta_soft = 4
const auto b4_delta = (stride == 4) ? 3 : ((is_last_line) ? 5 : 4);
// In block 2 (2 means we process 2 weight values together), we read
// 8 bytes of input data.
// lineIn0 + stride * (i + ids_min) + 8 <= lineIn0 + stride * (ids_size + ids_min)
// --> i <= ids_size - 8.0 / stride
// Strict boundary:
// --> i < ids_size + 1 - int(ceil(8.0 / stride)) = ids_size - b2_delta
// Soft boundary for reading inside the buffer except its boundaries:
// --> i < ids_size + 1 - int(8.0 / stride) = ids_size - b2_delta_soft
// RGBA: b2_delta = b2_delta_soft = 1
// RGB : b2_delta = 2
// RGB : b2_delta_soft = 1
const auto b2_delta = (stride == 4) ? 1 : ((is_last_line) ? 2 : 1);
const auto max_out_x_strided = out_xsize * stride;
const auto max_in_x_strided = in_xsize * stride;
for (const auto out_x : c10::irange(out_xsize)) {
__m128i sss;
const auto ids_min = idx_ptr_xmin[out_x];
const auto ids_size = idx_ptr_size[out_x];
const auto * k = &kk[out_x * kmax];
int64_t i = 0;
const auto * lineIn_min = lineIn + ids_min;
if (ids_size < 8) {
sss = _mm_set1_epi32(1 << (coefs_precision - 1));
} else {
// Lower part will be added to higher, use only half of the error
auto sss256 = _mm256_set1_epi32(1 << (coefs_precision - 2));
// block 8
for (; i < ids_size - b8_delta; i += 8) {
// Load 8 values from weight vector
auto tmp = _mm_loadu_si128((__m128i*)&k[i]);
// ksource = [
// wl_0 wh_0 wl_1 wh_1 wl_2 wh_2 wl_3 wh_3 wl_4 wh_4 wl_5 wh_5 wl_6 wh_6 wl_7 wh_7
// wl_0 wh_0 wl_1 wh_1 wl_2 wh_2 wl_3 wh_3 wl_4 wh_4 wl_5 wh_5 wl_6 wh_6 wl_7 wh_7
// ]
auto ksource = _mm256_insertf128_si256(_mm256_castsi128_si256(tmp), tmp, 1);
// RGBA: Load 8 pixels from input:
// source = [
// r0 g0 b0 a0 r1 g1 b1 a1 r2 g2 b2 a2 r3 g3 b3 a3
// r4 g4 b4 a4 r5 g5 b5 a5 r6 g6 b6 a6 r7 g7 b7 a7
// ]
// RGB: Load 10 pixels from input (however we can process only 8 pixels):
// source = [
// r0 g0 b0 r1 g1 b1 r2 g2 b2 r3 g3 b3 r4 g4 b4 r5
// r4 g4 b4 r5 g5 b5 r6 g6 b6 r7 g7 b7 r8 g8 b8 r9
// ]
auto source = _mm256_inserti128_si256(_mm256_castsi128_si256(
_mm_loadu_si128((__m128i *) (lineIn_min + stride * i))),
_mm_loadu_si128((__m128i *) (lineIn_min + stride * (i + 4))), 1);
// Extract lower part of each lane, cast to epi16 and reoder RGBARGBA -> RRGGBBAA
// RGBA: pix1 = [
// r0 0 r1 0 g0 0 g1 0 b0 0 b1 0 a0 0 a1 0
// r4 0 r5 0 g4 0 g5 0 b4 0 b5 0 a4 0 a5 0
// ]
// RGB: pix1 = [
// r0 0 r1 0 g0 0 g1 0 b0 0 b1 0 0 0 0 0
// r4 0 r5 0 g4 0 g5 0 b4 0 b5 0 0 0 0 0
// ]
auto pix1 = _mm256_shuffle_epi8(source, mask_low);
// mmk1 = [
// wl_0 wh_0 wl_1 wh_1 wl_0 wh_0 wl_1 wh_1 ... ...
// wl_4 wh_4 wl_5 wh_5 wl_4 wh_4 wl_5 wh_5 ... ...
// ]
auto mmk1 = _mm256_shuffle_epi8(ksource, kmask_low);
// Compute output value as
// C += w0 * C0 + w1 * C1
// C += w4 * C4 + w5 * C5 for each channel in 32-bit precision
sss256 = _mm256_add_epi32(sss256, _mm256_madd_epi16(pix1, mmk1));
// Same as above for higher part of each lane
auto pix2 = _mm256_shuffle_epi8(source, mask_high);
auto mmk2 = _mm256_shuffle_epi8(ksource, kmask_high);
// Compute output value as
// C += w2 * C2 + w3 * C3
// C += w6 * C6 + w7 * C7 for each channel in 32-bit precision
sss256 = _mm256_add_epi32(sss256, _mm256_madd_epi16(pix2, mmk2));
}
// block 4
for (; i < ids_size - b4_delta; i += 4) {
// Load 4 values from weight vector
auto tmp = _mm_loadl_epi64((__m128i *) &k[i]);
// ksource = [
// wl_0 wh_0 wl_1 wh_1 wl_2 wh_2 wl_3 wh_3 0 0 0 0 0 0 0 0
// wl_0 wh_0 wl_1 wh_1 wl_2 wh_2 wl_3 wh_3 0 0 0 0 0 0 0 0
// ]
auto ksource = _mm256_insertf128_si256(_mm256_castsi128_si256(tmp), tmp, 1);
// Load pixels from input line
tmp = _mm_loadu_si128((__m128i *) (lineIn_min + stride * i));
// RGBA: source = [
// r0 g0 b0 a0 r1 g1 b1 a1 r2 g2 b2 a2 r3 g3 b3 a3
// r0 g0 b0 a0 r1 g1 b1 a1 r2 g2 b2 a2 r3 g3 b3 a3
// ]
// RGB: source = [
// r0 g0 b0 r1 g1 b1 r2 g2 b2 r3 g3 b3 r4 g4 b4 r5
// r0 g0 b0 r1 g1 b1 r2 g2 b2 r3 g3 b3 r4 g4 b4 r5
// ]
auto source = _mm256_insertf128_si256(_mm256_castsi128_si256(tmp), tmp, 1);
// Cast source to epi16 and reorder RGBARGBA -> RRGGBBAA
// RGBA: pix = [
// r0 0 r1 0 g0 0 g1 0 b0 0 b1 0 a0 0 a1 0
// r2 0 r3 0 g2 0 g3 0 b2 0 b3 0 a2 0 a3 0
// ]
// RGB: pix = [
// r0 0 r1 0 g0 0 g1 0 b0 0 b1 0 0 0 0 0
// r2 0 r3 0 g2 0 g3 0 b2 0 b3 0 0 0 0 0
// ]
auto pix = _mm256_shuffle_epi8(source, mask_hl);
// mmk = [
// wl_0 wh_0 wl_1 wh_1 wl_0 wh_0 wl_1 wh_1 ... ...
// wl_2 wh_2 wl_3 wh_3 wl_2 wh_2 wl_3 wh_3 ... ...
// ]
auto mmk = _mm256_shuffle_epi8(ksource, kmask_hl);
// Compute output value as
// C += w0 * C0 + w1 * C1
// C += w2 * C2 + w3 * C3 for each channel in 32-bit precision
sss256 = _mm256_add_epi32(sss256, _mm256_madd_epi16(pix, mmk));
}
// Sum results between the lanes
sss = _mm_add_epi32(
_mm256_extracti128_si256(sss256, 0),
_mm256_extracti128_si256(sss256, 1));
}
// block 2
for (; i < ids_size - b2_delta; i += 2) {
// Load 2 values from weight vector
// mmk = [wl_0 wh_0 wl_1 wh_1 wl_0 wh_0 wl_1 wh_1 ...]
auto mmk = _mm_set1_epi32(*(int32_t*)&k[i]);
// Load pixels from input line
// RGBA: source = [
// r0 g0 b0 a0 r1 g1 b1 a1 0 0 0 0 0 0 0 0
// ]
// RGB: source = [
// r0 g0 b0 r1 g1 b1 r2 g2 0 0 0 0 0 0 0 0
// ]
auto source = _mm_loadl_epi64((__m128i *) (lineIn_min + stride * i));
// Cast source to epi16 and reorder RGBARGBA -> RRGGBBAA
auto pix = _mm_shuffle_epi8(source, mask_low128);
// Compute output value as C += w0 * C0 + w1 * C1 for each channel in 32-bit precision
sss = _mm_add_epi32(sss, _mm_madd_epi16(pix, mmk));
}
// block 1
const auto i32_aligned = num_channels == 4;
for (; i < ids_size - 1; i++) {
// Load 1 value from weight vector
// mmk = [wl_0 wh_0 0 0 wl_0 wh_0 0 0 ...]
auto mmk = _mm_set1_epi32(k[i]);
// Load one pixel from input line
// RGBA: pix = [
// r0 0 0 0 g0 0 0 0 b0 0 0 0 a0 0 0 0
// ]
// RGB: pix = [
// r0 0 0 0 g0 0 0 0 b0 0 0 0 r1 0 0 0