-
Notifications
You must be signed in to change notification settings - Fork 631
Expand file tree
/
Copy pathflash_attention.cc
More file actions
770 lines (742 loc) · 34.3 KB
/
flash_attention.cc
File metadata and controls
770 lines (742 loc) · 34.3 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
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
// Copyright 2025 Google LLC
// SPDX-License-Identifier: Apache-2.0
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <stddef.h>
#include <stdint.h>
#include <algorithm>
#include <cmath>
#include <limits>
#include "compression/types.h" // GEMMA_DISABLED_TARGETS
#include "util/threading_context.h"
#include "util/zones.h"
#ifndef HWY_DISABLED_TARGETS
#define HWY_DISABLED_TARGETS GEMMA_DISABLED_TARGETS
#endif // HWY_DISABLED_TARGETS
#include "gemma/activations.h"
#include "gemma/configs.h" // kMaxQKVDim
#include "gemma/gemma.h"
#include "util/threading.h"
#include "hwy/profiler.h"
// Compiles this file for multiple architectures via "foreach_target.h", to
// which we pass the filename via macro 'argument'.
// clang-format off
#undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "gemma/flash_attention.cc" // NOLINT
// clang-format on
#include "hwy/foreach_target.h" // IWYU pragma: keep
#include "hwy/highway.h"
// After highway.h
#include "compression/compress-inl.h"
#include "gemma/attention.h"
#include "ops/matmul-inl.h"
#include "ops/ops-inl.h"
HWY_BEFORE_NAMESPACE();
namespace gcpp {
namespace HWY_NAMESPACE {
static constexpr size_t kNFx8HTileSize = 8;
// Transposes q into q_t.
// Both are 4D tensors stuffed into a 2-D MatPtrT.
// q has shape [batch, qbatch][head, qkv_dim].
// q_t has shape [qkv_dim][qbatch, head, batch] in order to make the maximum
// possible consecutive elements have the same KV.
static void TransposeQ(const MatPtrT<float>& q, MatPtrT<float>& q_t,
const size_t qbatch_size, ThreadingContext& ctx) {
// Group floats by the number of floats in a cache line.
const size_t kNF = ctx.cache_info.LineBytes() / sizeof(float);
const size_t num_heads = q.Cols() / q_t.Rows();
const size_t batch_size = q.Rows() / qbatch_size;
const auto func = [&](const size_t task, size_t worker) HWY_ATTR {
GCPP_ZONE(ctx, worker, Zones::kFlashAttentionTransposeQ);
for (size_t lane = 0; lane < kNF; ++lane) {
size_t q_row = task * kNF + lane;
if (q_row >= q_t.Rows()) break;
float* HWY_RESTRICT qt_row = q_t.Row(q_row);
for (size_t qi = 0; qi < qbatch_size; ++qi) {
for (size_t h = 0; h < num_heads; ++h) {
for (size_t b = 0; b < batch_size; ++b) {
qt_row[(qi * num_heads + h) * batch_size + b] =
q.Row(b * qbatch_size + qi)[h * q_t.Rows() + q_row];
}
}
}
}
};
{
const size_t num_tasks = hwy::DivCeil(q_t.Rows(), kNF);
// Better than kFlat.
ParallelFor(ParallelismStrategy::kHierarchical, num_tasks, ctx,
/*cluster_idx=*/0, Callers::kFlashTransposeQ, func);
}
}
// Updates q in place for RMSNorm and positional encoding.
void RMSNormAndPositionalEncoding(const size_t num_tokens, const QBatch& qbatch,
MatPtrT<float>& q,
const MatPtr& query_norm_scale,
const size_t layer_idx,
const AttentionActivationsPtrs& activations,
ThreadingContext& ctx) {
const LayerConfig& layer_config = activations.config.layer_configs[layer_idx];
const float query_scale = activations.query_scale;
const hwy::Divisor div_qbatch(qbatch.Size());
const auto func = [&](const size_t task, size_t worker) HWY_ATTR {
GCPP_ZONE(ctx, worker, Zones::kFlashAttentionRmsNormAndPositionalEncoding);
size_t qi = div_qbatch.Remainder(task);
size_t batch_idx = div_qbatch.Divide(task);
for (size_t h = 0; h < layer_config.heads; ++h) {
const size_t tq_idx = qbatch.Size() * batch_idx + qi;
// Find the token position in the query and calculate
// the range of cache positions to attend to.
const size_t pos = qbatch.Pos(qi) + batch_idx;
float* HWY_RESTRICT q_row = q.Row(tq_idx) + h * layer_config.qkv_dim;
// Apply rope and scaling to Q.
if (query_norm_scale.HasPtr()) {
CallUpcasted(&query_norm_scale, [&](const auto* weights_t) {
RMSNormInplace(weights_t->PackedScale1(), /*w_ofs=*/0, q_row,
layer_config.qkv_dim, ctx, worker);
});
}
PositionalEncodingQK(q_row, layer_idx, activations, ctx, worker, pos,
query_scale);
}
};
{
// kHierarchical is not worth the extra sync overhead because the tasks are
// very lightweight.
ParallelFor(ParallelismStrategy::kFlat, num_tokens * qbatch.Size(), ctx,
/*cluster_idx=*/0, Callers::kFlashRMSNormAndPositionalEncoding,
func);
}
}
// Handles a single v row of flash attention for a single q.k dot product.
void HWY_INLINE SingleFlashAttentionStep(float x, float cap, float& old_max,
float& old_d,
const float* HWY_RESTRICT v,
const size_t v_cols,
float* HWY_RESTRICT att_out) {
if (cap > 0.0f) {
// Compute tanh(x / cap) * cap, being LogitsSoftCap on the scalar x.
x = cap * std::tanh(x / cap);
}
float m = std::max(x, old_max);
x = std::exp(x - m);
float scale = old_d * std::exp(old_max - m);
old_d = x + scale;
old_max = m;
float one_over_d = 1.0f / old_d;
scale *= one_over_d;
x *= one_over_d;
MulByConst(scale, att_out, v_cols);
MulByConstAndAdd(x, v, att_out, v_cols);
}
// Calculates the complete attention outputs for a single row of q.
void SingleFlashAttention(const size_t start_pos, const size_t last_pos,
const float* HWY_RESTRICT q, const MatPtrT<KV_t>& k,
const MatPtrT<KV_t>& v, const size_t layer_idx,
const AttentionActivationsPtrs& activations,
float* HWY_RESTRICT att_out, ThreadingContext& ctx,
const size_t worker) {
GCPP_ZONE(ctx, worker, Zones::kFlashAttentionSingleFlashAttention);
const size_t pos_mod = activations.div_seq_len.Remainder(start_pos);
float m = Dot(q, k.Row(pos_mod), k.Cols());
if (float cap = activations.config.att_cap; cap > 0.0f) {
// Compute tanh(x / cap) * cap, being LogitsSoftCap on the scalar x.
m = cap * std::tanh(m / cap);
}
float d = 1.0f;
// This is just a copy of the first token.
MulByConstTo(d, v.Row(pos_mod), att_out, v.Cols(), ctx, worker);
for (size_t pos = start_pos + 1; pos <= last_pos; ++pos) {
const size_t pos_mod = activations.div_seq_len.Remainder(pos);
float x = Dot(q, k.Row(pos_mod), k.Cols());
SingleFlashAttentionStep(x, activations.config.att_cap, m, d,
v.Row(pos_mod), v.Cols(), att_out);
}
}
// Computes and returns a single vector of NF Q.K dot products, which represents
// the dot products of NF rows of Q for a single K timestep.
template <class DF, class VF = hn::Vec<DF>>
VF QDotKVector(DF df, const uint32_t* HWY_RESTRICT q_offsets,
const size_t k_pos, const MatPtrT<KV_t>& q,
const MatPtrT<KV_t>& k) {
hn::TFromD<DF> results[hn::MaxLanes(df)];
for (size_t i = 0; i < hn::Lanes(df); ++i) {
results[i] = Dot(q.Row(0) + q_offsets[i], k.Row(k_pos), k.Cols());
}
return hn::LoadU(df, results);
}
// Returns an NF Q rows by 8 K rows tile of Q.K dot products, in single
// precision.
// This is the result of NF rows of Q against 8 K timesteps, with positions
// given by k_pos[0..7]. Q has been transposed so that the NF rows are read in
// consecutive elements, and other columns by adding q_stride.
template <class DF, class VF = hn::Vec<DF>>
void QDotKTileFloat(DF df, const float* HWY_RESTRICT q, const size_t q_stride,
const MatPtrT<KV_t>& k, const size_t* k_pos, VF& sum0,
VF& sum1, VF& sum2, VF& sum3, VF& sum4, VF& sum5, VF& sum6,
VF& sum7) {
constexpr size_t kHTileSize = kNFx8HTileSize;
sum0 = hn::Zero(df);
sum1 = hn::Zero(df);
sum2 = hn::Zero(df);
sum3 = hn::Zero(df);
sum4 = hn::Zero(df);
sum5 = hn::Zero(df);
sum6 = hn::Zero(df);
sum7 = hn::Zero(df);
const float* HWY_RESTRICT k_row[kHTileSize];
for (int i = 0; i < kHTileSize; ++i) {
k_row[i] = k.Row(k_pos[i]);
}
for (size_t i = 0; i < k.Cols(); ++i) {
VF q_vec = hn::Load(df, q);
VF k_0 = hn::Set(df, k_row[0][i]);
sum0 = hn::MulAdd(q_vec, k_0, sum0);
VF k_1 = hn::Set(df, k_row[1][i]);
sum1 = hn::MulAdd(q_vec, k_1, sum1);
VF k_2 = hn::Set(df, k_row[2][i]);
sum2 = hn::MulAdd(q_vec, k_2, sum2);
VF k_3 = hn::Set(df, k_row[3][i]);
sum3 = hn::MulAdd(q_vec, k_3, sum3);
VF k_4 = hn::Set(df, k_row[4][i]);
sum4 = hn::MulAdd(q_vec, k_4, sum4);
VF k_5 = hn::Set(df, k_row[5][i]);
sum5 = hn::MulAdd(q_vec, k_5, sum5);
VF k_6 = hn::Set(df, k_row[6][i]);
sum6 = hn::MulAdd(q_vec, k_6, sum6);
VF k_7 = hn::Set(df, k_row[7][i]);
sum7 = hn::MulAdd(q_vec, k_7, sum7);
q += q_stride;
}
}
// Returns the element-wise maximum of 8 vectors, in a single vector.
template <class DF, class VF = hn::Vec<DF>>
VF HWY_INLINE ElementwiseMaxOf8(DF df, const VF& x0, const VF& x1, const VF& x2,
const VF& x3, const VF& x4, const VF& x5,
const VF& x6, const VF& x7) {
VF m0 = hn::Max(x0, x1);
VF m1 = hn::Max(x2, x3);
VF m2 = hn::Max(x4, x5);
VF m3 = hn::Max(x6, x7);
m0 = hn::Max(m0, m1);
m2 = hn::Max(m2, m3);
return hn::Max(m0, m2);
}
// Returns the element-wise sum of 8 vectors, in a single vector.
template <class DF, class VF = hn::Vec<DF>>
VF HWY_INLINE ElementwiseSumOf8(DF df, const VF& x0, const VF& x1, const VF& x2,
const VF& x3, const VF& x4, const VF& x5,
const VF& x6, const VF& x7) {
VF sum0 = hn::Add(x0, x1);
VF sum1 = hn::Add(x2, x3);
VF sum2 = hn::Add(x4, x5);
VF sum3 = hn::Add(x6, x7);
sum0 = hn::Add(sum0, sum1);
sum2 = hn::Add(sum2, sum3);
return hn::Add(sum0, sum2);
}
// Sweeps a tile of NF Q rows by 8 K timesteps accumulators from start_pos to
// min_last_pos, then sweeps the remaining timesteps in the range (min_last_pos,
// max_last_pos].
void TileFlashAttention(const MatPtrT<float>& q,
const uint32_t* HWY_RESTRICT q_offsets,
const StridedView<float>& qT, const MatPtrT<KV_t>& k,
const size_t start_pos,
const uint32_t* HWY_RESTRICT last_pos,
const size_t min_last_pos, const size_t max_last_pos,
const MatPtrT<KV_t>& v, const size_t layer_idx,
const AttentionActivationsPtrs& activations,
MatPtrT<float>& att_out,
const uint32_t* HWY_RESTRICT out_offsets,
ThreadingContext& ctx, const size_t worker) {
GCPP_ZONE(ctx, worker, Zones::kFlashAttentionTileFlashAttention);
constexpr int kHTileSize = kNFx8HTileSize;
using DF = hn::ScalableTag<float>;
const DF df;
using VF = hn::Vec<DF>;
using DI = hn::ScalableTag<uint32_t>;
const DI di;
using VI = hn::Vec<DI>;
const int kVTileSize = hn::Lanes(df);
for (int i = 0; i < kVTileSize; ++i) {
hwy::ZeroBytes(att_out.Row(0) + out_offsets[i],
v.Cols() * sizeof(att_out.Row(0)[0]));
}
VI lasts = hn::LoadU(di, last_pos);
VF old_m = hn::Set(df, -std::numeric_limits<float>::max() / 2.0f);
VF old_d = hn::Zero(df);
const float* HWY_RESTRICT qT_row = qT.Row(0);
const size_t qT_stride = qT.Stride();
size_t position = start_pos;
while (position + kHTileSize - 1 <= min_last_pos) {
size_t k_pos[kHTileSize];
for (size_t i = 0; i < kHTileSize; ++i) {
k_pos[i] = activations.div_seq_len.Remainder(position + i);
}
VF x0, x1, x2, x3, x4, x5, x6, x7;
QDotKTileFloat(df, qT_row, qT_stride, k, k_pos, x0, x1, x2, x3, x4, x5, x6,
x7);
if (activations.config.att_cap > 0.0f) {
// Compute tanh(x / cap) * cap, being LogitsSoftCap on the tile.
VF cap = hn::Set(df, activations.config.att_cap);
VF one_over_cap = hn::Div(hn::Set(df, 1.0f), cap);
x0 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x0, one_over_cap)));
x1 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x1, one_over_cap)));
x2 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x2, one_over_cap)));
x3 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x3, one_over_cap)));
x4 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x4, one_over_cap)));
x5 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x5, one_over_cap)));
x6 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x6, one_over_cap)));
x7 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x7, one_over_cap)));
}
VF m = ElementwiseMaxOf8(df, x0, x1, x2, x3, x4, x5, x6, x7);
m = hn::Max(old_m, m);
x0 = hn::Exp(df, x0 - m);
x1 = hn::Exp(df, x1 - m);
x2 = hn::Exp(df, x2 - m);
x3 = hn::Exp(df, x3 - m);
x4 = hn::Exp(df, x4 - m);
x5 = hn::Exp(df, x5 - m);
x6 = hn::Exp(df, x6 - m);
x7 = hn::Exp(df, x7 - m);
VF scale = hn::Mul(old_d, hn::Exp(df, old_m - m));
old_d = ElementwiseSumOf8(df, x0, x1, x2, x3, x4, x5, x6, x7);
old_d = hn::Add(scale, old_d);
old_m = m;
VF one_over_d = hn::Div(hn::Set(df, 1.0f), old_d);
scale = hn::Mul(scale, one_over_d);
x0 = hn::Mul(x0, one_over_d);
x1 = hn::Mul(x1, one_over_d);
x2 = hn::Mul(x2, one_over_d);
x3 = hn::Mul(x3, one_over_d);
x4 = hn::Mul(x4, one_over_d);
x5 = hn::Mul(x5, one_over_d);
x6 = hn::Mul(x6, one_over_d);
x7 = hn::Mul(x7, one_over_d);
MulByConstAndAddTile(df, scale, x0, x1, x2, x3, x4, x5, x6, x7, v, k_pos,
att_out.Row(0), out_offsets, v.Cols());
position += kHTileSize;
}
while (position <= max_last_pos) {
size_t k_pos = activations.div_seq_len.Remainder(position);
VF x0 = QDotKVector(df, q_offsets, k_pos, q, k);
if (activations.config.att_cap > 0.0f) {
// Compute tanh(x / cap) * cap, being LogitsSoftCap on the vector.
VF cap = hn::Set(df, activations.config.att_cap);
VF one_over_cap = hn::Div(hn::Set(df, 1.0f), cap);
x0 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x0, one_over_cap)));
}
// Past the last position, x0 doesn't count.
auto mask = hn::Gt(hn::Set(di, position), lasts);
VF causal_offset = hn::MaskedSet(df, RebindMask(df, mask),
std::numeric_limits<float>::max() / 2.0f);
x0 = hn::Sub(x0, causal_offset);
VF m = hn::Max(old_m, x0);
x0 = hn::Exp(df, x0 - m);
VF scale = hn::Mul(old_d, hn::Exp(df, old_m - m));
old_m = m;
old_d = hn::Add(scale, x0);
VF one_over_d = hn::Div(hn::Set(df, 1.0f), old_d);
x0 = hn::Mul(x0, one_over_d);
scale = hn::Mul(scale, one_over_d);
MulByConstAndAddVector(df, scale, x0, v, k_pos, att_out.Row(0), out_offsets,
v.Cols());
++position;
}
}
// Returns an 4 Q rows by NF K tile of Q.K dot products, in single precision.
// This is the result of 4 rows of Q against NF K timesteps, with positions
// given by k_offsets[0..NF].
template <class DF, class VF = hn::Vec<DF>>
void QDotKTilex4(DF df, const float* HWY_RESTRICT q,
const uint32_t* HWY_RESTRICT q_offsets, const MatPtrT<KV_t>& k,
const int32_t* HWY_RESTRICT k_offsets, VF& sum0, VF& sum1,
VF& sum2, VF& sum3) {
sum0 = hn::Zero(df);
sum1 = hn::Zero(df);
sum2 = hn::Zero(df);
sum3 = hn::Zero(df);
const float* HWY_RESTRICT k_base = k.Row(0);
using DI = hn::ScalableTag<int32_t>;
const DI di;
using VI = hn::Vec<DI>;
VI k_offsets_vec = hn::LoadU(di, k_offsets);
for (size_t i = 0; i < k.Cols(); ++i) {
VF k_vec = hn::GatherIndex(df, k_base + i, k_offsets_vec);
VF q_0 = hn::Set(df, q[q_offsets[0] + i]);
sum0 = hn::MulAdd(q_0, k_vec, sum0);
VF q_1 = hn::Set(df, q[q_offsets[1] + i]);
sum1 = hn::MulAdd(q_1, k_vec, sum1);
VF q_2 = hn::Set(df, q[q_offsets[2] + i]);
sum2 = hn::MulAdd(q_2, k_vec, sum2);
VF q_3 = hn::Set(df, q[q_offsets[3] + i]);
sum3 = hn::MulAdd(q_3, k_vec, sum3);
}
}
// Handles NF v rows of flash attention for NF q.k dot products from one q row.
template <class DF, class VF = hn::Vec<DF>>
float HWY_INLINE SingleFlashAttentionRowVector(DF df, VF& x, float& old_max,
float& old_d) {
float m = hn::ReduceMax(df, x);
m = std::max(m, old_max);
x = hn::Exp(df, x - hn::Set(df, m));
float scale = old_d * std::exp(old_max - m);
old_d = hn::ReduceSum(df, x) + scale;
old_max = m;
float one_over_d = 1.0f / old_d;
scale *= one_over_d;
x = hn::Mul(x, hn::Set(df, one_over_d));
return scale;
}
// Sweeps a tile of 4 Q rows by NF K timesteps accumulators from start_pos to
// min_last_pos, then sweeps the remaining timesteps in the range (min_last_pos,
// max_last_pos].
void TileFlashAttention4(const MatPtrT<float>& q,
const uint32_t* HWY_RESTRICT q_offsets,
const MatPtrT<KV_t>& k, const size_t start_pos,
const uint32_t* HWY_RESTRICT last_pos,
const size_t min_last_pos, const size_t max_last_pos,
const MatPtrT<KV_t>& v, const size_t layer_idx,
const AttentionActivationsPtrs& activations,
MatPtrT<float>& att_out,
const uint32_t* HWY_RESTRICT out_offsets,
ThreadingContext& ctx, const size_t worker) {
GCPP_ZONE(ctx, worker, Zones::kFlashAttentionTileFlashAttention4);
using DF = hn::ScalableTag<float>;
const DF df;
using VF = hn::Vec<DF>;
constexpr size_t kMaxNF = hn::MaxLanes(df);
const size_t kHTileSize = hn::Lanes(df);
HWY_DASSERT(kHTileSize <= kMaxNF);
constexpr size_t kVTileSize = 4;
float scales[kVTileSize];
for (size_t i = 0; i < kVTileSize; ++i) {
hwy::ZeroBytes(att_out.Row(0) + out_offsets[i],
v.Cols() * sizeof(att_out.Row(0)[0]));
}
float old_m0 = -std::numeric_limits<float>::max() / 2.0f;
float old_m1 = -std::numeric_limits<float>::max() / 2.0f;
float old_m2 = -std::numeric_limits<float>::max() / 2.0f;
float old_m3 = -std::numeric_limits<float>::max() / 2.0f;
float old_d0 = 0.0f;
float old_d1 = 0.0f;
float old_d2 = 0.0f;
float old_d3 = 0.0f;
size_t position = start_pos;
while (position + kHTileSize - 1 <= min_last_pos) {
int32_t k_offsets[kMaxNF];
size_t v_pos[kMaxNF];
for (size_t i = 0; i < kHTileSize; ++i) {
v_pos[i] = activations.div_seq_len.Remainder(position + i);
k_offsets[i] = k.Row(v_pos[i]) - k.Row(0);
}
VF x0, x1, x2, x3;
QDotKTilex4(df, q.Row(0), q_offsets, k, k_offsets, x0, x1, x2, x3);
if (activations.config.att_cap > 0.0f) {
// Compute tanh(x / cap) * cap, being LogitsSoftCap on the tile.
VF cap = hn::Set(df, activations.config.att_cap);
VF one_over_cap = hn::Div(hn::Set(df, 1.0f), cap);
x0 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x0, one_over_cap)));
x1 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x1, one_over_cap)));
x2 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x2, one_over_cap)));
x3 = hn::Mul(cap, hn::Tanh(df, hn::Mul(x3, one_over_cap)));
}
scales[0] = SingleFlashAttentionRowVector(df, x0, old_m0, old_d0);
scales[1] = SingleFlashAttentionRowVector(df, x1, old_m1, old_d1);
scales[2] = SingleFlashAttentionRowVector(df, x2, old_m2, old_d2);
scales[3] = SingleFlashAttentionRowVector(df, x3, old_m3, old_d3);
MulByConstAndAddTile4(df, scales, x0, x1, x2, x3, v, v_pos, att_out.Row(0),
out_offsets, v.Cols());
position += kHTileSize;
}
while (position <= max_last_pos) {
size_t k_pos = activations.div_seq_len.Remainder(position);
if (position <= last_pos[0]) {
// Past the last position, x0 doesn't count.
float x0 = Dot(q.Row(0) + q_offsets[0], k.Row(k_pos), k.Cols());
SingleFlashAttentionStep(x0, activations.config.att_cap, old_m0, old_d0,
v.Row(k_pos), v.Cols(),
att_out.Row(0) + out_offsets[0]);
}
if (position <= last_pos[1]) {
// Past the last position, x1 doesn't count.
float x1 = Dot(q.Row(0) + q_offsets[1], k.Row(k_pos), k.Cols());
SingleFlashAttentionStep(x1, activations.config.att_cap, old_m1, old_d1,
v.Row(k_pos), v.Cols(),
att_out.Row(0) + out_offsets[1]);
}
if (position <= last_pos[2]) {
// Past the last position, x2 doesn't count.
float x2 = Dot(q.Row(0) + q_offsets[2], k.Row(k_pos), k.Cols());
SingleFlashAttentionStep(x2, activations.config.att_cap, old_m2, old_d2,
v.Row(k_pos), v.Cols(),
att_out.Row(0) + out_offsets[2]);
}
if (position <= last_pos[3]) {
// Past the last position, x3 doesn't count.
float x3 = Dot(q.Row(0) + q_offsets[3], k.Row(k_pos), k.Cols());
SingleFlashAttentionStep(x3, activations.config.att_cap, old_m3, old_d3,
v.Row(k_pos), v.Cols(),
att_out.Row(0) + out_offsets[3]);
}
++position;
}
}
// Rounds n to a number that can be used as the number of Q rows in a tile
// of flash attention.
static size_t RoundToSuitablePowerOf2(size_t n) {
if (n < 4) return 1;
if (n < 8) return 4;
if (n < 16) return 8;
if (n < 32) return 16;
return 32;
}
// The vertical tile size is determined by the ability to use tiling and the
// target_parallelism. In practice the possible tile sizes in order of
// preference for efficiency are kNF, 4, 1, where kNF is likely to be 4 8 or
// 16. The final tile size is chosen to be the largest possible that allows
// for target_parallelism parallel tasks.
size_t GetVTileSize(size_t kNF, size_t num_head_groups, size_t num_tokens,
size_t total_tasks, size_t target_parallelism) {
const size_t kMaxEqualK =
RoundToSuitablePowerOf2(num_head_groups * num_tokens);
const size_t kMinTileSize = (total_tasks / 4 >= target_parallelism) ? 4 : 1;
return (kNF <= kMaxEqualK && total_tasks / kNF >= target_parallelism)
? kNF
: std::min(kMinTileSize, kMaxEqualK);
}
// The nominal aim of attention is to combine 3 inputs Q[L,D], K[L,D], V[L,D]
// into a single output O[L,D].
// Conventional attention first computes A[L,L] = Q . KT
// followed by A = softmax(A) (over invididual rows).
// Then A is multiplied by V to get O[L,D].
// For each row of O, this takes a read of one row of Q L times, all of K,
// 3 write/reads of one row of A, read all of V, and read/write the one row of O
// L times. Ignoring the computation for now, and focusing just on memory,
// the one row of O takes L(4D+3) reads and L(D+3) writes.
// For the whole of Q, this is L^2(4D+3) reads and L^2(D+3) writes.
//
// Flash attention fuses these operations together, and has 3 operating modes:
// 1. NF rows of the result computed using tiles of registers of shape NFx8.
// 2. 4 rows of the result computed using tiles of registers of shape 4xNF.
// 3. One row (of Q and the result) at a time.
// In all cases the intermediate result (Q.KT) is never stored to memory.
// NF is the number of float lanes in a register, being 16 for AVX3. The softmax
// is converted to streaming form using the algorithm from:
// https://courses.cs.washington.edu/courses/cse599m/23sp/notes/flashattn.pdf.
// Q is transposed to Q_T[D,L] to make the dot product computation efficient.
//
// In mode 1:
// QDotKTileFloat computes NF Q rows x 8 K timesteps of Q.K dot products in one
// go, reducing reads of Q by 8 and reads of K by NF. The streaming softmax is
// computed entirely in registers, and a further NF registers to accumulate the
// results of the product of the softmax and V, reduce the number of reads of V
// by NF, and the reads/writes of O by 8.
// The reads are thus reduced to 2DL^2(1/8+1/NF) and writes reduced to DL^2/8,
// which on AVX3 is an overall reduction by about a factor of 10.
// Mode 1 can only be accessed if there is a large Qbatch size, or in multi-turn
// prefill, since in other cases, there is either a single K timestep (prefill)
// or a single num_heads set of Q rows (decode).
//
// In mode 2, the 4 rows of Q are computed against NF K timesteps in a tile,
// reducing the reads of Q by NF, and the reads of K by 4. The softmax and
// accumulation of the result is done in registers, cutting the reads of V by 4.
// The reads/writes of O are reduced by a factor of NF.
// The overall reduction is limited by the need to use gather to load K.
// Transposing K would be possible, but is complicated by the wraparound.
// Mode 2 can be used in all cases when there are at least 4 attention heads,
// but it may be prefereable to use mode 3 when the batch size is small to
// maximise parallelism.
//
// In mode 3, a single row of Q is computed against a single K timestep at a
// time, using SingleFlashAttention. In this case there is no reduction in the
// reads of Q or K, or V, or O, but the reads/writes of the intermediate A are
// still eliminated.
//
// A further complication is that real attention is not as simple as documented
// in the paper and above. There are multiple query heads, differing KV, and
// different sequence lengths, so a lot of the work in FlashAttention is making
// sure that a collection of q rows with the same KV and sequence length are
// grouped together so that mode 1 or 2 can be used, and choosing which of the
// 3 modes to use for best efficiency.
void FlashAttention(const size_t num_tokens, const size_t target_parallelism,
const size_t layer_idx, const MatPtr& query_norm_scale,
AttentionActivationsPtrs& activations, QBatch& qbatch,
ThreadingContext& ctx) {
GCPP_ZONE(ctx, 0, Zones::kFlashAttentionInclusive);
RMSNormAndPositionalEncoding(num_tokens, qbatch, activations.q,
query_norm_scale, layer_idx, activations, ctx);
const hwy::Divisor div_qbatch(qbatch.Size());
const LayerConfig& layer_config = activations.config.layer_configs[layer_idx];
const size_t qkv_dim = layer_config.qkv_dim;
// A "head group" in the context of GQA refers to a collection of query
// heads that share the same key and value heads.
const size_t kHeadGroups = layer_config.heads / layer_config.kv_heads;
const size_t cache_layer_size = layer_config.CacheLayerSize();
const size_t seq_len =
static_cast<size_t>(activations.div_seq_len.GetDivisor());
const size_t token_batch = num_tokens * div_qbatch.GetDivisor();
const size_t total_tasks = token_batch * layer_config.heads;
using DF = hn::ScalableTag<float>;
const DF df;
const size_t kNF = hn::Lanes(df);
constexpr size_t kMaxNF = hn::MaxLanes(df);
HWY_DASSERT(kNF <= kMaxNF);
const size_t kVTileSize = GetVTileSize(kNF, kHeadGroups, num_tokens,
total_tasks, target_parallelism);
// Only transpose Q if we are using tiling.
if (kVTileSize == kNF) {
size_t max_last = 0, min_start = std::numeric_limits<size_t>::max();
for (size_t qi = 0; qi < qbatch.Size(); ++qi) {
size_t pos = qbatch.Pos(qi);
const size_t start = StartPos(pos, activations.config, layer_idx);
pos += num_tokens - 1;
const size_t end = qbatch.PrefixEnd(qi);
if (end > 0 && end - 1 > pos) {
pos = end - 1;
}
max_last = std::max(max_last, pos);
min_start = std::min(min_start, start);
}
if (max_last - min_start + 1 >= kNFx8HTileSize) {
// q has shape [batch, qbatch][head, qkv_dim].
// We transpose it to [qkv_dim][qbatch, head, batch] in order to make the
// maximum possible number of consecutive columns have the same KV
// matrices. Each thread will process a tile of NF columns of QT so the
// starting column index of QT is just the task index * kVTileSize.
TransposeQ(activations.q, activations.q_T, qbatch.Size(), ctx);
}
}
const size_t num_thread_tasks = hwy::DivCeil(total_tasks, kVTileSize);
const hwy::Divisor div_tokens(num_tokens);
// All layers should have the same number of heads.
HWY_DASSERT(activations.div_heads.GetDivisor() == layer_config.heads);
// For each head/token/query, compute fused flash Q.K, softmax and weighted V.
const auto func = [&](const size_t task, size_t worker) HWY_ATTR {
GCPP_ZONE(ctx, worker, Zones::kFlashAttentionFlashAttention);
// Offsets into original Q for each row in the tile.
uint32_t q_offsets[kMaxNF];
// Offsets into att_out for each row in the tile.
uint32_t out_offsets[kMaxNF];
// Start positions for each row in the tile.
size_t start_positions[kMaxNF];
// Last positions for each row in the tile. Inclusive.
uint32_t last_pos[kMaxNF];
// min and max last positions across all rows in the tile determines when
// TileFlashAttention switches to single vector mode to handle the
// ragged sequence lengths.
size_t min_last_pos = std::numeric_limits<size_t>::max();
size_t max_last_pos = 0;
// Indices into the qbatch.KV for each row in the tile.
size_t qi_indices[kMaxNF];
// Indices into the kv_cache for each row in the tile.
size_t kv_offsets[kMaxNF];
// first_task is [qbatch, head, token].
const size_t first_task = task * kVTileSize;
const size_t last_task = first_task + kVTileSize - 1;
bool use_tile_attention = kVTileSize > 1 && last_task < total_tasks;
for (size_t offset = 0;
offset < kVTileSize && first_task + offset < total_tasks; ++offset) {
const size_t batch_idx = div_tokens.Remainder(first_task + offset);
const size_t qh = div_tokens.Divide(first_task + offset);
const size_t head = activations.div_heads.Remainder(qh);
const size_t qi = activations.div_heads.Divide(qh);
const size_t tq_idx = div_qbatch.GetDivisor() * batch_idx + qi;
qi_indices[offset] = qi;
// Find the token position in the query and calculate
// the range of cache positions to attend to.
const size_t pos = qbatch.Pos(qi) + batch_idx;
const size_t start_pos = StartPos(pos, activations.config, layer_idx);
start_positions[offset] = start_pos;
size_t last = pos;
const size_t prefix_end = qbatch.PrefixEnd(qi);
if (prefix_end > 0 && prefix_end - 1 > last) {
// last_pos in QDotK and WeightedSumV is inclusive.
last = prefix_end - 1;
}
last_pos[offset] = last;
min_last_pos = HWY_MIN(min_last_pos, last);
max_last_pos = HWY_MAX(max_last_pos, last);
q_offsets[offset] =
activations.q.Row(tq_idx) + head * qkv_dim - activations.q.Row(0);
out_offsets[offset] = activations.att_out.Row(tq_idx) + head * qkv_dim -
activations.att_out.Row(0);
const size_t kv_index = head / kHeadGroups;
const size_t head_offset = kv_index * qkv_dim * 2;
kv_offsets[offset] = layer_idx * cache_layer_size + head_offset;
// If any of the parameters in this if statement differ within this task,
// then we can't use TileFlashAttention. TileFlashAttention requires that
// all rows in the tile have the same K and V matrices, and Q starts at
// the same position. The end positions do not have to be the equal.
if (start_positions[offset] != start_positions[0] ||
qi_indices[offset] != qi_indices[0] ||
kv_offsets[offset] != kv_offsets[0]) {
use_tile_attention = false;
}
}
for (size_t offset = 0;
offset < kVTileSize && first_task + offset < total_tasks; ++offset) {
auto& kv_cache = qbatch.KV(qi_indices[offset]).kv_cache;
MatPtrT<KV_t> k("k_view", Extents2D(seq_len, qkv_dim));
k.SetPtr(kv_cache.Row(0) + kv_offsets[offset], kv_cache.Stride());
MatPtrT<KV_t> v("v_view", Extents2D(seq_len, qkv_dim));
v.SetPtr(kv_cache.Row(0) + kv_offsets[offset] + qkv_dim,
kv_cache.Stride());
if (use_tile_attention) {
// To avoid duplicating the code to setup K and V, the call to
// TileFlashAttention is inside the loop over tasks, even though it
// handles all rows in the task at once.
StridedView<float> qT =
StridedView<float>(activations.q_T.Row(0) + first_task, kVTileSize,
activations.q_T.Stride());
if (kVTileSize == kNF) {
// We can still use TileFlashAttention even if we didn't transpose Q
// above. The condition used for transposing Q above is more general
// and easier to compute than the condition used within
// TileFlashAttention that min_last_pos - start_positions[offset] <
// kNFx8HTileSize. In this case, qT is never used. Some tasks might
// use qT and some might not, which is why the more general condition
// is used above to catch all cases where qT will be used.
TileFlashAttention(activations.q, q_offsets, qT, k,
start_positions[offset], last_pos, min_last_pos,
max_last_pos, v, layer_idx, activations,
activations.att_out, out_offsets, ctx, worker);
} else if (kVTileSize == 4) {
TileFlashAttention4(activations.q, q_offsets, k,
start_positions[offset], last_pos, min_last_pos,
max_last_pos, v, layer_idx, activations,
activations.att_out, out_offsets, ctx, worker);
} else {
HWY_UNREACHABLE;
}
break;
} else {
SingleFlashAttention(start_positions[offset], last_pos[offset],
activations.q.Row(0) + q_offsets[offset], k, v,
layer_idx, activations,
activations.att_out.Row(0) + out_offsets[offset],
ctx, worker);
}
}
};
{
PROFILER_ZONE("Gen.FlashAttention.ForkJoin");
// Full parallelism is helpful, SmallParallelFor is insufficient.
HierarchicalParallelFor(num_thread_tasks, ctx, Callers::kFlashAttention,
func);
}
}
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace gcpp
HWY_AFTER_NAMESPACE();