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
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
|
//===- VectorToSCF.cpp - Convert vector to SCF dialect ----------*- C++ -*-===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This file implements lowering of vector transfer operations to SCF.
//
//===----------------------------------------------------------------------===//
#include <numeric>
#include <optional>
#include <type_traits>
#include "mlir/Conversion/VectorToSCF/VectorToSCF.h"
#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Vector/IR/VectorOps.h"
#include "mlir/Dialect/Vector/Transforms/LoweringPatterns.h"
#include "mlir/Dialect/Vector/Transforms/VectorTransforms.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/ImplicitLocOpBuilder.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "mlir/Transforms/Passes.h"
namespace mlir {
#define GEN_PASS_DEF_CONVERTVECTORTOSCF
#include "mlir/Conversion/Passes.h.inc"
} // namespace mlir
using namespace mlir;
using vector::TransferReadOp;
using vector::TransferWriteOp;
namespace {
/// Attribute name used for labeling transfer ops during progressive lowering.
static const char kPassLabel[] = "__vector_to_scf_lowering__";
/// Patterns that inherit from this struct have access to
/// VectorTransferToSCFOptions.
template <typename OpTy>
struct VectorToSCFPattern : public OpRewritePattern<OpTy> {
explicit VectorToSCFPattern(MLIRContext *context,
VectorTransferToSCFOptions opt)
: OpRewritePattern<OpTy>(context), options(opt) {}
VectorTransferToSCFOptions options;
};
/// Given a vector transfer op, calculate which dimension of the `source`
/// memref should be unpacked in the next application of TransferOpConversion.
/// A return value of std::nullopt indicates a broadcast.
template <typename OpTy>
static std::optional<int64_t> unpackedDim(OpTy xferOp) {
// TODO: support 0-d corner case.
assert(xferOp.getTransferRank() > 0 && "unexpected 0-d transfer");
auto map = xferOp.getPermutationMap();
if (auto expr = dyn_cast<AffineDimExpr>(map.getResult(0))) {
return expr.getPosition();
}
assert(xferOp.isBroadcastDim(0) &&
"Expected AffineDimExpr or AffineConstantExpr");
return std::nullopt;
}
/// Compute the permutation map for the new (N-1)-D vector transfer op. This
/// map is identical to the current permutation map, but the first result is
/// omitted.
template <typename OpTy>
static AffineMap unpackedPermutationMap(OpBuilder &b, OpTy xferOp) {
// TODO: support 0-d corner case.
assert(xferOp.getTransferRank() > 0 && "unexpected 0-d transfer");
auto map = xferOp.getPermutationMap();
return AffineMap::get(map.getNumDims(), 0, map.getResults().drop_front(),
b.getContext());
}
/// Calculate the indices for the new vector transfer op.
///
/// E.g.: transfer_read %A[%a, %b, %c, %d] ... : vector<5x4x3xf32> ...
/// --> transfer_read %A[%a, %b + iv, %c, %d] ... vector<4x3f32>
/// ^^^^^^
/// `iv` is the iteration variable of the (new) surrounding loop.
template <typename OpTy>
static void getXferIndices(OpBuilder &b, OpTy xferOp, Value iv,
SmallVector<Value, 8> &indices) {
typename OpTy::Adaptor adaptor(xferOp);
// Corresponding memref dim of the vector dim that is unpacked.
auto dim = unpackedDim(xferOp);
auto prevIndices = adaptor.getIndices();
indices.append(prevIndices.begin(), prevIndices.end());
Location loc = xferOp.getLoc();
bool isBroadcast = !dim.has_value();
if (!isBroadcast) {
AffineExpr d0, d1;
bindDims(xferOp.getContext(), d0, d1);
Value offset = adaptor.getIndices()[*dim];
indices[*dim] =
affine::makeComposedAffineApply(b, loc, d0 + d1, {offset, iv});
}
}
static void maybeYieldValue(OpBuilder &b, Location loc, bool hasRetVal,
Value value) {
if (hasRetVal) {
assert(value && "Expected non-empty value");
b.create<scf::YieldOp>(loc, value);
} else {
b.create<scf::YieldOp>(loc);
}
}
/// Generates a boolean Value that is true if the iv-th bit in xferOp's mask
/// is set to true. No such check is generated under following circumstances:
/// * xferOp does not have a mask.
/// * xferOp's mask is not 1D. (In case of (N>1)-D, a subvector of the mask is
/// computed and attached to the new transfer op in the pattern.)
/// * The to-be-unpacked dim of xferOp is a broadcast.
template <typename OpTy>
static Value generateMaskCheck(OpBuilder &b, OpTy xferOp, Value iv) {
if (!xferOp.getMask())
return Value();
if (xferOp.getMaskType().getRank() != 1)
return Value();
if (xferOp.isBroadcastDim(0))
return Value();
Location loc = xferOp.getLoc();
return b.create<vector::ExtractElementOp>(loc, xferOp.getMask(), iv);
}
/// Helper function TransferOpConversion and TransferOp1dConversion.
/// Generate an in-bounds check if the transfer op may go out-of-bounds on the
/// specified dimension `dim` with the loop iteration variable `iv`.
/// E.g., when unpacking dimension 0 from:
/// ```
/// %vec = vector.transfer_read %A[%a, %b] %cst
/// : vector<5x4xf32>, memref<?x?xf32>
/// ```
/// An if check similar to this will be generated inside the loop:
/// ```
/// %d = memref.dim %A, %c0 : memref<?x?xf32>
/// if (%a + iv < %d) {
/// (in-bounds case)
/// } else {
/// (out-of-bounds case)
/// }
/// ```
///
/// If the transfer is 1D and has a mask, this function generates a more complex
/// check also accounts for potentially masked out elements.
///
/// This function variant returns the value returned by `inBoundsCase` or
/// `outOfBoundsCase`. The MLIR type of the return value must be specified in
/// `resultTypes`.
template <typename OpTy>
static Value generateInBoundsCheck(
OpBuilder &b, OpTy xferOp, Value iv, std::optional<int64_t> dim,
TypeRange resultTypes,
function_ref<Value(OpBuilder &, Location)> inBoundsCase,
function_ref<Value(OpBuilder &, Location)> outOfBoundsCase = nullptr) {
bool hasRetVal = !resultTypes.empty();
Value cond; // Condition to be built...
// Condition check 1: Access in-bounds?
bool isBroadcast = !dim; // No in-bounds check for broadcasts.
Location loc = xferOp.getLoc();
ImplicitLocOpBuilder lb(xferOp.getLoc(), b);
if (!xferOp.isDimInBounds(0) && !isBroadcast) {
Value memrefDim =
vector::createOrFoldDimOp(b, loc, xferOp.getSource(), *dim);
AffineExpr d0, d1;
bindDims(xferOp.getContext(), d0, d1);
Value base = xferOp.getIndices()[*dim];
Value memrefIdx =
affine::makeComposedAffineApply(b, loc, d0 + d1, {base, iv});
cond = lb.create<arith::CmpIOp>(arith::CmpIPredicate::sgt, memrefDim,
memrefIdx);
}
// Condition check 2: Masked in?
if (auto maskCond = generateMaskCheck(b, xferOp, iv)) {
if (cond)
cond = lb.create<arith::AndIOp>(cond, maskCond);
else
cond = maskCond;
}
// If the condition is non-empty, generate an SCF::IfOp.
if (cond) {
auto check = lb.create<scf::IfOp>(
cond,
/*thenBuilder=*/
[&](OpBuilder &b, Location loc) {
maybeYieldValue(b, loc, hasRetVal, inBoundsCase(b, loc));
},
/*elseBuilder=*/
[&](OpBuilder &b, Location loc) {
if (outOfBoundsCase) {
maybeYieldValue(b, loc, hasRetVal, outOfBoundsCase(b, loc));
} else {
b.create<scf::YieldOp>(loc);
}
});
return hasRetVal ? check.getResult(0) : Value();
}
// Condition is empty, no need for an SCF::IfOp.
return inBoundsCase(b, loc);
}
/// In this function variant, `inBoundsCase` and `outOfBoundsCase` do not have
/// a return value. Consequently, this function does not have a return value.
template <typename OpTy>
static void generateInBoundsCheck(
OpBuilder &b, OpTy xferOp, Value iv, std::optional<int64_t> dim,
function_ref<void(OpBuilder &, Location)> inBoundsCase,
function_ref<void(OpBuilder &, Location)> outOfBoundsCase = nullptr) {
generateInBoundsCheck(
b, xferOp, iv, dim, /*resultTypes=*/TypeRange(),
/*inBoundsCase=*/
[&](OpBuilder &b, Location loc) {
inBoundsCase(b, loc);
return Value();
},
/*outOfBoundsCase=*/
[&](OpBuilder &b, Location loc) {
if (outOfBoundsCase)
outOfBoundsCase(b, loc);
return Value();
});
}
/// Given an ArrayAttr, return a copy where the first element is dropped.
static ArrayAttr dropFirstElem(OpBuilder &b, ArrayAttr attr) {
if (!attr)
return attr;
return ArrayAttr::get(b.getContext(), attr.getValue().drop_front());
}
/// Add the pass label to a vector transfer op if its rank is not the target
/// rank.
template <typename OpTy>
static void maybeApplyPassLabel(OpBuilder &b, OpTy newXferOp,
unsigned targetRank) {
if (newXferOp.getVectorType().getRank() > targetRank)
newXferOp->setAttr(kPassLabel, b.getUnitAttr());
}
/// Return true if this transfer op operates on a source tensor.
template <typename OpTy>
static bool isTensorOp(OpTy xferOp) {
if (isa<RankedTensorType>(xferOp.getShapedType())) {
if (xferOp.getOperationName().equals(TransferWriteOp::getOperationName())) {
// TransferWriteOps on tensors have a result.
assert(xferOp->getNumResults() > 0);
}
return true;
}
return false;
}
namespace lowering_n_d {
/// Helper data structure for data and mask buffers.
struct BufferAllocs {
Value dataBuffer;
Value maskBuffer;
};
// TODO: Parallelism and threadlocal considerations with a ParallelScope trait.
static Operation *getAutomaticAllocationScope(Operation *op) {
Operation *scope =
op->getParentWithTrait<OpTrait::AutomaticAllocationScope>();
assert(scope && "Expected op to be inside automatic allocation scope");
return scope;
}
/// Allocate temporary buffers for data (vector) and mask (if present).
template <typename OpTy>
static BufferAllocs allocBuffers(OpBuilder &b, OpTy xferOp) {
Location loc = xferOp.getLoc();
OpBuilder::InsertionGuard guard(b);
Operation *scope = getAutomaticAllocationScope(xferOp);
assert(scope->getNumRegions() == 1 &&
"AutomaticAllocationScope with >1 regions");
b.setInsertionPointToStart(&scope->getRegion(0).front());
BufferAllocs result;
auto bufferType = MemRefType::get({}, xferOp.getVectorType());
result.dataBuffer = b.create<memref::AllocaOp>(loc, bufferType);
if (xferOp.getMask()) {
auto maskType = MemRefType::get({}, xferOp.getMask().getType());
auto maskBuffer = b.create<memref::AllocaOp>(loc, maskType);
b.setInsertionPoint(xferOp);
b.create<memref::StoreOp>(loc, xferOp.getMask(), maskBuffer);
result.maskBuffer = b.create<memref::LoadOp>(loc, maskBuffer, ValueRange());
}
return result;
}
/// Given a MemRefType with VectorType element type, unpack one dimension from
/// the VectorType into the MemRefType.
///
/// E.g.: memref<9xvector<5x6xf32>> --> memref<9x5xvector<6xf32>>
static FailureOr<MemRefType> unpackOneDim(MemRefType type) {
auto vectorType = dyn_cast<VectorType>(type.getElementType());
// Vectors with leading scalable dims are not supported.
// It may be possible to support these in future by using dynamic memref dims.
if (vectorType.getScalableDims().front())
return failure();
auto memrefShape = type.getShape();
SmallVector<int64_t, 8> newMemrefShape;
newMemrefShape.append(memrefShape.begin(), memrefShape.end());
newMemrefShape.push_back(vectorType.getDimSize(0));
return MemRefType::get(newMemrefShape,
VectorType::Builder(vectorType).dropDim(0));
}
/// Given a transfer op, find the memref from which the mask is loaded. This
/// is similar to Strategy<TransferWriteOp>::getBuffer.
template <typename OpTy>
static Value getMaskBuffer(OpTy xferOp) {
assert(xferOp.getMask() && "Expected that transfer op has mask");
auto loadOp = xferOp.getMask().template getDefiningOp<memref::LoadOp>();
assert(loadOp && "Expected transfer op mask produced by LoadOp");
return loadOp.getMemRef();
}
/// Codegen strategy, depending on the operation.
template <typename OpTy>
struct Strategy;
/// Code strategy for vector TransferReadOp.
template <>
struct Strategy<TransferReadOp> {
/// Find the StoreOp that is used for writing the current TransferReadOp's
/// result to the temporary buffer allocation.
static memref::StoreOp getStoreOp(TransferReadOp xferOp) {
assert(xferOp->hasOneUse() && "Expected exactly one use of TransferReadOp");
auto storeOp = dyn_cast<memref::StoreOp>((*xferOp->use_begin()).getOwner());
assert(storeOp && "Expected TransferReadOp result used by StoreOp");
return storeOp;
}
/// Find the temporary buffer allocation. All labeled TransferReadOps are
/// used like this, where %buf is either the buffer allocation or a type cast
/// of the buffer allocation:
/// ```
/// %vec = vector.transfer_read ... { __vector_to_scf_lowering__ } ...
/// memref.store %vec, %buf[...] ...
/// ```
static Value getBuffer(TransferReadOp xferOp) {
return getStoreOp(xferOp).getMemRef();
}
/// Retrieve the indices of the current StoreOp that stores into the buffer.
static void getBufferIndices(TransferReadOp xferOp,
SmallVector<Value, 8> &indices) {
auto storeOp = getStoreOp(xferOp);
auto prevIndices = memref::StoreOpAdaptor(storeOp).getIndices();
indices.append(prevIndices.begin(), prevIndices.end());
}
/// Rewrite the TransferReadOp, assuming that there are no out-of-bounds
/// accesses on the to-be-unpacked dimension.
///
/// 1. Generate a new (N-1)-d TransferReadOp using the loop iteration
/// variable `iv`.
/// 2. Store the result into the (already `vector.type_cast`ed) buffer.
///
/// E.g.:
/// ```
/// %vec = vector.transfer_read %A[%a+%i, %b, %c], %cst
/// : memref<?x?x?xf32>, vector<4x3xf32>
/// memref.store %vec, %buf[%i] : memref<5xvector<4x3xf32>>
/// ```
/// Is rewritten to:
/// ```
/// %casted = vector.type_cast %buf
/// : memref<5xvector<4x3xf32>> to memref<5x4xvector<3xf32>>
/// for %j = 0 to 4 {
/// %vec = vector.transfer_read %A[%a+%i, %b+%j, %c], %cst
/// : memref<?x?x?xf32>, vector<3xf32>
/// memref.store %vec, %casted[%i, %j] : memref<5x4xvector<3xf32>>
/// }
/// ```
///
/// Note: The loop and type cast are generated in TransferOpConversion.
/// The original TransferReadOp and store op are deleted in `cleanup`.
/// Note: The `mask` operand is set in TransferOpConversion.
static TransferReadOp rewriteOp(OpBuilder &b,
VectorTransferToSCFOptions options,
TransferReadOp xferOp, Value buffer, Value iv,
ValueRange /*loopState*/) {
SmallVector<Value, 8> storeIndices;
getBufferIndices(xferOp, storeIndices);
storeIndices.push_back(iv);
SmallVector<Value, 8> xferIndices;
getXferIndices(b, xferOp, iv, xferIndices);
Location loc = xferOp.getLoc();
auto bufferType = dyn_cast<ShapedType>(buffer.getType());
auto vecType = dyn_cast<VectorType>(bufferType.getElementType());
auto inBoundsAttr = dropFirstElem(b, xferOp.getInBoundsAttr());
auto newXferOp = b.create<vector::TransferReadOp>(
loc, vecType, xferOp.getSource(), xferIndices,
AffineMapAttr::get(unpackedPermutationMap(b, xferOp)),
xferOp.getPadding(), Value(), inBoundsAttr);
maybeApplyPassLabel(b, newXferOp, options.targetRank);
b.create<memref::StoreOp>(loc, newXferOp.getVector(), buffer, storeIndices);
return newXferOp;
}
/// Handle out-of-bounds accesses on the to-be-unpacked dimension: Write
/// padding value to the temporary buffer.
static Value handleOutOfBoundsDim(OpBuilder &b, TransferReadOp xferOp,
Value buffer, Value iv,
ValueRange /*loopState*/) {
SmallVector<Value, 8> storeIndices;
getBufferIndices(xferOp, storeIndices);
storeIndices.push_back(iv);
Location loc = xferOp.getLoc();
auto bufferType = dyn_cast<ShapedType>(buffer.getType());
auto vecType = dyn_cast<VectorType>(bufferType.getElementType());
auto vec = b.create<vector::SplatOp>(loc, vecType, xferOp.getPadding());
b.create<memref::StoreOp>(loc, vec, buffer, storeIndices);
return Value();
}
/// Cleanup after rewriting the op.
static void cleanup(PatternRewriter &rewriter, TransferReadOp xferOp,
scf::ForOp /*forOp*/) {
rewriter.eraseOp(getStoreOp(xferOp));
rewriter.eraseOp(xferOp);
}
/// Return the initial loop state for the generated scf.for loop.
static Value initialLoopState(TransferReadOp xferOp) { return Value(); }
};
/// Codegen strategy for vector TransferWriteOp.
template <>
struct Strategy<TransferWriteOp> {
/// Find the temporary buffer allocation. All labeled TransferWriteOps are
/// used like this, where %buf is either the buffer allocation or a type cast
/// of the buffer allocation:
/// ```
/// %vec = memref.load %buf[...] ...
/// vector.transfer_write %vec ... { __vector_to_scf_lowering__ } ...
/// ```
static Value getBuffer(TransferWriteOp xferOp) {
auto loadOp = xferOp.getVector().getDefiningOp<memref::LoadOp>();
assert(loadOp && "Expected transfer op vector produced by LoadOp");
return loadOp.getMemRef();
}
/// Retrieve the indices of the current LoadOp that loads from the buffer.
static void getBufferIndices(TransferWriteOp xferOp,
SmallVector<Value, 8> &indices) {
auto loadOp = xferOp.getVector().getDefiningOp<memref::LoadOp>();
auto prevIndices = memref::LoadOpAdaptor(loadOp).getIndices();
indices.append(prevIndices.begin(), prevIndices.end());
}
/// Rewrite the TransferWriteOp, assuming that there are no out-of-bounds
/// accesses on the to-be-unpacked dimension.
///
/// 1. Load an (N-1)-d vector from the (already `vector.type_cast`ed) buffer,
/// using the loop iteration variable `iv`.
/// 2. Generate a new (N-1)-d TransferWriteOp, writing the loaded vector back
/// to memory.
///
/// Note: For more details, see comments on Strategy<TransferReadOp>.
static TransferWriteOp rewriteOp(OpBuilder &b,
VectorTransferToSCFOptions options,
TransferWriteOp xferOp, Value buffer,
Value iv, ValueRange loopState) {
SmallVector<Value, 8> loadIndices;
getBufferIndices(xferOp, loadIndices);
loadIndices.push_back(iv);
SmallVector<Value, 8> xferIndices;
getXferIndices(b, xferOp, iv, xferIndices);
Location loc = xferOp.getLoc();
auto vec = b.create<memref::LoadOp>(loc, buffer, loadIndices);
auto inBoundsAttr = dropFirstElem(b, xferOp.getInBoundsAttr());
auto source = loopState.empty() ? xferOp.getSource() : loopState[0];
Type type = isTensorOp(xferOp) ? xferOp.getShapedType() : Type();
auto newXferOp = b.create<vector::TransferWriteOp>(
loc, type, vec, source, xferIndices,
AffineMapAttr::get(unpackedPermutationMap(b, xferOp)), Value(),
inBoundsAttr);
maybeApplyPassLabel(b, newXferOp, options.targetRank);
return newXferOp;
}
/// Handle out-of-bounds accesses on the to-be-unpacked dimension.
static Value handleOutOfBoundsDim(OpBuilder &b, TransferWriteOp xferOp,
Value buffer, Value iv,
ValueRange loopState) {
return isTensorOp(xferOp) ? loopState[0] : Value();
}
/// Cleanup after rewriting the op.
static void cleanup(PatternRewriter &rewriter, TransferWriteOp xferOp,
scf::ForOp forOp) {
if (isTensorOp(xferOp)) {
assert(forOp->getNumResults() == 1 && "Expected one for loop result");
rewriter.replaceOp(xferOp, forOp->getResult(0));
} else {
rewriter.eraseOp(xferOp);
}
}
/// Return the initial loop state for the generated scf.for loop.
static Value initialLoopState(TransferWriteOp xferOp) {
return isTensorOp(xferOp) ? xferOp.getSource() : Value();
}
};
template <typename OpTy>
LogicalResult checkPrepareXferOp(OpTy xferOp,
VectorTransferToSCFOptions options) {
if (xferOp->hasAttr(kPassLabel))
return failure();
if (xferOp.getVectorType().getRank() <= options.targetRank)
return failure();
// Currently the unpacking of the leading dimension into the memref is not
// supported for scalable dimensions.
if (xferOp.getVectorType().getScalableDims().front())
return failure();
if (isTensorOp(xferOp) && !options.lowerTensors)
return failure();
// Transfer ops that modify the element type are not supported atm.
if (xferOp.getVectorType().getElementType() !=
xferOp.getShapedType().getElementType())
return failure();
return success();
}
/// Prepare a TransferReadOp for progressive lowering.
///
/// 1. Allocate a temporary buffer.
/// 2. Label the TransferReadOp, marking it eligible for progressive lowering.
/// 3. Store the result of the TransferReadOp into the temporary buffer.
/// 4. Load the result from the temporary buffer and replace all uses of the
/// original TransferReadOp with this load.
///
/// E.g.:
/// ```
/// %vec = vector.transfer_read %A[%a, %b, %c], %cst
/// : vector<5x4xf32>, memref<?x?x?xf32>
/// ```
/// is rewritten to:
/// ```
/// %0 = memref.alloca() : memref<vector<5x4xf32>>
/// %1 = vector.transfer_read %A[%a, %b, %c], %cst
/// { __vector_to_scf_lowering__ } : vector<5x4xf32>, memref<?x?x?xf32>
/// memref.store %1, %0[] : memref<vector<5x4xf32>>
/// %vec = memref.load %0[] : memref<vector<5x4xf32>>
/// ```
///
/// Note: A second temporary buffer may be allocated for the `mask` operand.
struct PrepareTransferReadConversion
: public VectorToSCFPattern<TransferReadOp> {
using VectorToSCFPattern<TransferReadOp>::VectorToSCFPattern;
LogicalResult matchAndRewrite(TransferReadOp xferOp,
PatternRewriter &rewriter) const override {
if (checkPrepareXferOp(xferOp, options).failed())
return failure();
auto buffers = allocBuffers(rewriter, xferOp);
auto *newXfer = rewriter.clone(*xferOp.getOperation());
newXfer->setAttr(kPassLabel, rewriter.getUnitAttr());
if (xferOp.getMask()) {
dyn_cast<TransferReadOp>(newXfer).getMaskMutable().assign(
buffers.maskBuffer);
}
Location loc = xferOp.getLoc();
rewriter.create<memref::StoreOp>(loc, newXfer->getResult(0),
buffers.dataBuffer);
rewriter.replaceOpWithNewOp<memref::LoadOp>(xferOp, buffers.dataBuffer);
return success();
}
};
/// Prepare a TransferWriteOp for progressive lowering.
///
/// 1. Allocate a temporary buffer.
/// 2. Store the vector into the buffer.
/// 3. Load the vector from the buffer again.
/// 4. Use the loaded vector as a TransferWriteOp operand and label the op,
/// marking it eligible for progressive lowering via TransferOpConversion.
///
/// E.g.:
/// ```
/// vector.transfer_write %vec, %A[%a, %b, %c]
/// : vector<5x4xf32>, memref<?x?x?xf32>
/// ```
/// is rewritten to:
/// ```
/// %0 = memref.alloca() : memref<vector<5x4xf32>>
/// memref.store %vec, %0[] : memref<vector<5x4xf32>>
/// %1 = memref.load %0[] : memref<vector<5x4xf32>>
/// vector.transfer_write %1, %A[%a, %b, %c] { __vector_to_scf_lowering__ }
/// : vector<5x4xf32>, memref<?x?x?xf32>
/// ```
///
/// Note: A second temporary buffer may be allocated for the `mask` operand.
struct PrepareTransferWriteConversion
: public VectorToSCFPattern<TransferWriteOp> {
using VectorToSCFPattern<TransferWriteOp>::VectorToSCFPattern;
LogicalResult matchAndRewrite(TransferWriteOp xferOp,
PatternRewriter &rewriter) const override {
if (checkPrepareXferOp(xferOp, options).failed())
return failure();
Location loc = xferOp.getLoc();
auto buffers = allocBuffers(rewriter, xferOp);
rewriter.create<memref::StoreOp>(loc, xferOp.getVector(),
buffers.dataBuffer);
auto loadedVec = rewriter.create<memref::LoadOp>(loc, buffers.dataBuffer);
rewriter.updateRootInPlace(xferOp, [&]() {
xferOp.getVectorMutable().assign(loadedVec);
xferOp->setAttr(kPassLabel, rewriter.getUnitAttr());
});
if (xferOp.getMask()) {
rewriter.updateRootInPlace(xferOp, [&]() {
xferOp.getMaskMutable().assign(buffers.maskBuffer);
});
}
return success();
}
};
/// Decompose a n-D PrintOp into a loop of elementary/scalar prints. This allows
/// printing both 1D scalable vectors and n-D fixed size vectors.
///
/// E.g.:
/// ```
/// vector.print %v : vector<[4]xi32>
/// ```
/// is rewritten to:
/// ```
/// %c0 = arith.constant 0 : index
/// %c4 = arith.constant 4 : index
/// %c1 = arith.constant 1 : index
/// %vscale = vector.vscale
/// %length = arith.muli %vscale, %c4 : index
/// %lastIndex = arith.subi %length, %c1 : index
/// vector.print punctuation <open>
/// scf.for %i = %c0 to %length step %c1 {
/// %el = vector.extractelement %v[%i : index] : vector<[4]xi32>
/// vector.print %el : i32 punctuation <no_punctuation>
/// %notLastIndex = arith.cmpi ult, %i, %lastIndex : index
/// scf.if %notLastIndex {
/// vector.print punctuation <comma>
/// }
/// }
/// vector.print punctuation <close>
/// vector.print
/// ```
struct DecomposePrintOpConversion : public VectorToSCFPattern<vector::PrintOp> {
using VectorToSCFPattern<vector::PrintOp>::VectorToSCFPattern;
LogicalResult matchAndRewrite(vector::PrintOp printOp,
PatternRewriter &rewriter) const override {
if (!printOp.getSource())
return failure();
VectorType vectorType = dyn_cast<VectorType>(printOp.getPrintType());
if (!vectorType)
return failure();
// Currently >= 2D scalable vectors are not supported.
// These can't be lowered to LLVM (as LLVM does not support scalable vectors
// of scalable vectors), and due to limitations of current ops can't be
// indexed with SSA values or flattened. This may change after
// https://reviews.llvm.org/D155034, though there still needs to be a path
// for lowering to LLVM.
if (vectorType.getRank() > 1 && vectorType.isScalable())
return failure();
auto loc = printOp.getLoc();
auto value = printOp.getSource();
if (auto intTy = dyn_cast<IntegerType>(vectorType.getElementType())) {
// Oddly sized integers are (somewhat) buggy on a lot of backends, so to
// avoid issues extend them to a more standard size.
// https://github.com/llvm/llvm-project/issues/30613
auto width = intTy.getWidth();
auto legalWidth = llvm::NextPowerOf2(std::max(8u, width) - 1);
auto legalIntTy = IntegerType::get(rewriter.getContext(), legalWidth,
intTy.getSignedness());
// arith can only take signless integers, so we must cast back and forth.
auto signlessSourceVectorType =
vectorType.cloneWith({}, getIntTypeWithSignlessSemantics(intTy));
auto signlessTargetVectorType =
vectorType.cloneWith({}, getIntTypeWithSignlessSemantics(legalIntTy));
auto targetVectorType = vectorType.cloneWith({}, legalIntTy);
value = rewriter.create<vector::BitCastOp>(loc, signlessSourceVectorType,
value);
if (value.getType() != signlessTargetVectorType) {
if (width == 1 || intTy.isUnsigned())
value = rewriter.create<arith::ExtUIOp>(loc, signlessTargetVectorType,
value);
else
value = rewriter.create<arith::ExtSIOp>(loc, signlessTargetVectorType,
value);
}
value = rewriter.create<vector::BitCastOp>(loc, targetVectorType, value);
vectorType = targetVectorType;
}
auto scalableDimensions = vectorType.getScalableDims();
auto shape = vectorType.getShape();
constexpr int64_t singletonShape[] = {1};
if (vectorType.getRank() == 0)
shape = singletonShape;
if (vectorType.getRank() != 1) {
// Flatten n-D vectors to 1D. This is done to allow indexing with a
// non-constant value (which can currently only be done via
// vector.extractelement for 1D vectors).
auto flatLength = std::accumulate(shape.begin(), shape.end(), 1,
std::multiplies<int64_t>());
auto flatVectorType =
VectorType::get({flatLength}, vectorType.getElementType());
value = rewriter.create<vector::ShapeCastOp>(loc, flatVectorType, value);
}
vector::PrintOp firstClose;
SmallVector<Value, 8> loopIndices;
for (unsigned d = 0; d < shape.size(); d++) {
// Setup loop bounds and step.
Value lowerBound = rewriter.create<arith::ConstantIndexOp>(loc, 0);
Value upperBound = rewriter.create<arith::ConstantIndexOp>(loc, shape[d]);
Value step = rewriter.create<arith::ConstantIndexOp>(loc, 1);
if (!scalableDimensions.empty() && scalableDimensions[d]) {
auto vscale = rewriter.create<vector::VectorScaleOp>(
loc, rewriter.getIndexType());
upperBound = rewriter.create<arith::MulIOp>(loc, upperBound, vscale);
}
auto lastIndex = rewriter.create<arith::SubIOp>(loc, upperBound, step);
// Create a loop to print the elements surrounded by parentheses.
rewriter.create<vector::PrintOp>(loc, vector::PrintPunctuation::Open);
auto loop =
rewriter.create<scf::ForOp>(loc, lowerBound, upperBound, step);
auto printClose = rewriter.create<vector::PrintOp>(
loc, vector::PrintPunctuation::Close);
if (!firstClose)
firstClose = printClose;
auto loopIdx = loop.getInductionVar();
loopIndices.push_back(loopIdx);
// Print a comma after all but the last element.
rewriter.setInsertionPointToStart(loop.getBody());
auto notLastIndex = rewriter.create<arith::CmpIOp>(
loc, arith::CmpIPredicate::ult, loopIdx, lastIndex);
rewriter.create<scf::IfOp>(loc, notLastIndex,
[&](OpBuilder &builder, Location loc) {
builder.create<vector::PrintOp>(
loc, vector::PrintPunctuation::Comma);
builder.create<scf::YieldOp>(loc);
});
rewriter.setInsertionPointToStart(loop.getBody());
}
// Compute the flattened index.
// Note: For the > rank 1 vectors this assumes non-scalable.
Value flatIndex;
auto currentStride = 1;
for (int d = shape.size() - 1; d >= 0; d--) {
auto stride = rewriter.create<arith::ConstantIndexOp>(loc, currentStride);
auto index = rewriter.create<arith::MulIOp>(loc, stride, loopIndices[d]);
if (flatIndex)
flatIndex = rewriter.create<arith::AddIOp>(loc, flatIndex, index);
else
flatIndex = index;
currentStride *= shape[d];
}
// Print the scalar elements in the inner most loop.
auto element =
rewriter.create<vector::ExtractElementOp>(loc, value, flatIndex);
rewriter.create<vector::PrintOp>(loc, element,
vector::PrintPunctuation::NoPunctuation);
rewriter.setInsertionPointAfter(firstClose);
rewriter.create<vector::PrintOp>(loc, printOp.getPunctuation());
rewriter.eraseOp(printOp);
return success();
}
static IntegerType getIntTypeWithSignlessSemantics(IntegerType intTy) {
return IntegerType::get(intTy.getContext(), intTy.getWidth(),
IntegerType::Signless);
};
};
/// Progressive lowering of vector transfer ops: Unpack one dimension.
///
/// 1. Unpack one dimension from the current buffer type and cast the buffer
/// to that new type. E.g.:
/// ```
/// %vec = memref.load %0[%1] : memref<5xvector<4x3xf32>>
/// vector.transfer_write %vec ...
/// ```
/// The following cast is generated:
/// ```
/// %casted = vector.type_cast %0
/// : memref<5xvector<4x3xf32>> to memref<5x4xvector<3xf32>>
/// ```
/// 2. Generate a for loop and rewrite the transfer op according to the
/// corresponding Strategy<OpTy>. If the to-be-unpacked dimension can be
/// out-of-bounds, generate an if-check and handle both cases separately.
/// 3. Clean up according to the corresponding Strategy<OpTy>.
///
/// Note: If the transfer op is a TransferWriteOp and operates on a tensor
/// source (as opposed to a memref source), then each iteration of the generated
/// scf.for loop yields the new tensor value. E.g.:
/// ```
/// %result = scf.for i = 0 to 5 {
/// %0 = memref.load %buffer[i] : memref<5xvector<4x3xf32>>
/// %1 = vector.transfer_write %0, %source[...]
/// : vector<4x3xf32>, tensor<5x4x3xf32>
/// scf.yield %1 : tensor<5x4x3xf32>
/// }
/// ```
template <typename OpTy>
struct TransferOpConversion : public VectorToSCFPattern<OpTy> {
using VectorToSCFPattern<OpTy>::VectorToSCFPattern;
void initialize() {
// This pattern recursively unpacks one dimension at a time. The recursion
// bounded as the rank is strictly decreasing.
this->setHasBoundedRewriteRecursion();
}
static void getMaskBufferLoadIndices(OpTy xferOp, Value castedMaskBuffer,
SmallVectorImpl<Value> &loadIndices,
Value iv) {
assert(xferOp.getMask() && "Expected transfer op to have mask");
// Add load indices from the previous iteration.
// The mask buffer depends on the permutation map, which makes determining
// the indices quite complex, so this is why we need to "look back" to the
// previous iteration to find the right indices.
Value maskBuffer = getMaskBuffer(xferOp);
for (Operation *user : maskBuffer.getUsers()) {
// If there is no previous load op, then the indices are empty.
if (auto loadOp = dyn_cast<memref::LoadOp>(user)) {
Operation::operand_range prevIndices = loadOp.getIndices();
loadIndices.append(prevIndices.begin(), prevIndices.end());
break;
}
}
// In case of broadcast: Use same indices to load from memref
// as before.
if (!xferOp.isBroadcastDim(0))
loadIndices.push_back(iv);
}
LogicalResult matchAndRewrite(OpTy xferOp,
PatternRewriter &rewriter) const override {
if (!xferOp->hasAttr(kPassLabel))
return failure();
// Find and cast data buffer. How the buffer can be found depends on OpTy.
ImplicitLocOpBuilder locB(xferOp.getLoc(), rewriter);
Value dataBuffer = Strategy<OpTy>::getBuffer(xferOp);
auto dataBufferType = dyn_cast<MemRefType>(dataBuffer.getType());
FailureOr<MemRefType> castedDataType = unpackOneDim(dataBufferType);
if (failed(castedDataType))
return failure();
auto castedDataBuffer =
locB.create<vector::TypeCastOp>(*castedDataType, dataBuffer);
// If the xferOp has a mask: Find and cast mask buffer.
Value castedMaskBuffer;
if (xferOp.getMask()) {
Value maskBuffer = getMaskBuffer(xferOp);
if (xferOp.isBroadcastDim(0) || xferOp.getMaskType().getRank() == 1) {
// Do not unpack a dimension of the mask, if:
// * To-be-unpacked transfer op dimension is a broadcast.
// * Mask is 1D, i.e., the mask cannot be further unpacked.
// (That means that all remaining dimensions of the transfer op must
// be broadcasted.)
castedMaskBuffer = maskBuffer;
} else {
// It's safe to assume the mask buffer can be unpacked if the data
// buffer was unpacked.
auto maskBufferType = cast<MemRefType>(maskBuffer.getType());
MemRefType castedMaskType = *unpackOneDim(maskBufferType);
castedMaskBuffer =
locB.create<vector::TypeCastOp>(castedMaskType, maskBuffer);
}
}
// Loop bounds and step.
auto lb = locB.create<arith::ConstantIndexOp>(0);
auto ub = locB.create<arith::ConstantIndexOp>(
castedDataType->getDimSize(castedDataType->getRank() - 1));
auto step = locB.create<arith::ConstantIndexOp>(1);
// TransferWriteOps that operate on tensors return the modified tensor and
// require a loop state.
auto loopState = Strategy<OpTy>::initialLoopState(xferOp);
// Generate for loop.
auto result = locB.create<scf::ForOp>(
lb, ub, step, loopState ? ValueRange(loopState) : ValueRange(),
[&](OpBuilder &b, Location loc, Value iv, ValueRange loopState) {
Type stateType = loopState.empty() ? Type() : loopState[0].getType();
auto result = generateInBoundsCheck(
b, xferOp, iv, unpackedDim(xferOp),
stateType ? TypeRange(stateType) : TypeRange(),
/*inBoundsCase=*/
[&](OpBuilder &b, Location loc) {
// Create new transfer op.
OpTy newXfer = Strategy<OpTy>::rewriteOp(
b, this->options, xferOp, castedDataBuffer, iv, loopState);
// If old transfer op has a mask: Set mask on new transfer op.
// Special case: If the mask of the old transfer op is 1D and
// the unpacked dim is not a broadcast, no mask is needed on
// the new transfer op.
if (xferOp.getMask() && (xferOp.isBroadcastDim(0) ||
xferOp.getMaskType().getRank() > 1)) {
OpBuilder::InsertionGuard guard(b);
b.setInsertionPoint(newXfer); // Insert load before newXfer.
SmallVector<Value, 8> loadIndices;
getMaskBufferLoadIndices(xferOp, castedMaskBuffer,
loadIndices, iv);
auto mask = b.create<memref::LoadOp>(loc, castedMaskBuffer,
loadIndices);
rewriter.updateRootInPlace(newXfer, [&]() {
newXfer.getMaskMutable().assign(mask);
});
}
return loopState.empty() ? Value() : newXfer->getResult(0);
},
/*outOfBoundsCase=*/
[&](OpBuilder &b, Location /*loc*/) {
return Strategy<OpTy>::handleOutOfBoundsDim(
b, xferOp, castedDataBuffer, iv, loopState);
});
maybeYieldValue(b, loc, !loopState.empty(), result);
});
Strategy<OpTy>::cleanup(rewriter, xferOp, result);
return success();
}
};
} // namespace lowering_n_d
namespace lowering_n_d_unrolled {
/// If the original transfer op has a mask, compute the mask of the new transfer
/// op (for the current iteration `i`) and assign it.
template <typename OpTy>
static void maybeAssignMask(OpBuilder &b, OpTy xferOp, OpTy newXferOp,
int64_t i) {
if (!xferOp.getMask())
return;
if (xferOp.isBroadcastDim(0)) {
// To-be-unpacked dimension is a broadcast, which does not have a
// corresponding mask dimension. Mask attribute remains unchanged.
newXferOp.getMaskMutable().assign(xferOp.getMask());
return;
}
if (xferOp.getMaskType().getRank() > 1) {
// Unpack one dimension of the mask.
OpBuilder::InsertionGuard guard(b);
b.setInsertionPoint(newXferOp); // Insert load before newXfer.
llvm::SmallVector<int64_t, 1> indices({i});
Location loc = xferOp.getLoc();
auto newMask = b.create<vector::ExtractOp>(loc, xferOp.getMask(), indices);
newXferOp.getMaskMutable().assign(newMask);
}
// If we end up here: The mask of the old transfer op is 1D and the unpacked
// dim is not a broadcast, so no mask is needed on the new transfer op.
// `generateInBoundsCheck` will have evaluated the mask already.
}
/// Progressive lowering of vector TransferReadOp with unrolling: Unpack one
/// dimension. This is similar to TransferOpConversion<TransferReadOp>, but no
/// memref buffer is allocated and the SCF loop is fully unrolled.
///
/// ```
/// E.g.:
/// ```
/// %vec = vector.transfer_read %A[%a, %b, %c], %padding
/// : memref<?x?x?xf32>, vector<5x4xf32>
/// ```
/// is rewritten to IR such as (simplified):
/// ```
/// %v_init = splat %padding : vector<5x4xf32>
/// %tmp0 = vector.transfer_read %A[%a, %b, %c], %padding
/// : memref<?x?x?xf32>, vector<4xf32>
/// %v0 = vector.insert %tmp0, %v_init[0] : vector<4xf32> into vector<5x4xf32>
/// %tmp1 = vector.transfer_read %A[%a, %b + 1, %c], %padding
/// : memref<?x?x?xf32>, vector<4xf32>
/// %v1 = vector.insert %tmp1, %v0[1] : vector<4xf32> into vector<5x4xf32>
/// ...
/// %tmp4 = vector.transfer_read %A[%a, %b + 4, %c], %padding
/// : memref<?x?x?xf32>, vector<4xf32>
/// %vec = vector.insert %tmp1, %v3[4] : vector<4xf32> into vector<5x4xf32>
/// ```
///
/// Note: As an optimization, if the result of the original TransferReadOp
/// was directly inserted into another vector, no new %v_init vector is created.
/// Instead, the new TransferReadOp results are inserted into that vector.
struct UnrollTransferReadConversion
: public VectorToSCFPattern<TransferReadOp> {
using VectorToSCFPattern<TransferReadOp>::VectorToSCFPattern;
void initialize() {
// This pattern recursively unpacks one dimension at a time. The recursion
// bounded as the rank is strictly decreasing.
setHasBoundedRewriteRecursion();
}
/// Return the vector into which the newly created TransferReadOp results
/// are inserted.
Value getResultVector(TransferReadOp xferOp,
PatternRewriter &rewriter) const {
if (auto insertOp = getInsertOp(xferOp))
return insertOp.getDest();
Location loc = xferOp.getLoc();
return rewriter.create<vector::SplatOp>(loc, xferOp.getVectorType(),
xferOp.getPadding());
}
/// If the result of the TransferReadOp has exactly one user, which is a
/// vector::InsertOp, return that operation.
vector::InsertOp getInsertOp(TransferReadOp xferOp) const {
if (xferOp->hasOneUse()) {
Operation *xferOpUser = *xferOp->getUsers().begin();
if (auto insertOp = dyn_cast<vector::InsertOp>(xferOpUser))
return insertOp;
}
return vector::InsertOp();
}
/// If the result of the TransferReadOp has exactly one user, which is a
/// vector::InsertOp, return that operation's indices.
void getInsertionIndices(TransferReadOp xferOp,
SmallVectorImpl<OpFoldResult> &indices) const {
if (auto insertOp = getInsertOp(xferOp)) {
auto pos = insertOp.getMixedPosition();
indices.append(pos.begin(), pos.end());
}
}
/// Rewrite the op: Unpack one dimension. Can handle masks, out-of-bounds
/// accesses, and broadcasts and transposes in permutation maps.
LogicalResult matchAndRewrite(TransferReadOp xferOp,
PatternRewriter &rewriter) const override {
if (xferOp.getVectorType().getRank() <= options.targetRank)
return failure();
if (isTensorOp(xferOp) && !options.lowerTensors)
return failure();
// Transfer ops that modify the element type are not supported atm.
if (xferOp.getVectorType().getElementType() !=
xferOp.getShapedType().getElementType())
return failure();
auto insertOp = getInsertOp(xferOp);
auto vec = getResultVector(xferOp, rewriter);
auto vecType = dyn_cast<VectorType>(vec.getType());
auto xferVecType = xferOp.getVectorType();
if (xferVecType.getScalableDims()[0]) {
// Cannot unroll a scalable dimension at compile time.
return failure();
}
VectorType newXferVecType = VectorType::Builder(xferVecType).dropDim(0);
int64_t dimSize = xferVecType.getShape()[0];
// Generate fully unrolled loop of transfer ops.
Location loc = xferOp.getLoc();
for (int64_t i = 0; i < dimSize; ++i) {
Value iv = rewriter.create<arith::ConstantIndexOp>(loc, i);
vec = generateInBoundsCheck(
rewriter, xferOp, iv, unpackedDim(xferOp), TypeRange(vecType),
/*inBoundsCase=*/
[&](OpBuilder &b, Location loc) {
// Indices for the new transfer op.
SmallVector<Value, 8> xferIndices;
getXferIndices(b, xferOp, iv, xferIndices);
// Indices for the new vector.insert op.
SmallVector<OpFoldResult, 8> insertionIndices;
getInsertionIndices(xferOp, insertionIndices);
insertionIndices.push_back(rewriter.getIndexAttr(i));
auto inBoundsAttr = dropFirstElem(b, xferOp.getInBoundsAttr());
auto newXferOp = b.create<vector::TransferReadOp>(
loc, newXferVecType, xferOp.getSource(), xferIndices,
AffineMapAttr::get(unpackedPermutationMap(b, xferOp)),
xferOp.getPadding(), Value(), inBoundsAttr);
maybeAssignMask(b, xferOp, newXferOp, i);
return b.create<vector::InsertOp>(loc, newXferOp, vec,
insertionIndices);
},
/*outOfBoundsCase=*/
[&](OpBuilder &b, Location loc) {
// Loop through original (unmodified) vector.
return vec;
});
}
if (insertOp) {
// Rewrite single user of the old TransferReadOp, which was an InsertOp.
rewriter.replaceOp(insertOp, vec);
rewriter.eraseOp(xferOp);
} else {
rewriter.replaceOp(xferOp, vec);
}
return success();
}
};
/// Progressive lowering of vector TransferWriteOp with unrolling: Unpack one
/// dimension. This is similar to TransferOpConversion<TransferWriteOp>, but no
/// memref buffer is allocated and the SCF loop is fully unrolled.
///
/// ```
/// E.g.:
/// ```
/// vector.transfer_write %vec, %A[%a, %b, %c]
/// : vector<5x4xf32>, memref<?x?x?xf32>
/// ```
/// is rewritten to IR such as (simplified):
/// ```
/// %v0 = vector.extract %vec[0] : vector<4xf32> from vector<5x4xf32>
/// vector.transfer_write %v0, %A[%a, %b, %c] : vector<4xf32>, memref<...>
/// %v1 = vector.extract %vec[1] : vector<4xf32> from vector<5x4xf32>
/// vector.transfer_write %v1, %A[%a, %b + 1, %c] : vector<4xf32>, memref<...>
/// ...
/// %v4 = vector.extract %vec[4] : vector<4xf32> from vector<5x4xf32>
/// vector.transfer_write %v4, %A[%a, %b + 4, %c] : vector<4xf32>, memref<...>
/// ```
///
/// Note: As an optimization, if the vector of the original TransferWriteOp
/// was directly extracted from another vector via an ExtractOp `a`, extract
/// the vectors for the newly generated TransferWriteOps from `a`'s input. By
/// doing so, `a` may become dead, and the number of ExtractOps generated during
/// recursive application of this pattern will be minimal.
struct UnrollTransferWriteConversion
: public VectorToSCFPattern<TransferWriteOp> {
using VectorToSCFPattern<TransferWriteOp>::VectorToSCFPattern;
void initialize() {
// This pattern recursively unpacks one dimension at a time. The recursion
// bounded as the rank is strictly decreasing.
setHasBoundedRewriteRecursion();
}
/// Return the vector from which newly generated ExtracOps will extract.
Value getDataVector(TransferWriteOp xferOp) const {
if (auto extractOp = getExtractOp(xferOp))
return extractOp.getVector();
return xferOp.getVector();
}
/// If the input of the given TransferWriteOp is an ExtractOp, return it.
vector::ExtractOp getExtractOp(TransferWriteOp xferOp) const {
if (auto *op = xferOp.getVector().getDefiningOp())
return dyn_cast<vector::ExtractOp>(op);
return vector::ExtractOp();
}
/// If the input of the given TransferWriteOp is an ExtractOp, return its
/// indices.
void getExtractionIndices(TransferWriteOp xferOp,
SmallVectorImpl<OpFoldResult> &indices) const {
if (auto extractOp = getExtractOp(xferOp)) {
auto pos = extractOp.getMixedPosition();
indices.append(pos.begin(), pos.end());
}
}
/// Rewrite the op: Unpack one dimension. Can handle masks, out-of-bounds
/// accesses, and broadcasts and transposes in permutation maps.
LogicalResult matchAndRewrite(TransferWriteOp xferOp,
PatternRewriter &rewriter) const override {
VectorType inputVectorTy = xferOp.getVectorType();
if (inputVectorTy.getRank() <= options.targetRank)
return failure();
if (isTensorOp(xferOp) && !options.lowerTensors)
return failure();
// Transfer ops that modify the element type are not supported atm.
if (inputVectorTy.getElementType() !=
xferOp.getShapedType().getElementType())
return failure();
auto vec = getDataVector(xferOp);
if (inputVectorTy.getScalableDims()[0]) {
// Cannot unroll a scalable dimension at compile time.
return failure();
}
int64_t dimSize = inputVectorTy.getShape()[0];
Value source = xferOp.getSource(); // memref or tensor to be written to.
auto sourceType = isTensorOp(xferOp) ? xferOp.getShapedType() : Type();
// Generate fully unrolled loop of transfer ops.
Location loc = xferOp.getLoc();
for (int64_t i = 0; i < dimSize; ++i) {
Value iv = rewriter.create<arith::ConstantIndexOp>(loc, i);
auto updatedSource = generateInBoundsCheck(
rewriter, xferOp, iv, unpackedDim(xferOp),
isTensorOp(xferOp) ? TypeRange(sourceType) : TypeRange(),
/*inBoundsCase=*/
[&](OpBuilder &b, Location loc) {
// Indices for the new transfer op.
SmallVector<Value, 8> xferIndices;
getXferIndices(b, xferOp, iv, xferIndices);
// Indices for the new vector.extract op.
SmallVector<OpFoldResult, 8> extractionIndices;
getExtractionIndices(xferOp, extractionIndices);
extractionIndices.push_back(b.getI64IntegerAttr(i));
auto extracted =
b.create<vector::ExtractOp>(loc, vec, extractionIndices);
auto inBoundsAttr = dropFirstElem(b, xferOp.getInBoundsAttr());
Value xferVec;
if (inputVectorTy.getRank() == 1) {
// When target-rank=0, unrolling would causes the vector input
// argument into `transfer_write` to become a scalar. We solve
// this by broadcasting the scalar to a 0D vector.
xferVec = b.create<vector::BroadcastOp>(
loc, VectorType::get({}, extracted.getType()), extracted);
} else {
xferVec = extracted;
}
auto newXferOp = b.create<vector::TransferWriteOp>(
loc, sourceType, xferVec, source, xferIndices,
AffineMapAttr::get(unpackedPermutationMap(b, xferOp)), Value(),
inBoundsAttr);
maybeAssignMask(b, xferOp, newXferOp, i);
return isTensorOp(xferOp) ? newXferOp->getResult(0) : Value();
},
/*outOfBoundsCase=*/
[&](OpBuilder &b, Location loc) {
return isTensorOp(xferOp) ? source : Value();
});
if (isTensorOp(xferOp))
source = updatedSource;
}
if (isTensorOp(xferOp))
rewriter.replaceOp(xferOp, source);
else
rewriter.eraseOp(xferOp);
return success();
}
};
} // namespace lowering_n_d_unrolled
namespace lowering_1_d {
/// Compute the indices into the memref for the LoadOp/StoreOp generated as
/// part of TransferOp1dConversion. Return the memref dimension on which
/// the transfer is operating. A return value of std::nullopt indicates a
/// broadcast.
template <typename OpTy>
static std::optional<int64_t>
get1dMemrefIndices(OpBuilder &b, OpTy xferOp, Value iv,
SmallVector<Value, 8> &memrefIndices) {
auto indices = xferOp.getIndices();
auto map = xferOp.getPermutationMap();
assert(xferOp.getTransferRank() > 0 && "unexpected 0-d transfer");
memrefIndices.append(indices.begin(), indices.end());
assert(map.getNumResults() == 1 &&
"Expected 1 permutation map result for 1D transfer");
if (auto expr = dyn_cast<AffineDimExpr>(map.getResult(0))) {
Location loc = xferOp.getLoc();
auto dim = expr.getPosition();
AffineExpr d0, d1;
bindDims(xferOp.getContext(), d0, d1);
Value offset = memrefIndices[dim];
memrefIndices[dim] =
affine::makeComposedAffineApply(b, loc, d0 + d1, {offset, iv});
return dim;
}
assert(xferOp.isBroadcastDim(0) &&
"Expected AffineDimExpr or AffineConstantExpr");
return std::nullopt;
}
/// Codegen strategy for TransferOp1dConversion, depending on the
/// operation.
template <typename OpTy>
struct Strategy1d;
/// Codegen strategy for TransferReadOp.
template <>
struct Strategy1d<TransferReadOp> {
static void generateForLoopBody(OpBuilder &b, Location loc,
TransferReadOp xferOp, Value iv,
ValueRange loopState) {
SmallVector<Value, 8> indices;
auto dim = get1dMemrefIndices(b, xferOp, iv, indices);
auto vec = loopState[0];
// In case of out-of-bounds access, leave `vec` as is (was initialized with
// padding value).
auto nextVec = generateInBoundsCheck(
b, xferOp, iv, dim, TypeRange(xferOp.getVectorType()),
/*inBoundsCase=*/
[&](OpBuilder &b, Location loc) {
Value val =
b.create<memref::LoadOp>(loc, xferOp.getSource(), indices);
return b.create<vector::InsertElementOp>(loc, val, vec, iv);
},
/*outOfBoundsCase=*/
[&](OpBuilder & /*b*/, Location loc) { return vec; });
b.create<scf::YieldOp>(loc, nextVec);
}
static Value initialLoopState(OpBuilder &b, TransferReadOp xferOp) {
// Inititalize vector with padding value.
Location loc = xferOp.getLoc();
return b.create<vector::SplatOp>(loc, xferOp.getVectorType(),
xferOp.getPadding());
}
};
/// Codegen strategy for TransferWriteOp.
template <>
struct Strategy1d<TransferWriteOp> {
static void generateForLoopBody(OpBuilder &b, Location loc,
TransferWriteOp xferOp, Value iv,
ValueRange /*loopState*/) {
SmallVector<Value, 8> indices;
auto dim = get1dMemrefIndices(b, xferOp, iv, indices);
// Nothing to do in case of out-of-bounds access.
generateInBoundsCheck(
b, xferOp, iv, dim,
/*inBoundsCase=*/[&](OpBuilder &b, Location loc) {
auto val =
b.create<vector::ExtractElementOp>(loc, xferOp.getVector(), iv);
b.create<memref::StoreOp>(loc, val, xferOp.getSource(), indices);
});
b.create<scf::YieldOp>(loc);
}
static Value initialLoopState(OpBuilder &b, TransferWriteOp xferOp) {
return Value();
}
};
/// Lower a 1D vector transfer op to SCF using scalar loads/stores. This is
/// necessary in cases where a 1D vector transfer op cannot be lowered into
/// vector load/stores due to non-unit strides or broadcasts:
///
/// * Transfer dimension is not the last memref dimension
/// * Transfer dimension is a broadcast (i.e., scalar load + broadcast)
/// * Memref has a layout map with non-unit stride on the last dimension
///
/// This pattern generates IR as follows:
///
/// 1. Generate a for loop iterating over each vector element.
/// 2. Inside the loop, generate a InsertElementOp or ExtractElementOp,
/// depending on OpTy.
///
/// TODO: In some cases (no masking, etc.), LLVM::MatrixColumnMajorLoadOp
/// can be generated instead of TransferOp1dConversion. Add such a pattern
/// to ConvertVectorToLLVM.
///
/// E.g.:
/// ```
/// vector.transfer_write %vec, %A[%a, %b]
/// {permutation_map = affine_map<(d0, d1) -> (d0)>, in_bounds = [true]}
/// : vector<9xf32>, memref<?x?xf32>
/// ```
/// Is rewritten to approximately the following pseudo-IR:
/// ```
/// for i = 0 to 9 {
/// %t = vector.extractelement %vec[i] : vector<9xf32>
/// memref.store %t, %arg0[%a + i, %b] : memref<?x?xf32>
/// }
/// ```
template <typename OpTy>
struct TransferOp1dConversion : public VectorToSCFPattern<OpTy> {
using VectorToSCFPattern<OpTy>::VectorToSCFPattern;
LogicalResult matchAndRewrite(OpTy xferOp,
PatternRewriter &rewriter) const override {
// TODO: support 0-d corner case.
if (xferOp.getTransferRank() == 0)
return failure();
auto map = xferOp.getPermutationMap();
auto memRefType = dyn_cast<MemRefType>(xferOp.getShapedType());
if (!memRefType)
return failure();
if (xferOp.getVectorType().getRank() != 1)
return failure();
if (map.isMinorIdentity() && isLastMemrefDimUnitStride(memRefType))
return failure(); // Handled by ConvertVectorToLLVM
// Loop bounds, step, state...
Location loc = xferOp.getLoc();
auto vecType = xferOp.getVectorType();
auto lb = rewriter.create<arith::ConstantIndexOp>(loc, 0);
Value ub =
rewriter.create<arith::ConstantIndexOp>(loc, vecType.getDimSize(0));
if (vecType.isScalable()) {
Value vscale =
rewriter.create<vector::VectorScaleOp>(loc, rewriter.getIndexType());
ub = rewriter.create<arith::MulIOp>(loc, ub, vscale);
}
auto step = rewriter.create<arith::ConstantIndexOp>(loc, 1);
auto loopState = Strategy1d<OpTy>::initialLoopState(rewriter, xferOp);
// Generate for loop.
rewriter.replaceOpWithNewOp<scf::ForOp>(
xferOp, lb, ub, step, loopState ? ValueRange(loopState) : ValueRange(),
[&](OpBuilder &b, Location loc, Value iv, ValueRange loopState) {
Strategy1d<OpTy>::generateForLoopBody(b, loc, xferOp, iv, loopState);
});
return success();
}
};
} // namespace lowering_1_d
} // namespace
void mlir::populateVectorToSCFConversionPatterns(
RewritePatternSet &patterns, const VectorTransferToSCFOptions &options) {
if (options.unroll) {
patterns.add<lowering_n_d_unrolled::UnrollTransferReadConversion,
lowering_n_d_unrolled::UnrollTransferWriteConversion>(
patterns.getContext(), options);
} else {
patterns.add<lowering_n_d::PrepareTransferReadConversion,
lowering_n_d::PrepareTransferWriteConversion,
lowering_n_d::TransferOpConversion<TransferReadOp>,
lowering_n_d::TransferOpConversion<TransferWriteOp>>(
patterns.getContext(), options);
}
if (options.targetRank == 1) {
patterns.add<lowering_1_d::TransferOp1dConversion<TransferReadOp>,
lowering_1_d::TransferOp1dConversion<TransferWriteOp>>(
patterns.getContext(), options);
}
patterns.add<lowering_n_d::DecomposePrintOpConversion>(patterns.getContext(),
options);
}
namespace {
struct ConvertVectorToSCFPass
: public impl::ConvertVectorToSCFBase<ConvertVectorToSCFPass> {
ConvertVectorToSCFPass() = default;
ConvertVectorToSCFPass(const VectorTransferToSCFOptions &options) {
this->fullUnroll = options.unroll;
this->targetRank = options.targetRank;
this->lowerTensors = options.lowerTensors;
}
void runOnOperation() override {
VectorTransferToSCFOptions options;
options.unroll = fullUnroll;
options.targetRank = targetRank;
options.lowerTensors = lowerTensors;
// Lower permutation maps first.
RewritePatternSet lowerTransferPatterns(&getContext());
mlir::vector::populateVectorTransferPermutationMapLoweringPatterns(
lowerTransferPatterns);
(void)applyPatternsAndFoldGreedily(getOperation(),
std::move(lowerTransferPatterns));
RewritePatternSet patterns(&getContext());
populateVectorToSCFConversionPatterns(patterns, options);
(void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns));
}
};
} // namespace
std::unique_ptr<Pass>
mlir::createConvertVectorToSCFPass(const VectorTransferToSCFOptions &options) {
return std::make_unique<ConvertVectorToSCFPass>(options);
}
|