1#include <gtest/gtest.h>
2
3#include <limits>
4#include <memory>
5#include <sstream>
6#include <stdexcept>
7#include <unordered_map>
8
9#include <test/cpp/tensorexpr/test_base.h>
10
11#include <c10/util/irange.h>
12#include <test/cpp/tensorexpr/padded_buffer.h>
13#include <torch/csrc/jit/tensorexpr/analysis.h>
14#include <torch/csrc/jit/tensorexpr/eval.h>
15#include <torch/csrc/jit/tensorexpr/ir.h>
16#include <torch/csrc/jit/tensorexpr/ir_printer.h>
17#include <torch/csrc/jit/tensorexpr/ir_simplifier.h>
18#include <torch/csrc/jit/tensorexpr/loopnest.h>
19#include <torch/csrc/jit/tensorexpr/tensor.h>
20#include <torch/csrc/jit/testing/file_check.h>
21
22namespace torch {
23namespace jit {
24
25using namespace torch::jit::tensorexpr;
26
27TEST(Reductions, ReduceSum0D_1) {
28 const int M = 10;
29
30 BufHandle b("b", {M}, kFloat);
31 std::vector<float> in(M);
32 for (const auto j : c10::irange(M)) {
33 in[j] = j;
34 }
35
36 std::vector<float> out(M, -1.f);
37
38 Tensor c = Reduce("sum", {M}, Sum(), b, {});
39 LoopNest loop({c});
40 loop.prepareForCodegen();
41 StmtPtr s = loop.root_stmt();
42 s = IRSimplifier::simplify(s);
43
44 SimpleIREvaluator cg(s, {b, c});
45
46 cg.call({in, out});
47 for (const auto i : c10::irange(M)) {
48 ASSERT_EQ(out[i], in[i]);
49 }
50}
51
52TEST(Reductions, ReduceSum0D_2) {
53 BufHandle b("b", {}, kFloat);
54 std::vector<float> in(1);
55 in[0] = 77.7;
56
57 std::vector<float> out(1, -1.f);
58
59 Tensor c = Reduce("sum", {}, Sum(), b, {});
60 LoopNest loop({c});
61 loop.prepareForCodegen();
62 StmtPtr s = loop.root_stmt();
63 s = IRSimplifier::simplify(s);
64
65 SimpleIREvaluator cg(s, {b, c});
66
67 cg.call({in, out});
68 ASSERT_EQ(out[0], in[0]);
69}
70
71// Sum an array to a single value.
72TEST(Reductions, ReduceSum1D) {
73 BufHandle b("b", {10}, kFloat);
74 std::vector<float> in(10);
75 for (const auto j : c10::irange(10)) {
76 in[j] = j;
77 }
78
79 std::vector<float> out(1, -1.f);
80
81 Tensor c = Reduce("sum", {}, Sum(), b, {10});
82 LoopNest loop({c});
83 loop.prepareForCodegen();
84 StmtPtr s = loop.root_stmt();
85 s = IRSimplifier::simplify(s);
86
87 SimpleIREvaluator cg(s, {b, c});
88
89 cg.call({in, out});
90 ASSERT_EQ(out[0], 45);
91}
92// Sum a 2D tensor to a 1D tensor with dynamic shapes.
93TEST(Reductions, ReduceSum2D) {
94 const int M = 3;
95 const int N = 7;
96
97 VarHandle m("m", kInt);
98 VarHandle n("n", kInt);
99
100 BufHandle b("b", {m, n}, kFloat);
101 std::vector<float> in(M * N);
102 for (const auto i : c10::irange(M)) {
103 for (const auto j : c10::irange(N)) {
104 in[i * N + j] = j;
105 }
106 }
107
108 std::vector<float> out(M, -1.f);
109
110 Tensor c = Reduce("sum", {M}, Sum(), b, {N});
111 LoopNest loop({c});
112 loop.prepareForCodegen();
113 StmtPtr s = loop.root_stmt();
114 s = IRSimplifier::simplify(s);
115
116 SimpleIREvaluator cg(s, {b, c, n, m});
117
118 cg.call({in, out, 5, 7});
119
120 float expected = 0;
121 for (const auto i : c10::irange(N)) {
122 // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
123 expected += i;
124 }
125
126 for (const auto i : c10::irange(M)) {
127 ASSERT_EQ(out[i], expected);
128 }
129}
130
131// Sum a 3D tensor to both a 2D and 1D tensor, then reduce the 2D tensor flat to
132// check our work.
133TEST(Reductions, ReduceSum3D) {
134 const int M = 10;
135 VarHandle m("m", kInt);
136
137 BufHandle b("b", {2, 3, m}, kFloat);
138
139 Tensor c = Reduce("sum", {2, 3}, Sum(), b, {m});
140 LoopNest loop({c});
141 loop.prepareForCodegen();
142 StmtPtr s = loop.root_stmt();
143 s = IRSimplifier::simplify(s);
144
145 SimpleIREvaluator cg(s, {b, c, m});
146
147 std::vector<float> bData(2 * 3 * M, 0);
148 std::vector<float> cData(2 * 3, 6.0f);
149 std::vector<float> dData(2, 1.0f);
150 std::vector<float> eData(2, 1.0f);
151
152 for (int i = 0; i < 2 * 3; ++i) {
153 for (const auto j : c10::irange(M)) {
154 bData[i * M + j] = j;
155 }
156 }
157
158 cg.call({bData, cData, M});
159 float expected = 0;
160 for (const auto i : c10::irange(M)) {
161 // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
162 expected += i;
163 }
164
165 for (int i = 0; i < 2 * 3; ++i) {
166 ASSERT_EQ(cData[i], expected);
167 }
168
169 Tensor d = Reduce("sum2", {2}, Sum(), b, {3, m});
170 LoopNest loop2({d});
171 loop2.prepareForCodegen();
172 StmtPtr s2 = loop2.root_stmt();
173 s2 = IRSimplifier::simplify(s2);
174
175 SimpleIREvaluator cg2(s2, {b, d, m});
176 cg2.call({bData, dData, M});
177
178 // We're combining an additional dimension of 3, so the sum is 3x.
179 expected = expected * 3;
180
181 for (const auto i : c10::irange(2)) {
182 ASSERT_EQ(dData[i], expected);
183 }
184
185 // This is the same as just reducing the original result across that axis.
186 BufHandle c_buf(c.buf());
187 Tensor e = Reduce("sum3", {2}, Sum(), c_buf, {3});
188 LoopNest loop3({e});
189 loop3.prepareForCodegen();
190 StmtPtr s3 = loop3.root_stmt();
191 s3 = IRSimplifier::simplify(s3);
192
193 SimpleIREvaluator cg3(s3, {c, e});
194 cg3.call({cData, eData});
195
196 for (const auto i : c10::irange(2)) {
197 ASSERT_EQ(eData[i], expected);
198 }
199}
200
201// Sum a large (10 D) Tensor 5 dimensions in.
202TEST(Reductions, ReduceSum10D) {
203 BufHandle in_("in_", {2, 3, 2, 3, 2, 3, 2, 3, 2, 3}, kFloat);
204 const int InputSize = 2 * 3 * 2 * 3 * 2 * 3 * 2 * 3 * 2 * 3;
205 BufHandle out_("out_", {2, 3, 2, 3, 2}, kFloat);
206 const int OutputSize = 2 * 3 * 2 * 3 * 2;
207
208 std::vector<float> in(InputSize, 1.f);
209 std::vector<float> out(OutputSize, -1.f);
210
211 Tensor c = Reduce("sum", {2, 3, 2, 3, 2}, Sum(), in_, {3, 2, 3, 2, 3});
212 LoopNest loop({c});
213 loop.prepareForCodegen();
214 StmtPtr s = loop.root_stmt();
215 s = IRSimplifier::simplify(s);
216
217 SimpleIREvaluator cg(s, {in_, c});
218
219 cg.call({in, out});
220
221 // NOLINTNEXTLINE(bugprone-integer-division)
222 float expected = InputSize / OutputSize;
223 for (const auto i : c10::irange(OutputSize)) {
224 ASSERT_EQ(out[i], expected);
225 }
226}
227
228// Reduce via Mul rather than Add using a custom Reducer.
229TEST(Reductions, ReduceProduct) {
230 const int M = 4;
231 const int N = 4;
232
233 BufHandle b("b", {M, N}, kFloat);
234 std::vector<float> in(M * N);
235 for (const auto i : c10::irange(M)) {
236 for (const auto j : c10::irange(N)) {
237 in[i * N + j] = 2 + j;
238 }
239 }
240
241 std::vector<float> out(M, -1.f);
242
243 Reducer product(
244 ExprHandle(1.f), [](ExprHandle a, ExprHandle b) { return a * b; });
245
246 Tensor c = Reduce("product", {M}, product, b, {N});
247 LoopNest loop({c});
248 loop.prepareForCodegen();
249 StmtPtr s = loop.root_stmt();
250 s = IRSimplifier::simplify(s);
251
252 SimpleIREvaluator cg(s, {b, c});
253
254 cg.call({in, out});
255
256 float expected = 1;
257 for (const auto i : c10::irange(N)) {
258 // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
259 expected *= 2 + i;
260 }
261
262 for (const auto i : c10::irange(M)) {
263 ASSERT_EQ(out[i], expected);
264 }
265}
266
267// Maximum reductions.
268TEST(Reductions, ReduceMax) {
269 BufHandle in_("b", {10}, kFloat);
270
271 std::vector<float> in(10);
272 std::vector<float> out(1, -1.f);
273 for (const auto j : c10::irange(10)) {
274 in[j] = j;
275 }
276
277 Tensor dm1 = Reduce("max", {}, Maximum(kFloat), in_, {10});
278
279 LoopNest loop({dm1});
280 loop.prepareForCodegen();
281 StmtPtr s = loop.root_stmt();
282 s = IRSimplifier::simplify(s);
283 SimpleIREvaluator cg(s, {in_, dm1});
284
285 cg.call({in, out});
286
287 ASSERT_EQ(out[0], 9);
288
289 BufHandle in2_("b", {2, 5}, kFloat);
290 std::vector<float> out2(2, -1.f);
291
292 Tensor m2d = Reduce("max", {2}, Maximum(kFloat), in2_, {5});
293
294 LoopNest loop2({m2d});
295 loop2.prepareForCodegen();
296 s = loop2.root_stmt();
297 s = IRSimplifier::simplify(s);
298
299 SimpleIREvaluator cg2(s, {in2_, m2d});
300 cg2.call({in, out2});
301
302 ASSERT_EQ(out2[0], 4);
303 ASSERT_EQ(out2[1], 9);
304}
305
306// Minimum reduction, with custom initialization.
307TEST(Reductions, ReduceMinCustomInitializer) {
308 VarHandle minInit("minInit", kFloat);
309 BufHandle in_("b", {10}, kFloat);
310
311 std::vector<float> in(10);
312 std::vector<float> out(1, -1.f);
313 for (const auto j : c10::irange(10)) {
314 in[j] = 10 + j;
315 }
316
317 Tensor min = Reduce(
318 "min",
319 {},
320 Minimum(ExprHandle(minInit)),
321 [&](ParameterList& v) { return in_.load(v); },
322 {10});
323
324 LoopNest loop({min});
325 loop.prepareForCodegen();
326 StmtPtr s = loop.root_stmt();
327 s = IRSimplifier::simplify(s);
328
329 SimpleIREvaluator cg(s, {in_, min, minInit});
330
331 // Works normally (note that out data starts lower than the correct
332 // minimum).
333 cg.call({in, out, std::numeric_limits<float>::max()});
334 ASSERT_EQ(out[0], 10);
335
336 // With an initalizer lower than the min, that's the min.
337 cg.call({in, out, 5.f});
338 ASSERT_EQ(out[0], 5);
339}
340
341// Example implementation of Any/All.
342// TODO: this is very awkward without logical And/Or operators.
343TEST(Reductions, ReduceAnyAll) {
344 VarHandle searchValue("searchValue", kInt);
345 BufHandle b("b", {4, 10}, kInt);
346
347 Reducer anyEqSV(ExprHandle(0), [](ExprHandle a, ExprHandle b) {
348 return CompareSelect::make(a, 1, 1, b, kEQ);
349 });
350
351 Tensor any = Reduce(
352 "anyEqual",
353 {4},
354 anyEqSV,
355 [&](const auto& i, const auto& j) {
356 return CompareSelect::make(b.load(i, j), searchValue, kEQ);
357 },
358 {10});
359
360 LoopNest loop({any});
361 loop.prepareForCodegen();
362 StmtPtr s = loop.root_stmt();
363 s = IRSimplifier::simplify(s);
364
365 SimpleIREvaluator cg(s, {b, any, searchValue});
366
367 std::vector<int> in(40, 0);
368 std::vector<int> out(4, 0);
369
370 // input has 0-39 in 4 rows.
371 for (const auto i : c10::irange(40)) {
372 in[i] = i;
373 }
374 cg.call({in, out, 1});
375
376 // only the first row has 1
377 ASSERT_EQ(out[0], 1);
378 ASSERT_EQ(out[1], 0);
379 ASSERT_EQ(out[2], 0);
380 ASSERT_EQ(out[3], 0);
381
382 cg.call({in, out, 15});
383
384 // 15 in the 3rd row
385 ASSERT_EQ(out[0], 0);
386 ASSERT_EQ(out[1], 1);
387 ASSERT_EQ(out[2], 0);
388 ASSERT_EQ(out[3], 0);
389
390 Reducer allGTSV(ExprHandle(1), [](ExprHandle a, ExprHandle b) {
391 return CompareSelect::make(a, 0, 0, b, kEQ);
392 });
393
394 Tensor allGreaterThan = Reduce(
395 "allGreaterThan",
396 {4},
397 allGTSV,
398 [&](const auto& i, const auto& j) {
399 return CompareSelect::make(b.load(i, j), searchValue, kGT);
400 },
401 {10});
402
403 LoopNest loop2({allGreaterThan});
404 loop2.prepareForCodegen();
405 s = loop2.root_stmt();
406 s = IRSimplifier::simplify(s);
407
408 SimpleIREvaluator cg2(s, {b, allGreaterThan, searchValue});
409
410 cg2.call({in, out, 11});
411
412 // 11 is in row 2.
413 ASSERT_EQ(out[0], 0);
414 ASSERT_EQ(out[1], 0);
415 ASSERT_EQ(out[2], 1);
416 ASSERT_EQ(out[3], 1);
417
418 cg2.call({in, out, -3});
419
420 // All are positive.
421 ASSERT_EQ(out[0], 1);
422 ASSERT_EQ(out[1], 1);
423 ASSERT_EQ(out[2], 1);
424 ASSERT_EQ(out[3], 1);
425}
426
427TEST(Reductions, ReduceMatmul2D) {
428 BufHandle tA("tA", {3, 2}, kFloat);
429 BufHandle tB("tB", {2, 3}, kFloat);
430
431 std::vector<float> tA_(6);
432 std::vector<float> tB_(6);
433
434 std::vector<float> out(9, -1.f);
435 for (const auto i : c10::irange(3)) {
436 for (const auto j : c10::irange(2)) {
437 tA_[i * 2 + j] = i * 2 + j;
438 tB_[j * 3 + i] = i * 2 + j;
439 }
440 }
441
442 Tensor mm = Reduce(
443 "mm",
444 {3, 3},
445 Sum(),
446 [&](const ExprHandle& m, const ExprHandle& n, const ExprHandle& k) {
447 return tA.load(m, k) * tB.load(k, n);
448 },
449 {2});
450
451 LoopNest loop({mm});
452 loop.prepareForCodegen();
453 StmtPtr s = loop.root_stmt();
454 s = IRSimplifier::simplify(s);
455
456 SimpleIREvaluator cg(s, {tA, tB, mm});
457 cg.call({tA_, tB_, out});
458
459 std::vector<float> expected(
460 {1.f, 3.f, 5.f, 3.f, 13.f, 23.f, 5.f, 23.f, 41.f});
461
462 for (const auto i : c10::irange(9)) {
463 ASSERT_EQ(out[i], expected[i]);
464 }
465}
466
467TEST(Reductions, ReduceRfactorLike) {
468 BufHandle in("in", {10, 10}, kFloat);
469 std::vector<float> in_(100);
470 for (const auto i : c10::irange(100)) {
471 in_[i] = i;
472 }
473 std::vector<float> in_rf_(10, -2.f);
474 std::vector<float> out(1, -1.f);
475
476 Tensor l1 = Reduce("l1", {10}, Sum(), in, {10});
477 BufHandle in_rf(l1.buf());
478
479 Tensor l2 = Reduce("l2", {}, Sum(), in_rf, {10});
480
481 LoopNest loop({l1, l2});
482 loop.prepareForCodegen();
483 StmtPtr s = loop.root_stmt();
484 s = IRSimplifier::simplify(s);
485
486 SimpleIREvaluator cg(s, {in, l1, l2});
487 cg.call({in_, in_rf_, out});
488
489 ASSERT_EQ(out[0], 99 * 50);
490}
491
492TEST(Reductions, ReduceAsProducer) {
493 const int M = 10;
494 VarHandle m("m", kInt);
495
496 BufHandle a("a", {2, 3}, kFloat);
497 BufHandle b("b", {2, 3, m}, kFloat);
498
499 Tensor c = Reduce("sum", {2, 3}, Sum(), b, {m});
500 Tensor d =
501 Compute("scale", {2, 3}, [&](const VarHandle& l, const VarHandle& n) {
502 return c.load(l, n) * a.load(l, n);
503 });
504 LoopNest loop({d}, {c, d});
505 loop.prepareForCodegen();
506 StmtPtr s = loop.root_stmt();
507 s = IRSimplifier::simplify(s);
508
509 SimpleIREvaluator cg(s, {a, b, d, m});
510
511 std::vector<float> aData(2 * 3, 0);
512 std::vector<float> bData(2 * 3 * M, 0);
513 std::vector<float> dData(2 * 3, 6.0f);
514
515 for (int i = 0; i < 2 * 3; ++i) {
516 aData[i] = 6 - i;
517 for (const auto j : c10::irange(M)) {
518 bData[i * M + j] = j;
519 }
520 }
521
522 cg.call({aData, bData, dData, M});
523 float expected = 0;
524 for (const auto i : c10::irange(M)) {
525 // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
526 expected += i;
527 }
528 for (int i = 0; i < 2 * 3; ++i) {
529 ASSERT_EQ(dData[i], expected * (6 - i));
530 }
531}
532
533TEST(Reductions, ReduceAsConsumer) {
534 const int M = 10;
535 VarHandle m("m", kInt);
536
537 BufHandle a("a", {2, 3, m}, kFloat);
538 BufHandle b("b", {2, 3, m}, kFloat);
539
540 Tensor c = Compute(
541 "scale",
542 {2, 3, m},
543 [&](const VarHandle& l, const VarHandle& n, const VarHandle& m) {
544 return b.load(l, n, m) * a.load(l, n, m);
545 });
546 Tensor d = Reduce("sum", {2}, Sum(), c, {3, m});
547 LoopNest loop({d}, {c, d});
548 loop.prepareForCodegen();
549 StmtPtr s = loop.root_stmt();
550 s = IRSimplifier::simplify(s);
551
552 SimpleIREvaluator cg(s, {a, b, d, m});
553
554 std::vector<float> aData(2 * 3 * M, 0);
555 std::vector<float> bData(2 * 3 * M, 0);
556 std::vector<float> dData(2, 6.0f);
557
558 for (int i = 0; i < 2 * 3; ++i) {
559 for (const auto j : c10::irange(M)) {
560 bData[i * M + j] = j + 1;
561 aData[i * M + j] = 6 - i;
562 }
563 }
564
565 cg.call({aData, bData, dData, M});
566 // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
567 float expected[2] = {0, 0};
568 for (const auto i : c10::irange(2)) {
569 for (const auto j : c10::irange(3)) {
570 for (const auto k : c10::irange(M)) {
571 // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers,bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
572 expected[i] += (k + 1) * (6 - (i * 3 + j));
573 }
574 }
575 }
576
577 for (const auto i : c10::irange(2)) {
578 ASSERT_EQ(dData[i], expected[i]);
579 }
580}
581
582TEST(Reductions, SplitReduceAxis) {
583 BufHandle in("in", {16, 8}, kFloat);
584
585 std::vector<float> in_(16 * 8);
586 for (const auto i : c10::irange(16)) {
587 for (const auto j : c10::irange(8)) {
588 in_[i * 8 + j] = i;
589 }
590 }
591 std::vector<float> out(16, -1.f);
592
593 Tensor tensor = Reduce("sum", {16}, Sum(), in, {8});
594 LoopNest l({tensor});
595 std::vector<ForPtr> loops = l.getLoopStmtsFor(tensor);
596 LoopNest::splitWithTail(loops[1], 2);
597
598 l.prepareForCodegen();
599
600 StmtPtr s = l.root_stmt();
601 s = IRSimplifier::simplify(s);
602
603 SimpleIREvaluator cg(s, {in, tensor});
604 cg.call({in_, out});
605
606 for (const auto i : c10::irange(16)) {
607 ASSERT_EQ(out[i], i * 8);
608 }
609}
610
611TEST(Reductions, SplitNonReduceAxis) {
612 BufHandle in("in", {16, 8}, kFloat);
613
614 std::vector<float> in_(16 * 8);
615 for (const auto i : c10::irange(16)) {
616 for (const auto j : c10::irange(8)) {
617 in_[i * 8 + j] = i;
618 }
619 }
620 std::vector<float> out(16, -1.f);
621 Tensor tensor = Reduce("sum", {16}, Sum(), in, {8});
622 LoopNest l({tensor});
623 std::vector<ForPtr> loops = l.getLoopStmtsFor(tensor);
624 LoopNest::splitWithTail(loops[0], 2);
625 LoopNest::splitWithTail(loops[0], 2);
626
627 l.prepareForCodegen();
628
629 StmtPtr s = l.root_stmt();
630 s = IRSimplifier::simplify(s);
631
632 SimpleIREvaluator cg(s, {in, tensor});
633 cg.call({in_, out});
634
635 for (const auto i : c10::irange(16)) {
636 ASSERT_EQ(out[i], i * 8);
637 }
638}
639
640TEST(Reductions, ReorderedReductionInitializer) {
641 /* From the quip:
642 for k in 0..1: // blockIdx
643 for m in 0..128:
644 for n in 0..64: // threadIdx
645 SumOp(c(k, n), 0, a(k, m, n), {m})
646 */
647
648 BufHandle in("in", {1, 12, 6}, kFloat);
649 std::vector<float> in_(12 * 6, 1.f);
650
651 Tensor tensor_ = Reduce("sum", {1, 12}, Sum(), in, {6});
652 LoopNest l_({tensor_});
653
654 l_.prepareForCodegen();
655 StmtPtr s_ = Stmt::clone(l_.root_stmt());
656 s_ = IRSimplifier::simplify(s_);
657
658 Tensor tensor = Reduce("sum", {1, 12}, Sum(), in, {6});
659 LoopNest l({tensor});
660
661 auto loops = l.getLoopStmtsFor(tensor);
662 loops[0]->set_gpu_block_index(0);
663 loops[1]->set_gpu_thread_index(0);
664
665 LoopNest::reorderAxis(loops[1], loops[2]);
666
667 StmtPtr s = l.root_stmt();
668 // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
669 s = IRSimplifier::simplify(s);
670
671 l.prepareForCodegen();
672
673 s = l.root_stmt();
674 s = IRSimplifier::simplify(s);
675
676 std::vector<float> out1(16, -1.f);
677 SimpleIREvaluator cg(s_, {in, tensor_});
678 cg.call({in_, out1});
679
680 std::vector<float> out2(16, -1.f);
681 SimpleIREvaluator cg2(s, {in, tensor});
682 cg2.call({in_, out2});
683
684 for (const auto i : c10::irange(16)) {
685 ASSERT_EQ(out1[i], out2[i]);
686 }
687}
688
689TEST(Reductions, ReduceRfactor) {
690 const int M = 10;
691 const int N = 10;
692 VarHandle m("m", kInt);
693 VarHandle n("n", kInt);
694
695 BufHandle b("b", {m, n}, kFloat);
696 std::vector<float> in(M * N);
697 for (int j = 0; j < M * N; ++j) {
698 in[j] = j;
699 }
700
701 std::vector<float> out(1, -1.f);
702
703 Tensor c = Reduce("sum", {}, Sum(), b, {m, n});
704 LoopNest loop({c});
705 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
706 auto c_body = loop.getAllWritesToBuf(c.buf())[1];
707 ASSERT_TRUE(loop.rfactor(c_body, loops.at(0)));
708 auto rc = NodeFinder<ReduceOp>::find(loop.root_stmt());
709 ASSERT_EQ(rc.size(), 2);
710 loop.prepareForCodegen();
711 StmtPtr s = loop.root_stmt();
712 s = IRSimplifier::simplify(s);
713
714 SimpleIREvaluator cg(s, {b, c, m, n});
715
716 cg.call({in, out, M, N});
717 ASSERT_EQ(out[0], 4950);
718}
719
720TEST(Reductions, Reduce3DRfactorInner) {
721 const int M = 10;
722 const int N = 10;
723 const int K = 10;
724 VarHandle m("m", kInt);
725 VarHandle n("n", kInt);
726 VarHandle k("k", kInt);
727
728 BufHandle b("b", {m, n, k}, kFloat);
729 std::vector<float> in(M * N * K);
730 for (int j = 0; j < M * N * K; ++j) {
731 in[j] = j;
732 }
733
734 std::vector<float> out(1, -1.f);
735
736 Tensor c = Reduce("sum", {}, Sum(), b, {m, n, k});
737 LoopNest loop({c});
738 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
739 auto c_body = loop.getAllWritesToBuf(c.buf())[1];
740 ASSERT_FALSE(loop.rfactor(c_body, loops.at(2)));
741 auto rc = NodeFinder<ReduceOp>::find(loop.root_stmt());
742 ASSERT_EQ(rc.size(), 1);
743 loop.prepareForCodegen();
744 StmtPtr s = loop.root_stmt();
745 s = IRSimplifier::simplify(s);
746
747 SimpleIREvaluator cg(s, {b, c, m, n, k});
748
749 cg.call({in, out, M, N, K});
750 ASSERT_EQ(out[0], 499500);
751}
752
753TEST(Reductions, Reduce3DRfactorOuter) {
754 const int M = 10;
755 const int N = 10;
756 const int K = 10;
757 VarHandle m("m", kInt);
758 VarHandle n("n", kInt);
759 VarHandle k("k", kInt);
760
761 BufHandle b("b", {m, n, k}, kFloat);
762 std::vector<float> in(M * N * K);
763 for (int j = 0; j < M * N * K; ++j) {
764 in[j] = j;
765 }
766
767 std::vector<float> out(1, -1.f);
768
769 Tensor c = Reduce("sum", {}, Sum(), b, {m, n, k});
770 LoopNest loop({c});
771 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
772 auto c_body = loop.getAllWritesToBuf(c.buf())[1];
773 ASSERT_TRUE(loop.rfactor(c_body, loops.at(0)));
774 auto rc = NodeFinder<ReduceOp>::find(loop.root_stmt());
775 ASSERT_EQ(rc.size(), 2);
776 loop.prepareForCodegen();
777 StmtPtr s = loop.root_stmt();
778 s = IRSimplifier::simplify(s);
779
780 SimpleIREvaluator cg(s, {b, c, m, n, k});
781 cg.call({in, out, M, N, K});
782 ASSERT_EQ(out[0], 499500);
783}
784
785TEST(Reductions, ReduceRepeatedInternalRfactor) {
786 BufHandle in_("in_", {2, 3, 4, 5, 6}, kFloat);
787 const int InputSize = 2 * 3 * 4 * 5 * 6;
788
789 std::vector<float> in(InputSize, 1.f);
790 std::vector<float> out(1, -1.f);
791 std::vector<float> ref(1, -1.f);
792
793 Tensor c = Reduce("sum", {}, Sum(), in_, {2, 3, 4, 5, 6});
794 LoopNest orig_loop({c});
795
796 // Try rfactoring N outer loops
797 for (const auto rfac_number : c10::irange(1, 5)) {
798 LoopNest refloop(orig_loop);
799 LoopNest loop(orig_loop);
800 refloop.prepareForCodegen();
801 SimpleIREvaluator ref_cg(
802 IRSimplifier::simplify(refloop.root_stmt()), {in_, c});
803 ref_cg.call({in, ref});
804
805 BufPtr tmp_buf = c.buf();
806
807 for (const auto idx : c10::irange(rfac_number)) {
808 auto reduce = loop.getAllWritesToBuf(tmp_buf)[1];
809 ASSERT_TRUE(loop.rfactor(
810 reduce, loop.getLoopStmtsFor(tmp_buf).at(idx), &tmp_buf));
811 }
812
813 loop.prepareForCodegen();
814 StmtPtr s = loop.root_stmt();
815 s = IRSimplifier::simplify(s);
816
817 SimpleIREvaluator cg(s, {in_, c});
818 cg.call({in, out});
819
820 ASSERT_EQ(ref[0], out[0]);
821 }
822}
823
824// Split a reduction axis with a tail loop.
825TEST(Reductions, ReduceSplitTail) {
826 const int M = 10;
827 const int N = 10;
828 const int K = 10;
829
830 BufHandle b("b", {M, N, K}, kFloat);
831 std::vector<float> in(M * N * K);
832 for (int j = 0; j < M * N * K; ++j) {
833 in[j] = j;
834 }
835
836 for (const auto i : c10::irange(3)) {
837 std::vector<float> out(M, -1.f);
838
839 Tensor c = Reduce("sum", {M}, Sum(), b, {N, K});
840 LoopNest loop({c});
841 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
842 LoopNest::splitWithTail(loops[i], 8);
843
844 loop.prepareForCodegen();
845 StmtPtr s = loop.root_stmt();
846 s = IRSimplifier::simplify(s);
847
848 SimpleIREvaluator cg(s, {b, c});
849
850 cg.call({in, out});
851 ASSERT_EQ(out[0], 4950);
852 }
853}
854
855// Split a reduction axis cleanly so there is no tail loop.
856TEST(Reductions, ReduceSplitNoTail) {
857 const int M = 10;
858 const int N = 10;
859 const int K = 10;
860 BufHandle b("b", {M, N, K}, kFloat);
861 std::vector<float> in(M * N * K);
862 for (int j = 0; j < M * N * K; ++j) {
863 in[j] = j;
864 }
865
866 for (const auto i : c10::irange(3)) {
867 std::vector<float> out(M, -1.f);
868
869 Tensor c = Reduce("sum", {M}, Sum(), b, {N, K});
870 LoopNest loop({c});
871 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
872 LoopNest::splitWithTail(loops[i], 5);
873
874 loop.prepareForCodegen();
875 StmtPtr s = loop.root_stmt();
876 s = IRSimplifier::simplify(s);
877
878 SimpleIREvaluator cg(s, {b, c});
879
880 cg.call({in, out});
881 ASSERT_EQ(out[0], 4950);
882 }
883}
884
885// Split a reduction axis with only a tail loop (the split loop will be size 0
886// and eliminated out).
887TEST(Reductions, ReduceOverSplitTail) {
888 const int M = 10;
889 const int N = 10;
890 const int K = 10;
891
892 BufHandle b("b", {M, N, K}, kFloat);
893 std::vector<float> in(M * N * K);
894 for (int j = 0; j < M * N * K; ++j) {
895 in[j] = j;
896 }
897
898 for (const auto i : c10::irange(3)) {
899 std::vector<float> out(M, -1.f);
900
901 Tensor c = Reduce("sum", {M}, Sum(), b, {N, K});
902 LoopNest loop({c});
903 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
904 LoopNest::splitWithTail(loops[i], 16);
905
906 loop.prepareForCodegen();
907 StmtPtr s = loop.root_stmt();
908 s = IRSimplifier::simplify(s);
909
910 SimpleIREvaluator cg(s, {b, c});
911
912 cg.call({in, out});
913 ASSERT_EQ(out[0], 4950);
914 }
915}
916
917// Split a reduction axis with a mask.
918TEST(Reductions, ReduceSplitMask) {
919 const int M = 10;
920 const int N = 10;
921 const int K = 10;
922
923 BufHandle b("b", {M, N, K}, kFloat);
924 std::vector<float> in(M * N * K);
925 for (int j = 0; j < M * N * K; ++j) {
926 in[j] = j;
927 }
928
929 for (const auto i : c10::irange(3)) {
930 std::vector<float> out(M, -1.f);
931
932 Tensor c = Reduce("sum", {M}, Sum(), b, {N, K});
933 LoopNest loop({c});
934 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
935 LoopNest::splitWithMask(loops[i], 8);
936
937 loop.prepareForCodegen();
938 StmtPtr s = loop.root_stmt();
939 s = IRSimplifier::simplify(s);
940
941 SimpleIREvaluator cg(s, {b, c});
942
943 cg.call({in, out});
944 ASSERT_EQ(out[0], 4950);
945 }
946}
947
948// Split a reduction axis cleanly not requiring a mask.
949TEST(Reductions, ReduceSplitNoMask) {
950 const int M = 10;
951 const int N = 10;
952 const int K = 10;
953 BufHandle b("b", {M, N, K}, kFloat);
954 std::vector<float> in(M * N * K);
955 for (int j = 0; j < M * N * K; ++j) {
956 in[j] = j;
957 }
958
959 for (const auto i : c10::irange(3)) {
960 std::vector<float> out(M, -1.f);
961
962 Tensor c = Reduce("sum", {M}, Sum(), b, {N, K});
963 LoopNest loop({c});
964 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
965 LoopNest::splitWithMask(loops[i], 5);
966
967 loop.prepareForCodegen();
968 StmtPtr s = loop.root_stmt();
969 s = IRSimplifier::simplify(s);
970
971 SimpleIREvaluator cg(s, {b, c});
972
973 cg.call({in, out});
974 ASSERT_EQ(out[0], 4950);
975 }
976}
977
978// Split a reduction axis with all logic in the mask.
979TEST(Reductions, ReduceOverSplitMask) {
980 const int M = 10;
981 const int N = 10;
982 const int K = 10;
983
984 BufHandle b("b", {M, N, K}, kFloat);
985 std::vector<float> in(M * N * K);
986 for (int j = 0; j < M * N * K; ++j) {
987 in[j] = j;
988 }
989
990 for (const auto i : c10::irange(3)) {
991 std::vector<float> out(M, -1.f);
992
993 Tensor c = Reduce("sum", {M}, Sum(), b, {N, K});
994 LoopNest loop({c});
995 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
996 LoopNest::splitWithMask(loops[i], 16);
997
998 loop.prepareForCodegen();
999 StmtPtr s = loop.root_stmt();
1000 s = IRSimplifier::simplify(s);
1001
1002 SimpleIREvaluator cg(s, {b, c});
1003
1004 cg.call({in, out});
1005 ASSERT_EQ(out[0], 4950);
1006 }
1007}
1008
1009// Test an rfactor when there are two ReduceOps in the graph due to a
1010// splitWithTail.
1011TEST(Reductions, ReduceSplitRfactor) {
1012 const int M = 2;
1013 const int N = 10;
1014 const int K = 10;
1015 const int SPLIT_FACTOR = 4;
1016
1017 BufHandle b("b", {M, N, K}, kFloat);
1018 std::vector<float> in(M * N * K);
1019 for (const auto m : c10::irange(M)) {
1020 for (int j = 0; j < N * K; ++j) {
1021 in[m * N * K + j] = j;
1022 }
1023 }
1024
1025 std::vector<float> out(M, -1.f);
1026
1027 Tensor c = Reduce("sum", {M}, Sum(), b, {N, K});
1028 LoopNest loop({c});
1029 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
1030 LoopNest::splitWithTail(loops[2], SPLIT_FACTOR);
1031
1032 auto c_body = loop.getAllWritesToBuf(c.buf())[2];
1033 auto all_loops = loop.getAllLoopNestsWritingToBuf(c.buf());
1034 ASSERT_TRUE(all_loops.size() == 3 && all_loops.at(2).size() == 3);
1035 LoopNest::reorderAxis(all_loops[2][1], all_loops[2][2]);
1036 all_loops = loop.getAllLoopNestsWritingToBuf(c.buf());
1037 ASSERT_TRUE(all_loops.size() == 3 && all_loops.at(2).size() == 3);
1038 ASSERT_TRUE(loop.rfactor(c_body, all_loops[2][1]));
1039 loop.prepareForCodegen();
1040 loop.simplify();
1041 StmtPtr s = loop.root_stmt();
1042
1043 SimpleIREvaluator cg(s, {b, c});
1044
1045 cg.call({in, out});
1046 for (const auto i : c10::irange(M)) {
1047 (void)i; // Suppress unused variable warning
1048 ASSERT_EQ(out[0], 4950);
1049 }
1050}
1051
1052// Test an rfactor which ends up being eliminated since the total loop size is
1053// smaller than the split factor.
1054TEST(Reductions, ReduceOverSplitRfactor) {
1055 const int N = 10;
1056 const int K = 10;
1057 const int SPLIT_FACTOR = 16;
1058
1059 BufHandle b("b", {N, K}, kFloat);
1060 std::vector<float> in(N * K);
1061 for (int j = 0; j < N * K; ++j) {
1062 in[j] = j;
1063 }
1064
1065 std::vector<float> out(1, -1.f);
1066
1067 Tensor c = Reduce("sum", {}, Sum(), b, {N, K});
1068 LoopNest loop({c});
1069 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
1070 // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
1071 ForPtr i, t;
1072 LoopNest::splitWithTail(loops[1], SPLIT_FACTOR, &i, &t);
1073 LoopNest::reorderAxis(loops[0], i);
1074
1075 auto all_loops = loop.getAllLoopNestsWritingToBuf(c.buf());
1076 ASSERT_TRUE(all_loops.size() == 3 && all_loops.at(1).size() == 3);
1077 auto c_body = loop.getAllWritesToBuf(c.buf())[1];
1078 ASSERT_TRUE(loop.rfactor(c_body, all_loops[1][0]));
1079 LoopNest::reorderAxis(all_loops[1][0], all_loops[1][2]);
1080
1081 loop.prepareForCodegen();
1082 loop.simplify();
1083 StmtPtr s = loop.root_stmt();
1084
1085 SimpleIREvaluator cg(s, {b, c});
1086
1087 cg.call({in, out});
1088 ASSERT_EQ(out[0], 4950);
1089
1090 std::ostringstream oss;
1091 oss << *cg.stmt();
1092
1093 // Check the IR to verify the rfactored reduce is eliminated.
1094 // TODO: The alloc free should be eliminated here since it is size 0.
1095 const std::string& verification_pattern =
1096 R"IR(
1097# CHECK: Allocate(tmp_buf); // dtype=float, dims=[0]
1098# CHECK: sum[0] = 0.f;
1099# CHECK: for (int n = 0; n < 10; n++) {
1100# CHECK: for (int k_tail = 0; k_tail < 10; k_tail++) {
1101# CHECK: sum[0] = (sum[0]) + (b[k_tail + 10 * n]);
1102# CHECK: }
1103# CHECK: }
1104# CHECK: Free(tmp_buf);)IR";
1105 // TODO: rfactor output is not consistent yet, will fix (@nickg).
1106 // torch::jit::testing::FileCheck().run(verification_pattern, oss.str());
1107}
1108
1109TEST(Reductions, ReduceInlineReduction) {
1110 const int M = 4;
1111 const int N = 5;
1112 const int K = 6;
1113
1114 BufHandle a_buf("a", {M}, kFloat);
1115 BufHandle b_buf("b", {M, N, K}, kFloat);
1116
1117 Tensor x = Reduce("x", {M}, Sum(), b_buf, {N, K});
1118 Tensor y = Compute(
1119 "y", {M}, [&](const VarHandle& m) { return a_buf.load(m) + x.load(m); });
1120
1121 PaddedBuffer<float> a_v(M);
1122 PaddedBuffer<float> b_v(M, N, K);
1123
1124 for (const auto i : c10::irange(M)) {
1125 a_v(i) = i * i;
1126 }
1127 for (const auto i : c10::irange(M)) {
1128 for (const auto j : c10::irange(N)) {
1129 for (const auto k : c10::irange(K)) {
1130 b_v(i, j, k) = j * j * k;
1131 }
1132 }
1133 }
1134
1135 LoopNest l1({y}, {x, y});
1136 // Cannot inline a reduction computation
1137 ASSERT_FALSE(l1.computeInline(x.buf()));
1138}
1139
1140TEST(Reductions, ReduceInlineConsumer) {
1141 const int M = 4;
1142 const int N = 5;
1143 const int K = 6;
1144
1145 BufHandle a_buf("a", {M, N, K}, kFloat);
1146 BufHandle b_buf("b", {M, N, K}, kFloat);
1147
1148 Tensor x = Compute(
1149 "x",
1150 {M, N, K},
1151 [&](const VarHandle& m, const VarHandle& n, const VarHandle& k) {
1152 return a_buf.load(m, n, k) + b_buf.load(m, n, k);
1153 });
1154 Tensor y = Reduce("y", {M}, Sum(), x, {N, K});
1155
1156 PaddedBuffer<float> a_v(M, N, K);
1157 PaddedBuffer<float> b_v(M, N, K);
1158
1159 for (const auto i : c10::irange(M)) {
1160 for (const auto j : c10::irange(N)) {
1161 for (const auto k : c10::irange(K)) {
1162 a_v(i, j, k) = i * i + k;
1163 b_v(i, j, k) = j * j + k;
1164 }
1165 }
1166 }
1167
1168 LoopNest l1({y}, {x, y});
1169 LoopNest l2(l1);
1170 l2.computeInline(x.buf());
1171
1172 l1.prepareForCodegen();
1173 l2.prepareForCodegen();
1174
1175 StmtPtr stmt1 = IRSimplifier::simplify(l1.root_stmt());
1176 StmtPtr stmt2 = IRSimplifier::simplify(l2.root_stmt());
1177
1178 SimpleIREvaluator eval1(stmt1, {a_buf, b_buf, y});
1179 SimpleIREvaluator eval2(stmt2, {a_buf, b_buf, y});
1180
1181 PaddedBuffer<float> y_1(M);
1182 PaddedBuffer<float> y_2(M);
1183
1184 eval1(a_v, b_v, y_1);
1185 eval2(a_v, b_v, y_2);
1186 ExpectAllNear(y_1, y_2, 1e-5);
1187 std::ostringstream oss1, oss2;
1188 oss1 << *stmt1;
1189 oss2 << *stmt2;
1190 ASSERT_GT(oss1.str().size(), oss2.str().size());
1191}
1192
1193TEST(Reductions, ReduceInlineReducerInternal) {
1194 const int M = 4;
1195 const int N = 5;
1196 const int K = 6;
1197
1198 BufHandle a_buf("a", {M, N, K}, kFloat);
1199 BufHandle b_buf("b", {M, N, K}, kFloat);
1200
1201 Tensor x = Compute(
1202 "x",
1203 {M, N, K},
1204 [&](const VarHandle& m, const VarHandle& n, const VarHandle& k) {
1205 return a_buf.load(m, n, k) + b_buf.load(m, n, k);
1206 });
1207
1208 Reducer minimum(ExprHandle(0.f), [&](ExprHandle a, ExprHandle b) {
1209 return Add::make(ExprHandle(1.f), Min::make(a, b, false));
1210 });
1211 Tensor y = Reduce("y", {M}, minimum, x, {N, K});
1212
1213 PaddedBuffer<float> a_v(M, N, K);
1214 PaddedBuffer<float> b_v(M, N, K);
1215
1216 for (const auto i : c10::irange(M)) {
1217 for (const auto j : c10::irange(N)) {
1218 for (const auto k : c10::irange(K)) {
1219 a_v(i, j, k) = i * i + k;
1220 b_v(i, j, k) = j * j + k;
1221 }
1222 }
1223 }
1224
1225 LoopNest l1({y}, {x, y});
1226 LoopNest l2(l1);
1227 l2.computeInline(x.buf());
1228
1229 l1.prepareForCodegen();
1230 l2.prepareForCodegen();
1231
1232 StmtPtr stmt1 = IRSimplifier::simplify(l1.root_stmt());
1233 StmtPtr stmt2 = IRSimplifier::simplify(l2.root_stmt());
1234
1235 SimpleIREvaluator eval1(stmt1, {a_buf, b_buf, y});
1236 SimpleIREvaluator eval2(stmt2, {a_buf, b_buf, y});
1237
1238 PaddedBuffer<float> y_1(M);
1239 PaddedBuffer<float> y_2(M);
1240
1241 eval1(a_v, b_v, y_1);
1242 eval2(a_v, b_v, y_2);
1243 ExpectAllNear(y_1, y_2, 1e-5);
1244 std::ostringstream oss1, oss2;
1245 oss1 << *stmt1;
1246 oss2 << *stmt2;
1247 ASSERT_GT(oss1.str().size(), oss2.str().size());
1248}
1249
1250TEST(Reductions, ReductionCacheAccessesOperatorAxis) {
1251 int L = 4;
1252 int N = 3;
1253 int M = 2;
1254
1255 BufHandle a("a", {L, N, M}, kFloat);
1256 BufHandle b("b", {L, N, M}, kFloat);
1257
1258 Tensor c = Compute(
1259 "scale",
1260 {L, N, M},
1261 [&](const VarHandle& l, const VarHandle& n, const VarHandle& m) {
1262 return b.load(l, n, m) * a.load(l, n, m);
1263 });
1264 Tensor d = Reduce("sum", {L}, Sum(), c, {N, M});
1265
1266 Tensor e = Compute("scale", {L}, [&](const VarHandle& l) {
1267 return b.load(0, 0, l) * d.load(l);
1268 });
1269
1270 LoopNest l({e}, {c, d, e});
1271 LoopNest l_before(l);
1272 l_before.prepareForCodegen();
1273 SimpleIREvaluator cg_before(
1274 LoopNest::sanitizeNames(l_before.root_stmt()), {a, b, e});
1275
1276 StmtPtr d_loop = l.getLoopStmtsFor(d)[0];
1277 l.cacheAccesses(d.buf(), "d_local", d_loop);
1278 l.prepareForCodegen();
1279
1280 StmtPtr result =
1281 LoopNest::sanitizeNames(IRSimplifier::simplify(l.root_stmt()));
1282 SimpleIREvaluator cg_after(result, {a, b, e});
1283
1284 std::ostringstream oss;
1285 oss << *cg_after.stmt();
1286 const std::string& expected_ir =
1287 R"IR(
1288#CHECK: Allocate(d_local); // dtype=float, dims=[4]
1289#CHECK: for (int i_2
1290#CHECK: d_local[i_2] = 0.f
1291#CHECK: for (int
1292#CHECK: for (int
1293#CHECK: d_local[i_2] = (d_local[i_2]) + (scale[
1294#CHECK: }
1295#CHECK: }
1296#CHECK: }
1297#CHECK: for (int i_3
1298#CHECK: sum[i_3] = d_local[i_3]
1299#CHECK: Free(d_local);
1300#CHECK-NOT: d_local
1301 )IR";
1302 torch::jit::testing::FileCheck().run(expected_ir, oss.str());
1303
1304 PaddedBuffer<float> a_v(L, M, N, "a");
1305 PaddedBuffer<float> b_v(L, M, N, "b");
1306 PaddedBuffer<float> c_v(L, M, N, "c");
1307 PaddedBuffer<float> d_v(L, "d");
1308 PaddedBuffer<float> e_before(L, "e_before");
1309 PaddedBuffer<float> e_after(L, "e_after");
1310
1311 for (const auto l : c10::irange(L)) {
1312 for (const auto m : c10::irange(M)) {
1313 for (const auto n : c10::irange(N)) {
1314 a_v(l, m, n) = at::randn({1}).item().to<float>();
1315 b_v(l, m, n) = at::randn({1}).item().to<float>();
1316 }
1317 }
1318 }
1319
1320 cg_before.call({a_v, b_v, e_before});
1321 cg_after.call({a_v, b_v, e_after});
1322
1323 // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
1324 ExpectAllNear(e_before, e_after, 1e-5);
1325}
1326
1327TEST(Reductions, ReductionCacheAccessesOuterReduceAxis) {
1328 int L = 4;
1329 int N = 3;
1330 int M = 2;
1331
1332 BufHandle a("a", {L, N, M}, kFloat);
1333 BufHandle b("b", {L, N, M}, kFloat);
1334
1335 Tensor c = Compute(
1336 "scale",
1337 {L, N, M},
1338 [&](const VarHandle& l, const VarHandle& n, const VarHandle& m) {
1339 return b.load(l, n, m) * a.load(l, n, m);
1340 });
1341 Tensor d = Reduce("sum", {L}, Sum(), c, {N, M});
1342
1343 Tensor e = Compute("scale", {L}, [&](const VarHandle& l) {
1344 return b.load(0, 0, l) * d.load(l);
1345 });
1346
1347 LoopNest l({e}, {c, d, e});
1348 LoopNest l_before(l);
1349 l_before.prepareForCodegen();
1350 SimpleIREvaluator cg_before(l_before.root_stmt(), {a, b, e});
1351
1352 StmtPtr d_loop = l.getLoopStmtsFor(d)[1];
1353 l.cacheAccesses(d.buf(), "d_local", d_loop);
1354 l.prepareForCodegen();
1355
1356 StmtPtr result =
1357 LoopNest::sanitizeNames(IRSimplifier::simplify(l.root_stmt()));
1358 SimpleIREvaluator cg_after(result, {a, b, e});
1359
1360 std::ostringstream oss;
1361 oss << *cg_after.stmt();
1362 const std::string& expected_ir =
1363 R"IR(
1364#CHECK: Allocate(d_local); // dtype=float, dims=[1]
1365#CHECK: sum[i_1] = 0
1366#CHECK: d_local[0] = sum[i_1]
1367#CHECK: for (int j_1
1368#CHECK: for (int k_1
1369#CHECK: d_local[0] = (d_local[0]) + (scale[
1370#CHECK: }
1371#CHECK: }
1372#CHECK: sum[i_1] = d_local[0]
1373#CHECK: Free(d_local);
1374#CHECK-NOT: d_local
1375 )IR";
1376 torch::jit::testing::FileCheck().run(expected_ir, oss.str());
1377
1378 PaddedBuffer<float> a_v(L, M, N, "a");
1379 PaddedBuffer<float> b_v(L, M, N, "b");
1380 PaddedBuffer<float> c_v(L, M, N, "c");
1381 PaddedBuffer<float> d_v(L, "d");
1382 PaddedBuffer<float> e_before(L, "e_before");
1383 PaddedBuffer<float> e_after(L, "e_after");
1384
1385 for (const auto l : c10::irange(L)) {
1386 for (const auto m : c10::irange(M)) {
1387 for (const auto n : c10::irange(N)) {
1388 a_v(l, m, n) = at::randn({1}).item().to<float>();
1389 b_v(l, m, n) = at::randn({1}).item().to<float>();
1390 }
1391 }
1392 }
1393
1394 cg_before.call({a_v, b_v, e_before});
1395 cg_after.call({a_v, b_v, e_after});
1396
1397 // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
1398 ExpectAllNear(e_before, e_after, 1e-5);
1399}
1400
1401TEST(Reductions, ReductionCacheAccessesInnerReduceAxis) {
1402 int L = 4;
1403 int N = 3;
1404 int M = 2;
1405
1406 BufHandle a("a", {L, N, M}, kFloat);
1407 BufHandle b("b", {L, N, M}, kFloat);
1408
1409 Tensor c = Compute(
1410 "scale",
1411 {L, N, M},
1412 [&](const VarHandle& l, const VarHandle& n, const VarHandle& m) {
1413 return b.load(l, n, m) * a.load(l, n, m);
1414 });
1415 Tensor d = Reduce("sum", {L}, Sum(), c, {N, M});
1416
1417 Tensor e = Compute("scale", {L}, [&](const VarHandle& l) {
1418 return b.load(0, 0, l) * d.load(l);
1419 });
1420
1421 LoopNest l({e}, {c, d, e});
1422 LoopNest l_before(l);
1423 l_before.prepareForCodegen();
1424 SimpleIREvaluator cg_before(l_before.root_stmt(), {a, b, e});
1425
1426 StmtPtr d_loop = l.getLoopStmtsFor(d)[2];
1427 l.cacheAccesses(d.buf(), "d_local", d_loop);
1428 l.prepareForCodegen();
1429
1430 StmtPtr result =
1431 LoopNest::sanitizeNames(IRSimplifier::simplify(l.root_stmt()));
1432 SimpleIREvaluator cg_after(result, {a, b, e});
1433
1434 std::ostringstream oss;
1435 oss << *cg_after.stmt();
1436 const std::string& expected_ir =
1437 R"IR(
1438#CHECK: Allocate(d_local); // dtype=float, dims=[1]
1439#CHECK: sum[i_1] = 0
1440#CHECK: for (int
1441#CHECK: d_local[0] = 0
1442#CHECK: for (int
1443#CHECK: d_local[0] = (d_local[0]) + (scale[
1444#CHECK: }
1445#CHECK: sum[i_1] = (sum[i_1]) + (d_local[0])
1446#CHECK: }
1447#CHECK: Free(d_local);
1448#CHECK-NOT: d_local
1449 )IR";
1450 torch::jit::testing::FileCheck().run(expected_ir, oss.str());
1451
1452 PaddedBuffer<float> a_v(L, M, N, "a");
1453 PaddedBuffer<float> b_v(L, M, N, "b");
1454 PaddedBuffer<float> c_v(L, M, N, "c");
1455 PaddedBuffer<float> d_v(L, "d");
1456 PaddedBuffer<float> e_before(L, "e_before");
1457 PaddedBuffer<float> e_after(L, "e_after");
1458
1459 for (const auto l : c10::irange(L)) {
1460 for (const auto m : c10::irange(M)) {
1461 for (const auto n : c10::irange(N)) {
1462 a_v(l, m, n) = at::randn({1}).item().to<float>();
1463 b_v(l, m, n) = at::randn({1}).item().to<float>();
1464 }
1465 }
1466 }
1467
1468 cg_before.call({a_v, b_v, e_before});
1469 cg_after.call({a_v, b_v, e_after});
1470
1471 // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
1472 ExpectAllNear(e_before, e_after, 1e-5);
1473}
1474
1475TEST(Reductions, ReductionCacheBodyAccess) {
1476 BufHandle a("a", {24, 32, 12}, kFloat);
1477 BufHandle b("b", {24, 32, 12}, kFloat);
1478
1479 Tensor c = Compute(
1480 "scale",
1481 {24, 32, 12},
1482 [&](const VarHandle& l, const VarHandle& n, const VarHandle& m) {
1483 return b.load(l, n, m) * a.load(l, n, m);
1484 });
1485 Tensor d = Reduce("sum", {24}, Sum(), c, {32, 12});
1486
1487 Tensor e = Compute("scale", {24}, [&](const VarHandle& l) {
1488 return b.load(0, 0, l) * d.load(l);
1489 });
1490
1491 LoopNest l({e}, {c, d, e});
1492
1493 StmtPtr d_loop = l.getLoopStmtsFor(d)[1];
1494 l.cacheAccesses(c.buf(), "scale_local", d_loop);
1495
1496 l.prepareForCodegen();
1497 StmtPtr result =
1498 LoopNest::sanitizeNames(IRSimplifier::simplify(l.root_stmt()));
1499 SimpleIREvaluator cg(result, {a, b, e});
1500
1501 std::ostringstream oss;
1502 oss << *cg.stmt();
1503 const std::string& expected_ir =
1504 R"IR(
1505#CHECK: Allocate(scale_local); // dtype=float, dims=[1, 32, 12]
1506#CHECK: for (int j_1 = 0; j_1 < 32; j_1++) {
1507#CHECK: for (int k_1 = 0; k_1 < 12; k_1++) {
1508#CHECK: scale_local[k_1 + 12 * j_1] = scale[(k_1 + 12 * j_1) + 384 * i_1];
1509#CHECK: sum[i_1] = (sum[i_1]) + (scale_local[k_2 + 12 * j_2]);
1510#CHECK: scale_1[i_2] = (b[i_2]) * (sum[i_2]);
1511#CHECK: Free(scale_local);
1512 )IR";
1513 torch::jit::testing::FileCheck().run(expected_ir, oss.str());
1514}
1515
1516TEST(Reductions, ReductionCacheConsumerAccess) {
1517 BufHandle a("a", {24, 32, 12}, kFloat);
1518 BufHandle b("b", {24, 32, 12}, kFloat);
1519
1520 Tensor c = Compute(
1521 "scale",
1522 {24, 32, 12},
1523 [&](const VarHandle& l, const VarHandle& n, const VarHandle& m) {
1524 return b.load(l, n, m) * a.load(l, n, m);
1525 });
1526 Tensor d = Reduce("sum", {24}, Sum(), c, {32, 12});
1527
1528 Tensor e = Compute("scale", {24}, [&](const VarHandle& l) {
1529 return b.load(0, 0, l) * d.load(l);
1530 });
1531
1532 LoopNest l({e}, {c, d, e});
1533
1534 LoopNest::splitWithMask(l.getLoopStmtsFor(e)[0], 4);
1535
1536 StmtPtr e_loop = l.getLoopStmtsFor(e)[1];
1537 l.cacheAccesses(d.buf(), "sum_local", e_loop);
1538 l.prepareForCodegen();
1539
1540 StmtPtr result =
1541 LoopNest::sanitizeNames(IRSimplifier::simplify(l.root_stmt()));
1542 SimpleIREvaluator cg(result, {a, b, e});
1543
1544 std::ostringstream oss;
1545 oss << *cg.stmt();
1546 const std::string& expected_ir =
1547 R"IR(
1548#CHECK: Alias(sum_local,scale);
1549#CHECK: sum[i_1] = (sum[i_1]) + (scale[
1550#CHECK: for (int j_2 = 0; j_2 < 4
1551#CHECK: sum_local[j_2] = sum[j_2 + 4 * i_2];
1552#CHECK: scale_1[j_3 + 4 * i_2] = (b[j_3 + 4 * i_2]) * (sum_local[j_3]);
1553 )IR";
1554 torch::jit::testing::FileCheck().run(expected_ir, oss.str());
1555}
1556
1557TEST(Reductions, ReductionSplitCacheConsumerAccess) {
1558 BufHandle a("a", {24, 32, 12}, kFloat);
1559 BufHandle b("b", {24, 32, 12}, kFloat);
1560
1561 Tensor c = Compute(
1562 "scale",
1563 {24, 32, 12},
1564 [&](const VarHandle& l, const VarHandle& n, const VarHandle& m) {
1565 return b.load(l, n, m) * a.load(l, n, m);
1566 });
1567 Tensor d = Reduce("sum", {24}, Sum(), c, {32, 12});
1568
1569 Tensor e = Compute("scale", {24}, [&](const VarHandle& l) {
1570 return b.load(0, 0, l) * d.load(l);
1571 });
1572
1573 LoopNest l({e}, {c, d, e});
1574
1575 // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
1576 ForPtr inner;
1577
1578 // Split outer reduction axis.
1579 LoopNest::splitWithMask(l.getLoopStmtsFor(d)[0], 4, &inner);
1580
1581 // Split reduction consumer.
1582 LoopNest::splitWithMask(l.getLoopStmtsFor(e)[0], 4, &inner);
1583
1584 l.cacheAccesses(d.buf(), "sum_local", inner);
1585 l.prepareForCodegen();
1586
1587 StmtPtr result =
1588 LoopNest::sanitizeNames(IRSimplifier::simplify(l.root_stmt()));
1589 SimpleIREvaluator cg(result, {a, b, e});
1590
1591 // reduction changes but cache does not.
1592 std::ostringstream oss;
1593 oss << *cg.stmt();
1594 const std::string& expected_ir =
1595 R"IR(
1596#CHECK: Alias(sum_local,scale);
1597#CHECK: sum[j_1 + 4 * i_1] = (sum[j_1 + 4 * i_1]) + (scale[((l + 12 * k_1) + 1536 * i_1) + 384 * j_1]);
1598#CHECK: for (int i_2 = 0; i_2 < 6
1599#CHECK: for (int j_2 = 0; j_2 < 4
1600#CHECK: sum_local[j_2] = sum[j_2 + 4 * i_2];
1601#CHECK: for (int j_3 = 0; j_3 < 4
1602#CHECK: scale_1[j_3 + 4 * i_2] = (b[j_3 + 4 * i_2]) * (sum_local[j_3]);
1603 )IR";
1604 torch::jit::testing::FileCheck().run(expected_ir, oss.str());
1605}
1606
1607TEST(Reductions, ReductionReorderCacheConsumerAccess) {
1608 BufHandle a("a", {24, 32, 12}, kFloat);
1609 BufHandle b("b", {24, 32, 12}, kFloat);
1610
1611 Tensor c = Compute(
1612 "scale",
1613 {24, 32, 12},
1614 [&](const VarHandle& l, const VarHandle& n, const VarHandle& m) {
1615 return b.load(l, n, m) * a.load(l, n, m);
1616 });
1617 Tensor d = Reduce("sum", {24}, Sum(), c, {32, 12});
1618
1619 Tensor e = Compute("scale", {24}, [&](const VarHandle& l) {
1620 return b.load(0, 0, l) * d.load(l);
1621 });
1622
1623 LoopNest l({e}, {c, d, e});
1624
1625 // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
1626 ForPtr inner;
1627
1628 // reorder outer reduction axes.
1629 auto loops = l.getLoopStmtsFor(d);
1630 LoopNest::reorderAxis(loops[0], loops[1]);
1631
1632 // Split reduction consumer.
1633 LoopNest::splitWithMask(l.getLoopStmtsFor(e)[0], 4, &inner);
1634
1635 l.cacheAccesses(d.buf(), "sum_local", inner);
1636 l.prepareForCodegen();
1637
1638 StmtPtr result =
1639 LoopNest::sanitizeNames(IRSimplifier::simplify(l.root_stmt()));
1640 SimpleIREvaluator cg(result, {a, b, e});
1641
1642 // neither reduction body not cache changes.
1643 std::ostringstream oss;
1644 oss << *cg.stmt();
1645 const std::string& expected_ir =
1646 R"IR(
1647#CHECK: sum[j_1] = (sum[j_1]) + (scale[(k_1 + 12 * i_2) + 384 * j_1]);
1648#CHECK: for (int i_3 = 0; i_3 < 6;
1649#CHECK: for (int j_2 = 0; j_2 < 4;
1650#CHECK: sum_local[j_2] = sum[j_2 + 4 * i_3];
1651#CHECK: for (int j_3 = 0; j_3 < 4;
1652#CHECK: scale_1[j_3 + 4 * i_3] = (b[j_3 + 4 * i_3]) * (sum_local[j_3]);
1653 )IR";
1654 torch::jit::testing::FileCheck().run(expected_ir, oss.str());
1655}
1656
1657TEST(Reductions, ReductionRfactorCacheTempOuter) {
1658 const int M = 10;
1659 const int N = 10;
1660 const int K = 10;
1661 VarHandle m("m", kInt);
1662 VarHandle n("n", kInt);
1663 VarHandle k("k", kInt);
1664
1665 BufHandle b("B", {m, n, k}, kFloat);
1666 std::vector<float> in(M * N * K);
1667 for (int j = 0; j < M * N * K; ++j) {
1668 in[j] = j;
1669 }
1670
1671 std::vector<float> out(1, -1.f);
1672
1673 Tensor c = Reduce("sum", {}, Sum(), b, {m, n, k});
1674 LoopNest loop({c});
1675
1676 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
1677 LoopNest::reorderAxis(loops.at(0), loops.at(1));
1678 loops = loop.getLoopStmtsFor(c);
1679 auto c_body = loop.getAllWritesToBuf(c.buf())[1];
1680 // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
1681 BufPtr rfac_buf;
1682 ASSERT_TRUE(loop.rfactor(c_body, loops.at(0), &rfac_buf));
1683 loop.distributeLoop(loops.at(0));
1684
1685 auto all_loops = loop.getAllLoopNestsWritingToBuf(rfac_buf);
1686 ASSERT_TRUE(all_loops.size() == 2 && all_loops.at(1).size() == 3);
1687 LoopNest::reorderAxis(all_loops[1][0], all_loops[1][1]);
1688
1689 all_loops = loop.getAllLoopNestsWritingToBuf(rfac_buf);
1690 LoopNest::cacheAccesses(rfac_buf, "tmp", all_loops[1][1]);
1691 loop.simplify();
1692 loop.prepareForCodegen();
1693 StmtPtr s = LoopNest::sanitizeNames(loop.root_stmt());
1694 SimpleIREvaluator cg(s, {b, c, m, n, k});
1695
1696 std::ostringstream oss;
1697 oss << *cg.stmt();
1698 const std::string& expected_ir =
1699 R"IR(
1700#CHECK: Allocate(sum_rfac); // dtype=float, dims=[n]
1701#CHECK: Allocate(tmp); // dtype=float, dims=[n]
1702#CHECK: for (int i_1 = 0; i_1 < m
1703#CHECK: for (int j = 0; j < n
1704#CHECK: tmp[j] = 0
1705#CHECK: }
1706#CHECK: for (int j_1 = 0; j_1 < n
1707#CHECK: for (int k
1708#CHECK: tmp[j_1] = (tmp[j_1]) + (B[
1709#CHECK: }
1710#CHECK: }
1711#CHECK: for (int j_2 = 0; j_2 < n
1712#CHECK: sum_rfac[j_2] = (sum_rfac[j_2]) + (tmp[j_2]);
1713#CHECK: }
1714#CHECK: Free(tmp);
1715#CHECK-NOT: tmp
1716 )IR";
1717 torch::jit::testing::FileCheck().run(expected_ir, oss.str());
1718
1719 cg.call({in, out, M, N, K});
1720 ASSERT_EQ(out[0], 499500);
1721}
1722
1723TEST(Reductions, ReductionRfactorCacheTempInner) {
1724 const int M = 10;
1725 const int N = 10;
1726 const int K = 10;
1727 VarHandle m("m", kInt);
1728 VarHandle n("n", kInt);
1729 VarHandle k("k", kInt);
1730
1731 BufHandle b("B", {m, n, k}, kFloat);
1732 std::vector<float> in(M * N * K);
1733 for (int j = 0; j < M * N * K; ++j) {
1734 in[j] = j;
1735 }
1736
1737 std::vector<float> out(1, -1.f);
1738
1739 Tensor c = Reduce("sum", {}, Sum(), b, {m, n, k});
1740 LoopNest loop({c});
1741 std::vector<ForPtr> loops = loop.getLoopStmtsFor(c);
1742 auto c_body = loop.getAllWritesToBuf(c.buf())[1];
1743
1744 LoopNest::reorderAxis(loops.at(0), loops.at(1));
1745 loops = loop.getLoopStmtsFor(c);
1746 // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
1747 BufPtr rfac_buf;
1748 ASSERT_TRUE(loop.rfactor(c_body, loops.at(0), &rfac_buf));
1749 loop.distributeLoop(loops.at(0));
1750 auto all_loops = loop.getAllLoopNestsWritingToBuf(rfac_buf);
1751 ASSERT_TRUE(all_loops.size() == 2 && all_loops.at(1).size() == 3);
1752 LoopNest::reorderAxis(all_loops[1][0], all_loops[1][1]);
1753
1754 all_loops = loop.getAllLoopNestsWritingToBuf(rfac_buf);
1755 ASSERT_TRUE(all_loops.size() == 2 && all_loops.at(1).size() == 3);
1756 LoopNest::cacheAccesses(rfac_buf, "tmp", all_loops[1][2]);
1757 loop.prepareForCodegen();
1758 loop.simplify();
1759 StmtPtr s = LoopNest::sanitizeNames(loop.root_stmt());
1760 SimpleIREvaluator cg(s, {b, c, m, n, k});
1761
1762 std::ostringstream oss;
1763 oss << *cg.stmt();
1764 const std::string& expected_ir =
1765 R"IR(
1766#CHECK: Allocate(sum_rfac); // dtype=float, dims=[n]
1767#CHECK: Allocate(tmp); // dtype=float, dims=[1]
1768#CHECK: for (int i_1 = 0; i_1 < m
1769#CHECK: for (int j = 0; j < n
1770#CHECK: tmp[0] = 0
1771#CHECK: for (int k
1772#CHECK: tmp[0] = (tmp[0]) + (B[
1773#CHECK: }
1774#CHECK: sum_rfac[j] = (sum_rfac[j]) + (tmp[0]);
1775#CHECK: Free(tmp);
1776#CHECK-NOT: tmp
1777 )IR";
1778 torch::jit::testing::FileCheck().run(expected_ir, oss.str());
1779
1780 cg.call({in, out, M, N, K});
1781 ASSERT_EQ(out[0], 499500);
1782}
1783
1784TEST(Reductions, ReductionVectorize) {
1785 std::vector<float> in_(8 * 8);
1786 for (const auto i : c10::irange(8)) {
1787 for (const auto j : c10::irange(8)) {
1788 in_[i * 8 + j] = i;
1789 }
1790 }
1791 std::vector<float> out_before(8, -1.f);
1792 std::vector<float> out_after(8, -1.f);
1793
1794 BufHandle in("in", {8, 8}, kFloat);
1795
1796 Tensor tensor = Reduce("sum", {8}, Sum(), in, {8});
1797 LoopNest l_before({tensor});
1798 LoopNest l(l_before);
1799 l_before.prepareForCodegen();
1800 SimpleIREvaluator cg_before(l_before.root_stmt(), {in, tensor});
1801 cg_before.call({in_, out_before});
1802
1803 ASSERT_TRUE(LoopNest::vectorize(l.getLoopStmtsFor(tensor)[0]));
1804
1805 StmtPtr s = l.root_stmt();
1806 s = LoopNest::sanitizeNames(IRSimplifier::simplify(s));
1807
1808 std::ostringstream oss;
1809 oss << *s;
1810 const std::string& expected_ir =
1811 R"IR(
1812#CHECK: sum[Ramp(0, 1, 8)] = Broadcast(0.f, 8);
1813#CHECK: for (int i = 0; i < 8; i++) {
1814#CHECK: sum[Ramp(0, 1, 8)] = ReduceOp((sum[Ramp(0, 1, 8)]) + (in[Ramp(i, 8, 8)]), reduce_args={i});
1815#CHECK: }
1816 )IR";
1817 torch::jit::testing::FileCheck().run(expected_ir, oss.str());
1818
1819 // Vectorizing should not change result.
1820 l.prepareForCodegen();
1821 s = IRSimplifier::simplify(l.root_stmt());
1822 SimpleIREvaluator cg_after(s, {in, tensor});
1823 cg_after.call({in_, out_after});
1824 for (const auto i : c10::irange(8)) {
1825 ASSERT_EQ(out_before[i], out_after[i]);
1826 }
1827}
1828
1829TEST(Reductions, ReductionVectorizeInner) {
1830 BufHandle in("in", {8, 8}, kFloat);
1831
1832 Tensor tensor = Reduce("sum", {8}, Sum(), in, {8});
1833 LoopNest l({tensor});
1834
1835 ASSERT_FALSE(LoopNest::vectorize(l.getLoopStmtsFor(tensor)[1]));
1836}
1837
1838TEST(Reductions, ReductionVectorizeRfactor) {
1839 std::vector<float> in_(8 * 8);
1840 for (const auto i : c10::irange(8)) {
1841 for (const auto j : c10::irange(8)) {
1842 in_[i * 8 + j] = i;
1843 }
1844 }
1845 std::vector<float> out_before(1, -1.f);
1846 std::vector<float> out_after(1, -1.f);
1847
1848 BufHandle in("in", {8, 8}, kFloat);
1849
1850 Tensor tensor = Reduce("sum", {}, Sum(), in, {8, 8});
1851
1852 LoopNest l_before({tensor});
1853 LoopNest l(l_before);
1854 l_before.prepareForCodegen();
1855 SimpleIREvaluator cg_before(l_before.root_stmt(), {in, tensor});
1856 cg_before.call({in_, out_before});
1857
1858 ASSERT_FALSE(LoopNest::vectorize(l.getLoopStmtsFor(tensor)[1]));
1859
1860 // But if we rfactor this so it's not a reduce axis we can vectorize that
1861 // loop.
1862 std::vector<ForPtr> loops = l.getLoopStmtsFor(tensor);
1863 LoopNest::reorderAxis(loops[0], loops[1]);
1864 loops = l.getLoopStmtsFor(tensor);
1865 auto tensor_body = l.getAllWritesToBuf(tensor.buf())[1];
1866 BufPtr rfac_buf = nullptr;
1867 ASSERT_TRUE(LoopNest::rfactor(tensor_body, loops.at(0), &rfac_buf));
1868
1869 LoopNest::distributeLoop(loops.at(0));
1870 auto rfac_loops = l.getAllLoopNestsWritingToBuf(rfac_buf);
1871
1872 ASSERT_TRUE(LoopNest::vectorize(rfac_loops[1][0]));
1873 l.simplify();
1874
1875 StmtPtr s = LoopNest::sanitizeNames(l.root_stmt());
1876
1877 std::ostringstream oss;
1878 oss << *s;
1879 const std::string& expected_ir =
1880 R"IR(
1881#CHECK: sum = 0.f;
1882#CHECK: for (int i = 0; i < 8; i++) {
1883#CHECK: sum_rfac[i] = 0.f;
1884#CHECK: }
1885#CHECK: for (int i_1 = 0; i_1 < 8; i_1++) {
1886#CHECK: sum_rfac[Ramp(0, 1, 8)] = ReduceOp((sum_rfac[Ramp(0, 1, 8)]) + (in[Ramp(8 * i_1, 1, 8)]), reduce_args={i_1});
1887#CHECK: }
1888#CHECK: for (int i_2 = 0; i_2 < 8; i_2++) {
1889#CHECK: sum = ReduceOp((sum) + (sum_rfac[i_2]), reduce_args={i_2});
1890#CHECK: }
1891 )IR";
1892 torch::jit::testing::FileCheck().run(expected_ir, oss.str());
1893
1894 // Vectorizing should not change result.
1895 l.prepareForCodegen();
1896 s = IRSimplifier::simplify(l.root_stmt());
1897 SimpleIREvaluator cg_after(s, {in, tensor});
1898 cg_after.call({in_, out_after});
1899
1900 ASSERT_EQ(out_before[0], out_after[0]);
1901}
1902
1903TEST(Reductions, InitFunction) {
1904 constexpr int M = 32;
1905 constexpr int N = 16;
1906 BufHandle A("A", {M, N}, kFloat);
1907 BufHandle B("B", {N}, kFloat);
1908 Tensor C = Reduce(
1909 "C",
1910 {N},
1911 Sum(),
1912 [&](const std::vector<VarHandle>& v) { return B.load(v[0]); },
1913 [&](const std::vector<VarHandle>& v) { return A.load(v[1], v[0]); },
1914 {M});
1915 LoopNest nest({C});
1916 nest.prepareForCodegen();
1917 StmtPtr s = LoopNest::sanitizeNames(IRSimplifier::simplify(nest.root_stmt()));
1918 std::ostringstream oss;
1919 oss << *s << "\n";
1920 const std::string& expected_ir =
1921 R"IR(
1922#CHECK: for (int i = 0; i < 16; i++) {
1923#CHECK: C[i] = B[i];
1924#CHECK: for (int j = 0; j < 32; j++) {
1925#CHECK: C[i] = (C[i]) + (A[i + 16 * j]);
1926#CHECK: }
1927#CHECK: }
1928 )IR";
1929 torch::jit::testing::FileCheck().run(expected_ir, oss.str());
1930}
1931} // namespace jit
1932} // namespace torch
1933