1 | #if defined(USE_CUDA) |
2 | #include <gmock/gmock-matchers.h> |
3 | #include <gtest/gtest.h> |
4 | |
5 | #include <arith.h> |
6 | #include <codegen.h> |
7 | #include <disjoint_set.h> |
8 | #include <executor.h> |
9 | #include <executor_launch_params.h> |
10 | #include <expr_evaluator.h> |
11 | #include <fusion.h> |
12 | #include <fusion_segmenter.h> |
13 | #include <grouped_reduction.h> |
14 | #include <inlining.h> |
15 | #include <ir_all_nodes.h> |
16 | #include <ir_builder.h> |
17 | #include <ir_graphviz.h> |
18 | #include <ir_iostream.h> |
19 | #include <ir_utils.h> |
20 | #include <iter_visitor.h> |
21 | #include <kernel_cache.h> |
22 | #include <kernel_expr_evaluator.h> |
23 | #include <kernel_ir.h> |
24 | #include <kernel_ir_dispatch.h> |
25 | #include <lower2device.h> |
26 | #include <lower_magic_zero.h> |
27 | #include <mutator.h> |
28 | #include <ops/all_ops.h> |
29 | #include <register_interface.h> |
30 | #include <root_domain_map.h> |
31 | #include <scheduler/all_schedulers.h> |
32 | #include <scheduler/reduction_utils.h> |
33 | #include <scheduler/utils.h> |
34 | #include <test/test_gpu_validator.h> |
35 | #include <test/test_utils.h> |
36 | #include <transform_replay.h> |
37 | #include <transform_rfactor.h> |
38 | |
39 | #include <test/cpp/jit/test_utils.h> |
40 | #include <torch/csrc/jit/api/function_impl.h> |
41 | #include <parser.h> |
42 | #include <torch/csrc/jit/ir/irparser.h> |
43 | #include <torch/torch.h> |
44 | |
45 | #include <ATen/cuda/CUDAContext.h> |
46 | #include <ATen/cuda/Exceptions.h> |
47 | #include <c10/cuda/CUDAStream.h> |
48 | |
49 | #include <algorithm> |
50 | #include <iostream> |
51 | #include <sstream> |
52 | #include <thread> |
53 | |
54 | // Tests go in torch::jit |
55 | namespace torch { |
56 | namespace jit { |
57 | |
58 | using namespace torch::jit::fuser::cuda; |
59 | using namespace at::indexing; |
60 | |
61 | TEST_F(NVFuserTest, FusionGlobalIntermediate_CUDA) { |
62 | Fusion fusion; |
63 | FusionGuard fg(&fusion); |
64 | |
65 | // Set up your input tensor views |
66 | TensorView* tv0 = makeSymbolicTensor(2); |
67 | TensorView* tv1 = |
68 | reductionOp(BinaryOpType::Add, {1}, IrBuilder::create<Double>(0), tv0); |
69 | fusion.addInput(tv0); |
70 | fusion.addOutput(tv1); |
71 | // tv1[I0, R1] = tv0[I0, I1] |
72 | |
73 | // Interface should just be a direct split with a Parallel type. We can |
74 | // include the parallelize call if we do this. |
75 | tv1->split(1, NamedScalar::getParallelDim(ParallelType::TIDx)); |
76 | // tv1[I0, R1o, R1i{BIDx}] = tv0[I0, I1] |
77 | |
78 | TensorView* tv2 = tv1->rFactor({2}); |
79 | tv2->setMemoryType(MemoryType::Global); |
80 | // tv2[I0, R1oo, Ir1i{BIDx}] = tv0[I0, I1] |
81 | // tv1[I0, R1i{BIDx}] = tv2[I0, R1oo, Ir1i{BIDx}] |
82 | |
83 | tv0->computeAt(tv1, 1); |
84 | |
85 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
86 | tv1->axis(0)->parallelize(ParallelType::BIDx); |
87 | |
88 | constexpr int numel_x = 65000, numel_y = 1024; |
89 | |
90 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
91 | at::Tensor input = at::randn({numel_x, numel_y}, options); |
92 | |
93 | // How many threads to use for the block reduction |
94 | constexpr int runtime_threadIdx_dim = 128; |
95 | |
96 | auto lparams = LaunchParams(-1, -1, -1, runtime_threadIdx_dim, -1, -1); |
97 | |
98 | FusionExecutor fe; |
99 | fe.compileFusion(&fusion, {input}, lparams); |
100 | auto cg_outputs = fe.runFusion({input}, lparams); |
101 | |
102 | auto aten_output = input.to(at::kDouble).sum({1}); |
103 | testValidate( |
104 | &fusion, |
105 | cg_outputs, |
106 | {input}, |
107 | {aten_output}, |
108 | __LINE__, |
109 | __FILE__, |
110 | "" , |
111 | lparams); |
112 | } |
113 | |
114 | TEST_F(NVFuserTest, FusionGlobalIntermediateDefaultSchedule_CUDA) { |
115 | Fusion fusion; |
116 | FusionGuard fg(&fusion); |
117 | |
118 | TensorView* tv0 = makeSymbolicTensor(2); |
119 | TensorView* tv1 = makeSymbolicTensor(2); |
120 | TensorView* tv2 = makeSymbolicTensor(2); |
121 | TensorView* tv3 = makeSymbolicTensor(2); |
122 | TensorView* tv4 = sub(tv2, tv3); |
123 | TensorView* tv5 = add(tv1, tv4); |
124 | TensorView* tv6 = sub(tv5, tv0); |
125 | fusion.addInput(tv0); |
126 | fusion.addInput(tv1); |
127 | fusion.addInput(tv2); |
128 | fusion.addInput(tv3); |
129 | fusion.addOutput(tv6); |
130 | // t6 = ((t1 + (t2 - t3)) - t0) |
131 | |
132 | tv4->setMemoryType(MemoryType::Global); |
133 | tv5->setMemoryType(MemoryType::Global); |
134 | tv6->setMemoryType(MemoryType::Global); |
135 | |
136 | constexpr int M = 32, N = 810; |
137 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
138 | at::Tensor t0 = at::randn({M, N}, options); |
139 | at::Tensor t1 = at::randn({M, N}, options); |
140 | at::Tensor t2 = at::randn({M, N}, options); |
141 | at::Tensor t3 = at::randn({M, N}, options); |
142 | |
143 | at::Tensor aten_output = (t1 + (t2 - t3)) - t0; |
144 | |
145 | std::vector<IValue> aten_inputs = {t0, t1, t2, t3}; |
146 | |
147 | FusionExecutor fe; |
148 | fe.compileFusion(&fusion, {t0, t1, t2, t3}); |
149 | auto cg_outputs = fe.runFusion({t0, t1, t2, t3}); |
150 | |
151 | testValidate( |
152 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
153 | } |
154 | |
155 | TEST_F(NVFuserTest, FusionConstCheck_CUDA) { |
156 | Fusion fusion; |
157 | FusionGuard fg(&fusion); |
158 | |
159 | auto one = IrBuilder::create<Int>(1); |
160 | TORCH_CHECK(one->isConstScalar()); |
161 | |
162 | auto one_x2 = mul(one, one); |
163 | TORCH_CHECK(one_x2->isConstScalar()); |
164 | |
165 | auto one_x3 = mul(one_x2, one); |
166 | TORCH_CHECK(one_x3->isConstScalar()); |
167 | |
168 | auto one_x4 = mul(one_x3, one); |
169 | TORCH_CHECK(one_x4->isConstScalar()); |
170 | } |
171 | |
172 | TEST_F(NVFuserTest, FusionUnrollWithAlloc_CUDA) { |
173 | const std::vector<int64_t> tensor_dims_in = {128, 128}; |
174 | Fusion fusion; |
175 | FusionGuard fg(&fusion); |
176 | |
177 | // Set up your input tensor views |
178 | TensorView* tv0 = makeSymbolicTensor(tensor_dims_in.size()); |
179 | fusion.addInput(tv0); |
180 | |
181 | TensorView* tv1 = add(tv0, IrBuilder::create<Double>(0)); |
182 | TensorView* tv2 = |
183 | reductionOp(BinaryOpType::Add, {1}, IrBuilder::create<Double>(0), tv1); |
184 | fusion.addOutput(tv2); |
185 | |
186 | const auto options = |
187 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
188 | at::Tensor input = at::randn(tensor_dims_in, options); |
189 | at::Tensor cg_output = at::empty({tensor_dims_in[0]}, options); |
190 | |
191 | // Schedule |
192 | tv2->split(1, 32); |
193 | tv2->split(1, 4); // unroll |
194 | |
195 | auto tv2_rf = tv2->rFactor({-3, -2}); |
196 | |
197 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
198 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
199 | |
200 | tv2_rf->axis(0)->parallelize(ParallelType::BIDx); |
201 | tv2_rf->axis(-1)->parallelize(ParallelType::TIDx); |
202 | tv2_rf->axis(-2)->parallelize(ParallelType::Unroll); |
203 | |
204 | tv1->computeAt(tv2_rf, -1); |
205 | |
206 | FusionExecutor fe; |
207 | fe.compileFusion(&fusion, {input}); |
208 | auto cg_outputs = fe.runFusion({input}); |
209 | |
210 | auto aten_output = (input + 0).to(at::kDouble).sum(1); |
211 | |
212 | testValidate(&fusion, cg_outputs, {input}, {aten_output}, __LINE__, __FILE__); |
213 | } |
214 | |
215 | // Test isZeroInt |
216 | TEST_F(NVFuserTest, FusionIsZeroInt_CUDA) { |
217 | Fusion fusion; |
218 | FusionGuard fg(&fusion); |
219 | |
220 | Int* x = IrBuilder::create<Int>(0); |
221 | Int* y = IrBuilder::create<Int>(1); |
222 | Val* z = mul(x, y); |
223 | TORCH_CHECK(x->isZeroInt()); |
224 | TORCH_CHECK(!y->isZeroInt()); |
225 | TORCH_CHECK(!z->isZeroInt()); |
226 | } |
227 | |
228 | // Test isOneInt |
229 | TEST_F(NVFuserTest, FusionIsOneInt_CUDA) { |
230 | Fusion fusion; |
231 | FusionGuard fg(&fusion); |
232 | |
233 | Int* x = IrBuilder::create<Int>(1); |
234 | Int* y = IrBuilder::create<Int>(1); |
235 | Val* z = mul(x, y); |
236 | TORCH_CHECK(x->isOneInt()); |
237 | TORCH_CHECK(y->isOneInt()); |
238 | TORCH_CHECK(!z->isOneInt()); |
239 | } |
240 | |
241 | // This is to verify no cycle of computeAt is created. A more complex |
242 | // variation of this pattern appears in one of the Python tests |
243 | // (test_random_topo). |
244 | TEST_F(NVFuserTest, FusionComputeAtNonterminatingOutput_CUDA) { |
245 | Fusion fusion; |
246 | FusionGuard fg(&fusion); |
247 | |
248 | TensorView* tv0 = makeSymbolicTensor(1); |
249 | fusion.addInput(tv0); |
250 | |
251 | // Common intermediate tensor |
252 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
253 | // tv1 -> tv2 |
254 | auto tv2 = add(tv1, IrBuilder::create<Double>(2)); |
255 | // tv1 -> tv3 -> tv4 |
256 | auto tv3 = add(tv1, IrBuilder::create<Double>(3)); |
257 | auto tv4 = add(tv3, IrBuilder::create<Double>(4)); |
258 | |
259 | // NOTE: This should no longer occur as of PR #201. |
260 | // The order of adding outputs matters. If tv3 is added before tv4, |
261 | // it should be fine. However, if tv4 is added before tv3, there |
262 | // will be a cycle of tv3->tv4 and tv4->tv3. tv3->tv4 is created |
263 | // first, and then tv4->tv3 is created at the final phase of |
264 | // computeAt (ComputeAt::setupOutputs). |
265 | fusion.addOutput(tv2); |
266 | fusion.addOutput(tv4); |
267 | fusion.addOutput(tv3); |
268 | |
269 | tv0->computeAt(tv2, -1); |
270 | |
271 | TORCH_CHECK(tv3->hasComputeAt()); |
272 | TORCH_CHECK(!tv4->hasComputeAt()); |
273 | |
274 | const auto options = |
275 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
276 | at::Tensor aten_input = at::randn(100, options); |
277 | |
278 | auto t1 = aten_input + 1; |
279 | auto t2 = t1 + 2; |
280 | auto t3 = t1 + 3; |
281 | auto t4 = t3 + 4; |
282 | |
283 | FusionExecutor fe; |
284 | fe.compileFusion(&fusion, {aten_input}); |
285 | auto cg_outputs = fe.runFusion({aten_input}); |
286 | |
287 | std::vector<at::Tensor> aten_outputs = {t2, t4, t3}; |
288 | testValidate( |
289 | &fusion, cg_outputs, {aten_input}, aten_outputs, __LINE__, __FILE__); |
290 | } |
291 | |
292 | TEST_F(NVFuserTest, FusionTraversalOrder1_CUDA) { |
293 | Fusion fusion; |
294 | FusionGuard fg(&fusion); |
295 | |
296 | // Set up your input tensor views |
297 | TensorView* tv0 = makeSymbolicTensor(2); |
298 | fusion.addInput(tv0); |
299 | |
300 | TensorView* tv1 = add(tv0, IrBuilder::create<Double>(1)); |
301 | TensorView* tv2 = add(tv0, IrBuilder::create<Double>(2)); |
302 | TensorView* tv3 = add(tv1, IrBuilder::create<Double>(3)); |
303 | TensorView* tv4 = add(tv1, IrBuilder::create<Double>(4)); |
304 | |
305 | fusion.addOutput(tv2); |
306 | fusion.addOutput(tv3); |
307 | fusion.addOutput(tv4); |
308 | |
309 | tv1->computeAt(tv3, -1); |
310 | |
311 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
312 | at::Tensor aten_input = at::randn({10, 10}, options); |
313 | |
314 | auto t1 = aten_input + 1; |
315 | auto t2 = aten_input + 2; |
316 | auto t3 = t1 + 3; |
317 | auto t4 = t1 + 4; |
318 | |
319 | std::vector<at::Tensor> aten_outputs = {t2, t3, t4}; |
320 | |
321 | std::vector<at::Tensor> cg_outputs = { |
322 | at::empty_like(aten_input, options), |
323 | at::empty_like(aten_input, options), |
324 | at::empty_like(aten_input, options)}; |
325 | |
326 | FusionExecutor fe; |
327 | fe.compileFusion(&fusion, {aten_input}); |
328 | fe.runFusion({aten_input}, cg_outputs); |
329 | testValidate( |
330 | &fusion, cg_outputs, {aten_input}, aten_outputs, __LINE__, __FILE__); |
331 | } |
332 | |
333 | TEST_F(NVFuserTest, FusionTraversalOrder2_CUDA) { |
334 | Fusion fusion; |
335 | FusionGuard fg(&fusion); |
336 | |
337 | // Set up your input tensor views |
338 | TensorView* tv0 = makeSymbolicTensor(2); |
339 | fusion.addInput(tv0); |
340 | |
341 | TensorView* tv1 = add(tv0, IrBuilder::create<Double>(1)); |
342 | TensorView* tv2 = add(tv1, IrBuilder::create<Double>(2)); |
343 | |
344 | TensorView* tv3 = add(tv0, IrBuilder::create<Double>(3)); |
345 | TensorView* tv4 = add(tv3, IrBuilder::create<Double>(4)); |
346 | |
347 | TensorView* tv5 = add(tv1, tv3); |
348 | |
349 | fusion.addOutput(tv2); |
350 | fusion.addOutput(tv4); |
351 | fusion.addOutput(tv5); |
352 | |
353 | tv1->computeAt(tv5, -1); |
354 | tv3->computeAt(tv5, -1); |
355 | |
356 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
357 | at::Tensor aten_input = at::randn({10, 10}, options); |
358 | |
359 | auto t1 = aten_input + 1; |
360 | auto t2 = t1 + 2; |
361 | auto t3 = aten_input + 3; |
362 | auto t4 = t3 + 4; |
363 | auto t5 = t1 + t3; |
364 | |
365 | std::vector<at::Tensor> aten_outputs = {t2, t4, t5}; |
366 | |
367 | std::vector<at::Tensor> cg_outputs = { |
368 | at::empty_like(aten_input, options), |
369 | at::empty_like(aten_input, options), |
370 | at::empty_like(aten_input, options)}; |
371 | |
372 | FusionExecutor fe; |
373 | fe.compileFusion(&fusion, {aten_input}); |
374 | fe.runFusion({aten_input}, cg_outputs); |
375 | |
376 | testValidate( |
377 | &fusion, cg_outputs, {aten_input}, aten_outputs, __LINE__, __FILE__); |
378 | } |
379 | |
380 | TEST_F(NVFuserTest, FusionTraversalOrder3_CUDA) { |
381 | for (const auto i : c10::irange(2)) { |
382 | Fusion fusion; |
383 | FusionGuard fg(&fusion); |
384 | |
385 | TensorView* tv0 = makeSymbolicTensor(1); |
386 | fusion.addInput(tv0); |
387 | |
388 | TensorView* tv1 = add(tv0, IrBuilder::create<Double>(1)); |
389 | TensorView* tv2 = add(tv1, IrBuilder::create<Double>(2)); |
390 | |
391 | TensorView* tv3 = add(tv0, IrBuilder::create<Double>(3)); |
392 | TensorView* tv4 = add(tv3, IrBuilder::create<Double>(4)); |
393 | |
394 | TensorView* tv5 = add(tv1, tv3); |
395 | |
396 | fusion.addOutput(tv2); |
397 | fusion.addOutput(tv4); |
398 | fusion.addOutput(tv5); |
399 | |
400 | const int tile = 32; |
401 | |
402 | tv1->split(-1, tile); |
403 | tv2->split(-1, tile); |
404 | tv3->split(-1, tile); |
405 | tv4->split(-1, tile); |
406 | tv5->split(-1, tile); |
407 | |
408 | auto compute_at_outer = tv1; |
409 | auto compute_at_inner = tv3; |
410 | if (i == 1) { |
411 | std::swap(compute_at_inner, compute_at_outer); |
412 | } |
413 | |
414 | compute_at_outer->computeAt(tv5, -2); |
415 | compute_at_inner->computeAt(tv5, -1); |
416 | |
417 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
418 | at::Tensor aten_input = at::randn({100}, options); |
419 | auto t1 = aten_input + 1; |
420 | auto t2 = t1 + 2; |
421 | auto t3 = aten_input + 3; |
422 | auto t4 = t3 + 4; |
423 | auto t5 = t1 + t3; |
424 | |
425 | std::vector<at::Tensor> aten_outputs = {t2, t4, t5}; |
426 | |
427 | std::vector<at::Tensor> cg_outputs = { |
428 | at::empty_like(aten_input, options), |
429 | at::empty_like(aten_input, options), |
430 | at::empty_like(aten_input, options)}; |
431 | |
432 | FusionExecutor fe; |
433 | fe.compileFusion(&fusion, {aten_input}); |
434 | fe.runFusion({aten_input}, cg_outputs); |
435 | |
436 | testValidate( |
437 | &fusion, cg_outputs, {aten_input}, aten_outputs, __LINE__, __FILE__); |
438 | } |
439 | } |
440 | |
441 | TEST_F(NVFuserTest, FusionTraversalOrder4_CUDA) { |
442 | Fusion fusion; |
443 | FusionGuard fg(&fusion); |
444 | |
445 | // First tree |
446 | TensorView* tv0 = makeSymbolicTensor(1); |
447 | fusion.addInput(tv0); |
448 | TensorView* tv1 = add(tv0, IrBuilder::create<Double>(1)); |
449 | TensorView* tv2 = add(tv1, IrBuilder::create<Double>(2)); |
450 | TensorView* tv3 = add(tv1, IrBuilder::create<Double>(3)); |
451 | fusion.addOutput(tv2); |
452 | fusion.addOutput(tv3); |
453 | |
454 | // Second tree |
455 | TensorView* tv4 = makeSymbolicTensor(1); |
456 | fusion.addInput(tv4); |
457 | TensorView* tv5 = add(tv4, IrBuilder::create<Double>(5)); |
458 | TensorView* tv6 = add(tv5, IrBuilder::create<Double>(6)); |
459 | TensorView* tv7 = add(tv5, IrBuilder::create<Double>(7)); |
460 | fusion.addOutput(tv6); |
461 | fusion.addOutput(tv7); |
462 | |
463 | tv1->computeAt(tv2, -1); |
464 | tv5->computeAt(tv6, -1); |
465 | |
466 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
467 | at::Tensor t0 = at::randn({100}, options); |
468 | at::Tensor t4 = at::rand_like(t0, options); |
469 | |
470 | auto t1 = t0 + 1; |
471 | auto t2 = t1 + 2; |
472 | auto t3 = t1 + 3; |
473 | auto t5 = t4 + 5; |
474 | auto t6 = t5 + 6; |
475 | auto t7 = t5 + 7; |
476 | |
477 | std::vector<at::Tensor> aten_outputs = {t2, t3, t6, t7}; |
478 | std::vector<IValue> aten_inputs = {t0, t4}; |
479 | std::vector<at::Tensor> cg_outputs = { |
480 | at::empty_like(t0, options), |
481 | at::empty_like(t0, options), |
482 | at::empty_like(t0, options), |
483 | at::empty_like(t0, options)}; |
484 | |
485 | FusionExecutor fe; |
486 | fe.compileFusion(&fusion, aten_inputs); |
487 | fe.runFusion(aten_inputs, cg_outputs); |
488 | |
489 | testValidate( |
490 | &fusion, cg_outputs, aten_inputs, aten_outputs, __LINE__, __FILE__); |
491 | } |
492 | |
493 | TEST_F(NVFuserTest, FusionTraversalOrder5_CUDA) { |
494 | Fusion fusion; |
495 | FusionGuard fg(&fusion); |
496 | |
497 | TensorView* tv0 = makeSymbolicTensor(1); |
498 | fusion.addInput(tv0); |
499 | TensorView* tv1 = add(tv0, IrBuilder::create<Double>(1)); |
500 | TensorView* tv2 = add(tv1, IrBuilder::create<Double>(2)); |
501 | TensorView* tv3 = add(tv0, IrBuilder::create<Double>(3)); |
502 | TensorView* tv4 = add(tv3, IrBuilder::create<Double>(4)); |
503 | TensorView* tv5 = add(tv2, tv4); |
504 | |
505 | fusion.addOutput(tv1); |
506 | fusion.addOutput(tv3); |
507 | fusion.addOutput(tv5); |
508 | |
509 | tv2->computeAt(tv5, -1); |
510 | tv4->computeAt(tv5, -1); |
511 | |
512 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
513 | at::Tensor aten_input = at::randn({100}, options); |
514 | std::vector<at::Tensor> cg_outputs = { |
515 | at::empty_like(aten_input, options), |
516 | at::empty_like(aten_input, options), |
517 | at::empty_like(aten_input, options)}; |
518 | |
519 | FusionExecutor fe; |
520 | fe.compileFusion(&fusion, {aten_input}); |
521 | fe.runFusion({aten_input}, cg_outputs); |
522 | |
523 | auto t1 = aten_input + 1; |
524 | auto t2 = t1 + 2; |
525 | auto t3 = aten_input + 3; |
526 | auto t4 = t3 + 4; |
527 | auto t5 = t2 + t4; |
528 | |
529 | std::vector<at::Tensor> aten_outputs = {t1, t3, t5}; |
530 | |
531 | testValidate( |
532 | &fusion, cg_outputs, {aten_input}, aten_outputs, __LINE__, __FILE__); |
533 | } |
534 | |
535 | TEST_F(NVFuserTest, FusionTraversalOrder6_CUDA) { |
536 | Fusion fusion; |
537 | FusionGuard fg(&fusion); |
538 | |
539 | TensorView* tv0 = makeSymbolicTensor(1); |
540 | fusion.addInput(tv0); |
541 | TensorView* tv1 = add(tv0, IrBuilder::create<Double>(1)); |
542 | TensorView* tv2 = add(tv0, IrBuilder::create<Double>(2)); |
543 | TensorView* tv3 = add(tv1, tv2); |
544 | TensorView* tv4 = add(tv3, IrBuilder::create<Double>(4)); |
545 | |
546 | fusion.addOutput(tv4); |
547 | |
548 | tv1->split(0, 32); |
549 | tv2->split(0, 32); |
550 | tv3->split(0, 32); |
551 | tv4->split(0, 32); |
552 | |
553 | tv3->computeAt(tv4, -2); |
554 | tv1->computeAt(tv3, -1); |
555 | tv2->computeAt(tv3, -2); |
556 | |
557 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
558 | at::Tensor aten_input = at::randn({100}, options); |
559 | |
560 | auto t1 = aten_input + 1; |
561 | auto t2 = aten_input + 2; |
562 | auto t3 = t1 + t2; |
563 | auto aten_output = t3 + 4; |
564 | |
565 | at::Tensor cg_output = at::empty_like(aten_input, options); |
566 | |
567 | FusionExecutor fe; |
568 | fe.compileFusion(&fusion, {aten_input}); |
569 | fe.runFusion({aten_input}, {cg_output}); |
570 | |
571 | testValidate( |
572 | &fusion, {cg_output}, {aten_input}, {aten_output}, __LINE__, __FILE__); |
573 | } |
574 | |
575 | TEST_F(NVFuserTest, FusionTraversalOrder7_CUDA) { |
576 | Fusion fusion; |
577 | FusionGuard fg(&fusion); |
578 | |
579 | TensorView* tv0 = makeSymbolicTensor(1); |
580 | fusion.addInput(tv0); |
581 | TensorView* tv1 = add(tv0, IrBuilder::create<Double>(1)); |
582 | TensorView* tv2 = add(tv1, IrBuilder::create<Double>(2)); |
583 | TensorView* tv3 = add(tv0, IrBuilder::create<Double>(3)); |
584 | TensorView* tv4 = add(tv3, IrBuilder::create<Double>(4)); |
585 | TensorView* tv5 = add(tv2, tv4); |
586 | |
587 | fusion.addOutput(tv5); |
588 | |
589 | TensorView* tvs[] = {tv1, tv2, tv3, tv4, tv5}; |
590 | for (auto tv : tvs) { |
591 | tv->split(0, 2); |
592 | tv->split(0, 4); |
593 | tv->split(0, 8); |
594 | } |
595 | |
596 | // computeAt into inner loop nests |
597 | tv1->computeAt(tv2, -1); |
598 | tv3->computeAt(tv4, -2); |
599 | |
600 | tv2->computeAt(tv5, -4); |
601 | tv4->computeAt(tv5, -3); |
602 | |
603 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
604 | at::Tensor aten_input = at::randn({100}, options); |
605 | |
606 | auto t1 = aten_input + 1; |
607 | auto t2 = t1 + 2; |
608 | auto t3 = aten_input + 3; |
609 | auto t4 = t3 + 4; |
610 | auto aten_output = t2 + t4; |
611 | |
612 | at::Tensor cg_output = at::empty_like(aten_input, options); |
613 | |
614 | FusionExecutor fe; |
615 | fe.compileFusion(&fusion, {aten_input}); |
616 | fe.runFusion({aten_input}, {cg_output}); |
617 | |
618 | testValidate( |
619 | &fusion, {cg_output}, {aten_input}, {aten_output}, __LINE__, __FILE__); |
620 | } |
621 | |
622 | // Test predication of grid reduction |
623 | TEST_F(NVFuserTest, FusionThreadPredicate_CUDA) { |
624 | const int gdimx = 4; |
625 | const int bdimx = 128; |
626 | |
627 | Fusion fusion; |
628 | FusionGuard fg(&fusion); |
629 | |
630 | TensorView* tv0 = makeSymbolicTensor(2); |
631 | fusion.addInput(tv0); |
632 | |
633 | TensorView* tv1 = |
634 | reductionOp(BinaryOpType::Add, {1}, IrBuilder::create<Double>(0), tv0); |
635 | TensorView* tv2 = unaryOp(UnaryOpType::Neg, tv1); |
636 | TensorView* tv3 = add(tv0, IrBuilder::create<Double>(2)); |
637 | |
638 | fusion.addOutput(tv3); |
639 | fusion.addOutput(tv2); |
640 | |
641 | tv1->split(1, bdimx); |
642 | tv1->split(1, gdimx); |
643 | tv3->split(1, bdimx); |
644 | tv3->split(1, gdimx); |
645 | |
646 | TensorView* tv1_rf = tv1->rFactor({1}); |
647 | |
648 | tv1->computeAt(tv2, -1); |
649 | |
650 | tv1->axis(0)->parallelize(ParallelType::BIDy); |
651 | tv1_rf->axis(0)->parallelize(ParallelType::BIDy); |
652 | tv2->axis(0)->parallelize(ParallelType::BIDy); |
653 | tv1->axis(-2)->parallelize(ParallelType::BIDx); |
654 | tv1_rf->axis(-2)->parallelize(ParallelType::BIDx); |
655 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
656 | tv1_rf->axis(-1)->parallelize(ParallelType::TIDx); |
657 | |
658 | tv3->axis(3)->parallelize(ParallelType::TIDx); |
659 | tv3->axis(2)->parallelize(ParallelType::BIDx); |
660 | tv3->axis(0)->parallelize(ParallelType::BIDy); |
661 | |
662 | int numel_x = 100; |
663 | int numel_y = 1000; |
664 | |
665 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
666 | at::Tensor aten_input = at::randn({numel_x, numel_y}, options); |
667 | |
668 | auto t2 = -aten_input.to(at::kDouble).sum({1}); |
669 | auto t3 = aten_input + 2.0; |
670 | |
671 | std::vector<at::Tensor> aten_outputs = {t3, t2}; |
672 | |
673 | std::vector<at::Tensor> cg_outputs = { |
674 | at::empty_like(aten_input, options), at::empty({numel_x}, options)}; |
675 | |
676 | FusionExecutor fe; |
677 | fe.compileFusion(&fusion, {aten_input}); |
678 | fe.runFusion({aten_input}, cg_outputs); |
679 | |
680 | testValidate( |
681 | &fusion, cg_outputs, {aten_input}, aten_outputs, __LINE__, __FILE__); |
682 | } |
683 | |
684 | TEST_F(NVFuserTest, FusionLSTMCell_CUDA) { |
685 | const int hidden_features = 512; |
686 | const int batch_size = 64; |
687 | |
688 | Fusion fusion; |
689 | FusionGuard fg(&fusion); |
690 | |
691 | TensorView* tvs[16]; |
692 | for (const auto i : c10::irange(16)) { |
693 | tvs[i] = makeSymbolicTensor(2); |
694 | fusion.addInput(tvs[i]); |
695 | } |
696 | |
697 | auto ingate = unaryOp( |
698 | UnaryOpType::Sigmoid, add(add(add(tvs[0], tvs[1]), tvs[2]), tvs[3])); |
699 | |
700 | auto forgetgate = unaryOp( |
701 | UnaryOpType::Sigmoid, add(add(add(tvs[4], tvs[5]), tvs[6]), tvs[7])); |
702 | |
703 | auto cellgate = unaryOp( |
704 | UnaryOpType::Tanh, add(add(add(tvs[8], tvs[9]), tvs[10]), tvs[11])); |
705 | |
706 | auto outgate = unaryOp( |
707 | UnaryOpType::Sigmoid, add(add(add(tvs[12], tvs[13]), tvs[14]), tvs[15])); |
708 | |
709 | auto cx = makeContigTensor(2); |
710 | fusion.addInput(cx); |
711 | |
712 | auto cy = add(mul(forgetgate, cx), mul(ingate, cellgate)); |
713 | |
714 | auto hy = mul(outgate, unaryOp(UnaryOpType::Tanh, cy)); |
715 | |
716 | fusion.addOutput(cy); |
717 | fusion.addOutput(hy); |
718 | |
719 | std::vector<c10::IValue> aten_inputs; |
720 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
721 | at::Tensor large_tensor0 = |
722 | at::randn({batch_size, hidden_features * 4}, options); |
723 | at::Tensor large_tensor1 = |
724 | at::randn({batch_size, hidden_features * 4}, options); |
725 | at::Tensor large_tensor2 = |
726 | at::randn({batch_size, hidden_features * 4}, options); |
727 | at::Tensor large_tensor3 = |
728 | at::randn({batch_size, hidden_features * 4}, options); |
729 | |
730 | auto chunked0 = large_tensor0.chunk(4, 1); |
731 | auto chunked1 = large_tensor1.chunk(4, 1); |
732 | auto chunked2 = large_tensor2.chunk(4, 1); |
733 | auto chunked3 = large_tensor3.chunk(4, 1); |
734 | |
735 | aten_inputs.insert(aten_inputs.end(), chunked0.begin(), chunked0.end()); |
736 | aten_inputs.insert(aten_inputs.end(), chunked1.begin(), chunked1.end()); |
737 | aten_inputs.insert(aten_inputs.end(), chunked2.begin(), chunked2.end()); |
738 | aten_inputs.insert(aten_inputs.end(), chunked3.begin(), chunked3.end()); |
739 | |
740 | auto at_ingate = |
741 | chunked0[0].add(chunked0[1]).add(chunked0[2]).add(chunked0[3]).sigmoid(); |
742 | auto at_forgetgate = |
743 | chunked1[0].add(chunked1[1]).add(chunked1[2]).add(chunked1[3]).sigmoid(); |
744 | auto at_cellgate = |
745 | chunked2[0].add(chunked2[1]).add(chunked2[2]).add(chunked2[3]).tanh(); |
746 | auto at_outgate = |
747 | chunked3[0].add(chunked3[1]).add(chunked3[2]).add(chunked3[3]).sigmoid(); |
748 | |
749 | auto at_cx = at::randn({batch_size, hidden_features}, options); |
750 | aten_inputs.push_back(at_cx); |
751 | auto at_cy = at_forgetgate.mul(at_cx).add(at_ingate.mul(at_cellgate)); |
752 | auto at_hy = at_outgate.mul(at_cy.tanh()); |
753 | |
754 | auto lparams = schedulePointwise(&fusion, aten_inputs); |
755 | |
756 | FusionExecutor fe; |
757 | fe.compileFusion(&fusion, aten_inputs, lparams); |
758 | auto cg_outputs = fe.runFusion(aten_inputs, lparams); |
759 | |
760 | testValidate( |
761 | &fusion, cg_outputs, aten_inputs, {at_cy, at_hy}, __LINE__, __FILE__); |
762 | } |
763 | |
764 | TEST_F(NVFuserTest, FusionReductionHalf_CUDA) { |
765 | Fusion fusion; |
766 | FusionGuard fg(&fusion); |
767 | |
768 | // Set up your input tensor views |
769 | TensorView* tv0 = makeSymbolicTensor(3, DataType::Half); |
770 | fusion.addInput(tv0); |
771 | |
772 | auto tv1 = castOp(DataType::Float, tv0); |
773 | auto tv2 = add(tv1, IrBuilder::create<Double>(1.0)); |
774 | auto tv3 = sum(tv2, {2}); |
775 | auto tv4 = castOp(DataType::Half, tv3); |
776 | |
777 | fusion.addOutput(tv4); |
778 | |
779 | const auto options = |
780 | at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0); |
781 | at::Tensor aten_input = at::randn({8, 8, 16}, options); |
782 | |
783 | auto reduction_tv = tv3; |
784 | |
785 | auto reduction_params = getReductionHeuristics(&fusion, {aten_input}); |
786 | TORCH_CHECK(reduction_params, "Reduction schedule was not generated!" ); |
787 | scheduleReduction(&fusion, *reduction_params); |
788 | |
789 | TORCH_CHECK(reduction_params, "Reduction schedule was not generated!" ); |
790 | |
791 | auto lparams = reduction_params->lparams; |
792 | |
793 | FusionExecutor fe; |
794 | fe.compileFusion(&fusion, {aten_input}, lparams); |
795 | // no broadcasting needed, omitting the last optional argument; |
796 | auto cg_outputs = fe.runFusion({aten_input}, lparams); |
797 | |
798 | auto aten_output = aten_input.add(1.0).to(at::kDouble).sum({2}); |
799 | |
800 | testValidate( |
801 | &fusion, |
802 | cg_outputs, |
803 | {aten_input}, |
804 | {aten_output}, |
805 | __LINE__, |
806 | __FILE__, |
807 | "" , |
808 | lparams); |
809 | } |
810 | |
811 | TEST_F(NVFuserTest, FusionReduceSingle_CUDA) { |
812 | Fusion fusion; |
813 | FusionGuard fg(&fusion); |
814 | |
815 | // Set up your input tensor views |
816 | TensorView* tv0 = makeConcreteTensor({100, 1}); |
817 | fusion.addInput(tv0); |
818 | auto tv1 = sum(tv0, {1}); |
819 | fusion.addOutput(tv1); |
820 | |
821 | const auto options = |
822 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
823 | at::Tensor aten_input = at::randn({100, 1}, options); |
824 | |
825 | // Grab only tensor views, though there shouldn't be any other type |
826 | FusionExecutor fe; |
827 | fe.compileFusion(&fusion, {aten_input}); |
828 | // no broadcasting needed, omitting the last optional argument; |
829 | auto cg_outputs = fe.runFusion({aten_input}); |
830 | |
831 | auto aten_output = aten_input.to(at::kDouble).sum({1}); |
832 | testValidate( |
833 | &fusion, cg_outputs, {aten_input}, {aten_output}, __LINE__, __FILE__); |
834 | } |
835 | |
836 | TEST_F(NVFuserTest, FusionReduceImplicitBroadcast_CUDA) { |
837 | constexpr int bid_x = 80; |
838 | constexpr int tid_x = 4096; |
839 | constexpr int red_dim = 1; |
840 | |
841 | Fusion fusion; |
842 | FusionGuard fg(&fusion); |
843 | |
844 | // Set up your input tensor views |
845 | TensorView* tv0 = makeConcreteTensor({bid_x, tid_x, 1}); |
846 | fusion.addInput(tv0); |
847 | |
848 | TensorView* tv1 = reductionOp( |
849 | BinaryOpType::Add, {red_dim, 2}, IrBuilder::create<Double>(0), tv0); |
850 | fusion.addOutput(tv1); |
851 | |
852 | const auto options = |
853 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
854 | at::Tensor aten_input = at::randn({bid_x, tid_x, 1}, options); |
855 | |
856 | // Apply reduction heuristic |
857 | auto reduction_params = getReductionHeuristics(&fusion, {aten_input}); |
858 | TORCH_CHECK(reduction_params, "Reduction schedule was not generated!" ); |
859 | scheduleReduction(&fusion, *reduction_params); |
860 | auto lparams = reduction_params->lparams; |
861 | |
862 | FusionExecutor fe; |
863 | fe.compileFusion(&fusion, {aten_input}, lparams); |
864 | // no broadcasting needed, omitting the last optional argument; |
865 | auto cg_outputs = fe.runFusion({aten_input}, lparams); |
866 | auto aten_output = aten_input.to(at::kDouble).sum({red_dim, 2}); |
867 | |
868 | testValidate( |
869 | &fusion, |
870 | cg_outputs, |
871 | {aten_input}, |
872 | {aten_output}, |
873 | __LINE__, |
874 | __FILE__, |
875 | "" , |
876 | lparams); |
877 | } |
878 | |
879 | TEST_F(NVFuserTest, FusionReduceImplicitBroadcast2_CUDA) { |
880 | constexpr int bid_x = 80; |
881 | constexpr int tid_x = 4096; |
882 | constexpr int red_dim = 1; |
883 | |
884 | Fusion fusion; |
885 | FusionGuard fg(&fusion); |
886 | |
887 | // Set up your input tensor views |
888 | TensorView* tv0 = makeConcreteTensor({bid_x, tid_x, 1}); |
889 | fusion.addInput(tv0); |
890 | |
891 | TensorView* tv1 = |
892 | reductionOp(BinaryOpType::Add, {2}, IrBuilder::create<Double>(0), tv0); |
893 | |
894 | TensorView* tv2 = reductionOp( |
895 | BinaryOpType::Add, {red_dim}, IrBuilder::create<Double>(0), tv1); |
896 | fusion.addOutput(tv2); |
897 | |
898 | const auto options = |
899 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
900 | at::Tensor aten_input = at::randn({bid_x, tid_x, 1}, options); |
901 | |
902 | // Apply reduction heuristic |
903 | auto reduction_params = getReductionHeuristics(&fusion, {aten_input}); |
904 | TORCH_CHECK(reduction_params, "Reduction schedule was not generated!" ); |
905 | |
906 | scheduleReduction(&fusion, *reduction_params); |
907 | auto lparams = reduction_params->lparams; |
908 | |
909 | FusionExecutor fe; |
910 | fe.compileFusion(&fusion, {aten_input}, lparams); |
911 | // no broadcasting needed, omitting the last optional argument; |
912 | auto cg_outputs = fe.runFusion({aten_input}, lparams); |
913 | auto aten_output = aten_input.to(at::kDouble).sum({1, 2}); |
914 | |
915 | testValidate( |
916 | &fusion, |
917 | cg_outputs, |
918 | {aten_input}, |
919 | {aten_output}, |
920 | __LINE__, |
921 | __FILE__, |
922 | "" , |
923 | lparams); |
924 | } |
925 | |
926 | TEST_F(NVFuserTest, FusionReduceImplicitBroadcast3_CUDA) { |
927 | constexpr int bid_x = 80; |
928 | constexpr int tid_x = 4096; |
929 | constexpr int red_dim = 1; |
930 | |
931 | Fusion fusion; |
932 | FusionGuard fg(&fusion); |
933 | |
934 | // Set up your input tensor views |
935 | TensorView* tv0 = makeConcreteTensor({bid_x, tid_x, 1}); |
936 | fusion.addInput(tv0); |
937 | |
938 | TensorView* tv1 = reductionOp( |
939 | BinaryOpType::Add, {red_dim}, IrBuilder::create<Double>(0), tv0); |
940 | |
941 | TensorView* tv2 = |
942 | reductionOp(BinaryOpType::Add, {1}, IrBuilder::create<Double>(0), tv1); |
943 | fusion.addOutput(tv2); |
944 | |
945 | const auto options = |
946 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
947 | at::Tensor aten_input = at::randn({bid_x, tid_x, 1}, options); |
948 | |
949 | // Apply reduction heuristic |
950 | auto reduction_params = getReductionHeuristics(&fusion, {aten_input}); |
951 | TORCH_CHECK(reduction_params, "Reduction schedule was not generated!" ); |
952 | scheduleReduction(&fusion, *reduction_params); |
953 | auto lparams = reduction_params->lparams; |
954 | |
955 | FusionExecutor fe; |
956 | fe.compileFusion(&fusion, {aten_input}, lparams); |
957 | // no broadcasting needed, omitting the last optional argument; |
958 | auto cg_outputs = fe.runFusion({aten_input}, lparams); |
959 | auto aten_output = aten_input.to(at::kDouble).sum({2, 1}); |
960 | |
961 | testValidate( |
962 | &fusion, |
963 | cg_outputs, |
964 | {aten_input}, |
965 | {aten_output}, |
966 | __LINE__, |
967 | __FILE__, |
968 | "" , |
969 | lparams); |
970 | } |
971 | |
972 | TEST_F(NVFuserTest, FusionTrivialReduction_CUDA) { |
973 | Fusion fusion; |
974 | FusionGuard fg(&fusion); |
975 | |
976 | // Set up your input tensor views |
977 | TensorView* tv0 = makeConcreteTensor({10, 20, 1}); |
978 | fusion.addInput(tv0); |
979 | TensorView* tv1 = |
980 | reductionOp(BinaryOpType::Add, {2}, IrBuilder::create<Double>(0), tv0); |
981 | fusion.addOutput(tv1); |
982 | |
983 | TORCH_CHECK( |
984 | ir_utils::getReductionOps(&fusion, true /* ignore_trivial */).empty(), |
985 | "Trivial reduction picked up by fusion" ); |
986 | |
987 | const auto options = |
988 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
989 | at::Tensor aten_input = at::randn({10, 20, 1}, options); |
990 | |
991 | FusionExecutor fe; |
992 | fe.compileFusion(&fusion, {aten_input}); |
993 | auto cg_outputs = fe.runFusion({aten_input}); |
994 | auto aten_output = aten_input.to(at::kDouble).sum({2}); |
995 | |
996 | testValidate( |
997 | &fusion, cg_outputs, {aten_input}, {aten_output}, __LINE__, __FILE__); |
998 | } |
999 | |
1000 | TEST_F(NVFuserTest, FusionTrivialReduction2_CUDA) { |
1001 | Fusion fusion; |
1002 | FusionGuard fg(&fusion); |
1003 | |
1004 | int w = 1, x = 1, y = 7, z = 8; |
1005 | |
1006 | auto tv0 = makeSymbolicTensor(2); |
1007 | auto tv1 = makeConcreteTensor({w, x, y, z}); |
1008 | fusion.addInput(tv0); |
1009 | fusion.addInput(tv1); |
1010 | |
1011 | auto tv2 = sum(tv1, {0}); |
1012 | auto tv3 = sum(tv2, {0}); |
1013 | auto tv4 = add(tv3, tv0); |
1014 | |
1015 | fusion.addOutput(tv4); |
1016 | |
1017 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1018 | at::Tensor t0 = at::randn({y, z}, options); |
1019 | at::Tensor t1 = at::randn({w, x, y, z}, options); |
1020 | auto aten_output = t1.to(at::kDouble).sum({0}).sum({0}).add(t0); |
1021 | |
1022 | std::vector<IValue> aten_inputs = {t0, t1}; |
1023 | |
1024 | auto lparams = schedulePointwise(&fusion, aten_inputs); |
1025 | |
1026 | FusionExecutor fe; |
1027 | fe.compileFusion(&fusion, aten_inputs, lparams); |
1028 | auto cg_outputs = fe.runFusion(aten_inputs, lparams); |
1029 | |
1030 | testValidate( |
1031 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
1032 | } |
1033 | |
1034 | TEST_F(NVFuserTest, FusionTrivialReduction3_CUDA) { |
1035 | Fusion fusion; |
1036 | FusionGuard fg(&fusion); |
1037 | |
1038 | int v = 1, w = 1, x = 1, y = 7, z = 8; |
1039 | |
1040 | auto tv0 = makeSymbolicTensor(2); |
1041 | auto tv1 = makeConcreteTensor({v, w, x, y, z}); |
1042 | fusion.addInput(tv0); |
1043 | fusion.addInput(tv1); |
1044 | |
1045 | auto tv2 = sum(tv1, {0, 1, 2}); |
1046 | auto tv3 = add(tv2, tv0); |
1047 | |
1048 | fusion.addOutput(tv3); |
1049 | |
1050 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1051 | at::Tensor t0 = at::randn({y, z}, options); |
1052 | at::Tensor t1 = at::randn({v, w, x, y, z}, options); |
1053 | auto aten_output = t1.sum({0, 1, 2}).add(t0); |
1054 | |
1055 | std::vector<IValue> aten_inputs = {t0, t1}; |
1056 | |
1057 | auto lparams = schedulePointwise(&fusion, aten_inputs); |
1058 | |
1059 | FusionExecutor fe; |
1060 | fe.compileFusion(&fusion, aten_inputs, lparams); |
1061 | auto cg_outputs = fe.runFusion(aten_inputs, lparams); |
1062 | |
1063 | testValidate( |
1064 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
1065 | } |
1066 | |
1067 | // Make sure trivial reductions are correctly detected even with |
1068 | // scheduling applied. |
1069 | TEST_F(NVFuserTest, FusionDetectTrivialReduction1_CUDA) { |
1070 | Fusion fusion; |
1071 | FusionGuard fg(&fusion); |
1072 | |
1073 | auto tv0 = makeSymbolicTensor(1); |
1074 | fusion.addInput(tv0); |
1075 | |
1076 | auto tv1 = broadcast(tv0, {false, true}); |
1077 | auto tv2 = sum(tv1, {1}); |
1078 | fusion.addOutput(tv2); |
1079 | |
1080 | tv2->split(1, 4); |
1081 | tv2->split(1, 8); |
1082 | auto tv3 = tv2->rFactor({-1}); |
1083 | auto tv4 = tv2->rFactor({-1}); |
1084 | |
1085 | auto tv5 = broadcast(tv0, {true, false}); |
1086 | auto tv6 = add(tv5, IrBuilder::create<Double>(1)); |
1087 | auto tv7 = sub(tv6, IrBuilder::create<Double>(1)); |
1088 | auto tv8 = sum(tv7, {0}); |
1089 | fusion.addOutput(tv8); |
1090 | |
1091 | auto tv9 = broadcast(tv0, {false, true, true}); |
1092 | auto tv10 = sum(tv9, {1}); |
1093 | auto tv11 = sum(tv10, {1}); |
1094 | fusion.addOutput(tv11); |
1095 | |
1096 | tv8->split(0, 3); |
1097 | tv10->split(1, 4); |
1098 | tv11->split(1, 5); |
1099 | |
1100 | tv0->computeAt(tv2, -1); |
1101 | tv0->computeAt(tv8, -1); |
1102 | tv0->computeAt(tv11, 1); |
1103 | |
1104 | // Test indexing to gmem-backed tensors |
1105 | tv3->setMemoryType(MemoryType::Global); |
1106 | tv8->setMemoryType(MemoryType::Global); |
1107 | |
1108 | GpuLower gpulw(&fusion); |
1109 | |
1110 | // No ReductionOp should be generated as all the reduction |
1111 | // exprs should be replaced with a unary set op. |
1112 | for (const auto expr : gpulw.kernel()->as<Fusion>()->exprs()) { |
1113 | TORCH_CHECK(!expr->isA<ReductionOp>()); |
1114 | } |
1115 | |
1116 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1117 | at::Tensor t0 = at::randn({100}, options); |
1118 | std::vector<IValue> aten_inputs = {t0}; |
1119 | |
1120 | FusionExecutor fe; |
1121 | fe.compileFusion(&fusion, aten_inputs); |
1122 | auto cg_outputs = fe.runFusion(aten_inputs); |
1123 | |
1124 | testValidate( |
1125 | &fusion, cg_outputs, aten_inputs, {t0, t0, t0}, __LINE__, __FILE__); |
1126 | } |
1127 | |
1128 | // Test detection of partially trivial reduction |
1129 | TEST_F(NVFuserTest, FusionDetectTrivialReduction2_CUDA) { |
1130 | Fusion fusion; |
1131 | FusionGuard fg(&fusion); |
1132 | |
1133 | auto tv0 = makeSymbolicTensor(2); |
1134 | fusion.addInput(tv0); |
1135 | auto tv1 = sum(tv0, {1}); |
1136 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
1137 | fusion.addOutput(tv2); |
1138 | |
1139 | tv1->split(1, 1); |
1140 | // tv1->axis(1): non-trivial |
1141 | // tv1->axis(2): trivial |
1142 | |
1143 | auto tv3 = tv1->rFactor({-1}); |
1144 | |
1145 | // Just to suppress register-allocation warning |
1146 | tv0->computeAt(tv2, 1); |
1147 | tv3->computeAt(tv1, -1); |
1148 | |
1149 | GpuLower gpulw(&fusion); |
1150 | |
1151 | // tv3's reduction axis is a trivial reduction. The only |
1152 | // ReductionOp should be for tv1. |
1153 | for (const auto expr : gpulw.kernel()->as<Fusion>()->exprs()) { |
1154 | if (expr->isA<ReductionOp>()) { |
1155 | auto reduction_out = |
1156 | expr->as<ReductionOp>()->outputs()[0]->as<TensorView>(); |
1157 | TORCH_CHECK(reduction_out->name() == 1); |
1158 | } |
1159 | } |
1160 | } |
1161 | |
1162 | TEST_F(NVFuserTest, FusionInputsIdLookup_CUDA) { |
1163 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1164 | at::Tensor t0 = at::randn({16, 8, 8}, options); |
1165 | at::Tensor t1 = at::randn({8, 8}, options); |
1166 | at::Tensor t2 = at::randn({6, 4}, options); |
1167 | |
1168 | // create a cache with max size 2; |
1169 | torch::jit::fuser::cuda::InputsIdLookup inputs_id_lookup(2); |
1170 | |
1171 | // testing basic function, same encoding for identical inputs |
1172 | auto id_0 = inputs_id_lookup.lookupId({t0, t1, 5.0}); |
1173 | auto id_0_lookup = inputs_id_lookup.lookupId({t0, t1, 2.5}); |
1174 | TORCH_CHECK(id_0.id == id_0_lookup.id); |
1175 | TORCH_CHECK(inputs_id_lookup.size() == 1); |
1176 | TORCH_CHECK(id_0.eviction == false); |
1177 | |
1178 | // new input (even tho same shape, but we have different signature because of |
1179 | // missing scalar input |
1180 | auto id_1 = inputs_id_lookup.lookupId({t0, t1}); |
1181 | auto id_1_lookup = inputs_id_lookup.lookupId({t0, t1}); |
1182 | TORCH_CHECK(id_1.id == id_1_lookup.id); |
1183 | TORCH_CHECK(inputs_id_lookup.size() == 2); |
1184 | TORCH_CHECK(id_1.eviction == false); |
1185 | |
1186 | // eviction should happen at this point |
1187 | auto id_2 = inputs_id_lookup.lookupId({t2, t1}); |
1188 | TORCH_CHECK(id_2.id != id_0.id); |
1189 | TORCH_CHECK(id_2.id != id_1.id); |
1190 | TORCH_CHECK(inputs_id_lookup.size() == 2); |
1191 | TORCH_CHECK(id_2.eviction == true); |
1192 | TORCH_CHECK(id_2.evict_id == id_0.id); |
1193 | |
1194 | // look at input 1 again |
1195 | auto id_1_relook = inputs_id_lookup.lookupId({t0, t1}); |
1196 | TORCH_CHECK(id_1_relook.id == id_1.id); |
1197 | TORCH_CHECK(id_1_relook.eviction == false); |
1198 | } |
1199 | |
1200 | TEST_F(NVFuserTest, FusionGroupGuardSimpleTensor_CUDA) { |
1201 | std::vector<int64_t> sizes_vec({16, 8, 8}); |
1202 | std::vector<int64_t> strides_vec({64, 8, 1}); |
1203 | auto tensor_type = TensorType::create( |
1204 | at::kFloat, c10::nullopt, sizes_vec, strides_vec, c10::nullopt); |
1205 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1206 | |
1207 | // pass with identical shape |
1208 | auto t0 = at::randn({16, 8, 8}, options); |
1209 | TORCH_CHECK(complyWith(t0, tensor_type)); |
1210 | |
1211 | // pass with dynamic shape |
1212 | auto t1 = at::randn({16, 16, 8}, options); |
1213 | TORCH_CHECK(complyWith(t1, tensor_type)); |
1214 | |
1215 | // broadcasting semantic change failure |
1216 | auto t2 = at::randn({16, 1, 8}, options); |
1217 | TORCH_CHECK(!complyWith(t2, tensor_type)); |
1218 | |
1219 | // contiguity failure via slicing |
1220 | auto t3 = t0.slice(1, 0, 8, 2); |
1221 | TORCH_CHECK(!complyWith(t3, tensor_type)); |
1222 | |
1223 | // contiguity failure via slicing |
1224 | auto t4 = t0.slice(2, 0, 8, 2); |
1225 | TORCH_CHECK(!complyWith(t4, tensor_type)); |
1226 | |
1227 | // rank failure |
1228 | auto t5 = at::randn({16, 8, 8, 8}, options); |
1229 | TORCH_CHECK(!complyWith(t5, tensor_type)); |
1230 | |
1231 | // contiguity on stride 1 dimension with implicit broadcasting |
1232 | auto t = at::randn({4}, options); |
1233 | auto t6 = t.unsqueeze(1).expand({4, 8}); |
1234 | TORCH_CHECK(complyWith(t6, TensorType::create(t6))); |
1235 | } |
1236 | |
1237 | TEST_F(NVFuserTest, FusionGroupGuardBroadcastTensor_CUDA) { |
1238 | std::vector<int64_t> sizes_vec({16, 1, 8}); |
1239 | std::vector<int64_t> strides_vec({8, 8, 1}); |
1240 | auto tensor_type = TensorType::create( |
1241 | at::kFloat, c10::nullopt, sizes_vec, strides_vec, c10::nullopt); |
1242 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1243 | |
1244 | // broadcasting semantic change |
1245 | auto t0 = at::randn({16, 8, 8}, options); |
1246 | TORCH_CHECK(!complyWith(t0, tensor_type)); |
1247 | |
1248 | // dtype failure |
1249 | auto t1 = at::randn({16, 1, 8}, options.dtype(at::kHalf)); |
1250 | TORCH_CHECK(!complyWith(t1, tensor_type)); |
1251 | |
1252 | // dtype failure |
1253 | auto t2 = at::randn({16, 1, 8}, options); |
1254 | TORCH_CHECK(complyWith(t2, tensor_type)); |
1255 | |
1256 | // device inconsistency shouldn't fail |
1257 | auto t3 = at::randn({16, 1, 8}, options.device(at::kCPU, 0)); |
1258 | TORCH_CHECK(complyWith(t3, tensor_type)); |
1259 | } |
1260 | |
1261 | TEST_F(NVFuserTest, FusionGroupGuardPermutedTensor_CUDA) { |
1262 | std::vector<int64_t> sizes_vec({16, 8, 8}); |
1263 | std::vector<int64_t> strides_vec({64, 1, 8}); |
1264 | auto tensor_type = TensorType::create( |
1265 | at::kFloat, c10::nullopt, sizes_vec, strides_vec, c10::nullopt); |
1266 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1267 | |
1268 | // failing permutation |
1269 | auto t0 = at::randn({16, 8, 8}, options); |
1270 | TORCH_CHECK(!complyWith(t0, tensor_type)); |
1271 | |
1272 | // passing with dynamic shape |
1273 | auto t1 = t0.permute({0, 2, 1}); |
1274 | TORCH_CHECK(complyWith(t1, tensor_type)); |
1275 | } |
1276 | |
1277 | TEST_F(NVFuserTest, FusionGroupGuardRelaxedCheck_CUDA) { |
1278 | std::vector<int64_t> sizes_vec({16, 8, 8}); |
1279 | std::vector<int64_t> strides_vec({128, 16, 1}); |
1280 | auto tensor_type = TensorType::create( |
1281 | at::kFloat, c10::nullopt, sizes_vec, strides_vec, c10::nullopt); |
1282 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1283 | |
1284 | // contiguity check passes although it differs |
1285 | auto t0 = at::randn({16, 16, 8}, options); |
1286 | TORCH_CHECK(complyWith(t0, tensor_type)); |
1287 | |
1288 | // passing with dynamic shape |
1289 | auto t1 = t0.slice(1, 0, 16, 2); |
1290 | TORCH_CHECK(complyWith(t1, tensor_type)); |
1291 | } |
1292 | |
1293 | TEST_F(NVFuserTest, FusionDisjointSet_CUDA) { |
1294 | DisjointSets<int> set; |
1295 | |
1296 | const std::set<int> group_x({0, 1, 2}); |
1297 | const std::set<int> group_y({3, 4, 5}); |
1298 | const std::set<int> group_z({6, 7, 8}); |
1299 | const std::vector<std::set<int>> groups({group_x, group_y, group_z}); |
1300 | std::set<int> group_all; |
1301 | std::for_each(groups.begin(), groups.end(), [&](const auto& g) { |
1302 | group_all.insert(g.begin(), g.end()); |
1303 | }); |
1304 | |
1305 | // Initially, nothing should be considered equivalent |
1306 | for (auto i : group_all) { |
1307 | for (auto j : group_all) { |
1308 | TORCH_CHECK(!set.permissiveAreMapped(i, j)); |
1309 | } |
1310 | } |
1311 | |
1312 | // Sets values in group_x are equivalent |
1313 | for (auto i : group_x) { |
1314 | for (auto j : group_x) { |
1315 | set.mapEntries(i, j); |
1316 | TORCH_CHECK(set.mappingExists(i)); |
1317 | TORCH_CHECK(set.mappingExists(j)); |
1318 | } |
1319 | } |
1320 | |
1321 | // All values in group_x shoudl be equivalent with each other |
1322 | for (auto i : group_x) { |
1323 | for (auto j : group_x) { |
1324 | TORCH_CHECK(set.permissiveAreMapped(i, j)); |
1325 | } |
1326 | } |
1327 | // But nothing else should be equivalent |
1328 | for (auto i : group_all) { |
1329 | for (auto j : group_y) { |
1330 | TORCH_CHECK(!set.permissiveAreMapped(i, j)); |
1331 | } |
1332 | for (auto j : group_z) { |
1333 | TORCH_CHECK(!set.permissiveAreMapped(i, j)); |
1334 | } |
1335 | } |
1336 | |
1337 | // Sets values in group_y are equivalent |
1338 | for (auto i : group_y) { |
1339 | for (auto j : group_y) { |
1340 | set.mapEntries(i, j); |
1341 | TORCH_CHECK(set.mappingExists(i)); |
1342 | TORCH_CHECK(set.mappingExists(j)); |
1343 | } |
1344 | } |
1345 | |
1346 | // group_x should be still equivalent |
1347 | for (auto i : group_x) { |
1348 | for (auto j : group_x) { |
1349 | TORCH_CHECK(set.permissiveAreMapped(i, j)); |
1350 | } |
1351 | } |
1352 | // group_y should be now equivalent |
1353 | for (auto i : group_y) { |
1354 | for (auto j : group_y) { |
1355 | TORCH_CHECK(set.permissiveAreMapped(i, j)); |
1356 | } |
1357 | } |
1358 | // But group_z should not be equivalent with anything yet |
1359 | for (auto i : group_all) { |
1360 | for (auto j : group_z) { |
1361 | TORCH_CHECK(!set.permissiveAreMapped(i, j)); |
1362 | } |
1363 | } |
1364 | |
1365 | // Sets values in group_z are equivalent |
1366 | for (auto i : group_z) { |
1367 | for (auto j : group_z) { |
1368 | set.mapEntries(i, j); |
1369 | TORCH_CHECK(set.mappingExists(i)); |
1370 | TORCH_CHECK(set.mappingExists(j)); |
1371 | } |
1372 | } |
1373 | |
1374 | // Now each of the three groups should be equivalent within each |
1375 | // group |
1376 | for (const auto gi : c10::irange(groups.size())) { |
1377 | for (const auto gj : c10::irange(groups.size())) { |
1378 | for (auto i : groups[gi]) { |
1379 | for (auto j : groups[gj]) { |
1380 | TORCH_CHECK( |
1381 | (gi == gj && set.permissiveAreMapped(i, j)) || |
1382 | (gi != gj && !set.permissiveAreMapped(i, j))); |
1383 | } |
1384 | } |
1385 | } |
1386 | } |
1387 | |
1388 | std::vector<int> all_elements = set.getAllElements().vector(); |
1389 | std::sort(all_elements.begin(), all_elements.end()); |
1390 | std::vector<int> group_all_vec(group_all.begin(), group_all.end()); |
1391 | std::sort(group_all_vec.begin(), group_all_vec.end()); |
1392 | TORCH_CHECK(all_elements == group_all_vec); |
1393 | |
1394 | set.clear(); |
1395 | TORCH_CHECK(set.getAllElements().vector().size() == 0); |
1396 | |
1397 | // All cleared. Nothing should be considered equivalent. |
1398 | for (auto i : group_all) { |
1399 | for (auto j : group_all) { |
1400 | TORCH_CHECK(!set.permissiveAreMapped(i, j)); |
1401 | } |
1402 | } |
1403 | } |
1404 | |
1405 | TEST_F(NVFuserTest, FusionNonUniqueBroadcastSize_CUDA) { |
1406 | Fusion fusion; |
1407 | FusionGuard fg(&fusion); |
1408 | |
1409 | auto tv0 = makeSymbolicTensor(1); |
1410 | auto tv1 = makeSymbolicTensor(2); |
1411 | auto tv2 = makeSymbolicTensor(2); |
1412 | fusion.addInput(tv0); |
1413 | fusion.addInput(tv1); |
1414 | fusion.addInput(tv2); |
1415 | |
1416 | auto tv3 = broadcast(tv0, {true, false}); |
1417 | auto tv4 = add(tv3, tv1); |
1418 | auto tv5 = add(tv3, tv2); |
1419 | |
1420 | fusion.addOutput(tv4); |
1421 | fusion.addOutput(tv5); |
1422 | |
1423 | // In order to do this, tv1->axis(1) and tv2->axis(1) must have the |
1424 | // same size, but we can't prove it, so this should throw an error. |
1425 | // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto) |
1426 | ASSERT_ANY_THROW(tv3->computeAt(tv4, -1)); |
1427 | } |
1428 | |
1429 | TEST_F(NVFuserTest, FusionBiasGeluFwd_CUDA) { |
1430 | Fusion fusion; |
1431 | FusionGuard fg(&fusion); |
1432 | |
1433 | const float k_079 = 0.79788456; |
1434 | const float k_004 = 0.044715; |
1435 | |
1436 | // bias vector |
1437 | auto t0 = makeSymbolicTensor(1, DataType::Half); |
1438 | fusion.addInput(t0); |
1439 | auto t1 = castOp(DataType::Float, t0); |
1440 | // input tensor |
1441 | auto t2 = makeSymbolicTensor(3, DataType::Half); |
1442 | fusion.addInput(t2); |
1443 | auto t3 = castOp(DataType::Float, t2); |
1444 | auto t4 = broadcast(t1, {true, true, false}); |
1445 | auto t5 = add(t4, t3); |
1446 | auto t6 = mul(t5, IrBuilder::create<Double>(0.5)); |
1447 | auto t7 = mul(t5, IrBuilder::create<Double>(k_079)); |
1448 | auto t8 = mul(t5, IrBuilder::create<Double>(k_004)); |
1449 | auto t9 = mul(t8, t5); |
1450 | auto t10 = add(t9, IrBuilder::create<Int>(1)); |
1451 | auto t11 = mul(t7, t10); |
1452 | auto t12 = unaryOp(UnaryOpType::Tanh, t11); |
1453 | auto t13 = add(t12, IrBuilder::create<Double>(1)); |
1454 | auto t14 = mul(t6, t13); |
1455 | auto t15 = castOp(DataType::Half, t14); |
1456 | fusion.addOutput(t15); |
1457 | |
1458 | auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0); |
1459 | at::manual_seed(0); |
1460 | std::vector<int64_t> input_shape{6, 512, 4096}; |
1461 | std::vector<int64_t> bias_shape{4096}; |
1462 | |
1463 | auto at_input = at::randn(input_shape, options); |
1464 | auto at_bias = at::randn(bias_shape, options); |
1465 | |
1466 | auto at_x = |
1467 | at_bias.to(c10::ScalarType::Float) + at_input.to(c10::ScalarType::Float); |
1468 | auto aten_output_float = |
1469 | at_x * 0.5 * (1.0 + (k_079 * at_x * (1 + k_004 * at_x * at_x)).tanh()); |
1470 | auto aten_output = aten_output_float.to(c10::ScalarType::Half); |
1471 | |
1472 | std::vector<IValue> aten_inputs = {at_bias, at_input}; |
1473 | auto lparams = schedulePointwise(&fusion, aten_inputs); |
1474 | |
1475 | FusionExecutor fe; |
1476 | fe.compileFusion(&fusion, aten_inputs, lparams); |
1477 | auto cg_outputs = fe.runFusion(aten_inputs, lparams); |
1478 | |
1479 | testValidate( |
1480 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
1481 | } |
1482 | |
1483 | TEST_F(NVFuserTest, FusionBiasGeluBwd_CUDA) { |
1484 | if (at::cuda::getDeviceProperties(0)->major < 6) { |
1485 | return; |
1486 | } |
1487 | Fusion fusion; |
1488 | FusionGuard fg(&fusion); |
1489 | |
1490 | const float k_079 = 0.79788456; |
1491 | const float k_004 = 0.044715; |
1492 | const float k_010 = 0.1070322243; |
1493 | |
1494 | // gradient tensor |
1495 | auto t0 = makeSymbolicTensor(3, DataType::Half); |
1496 | fusion.addInput(t0); |
1497 | auto t1 = castOp(DataType::Float, t0); |
1498 | // bias tensor |
1499 | auto t2 = makeSymbolicTensor(1, DataType::Half); |
1500 | fusion.addInput(t2); |
1501 | auto t3 = castOp(DataType::Float, t2); |
1502 | // input tensor |
1503 | auto t4 = makeSymbolicTensor(3, DataType::Half); |
1504 | fusion.addInput(t4); |
1505 | auto t5 = castOp(DataType::Float, t4); |
1506 | auto t6 = broadcast(t3, {true, true, false}); |
1507 | auto t7 = add(t6, t5); |
1508 | auto t8 = mul(t7, IrBuilder::create<Double>(k_079)); |
1509 | auto t9 = mul(t7, IrBuilder::create<Double>(k_004)); |
1510 | auto t10 = mul(t9, t7); |
1511 | auto t11 = add(t10, IrBuilder::create<Int>(1)); |
1512 | auto t12 = mul(t8, t11); |
1513 | auto t13 = unaryOp(UnaryOpType::Tanh, t12); |
1514 | auto t14 = mul(t7, IrBuilder::create<Double>(0.5)); |
1515 | auto t15 = mul(t13, t13); |
1516 | auto t16 = unaryOp(UnaryOpType::Neg, t15); |
1517 | auto t17 = add(t16, IrBuilder::create<Int>(1)); |
1518 | auto t18 = mul(t7, IrBuilder::create<Double>(k_010)); |
1519 | auto t19 = mul(t18, t7); |
1520 | auto t20 = add(t19, IrBuilder::create<Double>(k_079)); |
1521 | auto t21 = mul(t17, t20); |
1522 | auto t22 = mul(t14, t21); |
1523 | auto t23 = add(t13, IrBuilder::create<Int>(1)); |
1524 | auto t24 = mul(t23, IrBuilder::create<Double>(0.5)); |
1525 | auto t25 = add(t22, t24); |
1526 | auto t26 = mul(t25, t1); |
1527 | // Save float output for validation |
1528 | fusion.addOutput(t26); |
1529 | auto t27 = castOp(DataType::Half, t26); |
1530 | fusion.addOutput(t27); |
1531 | |
1532 | auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0); |
1533 | at::manual_seed(1); |
1534 | std::vector<int64_t> input_shape{6, 512, 4096}; |
1535 | std::vector<int64_t> bias_shape{4096}; |
1536 | auto at_input = at::randn(input_shape, options); |
1537 | auto at_bias = at::randn(bias_shape, options); |
1538 | auto at_grad = at::randn(input_shape, options); |
1539 | |
1540 | auto at_x = |
1541 | at_bias.to(c10::ScalarType::Float) + at_input.to(c10::ScalarType::Float); |
1542 | auto at_tanh_out = (k_079 * at_x * (1 + k_004 * at_x * at_x)).tanh(); |
1543 | auto at_ff = 0.5 * at_x * |
1544 | ((1 - at_tanh_out * at_tanh_out) * (k_079 + k_010 * at_x * at_x)) + |
1545 | 0.5 * (1 + at_tanh_out); |
1546 | auto at_out = at_ff * at_grad; |
1547 | auto at_out_half = at_out.to(c10::ScalarType::Half); |
1548 | |
1549 | std::vector<IValue> aten_inputs = {at_grad, at_bias, at_input}; |
1550 | std::vector<at::Tensor> aten_outputs = {at_out, at_out_half}; |
1551 | |
1552 | auto lparams = schedulePointwise(&fusion, aten_inputs); |
1553 | |
1554 | FusionExecutor fe; |
1555 | fe.compileFusion(&fusion, aten_inputs, lparams); |
1556 | auto cg_outputs = fe.runFusion(aten_inputs, lparams); |
1557 | |
1558 | testValidate( |
1559 | &fusion, cg_outputs, aten_inputs, aten_outputs, __LINE__, __FILE__); |
1560 | } |
1561 | |
1562 | // Reproducer of issue #459 |
1563 | TEST_F(NVFuserTest, FusionIssue459_CUDA) { |
1564 | Fusion fusion; |
1565 | FusionGuard fg(&fusion); |
1566 | |
1567 | auto tv0 = makeSymbolicTensor(1); |
1568 | fusion.addInput(tv0); |
1569 | auto tv1 = makeSymbolicTensor(2); |
1570 | fusion.addInput(tv1); |
1571 | |
1572 | auto tv2 = add(tv0, IrBuilder::create<Double>(1)); |
1573 | auto tv3 = broadcast(tv2, {true, false}); |
1574 | auto tv4 = add(tv1, tv3); |
1575 | |
1576 | // Create two outputs from the final arithmetic result |
1577 | auto tv5 = add(tv4, IrBuilder::create<Double>(1)); |
1578 | fusion.addOutput(tv5); |
1579 | auto tv6 = add(tv4, IrBuilder::create<Double>(1)); |
1580 | fusion.addOutput(tv6); |
1581 | |
1582 | // Scheduling |
1583 | for (auto output : ir_utils::filterByType<TensorView>(fusion.outputs())) { |
1584 | output->merge(-2, -1); |
1585 | } |
1586 | for (auto output : ir_utils::filterByType<TensorView>(fusion.outputs())) { |
1587 | output->split(0, 128); |
1588 | } |
1589 | |
1590 | tv0->computeAt(tv5, -1); |
1591 | |
1592 | tv6->axis(0)->parallelize(ParallelType::BIDx); |
1593 | tv6->axis(1)->parallelize(ParallelType::TIDx); |
1594 | |
1595 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1596 | at::manual_seed(0); |
1597 | const int numel_x = 10; |
1598 | const int numel_y = 20; |
1599 | auto t0 = at::randn({numel_x}, options); |
1600 | auto t1 = at::randn({numel_y, numel_x}, options); |
1601 | auto aten_output = (t0 + 1).unsqueeze(0) + t1 + 1; |
1602 | |
1603 | std::vector<IValue> aten_inputs = {t0, t1}; |
1604 | |
1605 | torch::jit::fuser::cuda::FusionExecutor fe; |
1606 | fe.compileFusion(&fusion, aten_inputs); |
1607 | auto cg_outputs = fe.runFusion(aten_inputs); |
1608 | |
1609 | testValidate( |
1610 | &fusion, |
1611 | cg_outputs, |
1612 | aten_inputs, |
1613 | {aten_output, aten_output}, |
1614 | __LINE__, |
1615 | __FILE__); |
1616 | } |
1617 | |
1618 | TEST_F(NVFuserTest, FusionSmemIndexingSimple_CUDA) { |
1619 | Fusion fusion; |
1620 | FusionGuard fg(&fusion); |
1621 | |
1622 | auto tv0 = makeSymbolicTensor(2); |
1623 | fusion.addInput(tv0); |
1624 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
1625 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
1626 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
1627 | fusion.addOutput(tv3); |
1628 | |
1629 | tv3->axis(0)->parallelize(ParallelType::BIDx); |
1630 | tv3->axis(1)->parallelize(ParallelType::TIDx); |
1631 | |
1632 | tv0->computeAt(tv3, -1); |
1633 | |
1634 | tv1->setMemoryType(MemoryType::Shared); |
1635 | tv2->setMemoryType(MemoryType::Global); |
1636 | |
1637 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1638 | |
1639 | auto aten_input = at::randn({12, 34}, options); |
1640 | at::Tensor aten_output = aten_input + 1.0 + 1.0 + 1.0; |
1641 | |
1642 | FusionExecutor fe; |
1643 | fe.compileFusion(&fusion, {aten_input}); |
1644 | auto cg_outputs = fe.runFusion({aten_input}); |
1645 | |
1646 | testValidate( |
1647 | &fusion, cg_outputs, {aten_input}, {aten_output}, __LINE__, __FILE__); |
1648 | } |
1649 | |
1650 | TEST_F(NVFuserTest, FusionSmemIndexing_CUDA) { |
1651 | Fusion fusion; |
1652 | FusionGuard fg(&fusion); |
1653 | |
1654 | // Symbolic integers we will use for runtime tiling |
1655 | Int* symbolic_m_tile_dim = IrBuilder::create<Int>(); |
1656 | Int* symbolic_split_k_tile_dim = IrBuilder::create<Int>(); |
1657 | Int* symbolic_block_k_tile_dim = IrBuilder::create<Int>(); |
1658 | // Compile-time integer for tiling |
1659 | int n_smem_tile = 32; |
1660 | |
1661 | // Symbolic 2D tensors TV0[M, K], TV1[K, N] |
1662 | TensorView* tv0 = makeSymbolicTensor(2); |
1663 | TensorView* tv1 = makeSymbolicTensor(2); |
1664 | |
1665 | // Broadcast tv0 to [M, K, *] |
1666 | TensorView* tv2 = broadcast(tv0, {false, false, true}); |
1667 | // Broadcast tv1 to [*, K, N] |
1668 | TensorView* tv3 = broadcast(tv1, {true, false, false}); |
1669 | |
1670 | // Pointwise multiplication resulting in tv3[M, K, N] |
1671 | TensorView* tv4 = mul(tv2, tv3); |
1672 | |
1673 | // Sum the K-dim |
1674 | TensorView* tv5 = sum(tv4, {1}); |
1675 | |
1676 | // Register inputs and outputs |
1677 | fusion.addInput(tv0); |
1678 | fusion.addInput(tv1); |
1679 | fusion.addOutput(tv5); |
1680 | |
1681 | // Register runtime tile dims as inputs |
1682 | fusion.addInput(symbolic_m_tile_dim); |
1683 | fusion.addInput(symbolic_split_k_tile_dim); |
1684 | fusion.addInput(symbolic_block_k_tile_dim); |
1685 | |
1686 | // Make a 3D tile, mix of symbolic and constant, do in reverse order because |
1687 | // dims are inserted |
1688 | // [M, rK, N] |
1689 | tv5->split(2, n_smem_tile); |
1690 | // [M, rK, No, Ni{32}] |
1691 | tv5->split(1, symbolic_block_k_tile_dim); |
1692 | // [M, rKo, rKi{i2}, No, Ni{32}] |
1693 | tv5->split(1, symbolic_split_k_tile_dim); |
1694 | // [M, rKoo, rKoi{i1}, rKi{i2}, No, Ni{32}] |
1695 | tv5->split(0, symbolic_m_tile_dim); |
1696 | // [Mo, Mi{i0}, rKoo, rKoi{i1}, rKi{i2}, No, Ni{32}] |
1697 | |
1698 | // Reorder so all outer tiles are in the leftmost 3 positions |
1699 | // [Mo, Mi{i0}, rKoo, rKoi{i1}, rKi{i2}, No, Ni{32}] |
1700 | // [Mo, No, rKoo, rKoi{i1}, rKi{i2}, Mi{i0}, Ni{32}] |
1701 | tv5->reorder({{1, 5}, {5, 1}}); |
1702 | |
1703 | // Factor out the outer reduction IterDomain, then run the inter-cta |
1704 | // reduction, and intra-cta reduction |
1705 | // [Mo, No, rKoo, Koi{i1}, Ki{i2}, Mi{i0}, Ni{32}] |
1706 | // [Mo, No, rKoi{i1}, rKi{i2}, Mi{i0}, Ni{32}] |
1707 | auto tv6 = tv5->rFactor({2}); |
1708 | |
1709 | // Scope computations |
1710 | tv6->computeAt(tv5, 2); |
1711 | |
1712 | // [Mo, No, rKoo, Koi{i1}, Ki{i2}, Mi{i0}, Ni{32}] |
1713 | // [Mo, No, Ki{i2}, Mi{i0}, Ni{32}, rKoo, Koi{i1}] |
1714 | tv6->reorder({ |
1715 | {5, -2}, |
1716 | {6, -1}, |
1717 | {2, 2}, |
1718 | {3, 3}, |
1719 | {4, 4}, |
1720 | }); |
1721 | |
1722 | // Setup compute at schedule |
1723 | tv0->computeAt(tv6, 3); |
1724 | tv1->computeAt(tv6, 3); |
1725 | tv4->computeAt(tv6, -1); |
1726 | |
1727 | // Cache smem tiles |
1728 | tv2->setMemoryType(MemoryType::Shared); |
1729 | tv3->setMemoryType(MemoryType::Shared); |
1730 | tv4->setMemoryType(MemoryType::Shared); |
1731 | tv6->setMemoryType(MemoryType::Shared); |
1732 | |
1733 | tv5->axis(0)->parallelize(ParallelType::BIDz); |
1734 | tv5->axis(1)->parallelize(ParallelType::BIDy); |
1735 | |
1736 | std::vector<TensorView*> tv_list = {tv2, tv3, tv4, tv5, tv6}; |
1737 | for (auto tv : tv_list) { |
1738 | tv->axis(-2)->parallelize(ParallelType::TIDz); |
1739 | tv->axis(-1)->parallelize(ParallelType::TIDy); |
1740 | } |
1741 | |
1742 | constexpr int M = 31, K = 65, N = 32; |
1743 | |
1744 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1745 | at::Tensor t0 = at::randn({M, K}, options); |
1746 | at::Tensor t1 = at::randn({K, N}, options); |
1747 | |
1748 | at::Tensor aten_output = |
1749 | mul(t0.unsqueeze(2), t1.unsqueeze(0)).to(at::kDouble).sum(1); |
1750 | |
1751 | // A, B, m_tile_dim, split_k, intra_cta_tile |
1752 | std::vector<IValue> aten_inputs = {t0, t1, 3, 4, 5}; |
1753 | |
1754 | FusionExecutor fe; |
1755 | fe.compileFusion(&fusion, aten_inputs); |
1756 | auto cg_outputs = fe.runFusion(aten_inputs); |
1757 | |
1758 | testValidate( |
1759 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
1760 | } |
1761 | |
1762 | // Reproducer of issue 408 |
1763 | TEST_F(NVFuserTest, FusionCacheBeforeReduction_CUDA) { |
1764 | Fusion fusion; |
1765 | FusionGuard fg(&fusion); |
1766 | |
1767 | auto tv0 = makeSymbolicTensor(2); |
1768 | fusion.addInput(tv0); |
1769 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
1770 | auto tv2 = sum(tv1, {1}); |
1771 | fusion.addOutput(tv2); |
1772 | |
1773 | tv2->split(0, 4); |
1774 | |
1775 | auto tv3 = tv2->cacheBefore(); |
1776 | |
1777 | tv0->computeAt(tv3, -1); |
1778 | tv3->computeAt(tv2, -1); |
1779 | |
1780 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
1781 | |
1782 | const int numel_x = 100; |
1783 | const int numel_y = 200; |
1784 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1785 | |
1786 | at::Tensor aten_input = at::randn({numel_x, numel_y}, options); |
1787 | at::Tensor cg_output = at::empty({numel_x}, options); |
1788 | |
1789 | auto aten_output = (aten_input + 1).to(at::kDouble).sum({1}); |
1790 | |
1791 | FusionExecutor fe; |
1792 | fe.compileFusion(&fusion, {aten_input}); |
1793 | fe.runFusion({aten_input}, {cg_output}); |
1794 | |
1795 | testValidate( |
1796 | &fusion, {cg_output}, {aten_input}, {aten_output}, __LINE__, __FILE__); |
1797 | } |
1798 | |
1799 | TEST_F(NVFuserTest, FusionCacheBeforeReduction2_CUDA) { |
1800 | Fusion fusion; |
1801 | FusionGuard fg(&fusion); |
1802 | |
1803 | auto tv0 = makeSymbolicTensor(3); |
1804 | fusion.addInput(tv0); |
1805 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
1806 | auto tv2 = sum(tv1, {1}); |
1807 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
1808 | fusion.addOutput(tv2); |
1809 | fusion.addOutput(tv3); |
1810 | |
1811 | auto tv4 = tv2->cacheBefore(); |
1812 | |
1813 | tv4->computeAt(tv3, 1); |
1814 | tv0->computeAt(tv4, -1); |
1815 | |
1816 | tv3->axis(0)->parallelize(ParallelType::BIDx); |
1817 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
1818 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
1819 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
1820 | tv4->axis(-1)->parallelize(ParallelType::TIDx); |
1821 | |
1822 | const int numel_x = 10; |
1823 | const int numel_y = 20; |
1824 | const int numel_z = 30; |
1825 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1826 | |
1827 | at::Tensor aten_input = at::randn({numel_x, numel_y, numel_z}, options); |
1828 | auto t2 = (aten_input + 1).to(at::kDouble).sum({1}); |
1829 | auto t3 = t2 + 1; |
1830 | std::vector<at::Tensor> aten_outputs = {t2, t3}; |
1831 | |
1832 | FusionExecutor fe; |
1833 | fe.compileFusion(&fusion, {aten_input}); |
1834 | auto cg_outputs = fe.runFusion({aten_input}); |
1835 | |
1836 | testValidate( |
1837 | &fusion, cg_outputs, {aten_input}, aten_outputs, __LINE__, __FILE__); |
1838 | } |
1839 | |
1840 | TEST_F(NVFuserTest, FusionIssue367_CUDA) { |
1841 | Fusion fusion; |
1842 | FusionGuard fg(&fusion); |
1843 | |
1844 | // Symbolic integers we will use for runtime tiling |
1845 | Int* symbolic_m_tile_dim = IrBuilder::create<Int>(); |
1846 | Int* symbolic_split_k_tile_dim = IrBuilder::create<Int>(); |
1847 | Int* symbolic_block_k_tile_dim = IrBuilder::create<Int>(); |
1848 | // Compile-time integer for tiling |
1849 | int n_smem_tile = 32; |
1850 | |
1851 | // Symbolic 2D tensors TV0[M, K], TV1[K, N] |
1852 | TensorView* tv0 = makeSymbolicTensor(2); |
1853 | TensorView* tv1 = makeSymbolicTensor(2); |
1854 | |
1855 | // Broadcast tv0 to [M, K, *] |
1856 | TensorView* tv2 = broadcast(tv0, {false, false, true}); |
1857 | // Broadcast tv1 to [*, K, N] |
1858 | TensorView* tv3 = broadcast(tv1, {true, false, false}); |
1859 | |
1860 | // Pointwise multiplication resulting in tv3[M, K, N] |
1861 | TensorView* tv4 = mul(tv2, tv3); |
1862 | |
1863 | // Sum the K-dim |
1864 | TensorView* tv5 = sum(tv4, {1}); |
1865 | |
1866 | // Register inputs and outputs |
1867 | fusion.addInput(tv0); |
1868 | fusion.addInput(tv1); |
1869 | fusion.addOutput(tv5); |
1870 | |
1871 | // Register runtime tile dims as inputs |
1872 | fusion.addInput(symbolic_m_tile_dim); |
1873 | fusion.addInput(symbolic_split_k_tile_dim); |
1874 | fusion.addInput(symbolic_block_k_tile_dim); |
1875 | |
1876 | // Make a 3D tile, mix of symbolic and constant, do in reverse order because |
1877 | // dims are inserted |
1878 | // [M, K, N] |
1879 | tv5->split(2, n_smem_tile); |
1880 | tv5->split(1, symbolic_block_k_tile_dim); |
1881 | tv5->split(1, symbolic_split_k_tile_dim); |
1882 | tv5->split(0, symbolic_m_tile_dim); |
1883 | // [Mo, Mi, Koo, Koi, Ki, No, Ni] |
1884 | tv5->reorder({{1, 5}, {5, 1}}); |
1885 | // [Mo, No, Koo, Koi, Ki, Mi, Ni] |
1886 | |
1887 | auto tv6 = tv5->rFactor({2}); |
1888 | auto tv7 = tv5->rFactor({2}); |
1889 | // [Mo, No, rKoo, Koi, Ki, Mi, Ni] |
1890 | // [Mo, No, rKoi, rKi, Mi, Ni] |
1891 | |
1892 | // Scope computations |
1893 | tv6->computeAt(tv5, 2); |
1894 | |
1895 | tv0->computeAt(tv6, 3); |
1896 | tv1->computeAt(tv6, 3); |
1897 | tv4->computeAt(tv6, -1); |
1898 | |
1899 | // Cache smem tiles |
1900 | tv2->setMemoryType(MemoryType::Shared); |
1901 | tv3->setMemoryType(MemoryType::Shared); |
1902 | tv4->setMemoryType(MemoryType::Local); |
1903 | tv6->setMemoryType(MemoryType::Local); |
1904 | tv7->setMemoryType(MemoryType::Local); |
1905 | |
1906 | tv5->axis(0)->parallelize(ParallelType::BIDz); |
1907 | tv5->axis(1)->parallelize(ParallelType::BIDy); |
1908 | |
1909 | std::vector<TensorView*> tv_list = {tv2, tv3, tv4, tv5, tv6, tv7}; |
1910 | for (auto tv : tv_list) { |
1911 | tv->axis(-2)->parallelize(ParallelType::TIDz); |
1912 | tv->axis(-1)->parallelize(ParallelType::TIDy); |
1913 | } |
1914 | tv2->axis(3)->parallelize(ParallelType::TIDx); |
1915 | tv3->axis(3)->parallelize(ParallelType::TIDx); |
1916 | tv4->axis(3)->parallelize(ParallelType::TIDx); |
1917 | tv6->axis(3)->parallelize(ParallelType::TIDx); |
1918 | tv7->axis(2)->parallelize(ParallelType::TIDx); |
1919 | |
1920 | tv2->axis(4)->parallelize(ParallelType::BIDx); |
1921 | tv3->axis(4)->parallelize(ParallelType::BIDx); |
1922 | tv4->axis(4)->parallelize(ParallelType::BIDx); |
1923 | tv6->axis(4)->parallelize(ParallelType::BIDx); |
1924 | tv7->axis(3)->parallelize(ParallelType::BIDx); |
1925 | tv5->axis(2)->parallelize(ParallelType::BIDx); |
1926 | |
1927 | constexpr int M = 3, K = 6, N = 16; |
1928 | |
1929 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1930 | |
1931 | at::Tensor t0 = at::randn({M, K}, options); |
1932 | at::Tensor t1 = at::randn({K, N}, options); |
1933 | |
1934 | // A, B, m, split_k, block_k |
1935 | std::vector<IValue> aten_inputs = {t0, t1, 2, 2, 3}; |
1936 | at::Tensor aten_output = |
1937 | mul(t0.unsqueeze(2), t1.unsqueeze(0)).to(at::kDouble).sum(1); |
1938 | |
1939 | torch::jit::fuser::cuda::FusionExecutor fe; |
1940 | fe.compileFusion(&fusion, aten_inputs); |
1941 | auto cg_outputs = fe.runFusion(aten_inputs); |
1942 | |
1943 | testValidate( |
1944 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
1945 | } |
1946 | |
1947 | TEST_F(NVFuserTest, FusionIssue468_CUDA) { |
1948 | Fusion fusion; |
1949 | FusionGuard fg(&fusion); |
1950 | |
1951 | auto tv0 = makeSymbolicTensor(2); |
1952 | fusion.addInput(tv0); |
1953 | auto tv1 = sum(tv0, {1}); |
1954 | auto tv2 = sum(tv1, {0}); |
1955 | fusion.addOutput(tv2); |
1956 | |
1957 | tv1->axis(0)->parallelize(ParallelType::TIDy); |
1958 | tv1->axis(1)->parallelize(ParallelType::TIDx); |
1959 | |
1960 | tv2->axis(0)->parallelize(ParallelType::TIDy); |
1961 | |
1962 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
1963 | at::Tensor aten_input = at::randn({10, 100}, options); |
1964 | at::Tensor aten_output = aten_input.to(at::kDouble).sum({1}).sum({0}); |
1965 | |
1966 | FusionExecutor fe; |
1967 | fe.compileFusion(&fusion, {aten_input}); |
1968 | auto cg_outputs = fe.runFusion({aten_input}); |
1969 | |
1970 | testValidate( |
1971 | &fusion, cg_outputs, {aten_input}, {aten_output}, __LINE__, __FILE__); |
1972 | } |
1973 | |
1974 | TEST_F(NVFuserTest, FusionIssue363_CUDA) { |
1975 | Fusion fusion; |
1976 | FusionGuard fg(&fusion); |
1977 | |
1978 | // Symbolic 2D tensors TV0[M, K], TV1[K, N] |
1979 | TensorView* tv0 = makeSymbolicTensor(2); |
1980 | TensorView* tv1 = makeSymbolicTensor(2); |
1981 | |
1982 | // Broadcast tv0 to [M, K, *] |
1983 | TensorView* tv2 = broadcast(tv0, {false, false, true}); |
1984 | // Broadcast tv1 to [*, K, N] |
1985 | TensorView* tv3 = broadcast(tv1, {true, false, false}); |
1986 | |
1987 | // Pointwise multiplication resulting in tv3[M, K, N] |
1988 | TensorView* tv4 = mul(tv2, tv3); |
1989 | |
1990 | // Sum the K-dim |
1991 | TensorView* tv5 = sum(tv4, {1}); |
1992 | |
1993 | // Register inputs and outputs |
1994 | fusion.addInput(tv0); |
1995 | fusion.addInput(tv1); |
1996 | fusion.addOutput(tv5); |
1997 | |
1998 | tv2->setMemoryType(MemoryType::Global); |
1999 | tv3->setMemoryType(MemoryType::Global); |
2000 | tv4->setMemoryType(MemoryType::Global); |
2001 | |
2002 | tv0->computeAt(tv5, -1); |
2003 | tv1->computeAt(tv5, -1); |
2004 | |
2005 | tv5->axis(0)->parallelize(ParallelType::BIDz); |
2006 | tv5->axis(1)->parallelize(ParallelType::BIDy); |
2007 | |
2008 | tv5->axis(2)->parallelize(ParallelType::BIDx); |
2009 | |
2010 | constexpr int M = 3, K = 6, N = 16; |
2011 | |
2012 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2013 | |
2014 | at::Tensor t0 = at::randn({M, K}, options); |
2015 | at::Tensor t1 = at::randn({K, N}, options); |
2016 | at::Tensor aten_output = |
2017 | mul(t0.unsqueeze(2), t1.unsqueeze(0)).to(at::kDouble).sum(1); |
2018 | |
2019 | std::vector<IValue> aten_inputs = {t0, t1}; |
2020 | |
2021 | torch::jit::fuser::cuda::FusionExecutor fe; |
2022 | fe.compileFusion(&fusion, aten_inputs); |
2023 | auto cg_outputs = fe.runFusion(aten_inputs); |
2024 | |
2025 | testValidate( |
2026 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
2027 | } |
2028 | |
2029 | TEST_F(NVFuserTest, FusionIssue484_CUDA) { |
2030 | Fusion fusion; |
2031 | FusionGuard fg(&fusion); |
2032 | |
2033 | auto tv0 = makeSymbolicTensor(2); |
2034 | fusion.addInput(tv0); |
2035 | auto tv1 = sum(tv0, {1}); |
2036 | auto tv2 = add(tv1, IrBuilder::create<Double>(0)); |
2037 | fusion.addOutput(tv2); |
2038 | |
2039 | tv1->setMemoryType(MemoryType::Global); |
2040 | tv1->axis(1)->parallelize(ParallelType::TIDx); |
2041 | |
2042 | constexpr int M = 100; |
2043 | |
2044 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2045 | |
2046 | at::Tensor aten_input = at::randn({M, M}, options); |
2047 | at::Tensor aten_output = aten_input.to(at::kDouble).sum({1}); |
2048 | |
2049 | torch::jit::fuser::cuda::FusionExecutor fe; |
2050 | fe.compileFusion(&fusion, {aten_input}); |
2051 | auto cg_outputs = fe.runFusion({aten_input}); |
2052 | |
2053 | testValidate( |
2054 | &fusion, cg_outputs, {aten_input}, {aten_output}, __LINE__, __FILE__); |
2055 | } |
2056 | |
2057 | TEST_F(NVFuserTest, FusionIssue329_CUDA) { |
2058 | Fusion fusion; |
2059 | FusionGuard fg(&fusion); |
2060 | |
2061 | auto tv0 = makeSymbolicTensor(2); |
2062 | fusion.addInput(tv0); |
2063 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
2064 | auto tv2 = sum(tv1, {1}); |
2065 | fusion.addOutput(tv2); |
2066 | auto tv3 = sum(tv1, {1}); |
2067 | fusion.addOutput(tv3); |
2068 | |
2069 | tv1->computeAt(tv2, -1); |
2070 | |
2071 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2072 | |
2073 | std::vector<int64_t> t0_shape{17, 19}; |
2074 | auto aten_input = at::randn(t0_shape, options); |
2075 | auto t2 = (aten_input + 1).to(at::kDouble).sum({1}); |
2076 | auto t3 = (aten_input + 1).to(at::kDouble).sum({1}); |
2077 | std::vector<at::Tensor> aten_outputs = {t2, t3}; |
2078 | |
2079 | FusionExecutor fe; |
2080 | fe.compileFusion(&fusion, {aten_input}); |
2081 | auto cg_outputs = fe.runFusion({aten_input}); |
2082 | |
2083 | testValidate( |
2084 | &fusion, cg_outputs, {aten_input}, aten_outputs, __LINE__, __FILE__); |
2085 | } |
2086 | |
2087 | TEST_F(NVFuserTest, FusionIssue382_CUDA) { |
2088 | Fusion fusion; |
2089 | FusionGuard fg(&fusion); |
2090 | |
2091 | auto tv0 = makeSymbolicTensor(2); |
2092 | fusion.addInput(tv0); |
2093 | |
2094 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
2095 | auto tv2 = broadcast(tv1, {false, false, true}); |
2096 | auto tv3 = makeSymbolicTensor(3); |
2097 | fusion.addInput(tv3); |
2098 | auto tv4 = add(tv2, tv3); |
2099 | fusion.addOutput(tv4); |
2100 | |
2101 | tv2->merge(1); |
2102 | tv4->merge(1); |
2103 | |
2104 | tv1->computeAt(tv4, 1); |
2105 | |
2106 | tv4->axis(0)->parallelize(ParallelType::BIDx); |
2107 | |
2108 | tv1->setMemoryType(MemoryType::Global); |
2109 | tv2->setMemoryType(MemoryType::Global); |
2110 | |
2111 | const int numel_x = 12; |
2112 | const int numel_y = 34; |
2113 | const int numel_z = 56; |
2114 | |
2115 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2116 | at::manual_seed(0); |
2117 | auto t0 = at::randn({numel_x, numel_y}, options); |
2118 | auto t3 = at::randn({numel_x, numel_y, numel_z}, options); |
2119 | |
2120 | std::vector<IValue> aten_inputs = {t0, t3}; |
2121 | auto aten_output = (t0 + 1).unsqueeze(-1) + t3; |
2122 | |
2123 | FusionExecutor fe; |
2124 | fe.compileFusion(&fusion, aten_inputs); |
2125 | auto cg_outputs = fe.runFusion(aten_inputs); |
2126 | |
2127 | testValidate( |
2128 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
2129 | } |
2130 | |
2131 | TEST_F(NVFuserTest, FusionIssue507_CUDA) { |
2132 | Fusion fusion; |
2133 | FusionGuard fg(&fusion); |
2134 | |
2135 | auto tv0 = makeSymbolicTensor(2); |
2136 | fusion.addInput(tv0); |
2137 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
2138 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
2139 | fusion.addOutput(tv2); |
2140 | |
2141 | tv1->setMemoryType(MemoryType::Shared); |
2142 | |
2143 | tv1->axis(1)->parallelize(ParallelType::TIDx); |
2144 | tv2->axis(1)->parallelize(ParallelType::TIDx); |
2145 | tv1->axis(0)->parallelize(ParallelType::BIDx); |
2146 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
2147 | |
2148 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2149 | |
2150 | std::vector<int64_t> t0_shape{17, 19}; |
2151 | auto aten_input = at::randn(t0_shape, options); |
2152 | auto t1 = (aten_input + 1); |
2153 | auto aten_output = (t1 + 1); |
2154 | |
2155 | FusionExecutor fe; |
2156 | fe.compileFusion(&fusion, {aten_input}); |
2157 | auto cg_outputs = fe.runFusion({aten_input}); |
2158 | |
2159 | testValidate( |
2160 | &fusion, cg_outputs, {aten_input}, {aten_output}, __LINE__, __FILE__); |
2161 | } |
2162 | |
2163 | TEST_F(NVFuserTest, FusionIssue532_CUDA) { |
2164 | Fusion fusion; |
2165 | FusionGuard fg(&fusion); |
2166 | |
2167 | // Algorithm |
2168 | TensorView* tv0 = makeSymbolicTensor(1); |
2169 | TensorView* tv1 = add(tv0, IrBuilder::create<Double>(1)); |
2170 | TensorView* tv2 = add(tv1, IrBuilder::create<Double>(1)); |
2171 | fusion.addInput(tv0); |
2172 | fusion.addOutput(tv2); |
2173 | |
2174 | const int M_BLOCK = 64; |
2175 | const int M_THREAD = 4; |
2176 | |
2177 | tv2->split(0, M_BLOCK); |
2178 | // tv2: [M/M_BLOCK, M_BLOCK] |
2179 | tv1->computeAt(tv2, 1); |
2180 | // tv1: [M/M_BLOCK, M_BLOCK] |
2181 | |
2182 | tv1->split(-1, M_BLOCK / M_THREAD); |
2183 | // tv1: [M/M_BLOCK, M_THREAD, M_BLOCK / M_THREAD] |
2184 | |
2185 | tv2->split(-1, M_THREAD); |
2186 | // tv2: [M/M_BLOCK, M_BLOCK / M_THREAD, M_THREAD] |
2187 | |
2188 | constexpr int M = 1000; |
2189 | |
2190 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2191 | at::manual_seed(0); |
2192 | at::Tensor t0 = at::randn({M}, options); |
2193 | std::vector<IValue> aten_inputs = {t0}; |
2194 | |
2195 | FusionExecutor fe; |
2196 | fe.compileFusion(&fusion, aten_inputs); |
2197 | auto outputs = fe.runFusion(aten_inputs); |
2198 | |
2199 | at::Tensor aten_output = t0 + 1 + 1; |
2200 | |
2201 | testValidate( |
2202 | &fusion, outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
2203 | } |
2204 | |
2205 | TEST_F(NVFuserTest, FusionLoopUnswitch_CUDA) { |
2206 | Fusion fusion; |
2207 | FusionGuard fg(&fusion); |
2208 | |
2209 | // Algorithm |
2210 | TensorView* tv0 = makeSymbolicTensor(1); |
2211 | TensorView* tv1 = add(tv0, IrBuilder::create<Double>(1)); |
2212 | TensorView* tv2 = add(tv1, IrBuilder::create<Double>(1)); |
2213 | fusion.addInput(tv0); |
2214 | fusion.addOutput(tv2); |
2215 | |
2216 | tv2->split(0, 32); |
2217 | tv1->computeAt(tv2, -1); |
2218 | |
2219 | tv2->axis(1)->parallelize(ParallelType::Unswitch); |
2220 | |
2221 | constexpr int M = 1000; |
2222 | |
2223 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2224 | at::manual_seed(0); |
2225 | at::Tensor t0 = at::randn({M}, options); |
2226 | std::vector<IValue> aten_inputs = {t0}; |
2227 | |
2228 | FusionExecutor fe; |
2229 | fe.compileFusion(&fusion, aten_inputs); |
2230 | auto outputs = fe.runFusion(aten_inputs); |
2231 | |
2232 | at::Tensor aten_output = t0 + 1 + 1; |
2233 | |
2234 | testValidate( |
2235 | &fusion, outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
2236 | } |
2237 | |
2238 | TEST_F(NVFuserTest, FusionIssue549_CUDA) { |
2239 | Fusion fusion; |
2240 | FusionGuard fg(&fusion); |
2241 | |
2242 | // Set up your input tensor views |
2243 | TensorView* tv0 = makeSymbolicTensor(2); // M, K |
2244 | TensorView* tv1 = makeSymbolicTensor(2); // K, N |
2245 | fusion.addInput(tv0); |
2246 | fusion.addInput(tv1); |
2247 | |
2248 | auto tv2 = add(tv0, IrBuilder::create<Double>(1)); |
2249 | |
2250 | TensorView* tv3 = broadcast(tv2, {false, false, true}); |
2251 | // tv3[I0, I1, B] = tv0[I0, I1] |
2252 | |
2253 | TensorView* tv4 = broadcast(tv1, {true, false, false}); |
2254 | // tv4[B, I1, I2] = tv1[I1, I2] |
2255 | |
2256 | // tv5[I0, I1, I2] = tv3[I0, I1, B] * tv4[B, I1, I2] |
2257 | TensorView* tv5 = mul(tv3, tv4); |
2258 | // tv6[I0, R1, I2] = tv5[I0, I1, I2] |
2259 | TensorView* tv6 = sum(tv5, {1}); |
2260 | fusion.addOutput(tv6); |
2261 | |
2262 | tv6->split(1, 32); |
2263 | // tv6[I0, R1o, R1i{32}, I2] |
2264 | |
2265 | auto tv7 = tv6->rFactor({1}); |
2266 | // tv7[I0, R1o, I1i{32}, I2] = tv5[I0, I1, I2] |
2267 | // tv6[I0, , R1i{32}, I2] = tv7[I0, R1o, I1i{32}, I2] |
2268 | |
2269 | tv6->split(0, 4); |
2270 | tv6->split(-1, 4); |
2271 | // tv6[I0o, I0i{4}, R1i{32}, I2o, I2i{4}] |
2272 | // tv6[I0o, I0i{4}, R1i{32}, I2o, I2i{4}] |
2273 | |
2274 | tv0->computeAt(tv6, -1); |
2275 | tv1->computeAt(tv6, -1); |
2276 | |
2277 | // tv7[I0o, I0i{4}, R1o, I1i{32}, I2o, I2i{4}] |
2278 | // tv6[I0o, I0i{4}, , R1i{32}, I2o, I2i{4}] |
2279 | //--> (line symbolizes compute at location) |
2280 | // tv5[I0o, I0i{4}, I1i{32}, I2o, I2i{4}|, I1o] |
2281 | // tv7[I0o, I0i{4}, I1i{32}, I2o, I2i{4}|, R1o] |
2282 | // tv6[I0o, I0i{4}, R1i{32}, I2o, I2i{4}|] |
2283 | |
2284 | tv0->computeAt(tv7, -1); |
2285 | tv1->computeAt(tv7, -1); |
2286 | // tv5[I0o, I0i{4}, I1i{32}, I2o, I2i{4}, I1o |] |
2287 | // tv7[I0o, I0i{4}, I1i{32}, I2o, I2i{4}, R1o |] |
2288 | // tv6[I0o, I0i{4}, R1i{32}, I2o, I2i{4}|] |
2289 | |
2290 | tv6->axis(0)->parallelize(ParallelType::BIDz); |
2291 | tv6->axis(1)->parallelize(ParallelType::TIDz); |
2292 | |
2293 | tv6->axis(-2)->parallelize(ParallelType::BIDy); |
2294 | tv6->axis(-1)->parallelize(ParallelType::TIDy); |
2295 | |
2296 | tv6->axis(2)->parallelize(ParallelType::TIDx); |
2297 | tv7->axis(2)->parallelize(ParallelType::TIDx); |
2298 | |
2299 | constexpr int M = 65, K = 33, N = 17; |
2300 | |
2301 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2302 | |
2303 | at::Tensor t0 = at::randn({M, K}, options); |
2304 | at::Tensor t1 = at::randn({K, N}, options); |
2305 | |
2306 | // Lets specify a few bounds in launch params to make sure it works |
2307 | LaunchParams lparams(1, -1, -1, 32, 4, 4); |
2308 | |
2309 | FusionExecutor fe; |
2310 | fe.compileFusion(&fusion, {t0, t1}, lparams); |
2311 | fe.runFusion({t0, t1}, lparams); |
2312 | |
2313 | // Make sure bad launch params throws |
2314 | // TODO: Re-enable once we have parallelization validation in. |
2315 | // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto) |
2316 | // ASSERT_ANY_THROW(fe.runFusion({t0, t1}, LaunchParams(1, 2, 3, 4, 5, 6))); |
2317 | |
2318 | // Don't specify any launch params |
2319 | auto cg_outputs = fe.runFusion({t0, t1}); |
2320 | |
2321 | auto aten_output = (t0 + 1).to(at::kDouble).matmul(t1.to(at::kDouble)); |
2322 | |
2323 | testValidate( |
2324 | &fusion, cg_outputs, {t0, t1}, {aten_output}, __LINE__, __FILE__); |
2325 | } |
2326 | |
2327 | TEST_F(NVFuserTest, FusionSimpleCompileRtc_CUDA) { |
2328 | FusionExecutor fe; |
2329 | std::string kernel = R"( |
2330 | __global__ void kernel1(Tensor<float, 1> T0, Tensor<float, 1> T1) { |
2331 | if(threadIdx.x==0){ |
2332 | for(size_t ki28 = 0; ki28 < T0.size[0]; ++ki28) { |
2333 | T1[ki28*T1.stride[0]] = T0[ki28*T0.stride[0]]*2; |
2334 | } |
2335 | } |
2336 | } |
2337 | )" ; |
2338 | fe.compileRtc(kernel, "CudaCodeGen::kernel1" ); |
2339 | LaunchParams lp( |
2340 | 256, // gdimx |
2341 | 1, // gdimy |
2342 | 1, // gdimz |
2343 | 1, // bdimx |
2344 | 1, // bdimy |
2345 | 1 // bdimz |
2346 | ); |
2347 | lp.setSmem(0); |
2348 | const auto options = |
2349 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2350 | const std::vector<int64_t> tensor_dims = {8}; |
2351 | auto in0 = at::randn(tensor_dims, options); |
2352 | auto out0 = at::empty_like(in0); |
2353 | fe.runRtc(lp, {in0, out0}); |
2354 | |
2355 | auto out_ref = in0 * 2; |
2356 | TORCH_CHECK(out_ref.allclose(out0)); |
2357 | } |
2358 | |
2359 | TEST_F(NVFuserTest, FusionSerialWelford_CUDA) { |
2360 | FusionExecutor fe; |
2361 | int x = 128, y = 64, z = 64; |
2362 | |
2363 | std::string kernel = R"( |
2364 | __global__ void kernel1( |
2365 | Tensor<float,3> inp, |
2366 | Tensor<float,1> out_var, |
2367 | Tensor<float,1> out_avg |
2368 | ){ |
2369 | for(int i0=0;i0<inp.size[0];i0++){ |
2370 | float tmp_M2=0; |
2371 | float tmp_avg=0; |
2372 | long tmp_N=0; |
2373 | for(int i1=0;i1<inp.size[1];i1++){ |
2374 | for(int i2=0;i2<inp.size[2];i2++){ |
2375 | welfordCombine( |
2376 | tmp_avg, |
2377 | tmp_M2, |
2378 | tmp_N, |
2379 | inp[i0*inp.stride[0]+ |
2380 | i1*inp.stride[1]+ |
2381 | i2*inp.stride[2]], |
2382 | 0.f, |
2383 | (long)1 |
2384 | ); |
2385 | } |
2386 | } |
2387 | out_var[i0*out_var.stride[0]]= |
2388 | tmp_M2/(tmp_N); |
2389 | out_avg[i0*out_avg.stride[0]]= |
2390 | tmp_avg; |
2391 | } |
2392 | } |
2393 | )" ; |
2394 | fe.compileRtc(kernel, "CudaCodeGen::kernel1" ); |
2395 | LaunchParams lp( |
2396 | 1, // gdimx |
2397 | 1, // gdimy |
2398 | 1, // gdimz |
2399 | 1, // bdimx |
2400 | 1, // bdimy |
2401 | 1 // bdimz |
2402 | ); |
2403 | lp.setSmem(0); |
2404 | const auto options = |
2405 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2406 | const std::vector<int64_t> tensor_dims = {x, y, z}; |
2407 | auto in0 = at::randn(tensor_dims, options); |
2408 | auto out_var = at::empty({x}, options); |
2409 | auto out_avg = at::empty({x}, options); |
2410 | fe.runRtc(lp, {in0, out_var, out_avg}); |
2411 | |
2412 | TORCH_CHECK(in0.var({1, 2}, false).allclose(out_var)); |
2413 | TORCH_CHECK(in0.mean({1, 2}).allclose(out_avg, /*rtol*/ 1e-5, /*atol*/ 1e-6)); |
2414 | } |
2415 | |
2416 | TEST_F(NVFuserTest, FusionBlockWelford_CUDA) { |
2417 | FusionExecutor fe; |
2418 | int x = 7, y = 8, z = 9; |
2419 | |
2420 | std::string kernel = R"( |
2421 | __global__ void kernel1( |
2422 | Tensor<float,2> inp, |
2423 | Tensor<float,1> out_avg, |
2424 | Tensor<float,1> out_var, |
2425 | Tensor<float,1> init_avg, |
2426 | Tensor<float,1> init_var, |
2427 | Tensor<long,0> init_N |
2428 | ){ |
2429 | //actual generated kernel will use dynamic shared mem, |
2430 | // here is just for prototype |
2431 | __shared__ float mem_avg[512]; |
2432 | __shared__ float mem_M2[512]; |
2433 | __shared__ long mem_N[512]; |
2434 | float in=inp[threadIdx.x*inp.stride[0]+ |
2435 | threadIdx.y*inp.stride[1]]; |
2436 | float tmp_avg=0; |
2437 | float tmp_M2=0; |
2438 | long tmp_N=0; |
2439 | blockWelford<false,true,false>( |
2440 | tmp_avg, |
2441 | tmp_M2, |
2442 | tmp_N, |
2443 | in, |
2444 | 0.f, |
2445 | (long)1, |
2446 | threadIdx, |
2447 | blockDim, |
2448 | (float*)mem_avg, |
2449 | (float*)mem_M2, |
2450 | (long*)mem_N, |
2451 | (bool)(threadIdx.x<inp.size[0]), |
2452 | 0.f); |
2453 | __syncthreads(); |
2454 | if(threadIdx.x<out_var.size[0] && threadIdx.y==0){ |
2455 | welfordCombine( |
2456 | tmp_avg, |
2457 | tmp_M2, |
2458 | tmp_N, |
2459 | init_avg[threadIdx.x*init_avg.stride[0]], |
2460 | init_var[threadIdx.x*init_var.stride[0]]*init_N[0], |
2461 | init_N[0] |
2462 | ); |
2463 | out_avg[threadIdx.x*out_avg.stride[0]]=tmp_avg; |
2464 | out_var[threadIdx.x*out_var.stride[0]]=tmp_M2/(tmp_N); |
2465 | } |
2466 | } |
2467 | )" ; |
2468 | fe.compileRtc(kernel, "CudaCodeGen::kernel1" ); |
2469 | LaunchParams lp( |
2470 | 1, // gdimx |
2471 | 1, // gdimy |
2472 | 1, // gdimz |
2473 | x, // bdimx |
2474 | y, // bdimy |
2475 | 1 // bdimz |
2476 | ); |
2477 | lp.setSmem(0); |
2478 | const auto options = |
2479 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2480 | const std::vector<int64_t> tensor_dims = {x, y}; |
2481 | const std::vector<int64_t> init_dims = {x, z}; |
2482 | |
2483 | // generate initial values |
2484 | auto init_in = at::randn(init_dims, options); |
2485 | auto init_var = init_in.var({1}, false); |
2486 | auto init_avg = init_in.mean({1}); |
2487 | auto init_N = |
2488 | at::tensor(z, at::TensorOptions().dtype(at::kLong).device(at::kCUDA, 0)); |
2489 | |
2490 | auto in0 = at::randn(tensor_dims, options); |
2491 | |
2492 | // run kernel |
2493 | auto out_var = at::zeros({x}, options); |
2494 | auto out_avg = at::zeros({x}, options); |
2495 | fe.runRtc(lp, {in0, out_avg, out_var, init_avg, init_var, init_N}); |
2496 | |
2497 | // compare with reference output |
2498 | auto cat_tensor = at::cat({init_in, in0}, 1); |
2499 | TORCH_CHECK(cat_tensor.var({1}, false).allclose(out_var)); |
2500 | TORCH_CHECK( |
2501 | cat_tensor.mean({1}).allclose(out_avg, /*rtol*/ 1e-5, /*atol*/ 1e-6)); |
2502 | } |
2503 | |
2504 | TEST_F(NVFuserTest, FusionBlockWelfordNoInit_CUDA) { |
2505 | FusionExecutor fe; |
2506 | int x = 7, y = 8, z = 9; |
2507 | |
2508 | // need support IValue for integer input as initial count |
2509 | std::string kernel = R"( |
2510 | __global__ void kernel1( |
2511 | Tensor<float,3> inp, |
2512 | Tensor<float,1> out_avg, |
2513 | Tensor<float,1> out_var |
2514 | ){ |
2515 | //actual generated kernel will use dynamic shared mem, |
2516 | // here is just for prototype |
2517 | __shared__ float mem_avg[512]; |
2518 | __shared__ float mem_M2[512]; |
2519 | __shared__ long mem_N[512]; |
2520 | float in=inp[threadIdx.x*inp.stride[0]+ |
2521 | threadIdx.y*inp.stride[1]+ |
2522 | threadIdx.z*inp.stride[2]]; |
2523 | float tmp_avg=0; |
2524 | float tmp_M2=0; |
2525 | long tmp_N=0; |
2526 | block_sync::init(); |
2527 | blockWelford<false,true,true>( |
2528 | tmp_avg, |
2529 | tmp_M2, |
2530 | tmp_N, |
2531 | in, |
2532 | 0.f, |
2533 | (long) 1, |
2534 | threadIdx, |
2535 | blockDim, |
2536 | (float*)mem_avg, |
2537 | (float*)mem_M2, |
2538 | (long*)mem_N, |
2539 | (bool)(threadIdx.x<inp.size[0]), |
2540 | 0.f); |
2541 | __syncthreads(); |
2542 | if(threadIdx.x<out_var.size[0] && threadIdx.y==0 && threadIdx.z==0){ |
2543 | out_avg[threadIdx.x*out_var.stride[0]]=tmp_avg; |
2544 | out_var[threadIdx.x*out_var.stride[0]]=tmp_M2/(tmp_N); |
2545 | } |
2546 | } |
2547 | )" ; |
2548 | fe.compileRtc(kernel, "CudaCodeGen::kernel1" ); |
2549 | LaunchParams lp( |
2550 | 1, // gdimx |
2551 | 1, // gdimy |
2552 | 1, // gdimz |
2553 | x, // bdimx |
2554 | y, // bdimy |
2555 | z // bdimz |
2556 | ); |
2557 | lp.setSmem(0); |
2558 | const auto options = |
2559 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2560 | const std::vector<int64_t> tensor_dims = {x, y, z}; |
2561 | auto in0 = at::randn(tensor_dims, options); |
2562 | auto out_var = at::empty({x}, options); |
2563 | auto out_avg = at::empty({x}, options); |
2564 | fe.runRtc(lp, {in0, out_avg, out_var}); |
2565 | |
2566 | TORCH_CHECK(in0.var({1, 2}, false).allclose(out_var)); |
2567 | TORCH_CHECK(in0.mean({1, 2}).allclose(out_avg, /*rtol*/ 1e-5, /*atol*/ 1e-6)); |
2568 | } |
2569 | |
2570 | TEST_F(NVFuserTest, FusionGridWelfordNoInit_CUDA) { |
2571 | FusionExecutor fe; |
2572 | int x = 128, y = 64, z = 128; |
2573 | |
2574 | std::string kernel = R"( |
2575 | __global__ void kernel1( |
2576 | Tensor<float,3> inp, |
2577 | Tensor<float,1> out_avg, |
2578 | Tensor<float,1> out_var, |
2579 | Tensor<float,1> work_buf_avg, |
2580 | Tensor<float,1> work_buf_M2, |
2581 | Tensor<long,1> work_buf_N, |
2582 | Tensor<int64_t,1> sync_flag |
2583 | ){ |
2584 | __shared__ float shared_buf_avg[512]; |
2585 | __shared__ float shared_buf_M2[512]; |
2586 | __shared__ long shared_buf_N[512]; |
2587 | float tmp_avg=0; |
2588 | float tmp_M2=0; |
2589 | long tmp_N=0; |
2590 | float in = inp[ blockIdx.x * inp.stride[0]+ |
2591 | blockIdx.y * inp.stride[1]+ |
2592 | threadIdx.x * inp.stride[2]]; |
2593 | block_sync::init(); |
2594 | welford::gridWelford< |
2595 | true,true,false, |
2596 | true,false,false, |
2597 | false |
2598 | >( |
2599 | tmp_avg, |
2600 | tmp_M2, |
2601 | tmp_N, |
2602 | in, |
2603 | 0.f, |
2604 | (long) 1, |
2605 | &work_buf_avg[0], |
2606 | &work_buf_M2[0], |
2607 | &work_buf_N[0], |
2608 | sync_flag, |
2609 | (float*)shared_buf_avg, |
2610 | (float*)shared_buf_M2, |
2611 | (long*)shared_buf_N, |
2612 | threadIdx.x<out_var.size[0], |
2613 | threadIdx.x<out_var.size[0], |
2614 | 0.f, |
2615 | 0, |
2616 | 1); |
2617 | if(blockIdx.x == gridDim.x - 1 && blockIdx.y == gridDim.y - 1){ |
2618 | out_avg[threadIdx.x*out_avg.stride[0]]=tmp_avg; |
2619 | out_var[threadIdx.x*out_var.stride[0]]=tmp_M2/tmp_N; |
2620 | } |
2621 | } |
2622 | )" ; |
2623 | fe.compileRtc(kernel, "CudaCodeGen::kernel1" ); |
2624 | LaunchParams lp( |
2625 | x, // gdimx |
2626 | y, // gdimy |
2627 | 1, // gdimz |
2628 | z, // bdimx |
2629 | 1, // bdimy |
2630 | 1 // bdimz |
2631 | ); |
2632 | lp.setSmem(0); |
2633 | const auto options = |
2634 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2635 | const auto options_int = |
2636 | at::TensorOptions().dtype(at::kLong).device(at::kCUDA, 0); |
2637 | |
2638 | const std::vector<int64_t> tensor_dims = {x, y, z}; |
2639 | auto in0 = at::randn(tensor_dims, options); |
2640 | |
2641 | auto out_avg = at::empty({z}, options); |
2642 | auto out_var = at::empty({z}, options); |
2643 | auto work_buf_avg = at::empty({x * y * z}, options); |
2644 | auto work_buf_var = at::empty({x * y * z}, options); |
2645 | auto work_buf_N = at::empty({x * y * z}, options_int); |
2646 | auto sync_flag = at::zeros({1}, options_int); |
2647 | fe.runRtc( |
2648 | lp, |
2649 | {in0, |
2650 | out_avg, |
2651 | out_var, |
2652 | work_buf_avg, |
2653 | work_buf_var, |
2654 | work_buf_N, |
2655 | sync_flag}); |
2656 | std::vector<int64_t> dims{0, 1}; |
2657 | |
2658 | TORCH_CHECK(in0.mean(dims).allclose(out_avg, /*rtol*/ 1e-5, /*atol*/ 1e-6)); |
2659 | TORCH_CHECK(in0.var(dims, false).allclose(out_var)); |
2660 | } |
2661 | |
2662 | TEST_F(NVFuserTest, FusionWelfordOp_CUDA) { |
2663 | Fusion fusion; |
2664 | FusionGuard fg(&fusion); |
2665 | |
2666 | int M = 64, N = 128; |
2667 | |
2668 | auto tv0 = makeSymbolicTensor(2); |
2669 | fusion.addInput(tv0); |
2670 | auto tv1 = mul(tv0, IrBuilder::create<Double>(1)); |
2671 | auto tvs = Welford(tv1, {1}); |
2672 | auto tv_avg = tvs.avg; |
2673 | auto tv_M2 = tvs.var_sum; |
2674 | auto tv_N = tvs.n; |
2675 | fusion.addOutput(tv_avg); |
2676 | fusion.addOutput(tv_M2); |
2677 | fusion.addOutput(tv_N); |
2678 | |
2679 | tv_avg->split(1, 32); |
2680 | tv_avg->split(0, 32); |
2681 | tv_avg->split(0, 4); |
2682 | tv_avg->reorder({{-1, -3}, {-3, -1}}); |
2683 | tv1->computeAt(tv_avg, -1); |
2684 | |
2685 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2686 | auto options_int = at::TensorOptions().dtype(at::kLong).device(at::kCUDA, 0); |
2687 | at::manual_seed(0); |
2688 | at::Tensor t0 = at::randn({M, N}, options); |
2689 | |
2690 | FusionExecutor fe; |
2691 | fe.compileFusion(&fusion, {t0}); |
2692 | auto outputs = fe.runFusion({t0}); |
2693 | |
2694 | // by default Welford outputs sum of square diff so need to divide to get var |
2695 | outputs[1] /= N; |
2696 | |
2697 | testValidate( |
2698 | fe.kernel(), |
2699 | outputs, |
2700 | {t0}, |
2701 | {t0.mean({1}), t0.var({1}, false), at::ones({M}, options_int) * N}, |
2702 | __LINE__, |
2703 | __FILE__); |
2704 | } |
2705 | |
2706 | TEST_F(NVFuserTest, FusionBlockWelfordOp_CUDA) { |
2707 | Fusion fusion; |
2708 | FusionGuard fg(&fusion); |
2709 | |
2710 | int M = 64, N = 128; |
2711 | |
2712 | auto tv0 = makeSymbolicTensor(2); |
2713 | fusion.addInput(tv0); |
2714 | auto tv1 = mul(tv0, IrBuilder::create<Double>(1)); |
2715 | auto tvs = Welford(tv1, {1}); |
2716 | auto tv_avg = tvs.avg; |
2717 | auto tv_M2 = tvs.var_sum; |
2718 | auto tv_N = tvs.n; |
2719 | fusion.addOutput(tv_avg); |
2720 | fusion.addOutput(tv_M2); |
2721 | fusion.addOutput(tv_N); |
2722 | |
2723 | tv_avg->axis(-1)->parallelize(ParallelType::TIDx); |
2724 | |
2725 | tv1->computeAt(tv_avg, -1); |
2726 | |
2727 | // |
2728 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2729 | auto options_int = at::TensorOptions().dtype(at::kLong).device(at::kCUDA, 0); |
2730 | at::manual_seed(0); |
2731 | at::Tensor t0 = at::randn({M, N}, options); |
2732 | at::Tensor t_var = at::empty({M}, options); |
2733 | at::Tensor t_avg = at::empty({M}, options); |
2734 | at::Tensor t_N = at::empty({M}, options_int); |
2735 | |
2736 | FusionExecutor fe; |
2737 | fe.compileFusion(&fusion, {t0}); |
2738 | auto outputs = fe.runFusion({t0}); |
2739 | |
2740 | // by default Welford outputs sum of square diff so need to divide to get var |
2741 | outputs[1] /= N; |
2742 | |
2743 | testValidate( |
2744 | fe.kernel(), |
2745 | outputs, |
2746 | {t0}, |
2747 | {t0.mean({1}), t0.var({1}, false), at::ones({M}, options_int) * N}, |
2748 | __LINE__, |
2749 | __FILE__); |
2750 | } |
2751 | |
2752 | TEST_F(NVFuserTest, FusionGridWelfordOp_CUDA) { |
2753 | Fusion fusion; |
2754 | FusionGuard fg(&fusion); |
2755 | |
2756 | int M = 64, N = 128; |
2757 | |
2758 | auto tv0 = makeSymbolicTensor(2); |
2759 | fusion.addInput(tv0); |
2760 | auto tv1 = mul(tv0, IrBuilder::create<Double>(1)); |
2761 | auto tvs = Welford(tv1, {1}); |
2762 | auto tv_avg = tvs.avg; |
2763 | auto tv_M2 = tvs.var_sum; |
2764 | auto tv_N = tvs.n; |
2765 | fusion.addOutput(tv_avg); |
2766 | fusion.addOutput(tv_M2); |
2767 | fusion.addOutput(tv_N); |
2768 | |
2769 | tv_avg->axis(0)->parallelize(ParallelType::TIDx); |
2770 | tv_avg->axis(-1)->parallelize(ParallelType::BIDx); |
2771 | |
2772 | tv1->computeAt(tv_avg, -1); |
2773 | |
2774 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2775 | auto options_int = at::TensorOptions().dtype(at::kLong).device(at::kCUDA, 0); |
2776 | at::manual_seed(0); |
2777 | at::Tensor t0 = at::randn({M, N}, options); |
2778 | at::Tensor t_avg = at::empty({M}, options); |
2779 | at::Tensor t_var = at::empty({M}, options); |
2780 | at::Tensor t_N = at::empty({M}, options_int); |
2781 | |
2782 | FusionExecutor fe; |
2783 | fe.compileFusion(&fusion, {t0}); |
2784 | auto outputs = fe.runFusion({t0}); |
2785 | |
2786 | // by default Welford outputs sum of square diff so need to divide to get var |
2787 | outputs[1] /= N; |
2788 | |
2789 | testValidate( |
2790 | fe.kernel(), |
2791 | outputs, |
2792 | {t0}, |
2793 | {t0.mean({1}), t0.var({1}, false), at::ones({M}, options_int) * N}, |
2794 | __LINE__, |
2795 | __FILE__); |
2796 | } |
2797 | |
2798 | TEST_F(NVFuserTest, FusionRfactorWelfordOp_CUDA) { |
2799 | Fusion fusion; |
2800 | FusionGuard fg(&fusion); |
2801 | |
2802 | int M = 64, N = 128; |
2803 | |
2804 | auto tv0 = makeSymbolicTensor(2); |
2805 | fusion.addInput(tv0); |
2806 | auto tv1 = mul(tv0, IrBuilder::create<Double>(1)); |
2807 | auto tvs = Welford(tv1, {1}); |
2808 | auto tv_avg = tvs.avg; |
2809 | auto tv_M2 = tvs.var_sum; |
2810 | auto tv_N = tvs.n; |
2811 | fusion.addOutput(tv_avg); |
2812 | fusion.addOutput(tv_M2); |
2813 | fusion.addOutput(tv_N); |
2814 | |
2815 | tv_avg->split(1, 4); |
2816 | ir_utils::rfactorHelper(tvs.avg, {2}); |
2817 | tv1->computeAt(tv_avg, -1); |
2818 | |
2819 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2820 | auto options_int = at::TensorOptions().dtype(at::kLong).device(at::kCUDA, 0); |
2821 | at::manual_seed(0); |
2822 | at::Tensor t0 = at::randn({M, N}, options); |
2823 | at::Tensor t_avg = at::empty({M}, options); |
2824 | at::Tensor t_var = at::empty({M}, options); |
2825 | at::Tensor t_N = at::empty({M}, options_int); |
2826 | |
2827 | FusionExecutor fe; |
2828 | fe.compileFusion(&fusion, {t0}); |
2829 | auto outputs = fe.runFusion({t0}); |
2830 | |
2831 | // by default Welford outputs sum of square diff so need to divide to get var |
2832 | outputs[1] /= N; |
2833 | |
2834 | testValidate( |
2835 | fe.kernel(), |
2836 | outputs, |
2837 | {t0}, |
2838 | {t0.mean({1}), t0.var({1}, false), at::ones({M}, options_int) * N}, |
2839 | __LINE__, |
2840 | __FILE__); |
2841 | } |
2842 | |
2843 | TEST_F(NVFuserTest, FusionWelfordSchedule_CUDA) { |
2844 | Fusion fusion; |
2845 | FusionGuard fg(&fusion); |
2846 | |
2847 | int M = 64, N = 128; |
2848 | |
2849 | auto tv0 = makeSymbolicTensor(2); |
2850 | fusion.addInput(tv0); |
2851 | auto tv1 = mul(tv0, IrBuilder::create<Double>(1)); |
2852 | auto tvs = Welford(tv1, {1}); |
2853 | auto tv_avg = tvs.avg; |
2854 | auto tv_M2 = tvs.var_sum; |
2855 | auto tv_N = tvs.n; |
2856 | fusion.addOutput(tv_avg); |
2857 | fusion.addOutput(tv_M2); |
2858 | fusion.addOutput(tv_N); |
2859 | |
2860 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
2861 | auto options_int = at::TensorOptions().dtype(at::kLong).device(at::kCUDA, 0); |
2862 | at::manual_seed(0); |
2863 | at::Tensor t0 = at::randn({M, N}, options); |
2864 | // TODO: Why do we use launch params from here, but not scheduling??? |
2865 | auto reduction_params = getReductionHeuristics(&fusion, {t0}); |
2866 | scheduleReduction(&fusion, *reduction_params); |
2867 | |
2868 | auto lparams = reduction_params->lparams; |
2869 | FusionExecutor fe; |
2870 | fe.compileFusion(&fusion, {t0}, lparams); |
2871 | auto outputs = fe.runFusion({t0}, lparams); |
2872 | |
2873 | // by default Welford outputs sum of square diff so need to divide to get var |
2874 | outputs[1] /= N; |
2875 | |
2876 | auto at_avg = t0.mean({1}); |
2877 | auto at_var = t0.var({1}, false); |
2878 | auto at_n = at::ones({M}, options_int) * N; |
2879 | |
2880 | testValidate( |
2881 | fe.kernel(), |
2882 | outputs, |
2883 | {t0}, |
2884 | {at_avg, at_var, at_n}, |
2885 | __LINE__, |
2886 | __FILE__, |
2887 | "validate welford" , |
2888 | reduction_params->lparams); |
2889 | } |
2890 | |
2891 | namespace { |
2892 | void testWelford(DataType dtype, int red_axis, int odim, int rdim) { |
2893 | const int axis = red_axis; |
2894 | at::ScalarType aten_dtype = data_type_to_aten(dtype); |
2895 | |
2896 | Fusion fusion; |
2897 | FusionGuard fg(&fusion); |
2898 | TensorView* tv0 = makeSymbolicTensor(2, dtype); |
2899 | bool is_fp16 = dtype == DataType::Half; |
2900 | bool is_bf16 = dtype == DataType::BFloat16; |
2901 | TensorView* tv0_cast = tv0; |
2902 | if (is_fp16 || is_bf16) { |
2903 | tv0_cast = castOp(DataType::Float, tv0); |
2904 | } |
2905 | fusion.addInput(tv0); |
2906 | auto tv1 = mul(tv0_cast, IrBuilder::create<Double>(1)); |
2907 | auto tvs = Welford(tv1, {axis}); |
2908 | auto tv_avg = tvs.avg; |
2909 | auto tv_M2 = tvs.var_sum; |
2910 | auto tv_N = tvs.n; |
2911 | |
2912 | TensorView* avg_cast = tv_avg; |
2913 | TensorView* M2_cast = tv_M2; |
2914 | |
2915 | if (is_fp16) { |
2916 | avg_cast = castOp(DataType::Half, tv_avg); |
2917 | M2_cast = castOp(DataType::Half, tv_M2); |
2918 | } |
2919 | if (is_bf16) { |
2920 | avg_cast = castOp(DataType::BFloat16, tv_avg); |
2921 | M2_cast = castOp(DataType::BFloat16, tv_M2); |
2922 | } |
2923 | |
2924 | fusion.addOutput(avg_cast); |
2925 | fusion.addOutput(M2_cast); |
2926 | fusion.addOutput(tv_N); |
2927 | |
2928 | auto options = at::TensorOptions().dtype(aten_dtype).device(at::kCUDA, 0); |
2929 | auto options_int = at::TensorOptions().dtype(at::kLong).device(at::kCUDA, 0); |
2930 | at::manual_seed(0); |
2931 | std::vector<TensorView*> outputs_of_red; |
2932 | at::Tensor aten_input = |
2933 | (axis ? at::randn({odim, rdim}, options) |
2934 | : at::randn({rdim, odim}, options)); |
2935 | |
2936 | if (is_fp16 || is_bf16) { |
2937 | outputs_of_red.push_back(avg_cast); |
2938 | outputs_of_red.push_back(M2_cast); |
2939 | } |
2940 | |
2941 | auto reduction_params = getReductionHeuristics(&fusion, {aten_input}); |
2942 | scheduleReduction(&fusion, *reduction_params); |
2943 | |
2944 | auto lparams = reduction_params->lparams; |
2945 | |
2946 | FusionExecutor fe; |
2947 | fe.compileFusion(&fusion, {aten_input}, lparams); |
2948 | auto outputs = fe.runFusion({aten_input}, lparams); |
2949 | |
2950 | // by default Welford outputs sum of square diff so need to divide to |
2951 | // get var |
2952 | |
2953 | outputs[1] /= rdim; |
2954 | |
2955 | auto at_avg = aten_input.mean({axis}); |
2956 | auto at_var = aten_input.var({axis}, false); |
2957 | auto at_n = |
2958 | (axis ? at::ones({odim, rdim}, options) |
2959 | : at::ones({rdim, odim}, options)); |
2960 | at_n = at_n.sum({axis}); |
2961 | |
2962 | testValidate( |
2963 | fe.kernel(), |
2964 | outputs, |
2965 | {aten_input}, |
2966 | {at_avg, at_var, at_n}, |
2967 | __LINE__, |
2968 | __FILE__, |
2969 | "validate welford" , |
2970 | reduction_params->lparams); |
2971 | } |
2972 | } // namespace |
2973 | |
2974 | TEST_F(NVFuserTest, FusionWelfordShmoo_CUDA) { |
2975 | std::vector<DataType> dtypes = { |
2976 | DataType::Double, DataType::Float, DataType::Half}; |
2977 | // TODO: enable this for complex. Currently, complex yields |
2978 | // silent wrong results: |
2979 | // Detected abs error of: 3.8062 |
2980 | // absolute tolerance was set to 2.23704e-06 |
2981 | // and relative tolerance set to 2.23704e-08 |
2982 | #if !defined(USE_ROCM) |
2983 | if (at::cuda::getDeviceProperties(0)->major >= 8) { |
2984 | dtypes.insert(dtypes.end(), DataType::BFloat16); |
2985 | } |
2986 | #endif |
2987 | |
2988 | std::vector<int> red_axis = {1, 0}; |
2989 | std::vector<int> output_dims = {160, 320}; |
2990 | std::vector<int> red_dims; |
2991 | |
2992 | // Tried to cut down the number iterations with just |
2993 | // doing every other power of 2. |
2994 | for (int i = 1; i <= 1024 * 1024; i <<= 2) { |
2995 | red_dims.push_back(i); |
2996 | } |
2997 | |
2998 | for (auto dtype : dtypes) { |
2999 | for (auto& axis : red_axis) { |
3000 | for (auto& odim : output_dims) { |
3001 | for (auto& rdim : red_dims) { |
3002 | // TODO: original welford algorithm actually keeps a running sum of |
3003 | // squares, i.e. M_{2n} in the |
3004 | // cf: |
3005 | // https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance |
3006 | // algorithm notation, and it can reach inf for large numbers |
3007 | // with half precision. skipping too large volumes for half for |
3008 | // nwo might need further numerical experiments to re-design |
3009 | // this. |
3010 | if (rdim > 32768 && |
3011 | (dtype == DataType::Half || dtype == DataType::BFloat16)) { |
3012 | continue; |
3013 | } |
3014 | testWelford(dtype, axis, odim, rdim); |
3015 | } |
3016 | } |
3017 | } |
3018 | } |
3019 | } |
3020 | |
3021 | namespace { |
3022 | void testVarMean(at::ScalarType dtype, int correction, bool keepdim) { |
3023 | auto fusion = std::make_unique<Fusion>(); |
3024 | FusionGuard fg(fusion.get()); |
3025 | |
3026 | int M = 64, N = 128; |
3027 | |
3028 | auto tv0 = makeSymbolicTensor(2, aten_to_data_type(dtype)); |
3029 | fusion->addInput(tv0); |
3030 | auto tvs = variance_mean(tv0, {1}, correction, keepdim); |
3031 | auto tv_mean = tvs.mean; |
3032 | auto tv_var = tvs.var; |
3033 | fusion->addOutput(tv_var); |
3034 | fusion->addOutput(tv_mean); |
3035 | |
3036 | auto options = at::TensorOptions().dtype(dtype).device(at::kCUDA, 0); |
3037 | at::manual_seed(0); |
3038 | at::Tensor t0 = at::randn({M, N}, options); |
3039 | |
3040 | FusionExecutorCache executor_cache(std::move(fusion)); |
3041 | auto outputs = executor_cache.runFusionWithInputs({t0}); |
3042 | |
3043 | auto at_var_mean = at::var_mean(t0, {1}, correction, keepdim); |
3044 | std::vector<at::Tensor> aten_outputs = { |
3045 | std::get<0>(at_var_mean), std::get<1>(at_var_mean)}; |
3046 | |
3047 | testValidate( |
3048 | executor_cache.fusion(), outputs, {t0}, aten_outputs, __LINE__, __FILE__); |
3049 | } |
3050 | } // namespace |
3051 | |
3052 | TEST_F(NVFuserTest, FusionVarMean_CUDA) { |
3053 | std::vector<at::ScalarType> dtypes = {at::kFloat, at::kDouble}; |
3054 | std::vector<int> corrections = {0, 1}; |
3055 | std::vector<bool> keepdims = {false, true}; |
3056 | for (auto correction : corrections) { |
3057 | for (auto keepdim : keepdims) { |
3058 | for (auto dtype : dtypes) { |
3059 | testVarMean(dtype, correction, keepdim); |
3060 | } |
3061 | } |
3062 | } |
3063 | } |
3064 | |
3065 | TEST_F(NVFuserTest, FusionSimpleGemmTransposed_CUDA) { |
3066 | Fusion fusion; |
3067 | FusionGuard fg(&fusion); |
3068 | |
3069 | // Set up your input tensor views |
3070 | |
3071 | TensorView* tv0 = makeSymbolicTensor(2); // K, M |
3072 | TensorView* tv1 = makeSymbolicTensor(2); // N, K |
3073 | fusion.addInput(tv0); |
3074 | fusion.addInput(tv1); |
3075 | |
3076 | TensorView* tv0_t = transpose(tv0); |
3077 | TensorView* tv1_t = transpose(tv1); |
3078 | |
3079 | TensorView* tv2 = broadcast(tv0_t, {false, false, true}); |
3080 | // tv2[I0, I1, B] = tv0[I0, I1] |
3081 | |
3082 | TensorView* tv3 = broadcast(tv1_t, {true, false, false}); |
3083 | // tv3[B, I1, I2] = tv1[I1, I2] |
3084 | |
3085 | // tv4[I0, I1, I2] = tv2[I0, I1, B] * tv3[B, I1, I2] |
3086 | TensorView* tv4 = mul(tv2, tv3); |
3087 | // tv5[I0, R1, I2] = tv4[I0, I1, I2] |
3088 | TensorView* tv5 = sum(tv4, {1}); |
3089 | fusion.addOutput(tv5); |
3090 | |
3091 | tv5->split(1, 32); |
3092 | // tv5[I0, R1o, R1i{32}, I2] |
3093 | |
3094 | auto tv6 = tv5->rFactor({1}); |
3095 | // tv6[I0, R1o, I1i{32}, I2] = tv4[I0, I1, I2] |
3096 | // tv5[I0, , R1i{32}, I2] = tv6[I0, R1o, I1i{32}, I2] |
3097 | |
3098 | tv5->split(0, 4); |
3099 | tv5->split(-1, 4); |
3100 | // tv5[I0o, I0i{4}, R1i{32}, I2o, I2i{4}] |
3101 | // tv5[I0o, I0i{4}, R1i{32}, I2o, I2i{4}] |
3102 | |
3103 | tv0_t->computeAt(tv5, -1); |
3104 | tv1_t->computeAt(tv5, -1); |
3105 | |
3106 | // tv6[I0o, I0i{4}, R1o, I1i{32}, I2o, I2i{4}] |
3107 | // tv5[I0o, I0i{4}, , R1i{32}, I2o, I2i{4}] |
3108 | //--> (line symbolizes compute at location) |
3109 | // tv4[I0o, I0i{4}, I1i{32}, I2o, I2i{4}|, I1o] |
3110 | // tv6[I0o, I0i{4}, I1i{32}, I2o, I2i{4}|, R1o] |
3111 | // tv5[I0o, I0i{4}, R1i{32}, I2o, I2i{4}|] |
3112 | |
3113 | tv0_t->computeAt(tv6, -1); |
3114 | tv1_t->computeAt(tv6, -1); |
3115 | // tv4[I0o, I0i{4}, I1i{32}, I2o, I2i{4}, I1o |] |
3116 | // tv6[I0o, I0i{4}, I1i{32}, I2o, I2i{4}, R1o |] |
3117 | // tv5[I0o, I0i{4}, R1i{32}, I2o, I2i{4}|] |
3118 | |
3119 | tv5->axis(0)->parallelize(ParallelType::BIDz); |
3120 | tv5->axis(1)->parallelize(ParallelType::TIDz); |
3121 | |
3122 | tv5->axis(-2)->parallelize(ParallelType::BIDy); |
3123 | tv5->axis(-1)->parallelize(ParallelType::TIDy); |
3124 | |
3125 | tv5->axis(2)->parallelize(ParallelType::TIDx); |
3126 | tv6->axis(2)->parallelize(ParallelType::TIDx); |
3127 | |
3128 | constexpr int M = 65, K = 33, N = 17; |
3129 | |
3130 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3131 | |
3132 | at::Tensor t0 = at::randn({K, M}, options); |
3133 | at::Tensor t1 = at::randn({N, K}, options); |
3134 | |
3135 | // Lets specify a few bounds in launch params to make sure it works |
3136 | LaunchParams lparams(1, -1, -1, 32, 4, 4); |
3137 | FusionExecutor fe; |
3138 | fe.compileFusion(&fusion, {t0, t1}, lparams); |
3139 | fe.runFusion({t0, t1}, lparams); |
3140 | |
3141 | // Don't specify any launch params |
3142 | auto cg_outputs = fe.runFusion({t0, t1}); |
3143 | |
3144 | auto aten_output = t0.t().to(at::kDouble).matmul(t1.t().to(at::kDouble)); |
3145 | |
3146 | testValidate( |
3147 | &fusion, cg_outputs, {t0, t1}, {aten_output}, __LINE__, __FILE__); |
3148 | } |
3149 | |
3150 | TEST_F(NVFuserTest, FusionSoftmax3DTransposed_CUDA) { |
3151 | Fusion fusion; |
3152 | FusionGuard fg(&fusion); |
3153 | |
3154 | const int tidx = 32; |
3155 | const int dimx = 32; |
3156 | const int dimy = 16; |
3157 | const int dimz = 130; |
3158 | |
3159 | // Set up your input tensor views |
3160 | TensorView* input_tv0 = makeSymbolicTensor(3); |
3161 | fusion.addInput(input_tv0); |
3162 | |
3163 | TensorView* input_t = transpose(input_tv0, 1, 2); |
3164 | |
3165 | TensorView* exp_tv1 = unaryOp(UnaryOpType::Exp, input_t); |
3166 | TensorView* sum_exp_tv2 = sum(exp_tv1, {-1}); |
3167 | TensorView* bcast_sum_tv3 = broadcast(sum_exp_tv2, {false, false, true}); |
3168 | |
3169 | // Replicate exp_tv4 as exp_tv4_copy because exp_tv4 is going to be |
3170 | // computed at sum_exp_rf_tv8. |
3171 | TensorView* input_t_copy = transpose(input_tv0, 1, 2); |
3172 | TensorView* exp_tv1_copy = unaryOp(UnaryOpType::Exp, input_t_copy); |
3173 | |
3174 | TensorView* output_tv4 = div(exp_tv1_copy, bcast_sum_tv3); |
3175 | |
3176 | fusion.addOutput(output_tv4); |
3177 | |
3178 | bcast_sum_tv3->split(-1, tidx); |
3179 | |
3180 | sum_exp_tv2->split(-1, tidx); |
3181 | TensorView* sum_exp_rf_tv5 = sum_exp_tv2->rFactor({-2}); |
3182 | |
3183 | output_tv4->split(-1, tidx); |
3184 | |
3185 | input_t->computeAt(sum_exp_rf_tv5, -1); |
3186 | input_t_copy->computeAt(output_tv4, -1); |
3187 | |
3188 | TensorView* tensors_to_parallelize[] = { |
3189 | sum_exp_tv2, bcast_sum_tv3, output_tv4, sum_exp_rf_tv5}; |
3190 | |
3191 | for (auto tv : tensors_to_parallelize) { |
3192 | tv->axis(0)->parallelize(ParallelType::BIDx); |
3193 | tv->axis(1)->parallelize(ParallelType::BIDy); |
3194 | tv->axis(-1)->parallelize(ParallelType::TIDx); |
3195 | } |
3196 | |
3197 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3198 | at::Tensor input = at::randn({dimx, dimz, dimy}, options); |
3199 | |
3200 | at::Tensor cg_output = at::empty({dimx, dimy, dimz}, options); |
3201 | |
3202 | FusionExecutor fe; |
3203 | fe.compileFusion(&fusion, {input}); |
3204 | fe.runFusion({input}, {cg_output}); |
3205 | |
3206 | auto aten_input_t = at::transpose(input, 1, 2); |
3207 | auto aten_output = at::_softmax(aten_input_t.to(at::kDouble), -1, false); |
3208 | |
3209 | testValidate( |
3210 | &fusion, {cg_output}, {input}, {aten_output}, __LINE__, __FILE__); |
3211 | } |
3212 | |
3213 | TEST_F(NVFuserTest, FusionAdvancedComputeAtTransposed1_CUDA) { |
3214 | // Case 1 |
3215 | // tv1 = tv0 * 0.5 |
3216 | // tv2 = tv1 * -1 |
3217 | // tv3 = tv1 + 3 |
3218 | // tv4 = tv1 * 2 |
3219 | // tv5 = tv3 + tv2 |
3220 | // tv6 = tv5 + tv4 |
3221 | // tv7 = tv1 + tv4 |
3222 | Fusion fusion; |
3223 | FusionGuard fg(&fusion); |
3224 | |
3225 | TensorView* tv0 = makeSymbolicTensor(2); |
3226 | fusion.addInput(tv0); |
3227 | |
3228 | tv0 = transpose(tv0); |
3229 | |
3230 | TensorView* tv1 = mul(tv0, IrBuilder::create<Double>(0.5)); |
3231 | TensorView* tv2 = mul(tv1, IrBuilder::create<Double>(-1.0)); |
3232 | TensorView* tv3 = add(tv1, IrBuilder::create<Double>(3.0)); |
3233 | TensorView* tv4 = mul(tv1, IrBuilder::create<Double>(2.0)); |
3234 | TensorView* tv5 = add(tv3, tv2); |
3235 | |
3236 | TensorView* tv6 = add(tv5, tv4); |
3237 | TensorView* tv7 = add(tv1, tv4); |
3238 | |
3239 | fusion.addOutput(tv6); |
3240 | fusion.addOutput(tv7); |
3241 | |
3242 | // Lets setup to actually run |
3243 | tv7->merge(0); |
3244 | tv7->split(0, 128); |
3245 | tv7->split(0, 4); |
3246 | |
3247 | tv7->axis(0)->parallelize(ParallelType::BIDx); |
3248 | |
3249 | tv0->computeAt(tv7, 1); |
3250 | |
3251 | // The this-position of the last tensor should be zero. |
3252 | TORCH_CHECK( |
3253 | tv7->nDims() == 3 && tv7->getComputeAtPosition() == 0 && |
3254 | tv7->getMaxProducerPosition() == 1); |
3255 | TORCH_CHECK( |
3256 | tv6->nDims() == 3 && tv6->getComputeAtPosition() == 0 && |
3257 | tv6->getMaxProducerPosition() == 1); |
3258 | // The position of every other tensor should be 1. |
3259 | for (auto tv : {tv1, tv2, tv3, tv4, tv5}) { |
3260 | TORCH_CHECK(tv->nDims() == 3 && tv->getComputeAtPosition() == 1); |
3261 | } |
3262 | |
3263 | for (Val* val : fusion.vals()) { |
3264 | if (!val->isFusionInput() && |
3265 | val->getValType().value() == ValType::TensorView) { |
3266 | TensorView* tv = static_cast<TensorView*>(val); |
3267 | tv->axis(1)->parallelize(ParallelType::Unroll); |
3268 | tv->axis(-1)->parallelize(ParallelType::TIDx); |
3269 | } |
3270 | } |
3271 | |
3272 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3273 | |
3274 | at::Tensor aten_input = at::randn({129, 127}, options); |
3275 | |
3276 | FusionExecutor fe; |
3277 | fe.compileFusion(&fusion, {aten_input}); |
3278 | auto cg_outputs = fe.runFusion({aten_input}); |
3279 | |
3280 | at::Tensor aten_input_t = aten_input.t(); |
3281 | |
3282 | auto t1 = aten_input_t.mul({0.5}); |
3283 | auto t2 = t1.mul({-1.0}); |
3284 | auto t3 = t1.add({3.0}); |
3285 | auto t4 = t1.mul({2.0}); |
3286 | auto t5 = t3.add(t2); |
3287 | auto t6 = t5.add(t4); |
3288 | auto t7 = t1.add(t4); |
3289 | |
3290 | std::vector<at::Tensor> aten_outputs = {t6, t7}; |
3291 | |
3292 | testValidate( |
3293 | &fusion, cg_outputs, {aten_input}, aten_outputs, __LINE__, __FILE__); |
3294 | } |
3295 | |
3296 | TEST_F(NVFuserTest, FusionAdvancedComputeAtTransposed2_CUDA) { |
3297 | // Case 2 |
3298 | // tv1 = tv0 * -1 |
3299 | // tv2 = tv0 + 3 |
3300 | // tv3 = tv0 * 2 |
3301 | // tv4 = tv2 + tv1 |
3302 | // tv5 = tv4 + tv3 |
3303 | // tv6 = tv5 + tv3 |
3304 | Fusion fusion; |
3305 | FusionGuard fg(&fusion); |
3306 | |
3307 | TensorView* tv0 = makeSymbolicTensor(2); |
3308 | fusion.addInput(tv0); |
3309 | |
3310 | tv0 = transpose(tv0); |
3311 | |
3312 | TensorView* tv1 = mul(tv0, IrBuilder::create<Double>(-1.0)); |
3313 | TensorView* tv2 = add(tv0, IrBuilder::create<Double>(3.0)); |
3314 | TensorView* tv3 = mul(tv0, IrBuilder::create<Double>(2.0)); |
3315 | TensorView* tv4 = add(tv2, tv1); |
3316 | |
3317 | TensorView* tv5 = add(tv4, tv3); |
3318 | TensorView* tv6 = add(tv5, tv3); |
3319 | |
3320 | fusion.addOutput(tv5); |
3321 | fusion.addOutput(tv6); |
3322 | |
3323 | // Lets setup to actually run |
3324 | tv6->merge(0); |
3325 | tv6->split(0, 128); |
3326 | tv6->split(0, 4); |
3327 | |
3328 | tv6->axis(0)->parallelize(ParallelType::BIDx); |
3329 | |
3330 | tv0->computeAt(tv6, 1); |
3331 | |
3332 | for (Val* val : fusion.vals()) { |
3333 | if (!val->isFusionInput() && |
3334 | val->getValType().value() == ValType::TensorView) { |
3335 | TensorView* tv = static_cast<TensorView*>(val); |
3336 | |
3337 | tv->axis(1)->parallelize(ParallelType::Unroll); |
3338 | tv->axis(-1)->parallelize(ParallelType::TIDx); |
3339 | } |
3340 | } |
3341 | |
3342 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3343 | at::Tensor input = at::randn({129, 127}, options); |
3344 | |
3345 | FusionExecutor fe; |
3346 | fe.compileFusion(&fusion, {input}); |
3347 | auto cg_outputs = fe.runFusion({input}); |
3348 | |
3349 | auto input_t = input.t(); |
3350 | auto t1 = input_t.mul({-1.0}); |
3351 | auto t2 = input_t.add({3.0}); |
3352 | auto t3 = input_t.mul({2.0}); |
3353 | auto t4 = t2.add(t1); |
3354 | auto t5 = t4.add(t3); |
3355 | auto t6 = t5.add(t3); |
3356 | |
3357 | std::vector<at::Tensor> aten_outputs = {t5, t6}; |
3358 | |
3359 | testValidate(&fusion, cg_outputs, {input}, aten_outputs, __LINE__, __FILE__); |
3360 | } |
3361 | |
3362 | TEST_F(NVFuserTest, FusionAdvancedComputeAtTransposed3_CUDA) { |
3363 | // Case 3 |
3364 | // T2 = T1 * 0.979361 |
3365 | // T3 = T2 * T0 |
3366 | Fusion fusion; |
3367 | FusionGuard fg(&fusion); |
3368 | |
3369 | TensorView* tv0 = makeSymbolicTensor(4); |
3370 | fusion.addInput(tv0); |
3371 | |
3372 | tv0 = permute(tv0, {3, 0, 1, 2}); |
3373 | |
3374 | TensorView* tv1 = makeSymbolicTensor(4); |
3375 | fusion.addInput(tv1); |
3376 | |
3377 | tv1 = permute(tv1, {3, 0, 1, 2}); |
3378 | |
3379 | TensorView* tv2 = mul(tv1, IrBuilder::create<Double>(.979361)); |
3380 | TensorView* tv3 = mul(tv2, tv0); |
3381 | |
3382 | fusion.addOutput(tv3); |
3383 | |
3384 | // Lets setup to actually run |
3385 | while (tv3->nDims() > 1) |
3386 | tv3->merge(0); |
3387 | tv3->split(0, 128); |
3388 | tv3->split(0, 4); |
3389 | |
3390 | tv0->computeAt(tv3, 1); |
3391 | tv1->computeAt(tv3, 1); |
3392 | |
3393 | tv3->axis(0)->parallelize(ParallelType::BIDx); |
3394 | |
3395 | for (Val* val : fusion.vals()) { |
3396 | if (!val->isFusionInput() && |
3397 | val->getValType().value() == ValType::TensorView) { |
3398 | TensorView* tv = static_cast<TensorView*>(val); |
3399 | |
3400 | tv->axis(1)->parallelize(ParallelType::Unroll); |
3401 | tv->axis(-1)->parallelize(ParallelType::TIDx); |
3402 | } |
3403 | } |
3404 | |
3405 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3406 | at::Tensor t0 = at::randn({129, 127, 63, 65}, options); |
3407 | at::Tensor t1 = at::rand_like(t0, options); |
3408 | |
3409 | std::vector<IValue> aten_inputs = {t0, t1}; |
3410 | |
3411 | FusionExecutor fe; |
3412 | fe.compileFusion(&fusion, aten_inputs); |
3413 | auto cg_outputs = fe.runFusion(aten_inputs); |
3414 | |
3415 | auto t0_t = t0.permute({3, 0, 1, 2}); |
3416 | auto t1_t = t1.permute({3, 0, 1, 2}); |
3417 | auto t2 = t1_t.mul({0.979361}); |
3418 | auto aten_output = t2.mul(t0_t); |
3419 | |
3420 | testValidate( |
3421 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
3422 | } |
3423 | |
3424 | TEST_F(NVFuserTest, FusionAdvancedComputeAtTransposed4_CUDA) { |
3425 | // Case 4 |
3426 | // T4 = T2 - T3 |
3427 | // T5 = T1 + T4 |
3428 | // T6 = T5 - T0 |
3429 | Fusion fusion; |
3430 | FusionGuard fg(&fusion); |
3431 | |
3432 | TensorView* tv0 = makeSymbolicTensor(4); |
3433 | fusion.addInput(tv0); |
3434 | |
3435 | tv0 = permute(tv0, {3, 0, 1, 2}); |
3436 | |
3437 | TensorView* tv1 = makeSymbolicTensor(4); |
3438 | fusion.addInput(tv1); |
3439 | |
3440 | tv1 = permute(tv1, {3, 0, 1, 2}); |
3441 | |
3442 | TensorView* tv2 = makeSymbolicTensor(4); |
3443 | fusion.addInput(tv2); |
3444 | |
3445 | tv2 = permute(tv2, {3, 0, 1, 2}); |
3446 | |
3447 | TensorView* tv3 = makeSymbolicTensor(4); |
3448 | fusion.addInput(tv3); |
3449 | |
3450 | tv3 = permute(tv3, {3, 0, 1, 2}); |
3451 | |
3452 | TensorView* tv4 = sub(tv2, tv3); |
3453 | TensorView* tv5 = add(tv1, tv4); |
3454 | TensorView* tv6 = sub(tv5, tv0); |
3455 | |
3456 | fusion.addOutput(tv6); |
3457 | |
3458 | // Lets setup to actually run |
3459 | while (tv6->nDims() > 1) |
3460 | tv6->merge(0); |
3461 | tv6->split(0, 128); |
3462 | tv6->split(0, 4); |
3463 | |
3464 | tv0->computeAt(tv6, 1); |
3465 | tv1->computeAt(tv6, 1); |
3466 | tv2->computeAt(tv6, 1); |
3467 | tv3->computeAt(tv6, 1); |
3468 | |
3469 | tv6->axis(0)->parallelize(ParallelType::BIDx); |
3470 | |
3471 | for (Val* val : fusion.vals()) { |
3472 | if (!val->isFusionInput() && |
3473 | val->getValType().value() == ValType::TensorView) { |
3474 | TensorView* tv = static_cast<TensorView*>(val); |
3475 | |
3476 | tv->axis(1)->parallelize(ParallelType::Unroll); |
3477 | tv->axis(-1)->parallelize(ParallelType::TIDx); |
3478 | } |
3479 | } |
3480 | |
3481 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3482 | at::Tensor t0 = at::randn({129, 127, 63, 65}, options); |
3483 | at::Tensor t1 = at::rand_like(t0, options); |
3484 | at::Tensor t2 = at::rand_like(t0, options); |
3485 | at::Tensor t3 = at::rand_like(t0, options); |
3486 | |
3487 | std::vector<IValue> aten_inputs = {t0, t1, t2, t3}; |
3488 | |
3489 | FusionExecutor fe; |
3490 | fe.compileFusion(&fusion, aten_inputs); |
3491 | auto cg_outputs = fe.runFusion(aten_inputs); |
3492 | |
3493 | auto t0_t = t0.permute({3, 0, 1, 2}); |
3494 | auto t1_t = t1.permute({3, 0, 1, 2}); |
3495 | auto t2_t = t2.permute({3, 0, 1, 2}); |
3496 | auto t3_t = t3.permute({3, 0, 1, 2}); |
3497 | auto t4 = t2_t.sub(t3_t); |
3498 | auto t5 = t1_t.add(t4); |
3499 | auto aten_output = t5.sub(t0_t); |
3500 | |
3501 | testValidate( |
3502 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
3503 | } |
3504 | |
3505 | TEST_F(NVFuserTest, FusionAdvancedComputeAtTransposed5_CUDA) { |
3506 | // Case 5 |
3507 | // tv2 = tv0 + 2.0 |
3508 | // tv3 = tv1 * tv2 |
3509 | Fusion fusion; |
3510 | FusionGuard fg(&fusion); |
3511 | |
3512 | // Set up your input tensor views |
3513 | TensorView* tv0 = makeSymbolicTensor(2); |
3514 | fusion.addInput(tv0); |
3515 | tv0 = transpose(tv0); |
3516 | TensorView* tv1 = makeSymbolicTensor(2); |
3517 | fusion.addInput(tv1); |
3518 | tv1 = transpose(tv1); |
3519 | TensorView* tv2 = add(tv0, IrBuilder::create<Double>(2.0)); |
3520 | TensorView* tv3 = mul(tv1, tv2); |
3521 | fusion.addOutput(tv3); |
3522 | |
3523 | tv3->merge(0); |
3524 | tv3->split(-1, 8); |
3525 | tv3->split(-1, 4); |
3526 | |
3527 | tv0->computeAt(tv3, 1); |
3528 | tv1->computeAt(tv3, 1); |
3529 | tv3->axis(0)->parallelize(ParallelType::BIDx); |
3530 | |
3531 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3532 | at::Tensor t0 = at::randn({63, 65}, options); |
3533 | at::Tensor t1 = at::rand_like(t0, options); |
3534 | |
3535 | std::vector<IValue> aten_inputs = {t0, t1}; |
3536 | |
3537 | FusionExecutor fe; |
3538 | fe.compileFusion(&fusion, aten_inputs); |
3539 | auto cg_outputs = fe.runFusion(aten_inputs); |
3540 | |
3541 | auto t2 = t0.t().add(2.0); |
3542 | auto aten_output = t1.t().mul(t2); |
3543 | |
3544 | testValidate( |
3545 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
3546 | } |
3547 | |
3548 | TEST_F(NVFuserTest, FusionAdvancedComputeAtTransposed6_CUDA) { |
3549 | Fusion fusion; |
3550 | FusionGuard fg(&fusion); |
3551 | |
3552 | TensorView* tv0 = makeSymbolicTensor(2); |
3553 | fusion.addInput(tv0); |
3554 | tv0 = transpose(tv0); |
3555 | TensorView* tv1 = makeSymbolicTensor(2); |
3556 | fusion.addInput(tv1); |
3557 | tv1 = transpose(tv1); |
3558 | TensorView* tv2 = add(tv0, IrBuilder::create<Double>(2.0)); |
3559 | TensorView* tv3 = mul(tv1, tv2); |
3560 | fusion.addOutput(tv3); |
3561 | |
3562 | tv2->merge(0); |
3563 | tv2->split(-1, 8); |
3564 | tv2->split(-1, 4); |
3565 | tv3->merge(0); |
3566 | tv3->split(-1, 8); |
3567 | |
3568 | tv0->computeAt(tv3, 1); |
3569 | tv1->computeAt(tv3, 1); |
3570 | |
3571 | tv3->axis(0)->parallelize(ParallelType::BIDx); |
3572 | |
3573 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3574 | at::Tensor t0 = at::randn({63, 65}, options); |
3575 | at::Tensor t1 = at::rand_like(t0, options); |
3576 | |
3577 | std::vector<IValue> aten_inputs = {t0, t1}; |
3578 | |
3579 | FusionExecutor fe; |
3580 | fe.compileFusion(&fusion, aten_inputs); |
3581 | auto cg_outputs = fe.runFusion(aten_inputs); |
3582 | |
3583 | auto t2 = t0.t().add(2.0); |
3584 | auto aten_output = t1.t().mul(t2); |
3585 | |
3586 | testValidate( |
3587 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
3588 | } |
3589 | |
3590 | TEST_F(NVFuserTest, FusionSegmentReducePointwise_CUDA) { |
3591 | auto fusion = std::make_unique<Fusion>(); |
3592 | FusionGuard fg(fusion.get()); |
3593 | |
3594 | TensorView* tv0 = makeSymbolicTensor(2); |
3595 | TensorView* tv1 = makeSymbolicTensor(1); |
3596 | TensorView* tv2 = makeSymbolicTensor(2); |
3597 | |
3598 | fusion->addInput(tv0); |
3599 | fusion->addInput(tv1); |
3600 | fusion->addInput(tv2); |
3601 | |
3602 | TensorView* tv3 = add(tv0, IrBuilder::create<Double>(1)); // Group 0 |
3603 | TensorView* tv4 = |
3604 | max(tv3, {0}); // Group 0 (use max instead to avoid numerical issues) |
3605 | TensorView* tv5 = add(tv4, tv1); // Group 0 (Non Broadcast after reduce, |
3606 | // keeps normalization scheduler away) |
3607 | TensorView* tv6 = add(tv5, tv2); // Group 1 (Broadcast after reduce) |
3608 | |
3609 | fusion->addOutput(tv6); |
3610 | |
3611 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3612 | at::Tensor t0 = at::randn({128, 65}, options); |
3613 | at::Tensor t1 = at::randn({65}, options); |
3614 | at::Tensor t2 = at::randn({128, 65}, options); |
3615 | |
3616 | auto t3 = t0.add(1.0); |
3617 | auto t4 = std::get<0>(at::max(t3, 0)); |
3618 | auto t5 = t4.add(t1); |
3619 | auto t6 = t5.add(t2); |
3620 | |
3621 | FusionExecutorCache executor_cache(std::move(fusion)); |
3622 | |
3623 | auto outputs = executor_cache.runFusionWithInputs({t0, t1, t2}); |
3624 | |
3625 | TORCH_CHECK( |
3626 | executor_cache.getMostRecentKernelRuntime()->isSegmented(), |
3627 | "segmentation didn't happen" ); |
3628 | TORCH_CHECK( |
3629 | executor_cache.getMostRecentKernelRuntime() |
3630 | ->fusionSegments() |
3631 | ->groups() |
3632 | .size() == 2, |
3633 | "segmentation didn't happen as expected" ); |
3634 | |
3635 | testValidate( |
3636 | executor_cache.fusion(), outputs, {t0, t1, t2}, {t6}, __LINE__, __FILE__); |
3637 | } |
3638 | |
3639 | TEST_F(NVFuserTest, FusionMultipleVectorize_CUDA) { |
3640 | auto fusion = std::make_unique<Fusion>(); |
3641 | FusionGuard fg(fusion.get()); |
3642 | |
3643 | TensorView* tv0 = makeContigTensor(1); |
3644 | TensorView* tv1 = makeContigTensor(1); |
3645 | |
3646 | fusion->addInput(tv0); |
3647 | fusion->addInput(tv1); |
3648 | |
3649 | TensorView* tv3 = add(tv0, tv1); |
3650 | fusion->addOutput(tv3); |
3651 | |
3652 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3653 | at::Tensor t0 = at::randn({40960}, options); |
3654 | at::Tensor t1 = at::randn({40960}, options); |
3655 | auto t2 = t0 + t1; |
3656 | |
3657 | FusionExecutorCache executor_cache(std::move(fusion)); |
3658 | executor_cache.profile(true); |
3659 | |
3660 | auto outputs = executor_cache.runFusionWithInputs({t0, t1}); |
3661 | auto runtime1 = executor_cache.getMostRecentKernelRuntime(); |
3662 | auto log1 = |
3663 | executor_cache.getMostRecentExecutorInfo().params->as<PointwiseParams>(); |
3664 | TORCH_CHECK(log1 != nullptr); |
3665 | TORCH_CHECK(log1->vectorize); |
3666 | |
3667 | testValidate( |
3668 | executor_cache.fusion(), outputs, {t0, t1}, {t2}, __LINE__, __FILE__); |
3669 | |
3670 | t0 = at::randn({40964}, options); |
3671 | t1 = at::randn({40964}, options); |
3672 | t2 = t0 + t1; |
3673 | |
3674 | outputs = executor_cache.runFusionWithInputs({t0, t1}); |
3675 | auto runtime2 = executor_cache.getMostRecentKernelRuntime(); |
3676 | auto log2 = |
3677 | executor_cache.getMostRecentExecutorInfo().params->as<PointwiseParams>(); |
3678 | TORCH_CHECK(log2 != nullptr); |
3679 | TORCH_CHECK(log2->vectorize); |
3680 | |
3681 | testValidate( |
3682 | executor_cache.fusion(), outputs, {t0, t1}, {t2}, __LINE__, __FILE__); |
3683 | |
3684 | t0 = at::randn({40962}, options); |
3685 | t1 = at::randn({40962}, options); |
3686 | t2 = t0 + t1; |
3687 | |
3688 | outputs = executor_cache.runFusionWithInputs({t0, t1}); |
3689 | auto runtime3 = executor_cache.getMostRecentKernelRuntime(); |
3690 | auto log3 = |
3691 | executor_cache.getMostRecentExecutorInfo().params->as<PointwiseParams>(); |
3692 | TORCH_CHECK(log3 != nullptr); |
3693 | TORCH_CHECK(log3->vectorize); |
3694 | |
3695 | testValidate( |
3696 | executor_cache.fusion(), outputs, {t0, t1}, {t2}, __LINE__, __FILE__); |
3697 | |
3698 | TORCH_CHECK(runtime1 == runtime2); |
3699 | TORCH_CHECK(runtime1 != runtime3); |
3700 | } |
3701 | |
3702 | TEST_F(NVFuserTest, FusionVectorizeSimple_CUDA) { |
3703 | Fusion fusion; |
3704 | FusionGuard fg(&fusion); |
3705 | |
3706 | TensorView* tv0 = makeContigTensor(3); |
3707 | |
3708 | fusion.addInput(tv0); |
3709 | |
3710 | auto tv1 = unaryOp(UnaryOpType::Sin, tv0); |
3711 | |
3712 | fusion.addOutput(tv1); |
3713 | |
3714 | auto tv0_cache = tv0->cacheAfter(); |
3715 | |
3716 | auto tv1_cache = tv1->cacheBefore(); |
3717 | |
3718 | tv1->merge(0); |
3719 | tv1->merge(0); |
3720 | tv1->split(0, 4); |
3721 | tv1->split(0, 128); |
3722 | |
3723 | tv1->axis(0)->parallelize(ParallelType::BIDx); |
3724 | tv1->axis(1)->parallelize(ParallelType::TIDx); |
3725 | |
3726 | tv0->computeAt(tv1, 2); |
3727 | |
3728 | tv0_cache->axis(2)->parallelize(ParallelType::Vectorize); |
3729 | tv1->axis(2)->parallelize(ParallelType::Vectorize); |
3730 | |
3731 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3732 | |
3733 | at::Tensor aten_input = at::empty({2, 6, 32}, options); |
3734 | |
3735 | FusionExecutor fe; |
3736 | fe.compileFusion(&fusion, {aten_input}); |
3737 | auto cg_outputs = fe.runFusion({aten_input}); |
3738 | |
3739 | at::Tensor aten_output = aten_input.sin(); |
3740 | |
3741 | testValidate( |
3742 | &fusion, cg_outputs, {aten_input}, {aten_output}, __LINE__, __FILE__); |
3743 | } |
3744 | |
3745 | TEST_F(NVFuserTest, FusionSimpleVectorizeUnroll_CUDA) { |
3746 | Fusion fusion; |
3747 | FusionGuard fg(&fusion); |
3748 | // dimensionality of the problem |
3749 | int nDims = 3; |
3750 | |
3751 | // Set up your input tensor views |
3752 | TensorView* tv0 = makeContigTensor(nDims); |
3753 | TensorView* tv1 = makeContigTensor(nDims); |
3754 | |
3755 | // Register your inputs |
3756 | fusion.addInput(tv0); |
3757 | fusion.addInput(tv1); |
3758 | |
3759 | // Do math with it, it returns a `Val*` but can be static_casted back to |
3760 | // TensorView |
3761 | TensorView* tv2 = add(tv1, IrBuilder::create<Double>(2.0)); |
3762 | TensorView* tv3 = add(tv0, tv2); |
3763 | |
3764 | // Register your outputs |
3765 | fusion.addOutput(tv3); |
3766 | |
3767 | auto tv0_cache = tv0->cacheAfter(); |
3768 | auto tv1_cache = tv1->cacheAfter(); |
3769 | auto tv3_cache = tv3->cacheBefore(); |
3770 | |
3771 | // Do transformations, remember, transformations are outputs to inputs |
3772 | // This doesn't have to be in this order |
3773 | tv3->merge(1); |
3774 | |
3775 | // Split by n_threads |
3776 | tv3->split(1, 2); |
3777 | tv3->split(0, 3); |
3778 | tv3->split(0, 1); |
3779 | |
3780 | // [bidx, unswitch, unroll{2}, tidx, vectorize{2}] |
3781 | |
3782 | // Parallelize TV3 |
3783 | tv3->axis(0)->parallelize(ParallelType::BIDx); |
3784 | tv3->axis(1)->parallelize(ParallelType::Unswitch); |
3785 | tv3->axis(2)->parallelize(ParallelType::Unroll); |
3786 | tv3->axis(3)->parallelize(ParallelType::TIDx); |
3787 | |
3788 | tv3->reorder({{4, 2}}); |
3789 | // [bidx, unswitch, vectorize{2}, unroll{2}, tidx] |
3790 | |
3791 | TransformPropagatorWithCheck propagator(tv3); |
3792 | MaxRootDomainInfoSpanningTree(tv3).traverse(&propagator); |
3793 | scheduler_utils::parallelizeAllLike(tv3); |
3794 | |
3795 | tv0_cache->axis(2)->parallelize(ParallelType::Vectorize); |
3796 | tv1_cache->axis(2)->parallelize(ParallelType::Vectorize); |
3797 | tv3->axis(2)->parallelize(ParallelType::Vectorize); |
3798 | |
3799 | // For all inputs, computeAt the output inline, temporaries should be squeezed |
3800 | // between them |
3801 | tv0->computeAt(tv3, -1, ComputeAtMode::MostInlined); |
3802 | tv1->computeAt(tv3, -1, ComputeAtMode::MostInlined); |
3803 | |
3804 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3805 | |
3806 | at::Tensor input1 = at::randn({64, 2, 128}, options); |
3807 | at::Tensor input2 = at::rand_like(input1); |
3808 | at::Tensor output = at::empty_like(input1); |
3809 | |
3810 | FusionExecutor fe; |
3811 | fe.compileFusion(&fusion, {input1, input2}); |
3812 | fe.runFusion({input1, input2}, {output}); |
3813 | |
3814 | at::Tensor tv2_ref = input2 + 2.0; |
3815 | at::Tensor output_ref = input1 + tv2_ref; |
3816 | |
3817 | TORCH_CHECK(output_ref.equal(output)); |
3818 | } |
3819 | |
3820 | TEST_F(NVFuserTest, FusionSegmentReduceSoftmax_CUDA) { |
3821 | auto fusion = std::make_unique<Fusion>(); |
3822 | FusionGuard fg(fusion.get()); |
3823 | |
3824 | std::vector<int64_t> input_shape{32, 64, 8}; |
3825 | const int kReductionAxis = 1; |
3826 | |
3827 | auto tv0 = TensorViewBuilder() |
3828 | .ndims(input_shape.size()) |
3829 | .dtype(DataType::Double) |
3830 | .build(); |
3831 | |
3832 | fusion->addInput(tv0); |
3833 | |
3834 | auto tv1 = add(tv0, IrBuilder::create<Double>(1.0)); |
3835 | auto tv2 = sum(tv1, {2}); // Group 0 |
3836 | |
3837 | auto output = softmax(tv2, kReductionAxis); // Group 1 |
3838 | fusion->addOutput(output); |
3839 | |
3840 | auto options = at::TensorOptions().dtype(at::kDouble).device(at::kCUDA, 0); |
3841 | at::Tensor at_x = at::randn(input_shape, options); |
3842 | |
3843 | FusionExecutorCache executor_cache(std::move(fusion)); |
3844 | |
3845 | auto outputs = executor_cache.runFusionWithInputs({at_x}); |
3846 | |
3847 | auto t1 = at_x.add(1.0); |
3848 | auto t2 = t1.sum({2}); |
3849 | auto t3 = at::_softmax(t2.to(at::kDouble), -1, false); |
3850 | |
3851 | auto optimized_fusion = executor_cache.getMostRecentKernelRuntime(); |
3852 | TORCH_CHECK(optimized_fusion->isSegmented(), "segmentation didn't happen" ); |
3853 | TORCH_CHECK( |
3854 | optimized_fusion->fusionSegments()->groups().size() == 2, |
3855 | "segmentation didn't happen as expected" ); |
3856 | |
3857 | testValidate( |
3858 | executor_cache.fusion(), outputs, {at_x}, {t3}, __LINE__, __FILE__); |
3859 | } |
3860 | |
3861 | TEST_F(NVFuserTest, FusionSwizzle1_CUDA) { |
3862 | Fusion fusion; |
3863 | FusionGuard fg(&fusion); |
3864 | |
3865 | auto tv0 = makeSymbolicTensor(1); |
3866 | fusion.addInput(tv0); |
3867 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
3868 | auto tv2 = mul(tv1, IrBuilder::create<Double>(2)); |
3869 | fusion.addOutput(tv2); |
3870 | |
3871 | tv2->split(0, 7); |
3872 | tv2->split(0, 9); |
3873 | |
3874 | tv0->computeAt(tv2, 1); |
3875 | |
3876 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
3877 | |
3878 | tv1->setMemoryType(MemoryType::Shared); |
3879 | tv1->swizzle(SwizzleType::Transpose, {1, 2}); |
3880 | |
3881 | tv1->axis(1)->parallelize(ParallelType::TIDx); |
3882 | tv1->axis(2)->parallelize(ParallelType::TIDy); |
3883 | |
3884 | tv2->axis(1)->parallelize(ParallelType::TIDx); |
3885 | tv2->axis(2)->parallelize(ParallelType::TIDy); |
3886 | |
3887 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3888 | at::Tensor t0 = at::randn({100}, options); |
3889 | |
3890 | std::vector<IValue> aten_inputs = {t0}; |
3891 | |
3892 | FusionExecutor fe; |
3893 | fe.compileFusion(&fusion, aten_inputs); |
3894 | auto cg_outputs = fe.runFusion(aten_inputs); |
3895 | |
3896 | auto aten_output = (t0 + 1) * 2; |
3897 | |
3898 | testValidate( |
3899 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
3900 | } |
3901 | |
3902 | TEST_F(NVFuserTest, FusionSwizzle2_CUDA) { |
3903 | Fusion fusion; |
3904 | FusionGuard fg(&fusion); |
3905 | |
3906 | auto tv0 = makeSymbolicTensor(1); |
3907 | fusion.addInput(tv0); |
3908 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
3909 | auto tv2 = mul(tv1, IrBuilder::create<Double>(2)); |
3910 | fusion.addOutput(tv2); |
3911 | |
3912 | tv1->split(-1, 4); |
3913 | tv1->split(-2, 4); |
3914 | |
3915 | tv2->split(-1, 4); |
3916 | tv2->split(-2, 4); |
3917 | |
3918 | tv0->computeAt(tv2, 1); |
3919 | |
3920 | tv2->reorder({{-1, -2}}); |
3921 | |
3922 | tv1->setMemoryType(MemoryType::Shared); |
3923 | tv1->swizzle(SwizzleType::Transpose, {-2, -1}); |
3924 | |
3925 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
3926 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
3927 | tv2->axis(-2)->parallelize(ParallelType::TIDy); |
3928 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
3929 | tv1->axis(-2)->parallelize(ParallelType::TIDy); |
3930 | |
3931 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3932 | at::Tensor t0 = at::randn({123}, options); |
3933 | |
3934 | std::vector<IValue> aten_inputs = {t0}; |
3935 | |
3936 | FusionExecutor fe; |
3937 | fe.compileFusion(&fusion, aten_inputs); |
3938 | auto cg_outputs = fe.runFusion(aten_inputs); |
3939 | |
3940 | auto aten_output = (t0 + 1) * 2; |
3941 | |
3942 | testValidate( |
3943 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
3944 | } |
3945 | |
3946 | TEST_F(NVFuserTest, FusionGridPersistence_CUDA) { |
3947 | Fusion fusion; |
3948 | FusionGuard fg(&fusion); |
3949 | |
3950 | auto tv0 = makeSymbolicTensor(1); |
3951 | fusion.addInput(tv0); |
3952 | |
3953 | auto tv1 = sum(tv0, {0}); |
3954 | auto tv2 = broadcast(tv1, {true}); |
3955 | auto tv3 = add(tv0, tv2); |
3956 | fusion.addOutput(tv3); |
3957 | |
3958 | std::vector<TensorView*> tvs = {tv1, tv2, tv3}; |
3959 | for (auto tv : tvs) { |
3960 | tv->split(0, 2); |
3961 | tv->axis(0)->parallelize(ParallelType::BIDx); |
3962 | tv->axis(1)->parallelize(ParallelType::BIDy); |
3963 | } |
3964 | |
3965 | const int numel_x = 10; |
3966 | |
3967 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
3968 | at::Tensor input = at::randn({numel_x}, options); |
3969 | |
3970 | FusionExecutor fe; |
3971 | fe.compileFusion(&fusion, {input}); |
3972 | auto out = fe.runFusion({input}); |
3973 | |
3974 | auto aten_output = input.sum({0}).unsqueeze(-1).add(input); |
3975 | |
3976 | testValidate(&fusion, out, {input}, {aten_output}, __LINE__, __FILE__); |
3977 | } |
3978 | |
3979 | TEST_F(NVFuserTest, FusionGridPersistence2_CUDA) { |
3980 | Fusion fusion; |
3981 | FusionGuard fg(&fusion); |
3982 | |
3983 | auto tv0 = makeSymbolicTensor(2); |
3984 | fusion.addInput(tv0); |
3985 | |
3986 | auto tv1 = sum(tv0, {0}); |
3987 | auto tv2 = broadcast(tv1, {true, false}); |
3988 | auto tv3 = add(tv0, tv2); |
3989 | fusion.addOutput(tv3); |
3990 | |
3991 | std::vector<TensorView*> tvs = {tv1, tv2, tv3}; |
3992 | for (auto tv : tvs) { |
3993 | tv->split(0, 2); |
3994 | tv->axis(0)->parallelize(ParallelType::BIDx); |
3995 | tv->axis(1)->parallelize(ParallelType::TIDy); |
3996 | tv->axis(2)->parallelize(ParallelType::TIDx); |
3997 | } |
3998 | |
3999 | const int numel_x = 10; |
4000 | const int numel_y = 3; |
4001 | |
4002 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4003 | at::Tensor input = at::randn({numel_x, numel_y}, options); |
4004 | |
4005 | FusionExecutor fe; |
4006 | fe.compileFusion(&fusion, {input}); |
4007 | auto out = fe.runFusion({input}); |
4008 | |
4009 | auto aten_output = input.sum({0}).unsqueeze(0).add(input); |
4010 | |
4011 | testValidate(&fusion, out, {input}, {aten_output}, __LINE__, __FILE__); |
4012 | } |
4013 | |
4014 | TEST_F(NVFuserTest, FusionWelfordPersistence_CUDA) { |
4015 | Fusion fusion; |
4016 | FusionGuard fg(&fusion); |
4017 | |
4018 | auto tv0 = makeSymbolicTensor(1); |
4019 | fusion.addInput(tv0); |
4020 | |
4021 | auto tvs = Welford(tv0, {0}); |
4022 | auto tv4 = add(tvs.avg, tvs.var_sum); |
4023 | auto tv5 = broadcast(tv4, {true}); |
4024 | auto tv6 = add(tv0, tv5); |
4025 | fusion.addOutput(tv6); |
4026 | |
4027 | std::vector<TensorView*> schedule_tvs = { |
4028 | tvs.avg, tvs.var_sum, tvs.n, tv5, tv6}; |
4029 | |
4030 | for (auto tv : schedule_tvs) { |
4031 | tv->split(0, 2); |
4032 | tv->axis(0)->parallelize(ParallelType::BIDx); |
4033 | tv->axis(1)->parallelize(ParallelType::BIDy); |
4034 | } |
4035 | |
4036 | const int numel_x = 10; |
4037 | |
4038 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4039 | at::Tensor input = at::randn({numel_x}, options); |
4040 | |
4041 | FusionExecutor fe; |
4042 | fe.compileFusion(&fusion, {input}); |
4043 | auto out = fe.runFusion({input}); |
4044 | |
4045 | auto aten_output = (input.mean({0}) + (input.var({0}, false) * numel_x)) |
4046 | .unsqueeze(-1) |
4047 | .add(input); |
4048 | |
4049 | testValidate(&fusion, out, {input}, {aten_output}, __LINE__, __FILE__); |
4050 | } |
4051 | |
4052 | TEST_F(NVFuserTest, FusionWelfordPersistence2_CUDA) { |
4053 | Fusion fusion; |
4054 | FusionGuard fg(&fusion); |
4055 | |
4056 | auto tv0 = makeSymbolicTensor(2); |
4057 | fusion.addInput(tv0); |
4058 | |
4059 | auto tvs = Welford(tv0, {0}); |
4060 | auto tv4 = add(tvs.avg, tvs.var_sum); |
4061 | auto tv5 = broadcast(tv4, {true, false}); |
4062 | auto tv6 = add(tv0, tv5); |
4063 | fusion.addOutput(tv6); |
4064 | |
4065 | std::vector<TensorView*> schedule_tvs = { |
4066 | tvs.avg, tvs.var_sum, tvs.n, tv5, tv6}; |
4067 | for (auto tv : schedule_tvs) { |
4068 | tv->split(0, 2); |
4069 | tv->axis(0)->parallelize(ParallelType::BIDx); |
4070 | tv->axis(1)->parallelize(ParallelType::TIDy); |
4071 | tv->axis(2)->parallelize(ParallelType::TIDx); |
4072 | } |
4073 | tv4->axis(0)->parallelize(ParallelType::TIDx); |
4074 | |
4075 | const int numel_x = 10; |
4076 | const int numel_y = 3; |
4077 | |
4078 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4079 | at::Tensor input = at::randn({numel_x, numel_y}, options); |
4080 | |
4081 | FusionExecutor fe; |
4082 | fe.compileFusion(&fusion, {input}); |
4083 | auto out = fe.runFusion({input}); |
4084 | |
4085 | auto aten_output = (input.mean({0}) + (input.var({0}, false) * numel_x)) |
4086 | .unsqueeze(0) |
4087 | .add(input); |
4088 | |
4089 | testValidate(&fusion, out, {input}, {aten_output}, __LINE__, __FILE__); |
4090 | } |
4091 | |
4092 | TEST_F(NVFuserTest, FusionIssue633_CUDA) { |
4093 | Fusion fusion; |
4094 | FusionGuard fg(&fusion); |
4095 | |
4096 | const int dx = 10; |
4097 | const int dy = 11; |
4098 | const int dz = 12; |
4099 | |
4100 | auto tv0 = makeConcreteTensor({dx, dy, dz}); |
4101 | fusion.addInput(tv0); |
4102 | auto tv1 = makeConcreteTensor({dx, dy, 1}); |
4103 | fusion.addInput(tv1); |
4104 | auto tv2 = add(tv0, tv1); |
4105 | fusion.addOutput(tv2); |
4106 | |
4107 | tv2->merge(1); |
4108 | tv2->merge(0); |
4109 | tv2->split(-1, 128); |
4110 | |
4111 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
4112 | tv2->axis(1)->parallelize(ParallelType::TIDx); |
4113 | |
4114 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4115 | at::Tensor t0 = at::randn({dx, dy, dz}, options); |
4116 | at::Tensor t1 = at::randn({dx, dy, 1}, options); |
4117 | std::vector<IValue> aten_inputs = {t0, t1}; |
4118 | |
4119 | FusionExecutor fe; |
4120 | fe.compileFusion(&fusion, aten_inputs); |
4121 | auto cg_outputs = fe.runFusion(aten_inputs); |
4122 | |
4123 | auto aten_output = t0 + t1; |
4124 | |
4125 | testValidate( |
4126 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
4127 | } |
4128 | |
4129 | TEST_F(NVFuserTest, FusionBroadcastAcrossComputeAt_CUDA) { |
4130 | Fusion fusion; |
4131 | FusionGuard fg(&fusion); |
4132 | |
4133 | std::vector<int64_t> shape{17, 19}; |
4134 | |
4135 | auto tv0 = makeSymbolicTensor(1); |
4136 | fusion.addInput(tv0); |
4137 | auto tv1 = makeSymbolicTensor(2); |
4138 | fusion.addInput(tv1); |
4139 | auto tv2 = broadcast(tv0, {false, true}); |
4140 | auto tv3 = add(tv1, tv2); |
4141 | fusion.addOutput(tv3); |
4142 | |
4143 | tv3->split(1, 128); |
4144 | tv0->computeAt(tv3, 2); |
4145 | |
4146 | for (auto tv : {tv2, tv3}) { |
4147 | tv->axis(-1)->parallelize(ParallelType::TIDx); |
4148 | } |
4149 | |
4150 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4151 | at::Tensor t0 = at::randn({shape[0]}, options); |
4152 | at::Tensor t1 = at::randn(shape, options); |
4153 | std::vector<IValue> aten_inputs = {t0, t1}; |
4154 | |
4155 | FusionExecutor fe; |
4156 | fe.compileFusion(&fusion, aten_inputs); |
4157 | auto cg_outputs = fe.runFusion(aten_inputs); |
4158 | |
4159 | auto t3 = t0.unsqueeze(-1).expand(shape) + t1; |
4160 | |
4161 | testValidate(&fusion, cg_outputs, aten_inputs, {t3}, __LINE__, __FILE__); |
4162 | } |
4163 | |
4164 | TEST_F(NVFuserTest, FusionVectorizeMisalignedPointwise_CUDA) { |
4165 | Fusion fusion; |
4166 | FusionGuard fg(&fusion); |
4167 | |
4168 | auto tv0 = makeContigTensor(2); |
4169 | auto tv1 = makeContigTensor(2); |
4170 | fusion.addInput(tv0); |
4171 | fusion.addInput(tv1); |
4172 | |
4173 | auto tv2 = add(tv0, tv1); |
4174 | fusion.addOutput(tv2); |
4175 | |
4176 | const int kTDX = 64; |
4177 | const int kVecSize = 4; |
4178 | const int kNumElems = kTDX * kVecSize; |
4179 | |
4180 | tv2->split(1, kNumElems); |
4181 | |
4182 | auto c0 = tv0->cacheAfter(); |
4183 | auto c1 = tv1->cacheAfter(); |
4184 | auto c2 = tv2->cacheBefore(); |
4185 | |
4186 | tv2->split(-1, kVecSize); |
4187 | |
4188 | c0->computeAt(tv2, -2); |
4189 | c1->computeAt(tv2, -2); |
4190 | |
4191 | c0->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4192 | c1->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4193 | |
4194 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
4195 | tv2->axis(-2)->parallelize(ParallelType::TIDx); |
4196 | tv2->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4197 | |
4198 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4199 | const int bx = 128; |
4200 | const int by = 457; |
4201 | at::Tensor t0 = at::randn({bx, by}, options); |
4202 | at::Tensor t1 = at::randn({bx, by}, options); |
4203 | |
4204 | std::vector<IValue> aten_inputs = {t0, t1}; |
4205 | |
4206 | FusionExecutor fe; |
4207 | fe.compileFusion(&fusion, aten_inputs); |
4208 | auto cg_outputs = fe.runFusion(aten_inputs); |
4209 | |
4210 | auto aten_output = t0 + t1; |
4211 | testValidate( |
4212 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
4213 | } |
4214 | |
4215 | TEST_F(NVFuserTest, FusionVectorizeMisalignedPointwiseMergeContig_CUDA) { |
4216 | Fusion fusion; |
4217 | FusionGuard fg(&fusion); |
4218 | |
4219 | auto tv0 = makeContigTensor(4); |
4220 | auto tv1 = makeContigTensor(4); |
4221 | fusion.addInput(tv0); |
4222 | fusion.addInput(tv1); |
4223 | |
4224 | auto tv2 = add(tv0, tv1); |
4225 | fusion.addOutput(tv2); |
4226 | |
4227 | tv2->reorder({{0, 1}, {1, 0}}); |
4228 | tv2->merge(-2); |
4229 | |
4230 | const int kTDX = 64; |
4231 | const int kVecSize = 2; |
4232 | const int kNumElems = kTDX * kVecSize; |
4233 | |
4234 | tv2->split(-1, kNumElems); |
4235 | |
4236 | auto c0 = tv0->cacheAfter(); |
4237 | auto c1 = tv1->cacheAfter(); |
4238 | auto c2 = tv2->cacheBefore(); |
4239 | |
4240 | tv2->split(0, 128); |
4241 | tv2->split(-1, kVecSize); |
4242 | |
4243 | c0->computeAt(tv2, -2); |
4244 | c1->computeAt(tv2, -2); |
4245 | |
4246 | c0->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4247 | c1->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4248 | |
4249 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
4250 | tv2->axis(1)->parallelize(ParallelType::BIDy); |
4251 | tv2->axis(-2)->parallelize(ParallelType::TIDx); |
4252 | tv2->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4253 | |
4254 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4255 | const int n = 32; |
4256 | const int c = 127; |
4257 | const int h = 51; |
4258 | const int w = 23; |
4259 | at::Tensor t0 = at::randn({n, c, h, w}, options); |
4260 | at::Tensor t1 = at::randn({n, c, h, w}, options); |
4261 | |
4262 | std::vector<IValue> aten_inputs = {t0, t1}; |
4263 | |
4264 | FusionExecutor fe; |
4265 | fe.compileFusion(&fusion, aten_inputs); |
4266 | auto cg_outputs = fe.runFusion(aten_inputs); |
4267 | |
4268 | auto aten_output = t0 + t1; |
4269 | testValidate( |
4270 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
4271 | } |
4272 | |
4273 | TEST_F(NVFuserTest, FusionVectorizeMisalignedPointwiseMergeSymbolicPass_CUDA) { |
4274 | Fusion fusion; |
4275 | FusionGuard fg(&fusion); |
4276 | |
4277 | constexpr int kNumDims = 4; |
4278 | constexpr int kTDX = 64; |
4279 | constexpr int kVecSize = 2; |
4280 | constexpr int kNumElems = kTDX * kVecSize; |
4281 | |
4282 | auto tv0 = makeSymbolicTensor(kNumDims); |
4283 | auto tv1 = makeSymbolicTensor(kNumDims); |
4284 | fusion.addInput(tv0); |
4285 | fusion.addInput(tv1); |
4286 | |
4287 | auto tv2 = add(tv0, tv1); |
4288 | fusion.addOutput(tv2); |
4289 | |
4290 | // Create caches for vectorization |
4291 | auto c0 = tv0->cacheAfter(); |
4292 | auto c1 = tv1->cacheAfter(); |
4293 | auto c2 = tv2->cacheBefore(); |
4294 | |
4295 | // Merge all dimensions together except inner-most dim |
4296 | for (const auto idx : c10::irange(kNumDims - 2)) { |
4297 | tv2->merge(0); |
4298 | } |
4299 | // Split inner-most dim |
4300 | tv2->split(-1, kNumElems); |
4301 | tv2->split(-1, kVecSize); |
4302 | TransformPropagatorWithCheck propagator(tv2); |
4303 | MaxRootDomainInfoSpanningTree(tv2).traverse(&propagator); |
4304 | |
4305 | c0->computeAt(tv2, -2); |
4306 | c1->computeAt(tv2, -2); |
4307 | |
4308 | // Parallelization Strategy |
4309 | c0->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4310 | c1->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4311 | |
4312 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
4313 | tv2->axis(2)->parallelize(ParallelType::TIDx); |
4314 | tv2->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4315 | |
4316 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4317 | const int n = 5; |
4318 | const int c = 3; |
4319 | const int h = 51; |
4320 | const int w = 257; |
4321 | at::Tensor t0 = at::randn({n, c, h, w}, options); |
4322 | at::Tensor t1 = at::randn({n, c, h, w}, options); |
4323 | |
4324 | std::vector<IValue> aten_inputs = {t0, t1}; |
4325 | |
4326 | FusionExecutor fe; |
4327 | fe.compileFusion(&fusion, aten_inputs); |
4328 | auto cg_outputs = fe.runFusion(aten_inputs); |
4329 | |
4330 | auto aten_output = t0 + t1; |
4331 | testValidate( |
4332 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
4333 | } |
4334 | |
4335 | TEST_F(NVFuserTest, FusionVectorizeMisalignedPointwiseMergeSymbolicFail_CUDA) { |
4336 | Fusion fusion; |
4337 | FusionGuard fg(&fusion); |
4338 | |
4339 | constexpr int kNumDims = 4; |
4340 | constexpr int kTDX = 64; |
4341 | constexpr int kVecSize = 2; |
4342 | constexpr int kNumElems = kTDX * kVecSize; |
4343 | std::vector<int64_t> bcast_shape{1, 1, 1, -1}; |
4344 | |
4345 | auto tv0 = makeContigTensor(kNumDims); |
4346 | auto tv1 = TensorViewBuilder().shape(bcast_shape).build(); |
4347 | fusion.addInput(tv0); |
4348 | fusion.addInput(tv1); |
4349 | |
4350 | auto tv2 = add(tv0, tv1); |
4351 | fusion.addOutput(tv2); |
4352 | |
4353 | // Create caches for vectorization |
4354 | auto c0 = tv0->cacheAfter(); |
4355 | auto c1 = tv1->cacheAfter(); |
4356 | auto c2 = tv2->cacheBefore(); |
4357 | |
4358 | // Merge all dimensions together |
4359 | // Backward merge order is necessary for vectorize validation |
4360 | for (int idx = kNumDims - 1; idx > 0; --idx) { |
4361 | tv2->merge(idx - 1); |
4362 | } |
4363 | tv2->split(-1, kNumElems); |
4364 | tv2->split(-1, kVecSize); |
4365 | TransformPropagatorWithCheck propagator(tv2); |
4366 | MaxRootDomainInfoSpanningTree(tv2).traverse(&propagator); |
4367 | |
4368 | c0->computeAt(tv2, -2); |
4369 | c1->computeAt(tv2, -2); |
4370 | |
4371 | // Parallelization Strategy |
4372 | c0->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4373 | c1->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4374 | |
4375 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
4376 | tv2->axis(1)->parallelize(ParallelType::TIDx); |
4377 | tv2->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4378 | |
4379 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4380 | const int n = 32; |
4381 | const int c = 128; |
4382 | const int h = 51; |
4383 | const int w = 23; |
4384 | at::Tensor t0 = at::randn({n, c, h, w}, options); |
4385 | at::Tensor t1 = at::randn({1, 1, 1, w}, options); |
4386 | |
4387 | std::vector<IValue> aten_inputs = {t0, t1}; |
4388 | |
4389 | FusionExecutor fe; |
4390 | // TODO: throw assertion - cannot merge non-contiguous vectorization axes |
4391 | // Make sure compilation fails |
4392 | // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto) |
4393 | ASSERT_ANY_THROW(fe.compileFusion(&fusion)); |
4394 | } |
4395 | |
4396 | TEST_F(NVFuserTest, FusionVectorizeMisalignedRFactor_CUDA) { |
4397 | Fusion fusion; |
4398 | FusionGuard fg(&fusion); |
4399 | |
4400 | auto tv0 = makeContigTensor(2); |
4401 | auto tv1 = makeContigTensor(2); |
4402 | |
4403 | fusion.addInput(tv0); |
4404 | fusion.addInput(tv1); |
4405 | |
4406 | auto tv2 = add(tv0, tv1); |
4407 | |
4408 | auto tv3 = sum(tv2, {-1}); |
4409 | |
4410 | fusion.addOutput(tv3); |
4411 | |
4412 | auto c0 = tv0->cacheAfter(); |
4413 | auto c1 = tv1->cacheAfter(); |
4414 | |
4415 | tv3->split(-1, 128 * 4); |
4416 | tv3->split(-1, 4); |
4417 | // Reduce outer dim first |
4418 | auto tv4 = tv3->rFactor({-3, -1}); |
4419 | // Tv3 will reduce threads |
4420 | |
4421 | tv0->computeAt(tv3, 1); |
4422 | tv1->computeAt(tv3, 1); |
4423 | |
4424 | tv3->axis(0)->parallelize(ParallelType::BIDx); |
4425 | |
4426 | tv0->computeAt(tv4, -2); |
4427 | tv1->computeAt(tv4, -2); |
4428 | |
4429 | c0->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4430 | c1->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4431 | |
4432 | tv4->axis(-2)->parallelize(ParallelType::TIDx); |
4433 | tv3->axis(1)->parallelize(ParallelType::TIDx); |
4434 | |
4435 | tv2->computeAt(tv4, -1); |
4436 | |
4437 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4438 | const int bx = 128; |
4439 | const int by = 2050; |
4440 | at::Tensor t0 = at::randn({bx, by}, options); |
4441 | at::Tensor t1 = at::randn({bx, by}, options); |
4442 | |
4443 | std::vector<IValue> aten_inputs = {t0, t1}; |
4444 | |
4445 | FusionExecutor fe; |
4446 | fe.compileFusion(&fusion, aten_inputs); |
4447 | auto cg_outputs = fe.runFusion(aten_inputs); |
4448 | |
4449 | auto aten_output = t0.add(t1).sum(1); |
4450 | testValidate( |
4451 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
4452 | } |
4453 | |
4454 | TEST_F(NVFuserTest, FusionVectorizeMisalignedWrongDimFail_CUDA) { |
4455 | Fusion fusion; |
4456 | FusionGuard fg(&fusion); |
4457 | |
4458 | auto tv0 = makeContigTensor(2); |
4459 | auto tv1 = makeContigTensor(2); |
4460 | |
4461 | fusion.addInput(tv0); |
4462 | fusion.addInput(tv1); |
4463 | |
4464 | auto tv2 = add(tv0, tv1); |
4465 | fusion.addOutput(tv2); |
4466 | |
4467 | tv2->split(1, 16); |
4468 | tv2->split(1, 64); |
4469 | |
4470 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
4471 | tv2->axis(2)->parallelize(ParallelType::TIDx); |
4472 | |
4473 | auto c0 = tv0->cacheAfter(); |
4474 | auto c1 = tv1->cacheAfter(); |
4475 | auto c2 = tv2->cacheBefore(); |
4476 | |
4477 | c0->computeAt(tv2, -2); |
4478 | c1->computeAt(tv2, -2); |
4479 | |
4480 | std::vector<TensorView*> vectorized_tvs = {c0, c1, tv2}; |
4481 | for (auto tv : vectorized_tvs) { |
4482 | tv->split(-1, 4); |
4483 | // Vectorize the wrong dimension |
4484 | tv->axis(-2)->parallelize(ParallelType::MisalignedVectorize); |
4485 | } |
4486 | |
4487 | FusionExecutor fe; |
4488 | // Make sure compilation fails |
4489 | // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto) |
4490 | ASSERT_ANY_THROW(fe.compileFusion(&fusion)); |
4491 | } |
4492 | |
4493 | TEST_F(NVFuserTest, FusionVectorizeMisalignedStride_CUDA) { |
4494 | Fusion fusion; |
4495 | FusionGuard fg(&fusion); |
4496 | |
4497 | auto tv0 = makeSymbolicTensor(2); |
4498 | auto tv1 = makeSymbolicTensor(2); |
4499 | |
4500 | fusion.addInput(tv0); |
4501 | fusion.addInput(tv1); |
4502 | |
4503 | auto tv2 = add(tv0, tv1); |
4504 | fusion.addOutput(tv2); |
4505 | |
4506 | const int kTDX = 64; |
4507 | const int kVecSize = 4; |
4508 | const int kNumElems = kTDX * kVecSize; |
4509 | |
4510 | tv2->split(1, kNumElems); |
4511 | |
4512 | auto c0 = tv0->cacheAfter(); |
4513 | auto c1 = tv1->cacheAfter(); |
4514 | |
4515 | tv2->split(-1, kVecSize); |
4516 | |
4517 | c0->computeAt(tv2, -2); |
4518 | c1->computeAt(tv2, -2); |
4519 | |
4520 | c0->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4521 | c1->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4522 | |
4523 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
4524 | tv2->axis(-2)->parallelize(ParallelType::TIDx); |
4525 | |
4526 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4527 | const int bx = 128; |
4528 | const int by = 2049; |
4529 | at::Tensor t0 = at::randn({bx, by}, options).index({"..." , Slice(3)}); |
4530 | at::Tensor t1 = at::randn({bx, by}, options).index({"..." , Slice(3)}); |
4531 | std::vector<IValue> aten_inputs = {t0, t1}; |
4532 | |
4533 | FusionExecutor fe; |
4534 | fe.compileFusion(&fusion, aten_inputs); |
4535 | auto cg_outputs = fe.runFusion(aten_inputs); |
4536 | |
4537 | auto aten_output = t0 + t1; |
4538 | testValidate( |
4539 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
4540 | } |
4541 | |
4542 | TEST_F(NVFuserTest, FusionVectorizeMisalignedStrideFail_CUDA) { |
4543 | Fusion fusion; |
4544 | FusionGuard fg(&fusion); |
4545 | |
4546 | auto tv0 = makeSymbolicTensor(2); |
4547 | auto tv1 = makeSymbolicTensor(2); |
4548 | |
4549 | fusion.addInput(tv0); |
4550 | fusion.addInput(tv1); |
4551 | |
4552 | auto tv2 = add(tv0, tv1); |
4553 | fusion.addOutput(tv2); |
4554 | |
4555 | const int kTDX = 64; |
4556 | const int kVecSize = 4; |
4557 | const int kNumElems = kTDX * kVecSize; |
4558 | |
4559 | tv2->split(1, kNumElems); |
4560 | |
4561 | auto c0 = tv0->cacheAfter(); |
4562 | auto c1 = tv1->cacheAfter(); |
4563 | auto c2 = tv2->cacheBefore(); |
4564 | |
4565 | tv2->split(-1, kVecSize); |
4566 | |
4567 | c0->computeAt(tv2, -2); |
4568 | c1->computeAt(tv2, -2); |
4569 | |
4570 | c0->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4571 | c1->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4572 | |
4573 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
4574 | tv2->axis(-2)->parallelize(ParallelType::TIDx); |
4575 | tv2->axis(-1)->parallelize(ParallelType::MisalignedVectorize); |
4576 | |
4577 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4578 | const int bx = 128; |
4579 | const int by = 2049; |
4580 | at::Tensor t0 = at::randn({bx, by}, options).index({"..." , Slice(3)}); |
4581 | at::Tensor t1 = at::randn({bx, by}, options).index({"..." , Slice(3)}); |
4582 | std::vector<IValue> aten_inputs = {t0, t1}; |
4583 | |
4584 | FusionExecutor fe; |
4585 | fe.compileFusion(&fusion, aten_inputs); |
4586 | |
4587 | // Failure because the input + output tensors do not have the same stride |
4588 | // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto) |
4589 | ASSERT_ANY_THROW(fe.runFusion(aten_inputs)); |
4590 | } |
4591 | |
4592 | TEST_F(NVFuserTest, FusionVectorization1_CUDA) { |
4593 | Fusion fusion; |
4594 | FusionGuard fg(&fusion); |
4595 | |
4596 | auto tv0 = makeSymbolicTensor(2); |
4597 | |
4598 | auto tv1 = makeSymbolicTensor(2); |
4599 | fusion.addInput(tv0); |
4600 | fusion.addInput(tv1); |
4601 | |
4602 | auto tv2 = add(tv0, tv1); |
4603 | fusion.addOutput(tv2); |
4604 | |
4605 | tv2->split(1, 16); |
4606 | tv2->split(1, 64); |
4607 | |
4608 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
4609 | tv2->axis(2)->parallelize(ParallelType::TIDx); |
4610 | |
4611 | auto c0 = tv0->cacheAfter(); |
4612 | auto c1 = tv1->cacheAfter(); |
4613 | auto c2 = tv2->cacheBefore(); |
4614 | |
4615 | c0->computeAt(tv2, -2); |
4616 | c1->computeAt(tv2, -2); |
4617 | |
4618 | std::vector<TensorView*> vectorized_tvs = {c0, c1, tv2}; |
4619 | for (auto tv : vectorized_tvs) { |
4620 | tv->split(-1, 4); |
4621 | tv->axis(-1)->parallelize(ParallelType::Vectorize); |
4622 | } |
4623 | |
4624 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4625 | const int bx = 128; |
4626 | const int by = 2048; |
4627 | at::Tensor t0 = at::randn({bx, by}, options); |
4628 | at::Tensor t1 = at::randn({bx, by}, options); |
4629 | |
4630 | std::vector<IValue> aten_inputs = {t0, t1}; |
4631 | |
4632 | FusionExecutor fe; |
4633 | fe.compileFusion(&fusion, aten_inputs); |
4634 | auto cg_outputs = fe.runFusion(aten_inputs); |
4635 | |
4636 | auto aten_output = t0 + t1; |
4637 | testValidate( |
4638 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
4639 | } |
4640 | |
4641 | TEST_F(NVFuserTest, FusionVectorization2_CUDA) { |
4642 | Fusion fusion; |
4643 | FusionGuard fg(&fusion); |
4644 | |
4645 | auto tv0 = makeSymbolicTensor(2); |
4646 | |
4647 | auto tv1 = makeSymbolicTensor(2); |
4648 | fusion.addInput(tv0); |
4649 | fusion.addInput(tv1); |
4650 | |
4651 | auto tv2 = add(tv0, tv1); |
4652 | fusion.addOutput(tv2); |
4653 | |
4654 | tv2->split(1, 16); |
4655 | tv2->split(1, 64); |
4656 | |
4657 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
4658 | tv2->axis(2)->parallelize(ParallelType::TIDx); |
4659 | |
4660 | auto c0 = tv0->cacheAfter(); |
4661 | auto c1 = tv1->cacheAfter(); |
4662 | auto c2 = tv2->cacheBefore(); |
4663 | |
4664 | c0->computeAt(tv2, -2); |
4665 | c1->computeAt(tv2, -2); |
4666 | |
4667 | std::vector<TensorView*> vectorized_tvs = {c0, c1, tv2}; |
4668 | for (auto tv : vectorized_tvs) { |
4669 | tv->split(-1, 4); |
4670 | // Vectorize the wrong dimension |
4671 | tv->axis(-2)->parallelize(ParallelType::Vectorize); |
4672 | } |
4673 | |
4674 | FusionExecutor fe; |
4675 | // Make sure compilation fails |
4676 | // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto) |
4677 | ASSERT_ANY_THROW(fe.compileFusion(&fusion)); |
4678 | } |
4679 | |
4680 | TEST_F(NVFuserTest, FusionVectorization3_CUDA) { |
4681 | Fusion fusion; |
4682 | FusionGuard fg(&fusion); |
4683 | |
4684 | auto tv0 = makeSymbolicTensor(2); |
4685 | |
4686 | auto tv1 = makeSymbolicTensor(2); |
4687 | fusion.addInput(tv0); |
4688 | fusion.addInput(tv1); |
4689 | |
4690 | auto tv2 = add(tv0, tv1); |
4691 | fusion.addOutput(tv2); |
4692 | |
4693 | tv2->split(1, 16); |
4694 | tv2->split(1, 64); |
4695 | |
4696 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
4697 | tv2->axis(2)->parallelize(ParallelType::TIDx); |
4698 | |
4699 | auto c0 = tv0->cacheAfter(); |
4700 | auto c1 = tv1->cacheAfter(); |
4701 | auto c2 = tv2->cacheBefore(); |
4702 | |
4703 | c0->computeAt(tv2, -2); |
4704 | c1->computeAt(tv2, -2); |
4705 | |
4706 | std::vector<TensorView*> vectorized_tvs = {c0, c1, tv2}; |
4707 | for (auto tv : vectorized_tvs) { |
4708 | tv->split(-1, 4); |
4709 | tv->axis(-1)->parallelize(ParallelType::Vectorize); |
4710 | } |
4711 | |
4712 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4713 | const int bx = 128; |
4714 | const int by = 2049; |
4715 | at::Tensor t0 = at::randn({bx, by}, options); |
4716 | at::Tensor t1 = at::randn({bx, by}, options); |
4717 | std::vector<IValue> aten_inputs = {t0, t1}; |
4718 | |
4719 | FusionExecutor fe; |
4720 | fe.compileFusion(&fusion, aten_inputs); |
4721 | // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto) |
4722 | ASSERT_ANY_THROW(fe.runFusion(aten_inputs)); |
4723 | |
4724 | aten_inputs[0] = t0.index({"..." , Slice(1)}); |
4725 | aten_inputs[1] = t1.index({"..." , Slice(1)}); |
4726 | // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto) |
4727 | ASSERT_ANY_THROW(fe.runFusion(aten_inputs)); |
4728 | |
4729 | t0 = at::randn({bx, 2048}, options).index({"..." , Slice(4)}); |
4730 | t1 = at::randn({bx, 2048}, options).index({"..." , Slice(4)}); |
4731 | aten_inputs = {t0, t1}; |
4732 | auto cg_outputs = fe.runFusion(aten_inputs); |
4733 | |
4734 | auto aten_output = t0 + t1; |
4735 | testValidate( |
4736 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
4737 | } |
4738 | |
4739 | TEST_F(NVFuserTest, FusionVectorizationRFactor_CUDA) { |
4740 | Fusion fusion; |
4741 | FusionGuard fg(&fusion); |
4742 | |
4743 | auto tv0 = makeSymbolicTensor(2); |
4744 | |
4745 | auto tv1 = makeSymbolicTensor(2); |
4746 | fusion.addInput(tv0); |
4747 | fusion.addInput(tv1); |
4748 | |
4749 | auto tv2 = add(tv0, tv1); |
4750 | |
4751 | auto tv3 = sum(tv2, {-1}); |
4752 | |
4753 | fusion.addOutput(tv3); |
4754 | |
4755 | tv3->split(-1, 128 * 4); |
4756 | tv3->split(-1, 4); |
4757 | // Reduce outer dim first |
4758 | auto tv4 = tv3->rFactor({-3, -1}); |
4759 | // Tv3 will reduce threads |
4760 | |
4761 | auto tv6 = tv0->cacheAfter(); |
4762 | auto tv7 = tv1->cacheAfter(); |
4763 | |
4764 | tv0->computeAt(tv3, 1); |
4765 | tv1->computeAt(tv3, 1); |
4766 | |
4767 | tv3->axis(0)->parallelize(ParallelType::BIDx); |
4768 | |
4769 | tv0->computeAt(tv4, -2); |
4770 | tv1->computeAt(tv4, -2); |
4771 | |
4772 | tv6->axis(-1)->parallelize(ParallelType::Vectorize); |
4773 | tv7->axis(-1)->parallelize(ParallelType::Vectorize); |
4774 | |
4775 | tv4->axis(-2)->parallelize(ParallelType::TIDx); |
4776 | tv3->axis(1)->parallelize(ParallelType::TIDx); |
4777 | |
4778 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4779 | const int bx = 128; |
4780 | const int by = 2048; |
4781 | at::Tensor t0 = at::randn({bx, by}, options); |
4782 | at::Tensor t1 = at::randn({bx, by}, options); |
4783 | |
4784 | std::vector<IValue> aten_inputs = {t0, t1}; |
4785 | |
4786 | FusionExecutor fe; |
4787 | fe.compileFusion(&fusion, aten_inputs); |
4788 | auto cg_outputs = fe.runFusion(aten_inputs); |
4789 | |
4790 | auto aten_output = t0.add(t1).sum(1); |
4791 | testValidate( |
4792 | &fusion, cg_outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
4793 | |
4794 | auto t3 = t0.add(t1).sum(1); |
4795 | |
4796 | testValidate(&fusion, cg_outputs, aten_inputs, {t3}, __LINE__, __FILE__); |
4797 | } |
4798 | |
4799 | // Unswitched loops with extent one may omit else clause. |
4800 | TEST_F(NVFuserTest, FusionSizeOneLoop1_CUDA) { |
4801 | Fusion fusion; |
4802 | FusionGuard fg(&fusion); |
4803 | |
4804 | // Progressively broadcast tensors |
4805 | TensorView* tv0 = makeSymbolicTensor(1); |
4806 | fusion.addInput(tv0); |
4807 | TensorView* tv1 = makeSymbolicTensor(2); |
4808 | fusion.addInput(tv1); |
4809 | TensorView* tv2 = makeSymbolicTensor(3); |
4810 | fusion.addInput(tv2); |
4811 | |
4812 | TensorView* tv3 = broadcast(tv0, {false, true}); |
4813 | TensorView* tv4 = add(tv3, tv1); |
4814 | TensorView* tv5 = add(tv4, tv2); |
4815 | |
4816 | fusion.addOutput(tv5); |
4817 | |
4818 | // Split inner dimension |
4819 | tv5->split(1, 8); |
4820 | // Merge middle dims with outer dimensions |
4821 | tv5->merge(2); |
4822 | tv5->merge(0); |
4823 | |
4824 | // tv5[I0*I1o, I1i*I2] |
4825 | // Get a dim of size 1 to unswitch |
4826 | tv5->split(0, 1, false); |
4827 | |
4828 | // Compute everything inline |
4829 | tv0->computeAt(tv5, -1); |
4830 | |
4831 | tv5->axis(0)->parallelize(ParallelType::Unswitch); |
4832 | tv5->axis(1)->parallelize(ParallelType::BIDx); |
4833 | tv5->axis(2)->parallelize(ParallelType::TIDx); |
4834 | |
4835 | // Make sure the unswitched loop does not have an else clause. |
4836 | GpuLower gpulw(&fusion); |
4837 | TORCH_CHECK(!UnswitchInElseChecker::check(gpulw)); |
4838 | |
4839 | const int x = 11; |
4840 | const int y = 12; |
4841 | const int z = 13; |
4842 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4843 | at::Tensor t0 = at::randn({x}, options); |
4844 | at::Tensor t1 = at::randn({x, y}, options); |
4845 | at::Tensor t2 = at::randn({z, x, y}, options); |
4846 | std::vector<IValue> aten_inputs = {t0, t1, t2}; |
4847 | |
4848 | FusionExecutor fe; |
4849 | fe.compileFusion(&fusion, aten_inputs); |
4850 | auto cg_outputs = fe.runFusion(aten_inputs); |
4851 | auto t6 = (t0.unsqueeze(-1) + t1).unsqueeze(0) + t2; |
4852 | |
4853 | testValidate(&fusion, cg_outputs, aten_inputs, {t6}, __LINE__, __FILE__); |
4854 | } |
4855 | |
4856 | // The unswitched loop has extent one but inner loops don't. The else |
4857 | // part should not be omitted. |
4858 | TEST_F(NVFuserTest, FusionSizeOneLoop2_CUDA) { |
4859 | Fusion fusion; |
4860 | FusionGuard fg(&fusion); |
4861 | |
4862 | const int x = 15; |
4863 | auto tv0 = makeConcreteTensor({x}); |
4864 | fusion.addInput(tv0); |
4865 | |
4866 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
4867 | fusion.addOutput(tv1); |
4868 | |
4869 | tv1->split(-1, 4); |
4870 | tv1->split(-2, 1); |
4871 | |
4872 | tv1->axis(-2)->parallelize(ParallelType::Unswitch); |
4873 | |
4874 | // Make sure the size-one unswitched loop does not omit the else clause. |
4875 | GpuLower gpulw(&fusion); |
4876 | TORCH_CHECK(UnswitchInElseChecker::check(gpulw)); |
4877 | |
4878 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
4879 | at::Tensor t0 = at::randn({x}, options); |
4880 | std::vector<IValue> aten_inputs = {t0}; |
4881 | |
4882 | FusionExecutor fe; |
4883 | fe.compileFusion(&fusion, aten_inputs); |
4884 | auto cg_outputs = fe.runFusion(aten_inputs); |
4885 | auto t1 = t0 + 1; |
4886 | |
4887 | testValidate(&fusion, cg_outputs, aten_inputs, {t1}, __LINE__, __FILE__); |
4888 | } |
4889 | |
4890 | TEST_F(NVFuserTest, FusionValidateParallelize1_CUDA) { |
4891 | Fusion fusion; |
4892 | FusionGuard fg(&fusion); |
4893 | |
4894 | auto tv0 = makeSymbolicTensor(1); |
4895 | fusion.addInput(tv0); |
4896 | |
4897 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
4898 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
4899 | fusion.addOutput(tv2); |
4900 | |
4901 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
4902 | tv2->axis(-1)->parallelize(ParallelType::TIDy); |
4903 | |
4904 | // Invalid as tv1 and tv2 do have the same ParallelType |
4905 | FusionExecutor fe; |
4906 | // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto) |
4907 | ASSERT_ANY_THROW(fe.compileFusion(&fusion)); |
4908 | } |
4909 | |
4910 | TEST_F(NVFuserTest, FusionValidateParallelize2_CUDA) { |
4911 | Fusion fusion; |
4912 | FusionGuard fg(&fusion); |
4913 | |
4914 | auto tv0 = makeSymbolicTensor(1); |
4915 | fusion.addInput(tv0); |
4916 | |
4917 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
4918 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
4919 | fusion.addOutput(tv2); |
4920 | |
4921 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
4922 | tv2->axis(-1)->parallelize(ParallelType::TIDy); |
4923 | tv1->setMemoryType(MemoryType::Shared); |
4924 | |
4925 | // tv1 and tv2 do have the same ParallelType, but tv1 is on shared |
4926 | // memory, so it is valid |
4927 | FusionExecutor fe; |
4928 | fe.compileFusion(&fusion); |
4929 | } |
4930 | |
4931 | TEST_F(NVFuserTest, FusionValidateParallelize3_CUDA) { |
4932 | Fusion fusion; |
4933 | FusionGuard fg(&fusion); |
4934 | |
4935 | auto tv0 = makeSymbolicTensor(1); |
4936 | fusion.addInput(tv0); |
4937 | |
4938 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
4939 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
4940 | fusion.addOutput(tv2); |
4941 | |
4942 | tv1->split(-1, 4); |
4943 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
4944 | tv2->split(-1, 4); |
4945 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
4946 | |
4947 | tv1->setMemoryType(MemoryType::Global); |
4948 | |
4949 | // tv1 and tv2 have the same shape and ParallelType |
4950 | FusionExecutor fe; |
4951 | fe.compileFusion(&fusion); |
4952 | } |
4953 | |
4954 | TEST_F(NVFuserTest, FusionValidateParallelize4_CUDA) { |
4955 | Fusion fusion; |
4956 | FusionGuard fg(&fusion); |
4957 | |
4958 | auto tv0 = makeSymbolicTensor(1); |
4959 | fusion.addInput(tv0); |
4960 | |
4961 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
4962 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
4963 | fusion.addOutput(tv2); |
4964 | |
4965 | tv1->split(-1, 4); |
4966 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
4967 | tv2->split(-1, 8); |
4968 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
4969 | |
4970 | tv1->setMemoryType(MemoryType::Global); |
4971 | |
4972 | // tv1 and tv2 do not have the same shape but global memory comm is supported. |
4973 | FusionExecutor fe; |
4974 | fe.compileFusion(&fusion); |
4975 | } |
4976 | |
4977 | TEST_F(NVFuserTest, FusionValidateParallelize5_CUDA) { |
4978 | Fusion fusion; |
4979 | FusionGuard fg(&fusion); |
4980 | |
4981 | auto tv0 = makeSymbolicTensor(1); |
4982 | fusion.addInput(tv0); |
4983 | |
4984 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
4985 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
4986 | fusion.addOutput(tv2); |
4987 | |
4988 | tv1->split(-1, 4); |
4989 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
4990 | tv1->setMemoryType(MemoryType::Shared); |
4991 | |
4992 | tv2->split(-1, 8); |
4993 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
4994 | |
4995 | // tv1 and tv2 do not have the same shape, but tv1 is on shared |
4996 | // memory, so it is valid |
4997 | FusionExecutor fe; |
4998 | fe.compileFusion(&fusion); |
4999 | } |
5000 | |
5001 | // See issue #995 |
5002 | TEST_F(NVFuserTest, FusionValidateParallelize6_CUDA) { |
5003 | Fusion fusion; |
5004 | FusionGuard fg(&fusion); |
5005 | |
5006 | int64_t W = 5, X = 6, Y = 7, Z = 8; |
5007 | |
5008 | auto tv0 = makeConcreteTensor({X, Y, Z}); |
5009 | auto tv1 = makeConcreteTensor({W, X, Y, Z}); |
5010 | fusion.addInput(tv0); |
5011 | fusion.addInput(tv1); |
5012 | |
5013 | auto tv2 = add(tv0, IrBuilder::create<Double>(1)); |
5014 | auto tv3 = broadcast(tv2, {true, false, false, false}); |
5015 | auto tv4 = add(tv3, tv1); |
5016 | fusion.addOutput(tv4); |
5017 | |
5018 | tv4->merge(0); |
5019 | tv4->merge(0); |
5020 | tv4->merge(0); |
5021 | tv4->split(0, 4); |
5022 | tv4->split(0, 3); |
5023 | tv4->split(0, 2); |
5024 | |
5025 | TransformPropagatorWithCheck propagator(tv4); |
5026 | MaxRootDomainInfoSpanningTree(tv4).traverse(&propagator); |
5027 | |
5028 | tv0->computeAt(tv2, 2); |
5029 | tv3->computeAt(tv4, 2); |
5030 | |
5031 | tv4->axis(0)->parallelize(ParallelType::BIDx); |
5032 | tv4->axis(-1)->parallelize(ParallelType::TIDx); |
5033 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
5034 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
5035 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
5036 | |
5037 | // Validation should throw an exception saying the first axes of tv2 |
5038 | // and tv3 have incompatible parallelization. See also issue #995. |
5039 | // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto) |
5040 | ASSERT_ANY_THROW(fusion.printKernel()); |
5041 | } |
5042 | |
5043 | // Repro of #2046 |
5044 | TEST_F(NVFuserTest, FusionValidateParallelize7_CUDA) { |
5045 | Fusion fusion; |
5046 | FusionGuard fg(&fusion); |
5047 | |
5048 | auto tv0 = makeSymbolicTensor(2); |
5049 | fusion.addInput(tv0); |
5050 | |
5051 | auto tv1 = set(tv0); |
5052 | auto tv2 = set(tv1); |
5053 | auto tv3 = set(tv1); |
5054 | fusion.addOutput(tv2); |
5055 | fusion.addOutput(tv3); |
5056 | |
5057 | tv1->setMemoryType(MemoryType::Global); |
5058 | |
5059 | tv1->axis(0)->parallelize(ParallelType::BIDx); |
5060 | tv1->axis(1)->parallelize(ParallelType::TIDx); |
5061 | |
5062 | tv2->axis(1)->parallelize(ParallelType::TIDy); |
5063 | tv3->axis(0)->parallelize(ParallelType::BIDx); |
5064 | |
5065 | // tv2 uses tv1 but is not parallelized with BIDx, so a grid sync is |
5066 | // required. It should be placed as a top-level expression. |
5067 | |
5068 | GpuLower gpulw(&fusion); |
5069 | TORCH_CHECK( |
5070 | std::any_of( |
5071 | gpulw.kernel()->topLevelExprs().begin(), |
5072 | gpulw.kernel()->topLevelExprs().end(), |
5073 | [](Expr* expr) { return expr->isA<kir::GridSync>(); }), |
5074 | "Grid sync not found" ); |
5075 | } |
5076 | |
5077 | TEST_F(NVFuserTest, FusionDAGMerging_CUDA) { |
5078 | Fusion fusion; |
5079 | FusionGuard fg(&fusion); |
5080 | |
5081 | auto tv0 = makeSymbolicTensor(5); |
5082 | auto tv1 = makeSymbolicTensor(1); |
5083 | fusion.addInput(tv0); |
5084 | fusion.addInput(tv1); |
5085 | |
5086 | // Branch 0 |
5087 | auto tv2 = sum(tv0, {0}); // 0 |
5088 | auto tv3 = sum(tv2, {0}); // 1 |
5089 | auto tv4 = sum(tv3, {0}); // 2 |
5090 | auto tv5 = sum(tv4, {0}); // 3 |
5091 | |
5092 | // Branch 1 |
5093 | auto tv6 = add(tv1, IrBuilder::create<Double>(1)); // 4 |
5094 | |
5095 | // Merge |
5096 | auto tv7 = add(tv6, tv5); // 5 |
5097 | |
5098 | // Maximum expected output groups (can improve overtime): |
5099 | // {0}, {1}, {2}, {3,4,5} |
5100 | // without final merge would have been {0}, {1}, {2}, {3,4}, {5} |
5101 | |
5102 | fusion.addOutput(tv7); |
5103 | |
5104 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5105 | at::Tensor t0 = at::randn({2, 2, 2, 2, 2}, options); |
5106 | at::Tensor t1 = at::randn({2}, options); |
5107 | |
5108 | std::vector<at::Tensor> aten_inputs = {t0, t1}; |
5109 | |
5110 | KernelArgumentHolder args(KernelIndexMode::INT32); |
5111 | args.setDeviceIndex(0); |
5112 | args.push(aten_inputs); |
5113 | |
5114 | auto fusion_segments = fusion.segment(args); |
5115 | TORCH_CHECK(fusion_segments->groups().size() <= 4); |
5116 | } |
5117 | |
5118 | TEST_F(NVFuserTest, FusionDAGScalarMerging_CUDA) { |
5119 | auto fusion = std::make_unique<Fusion>(); |
5120 | FusionGuard fg(fusion.get()); |
5121 | |
5122 | auto tv0 = makeSymbolicTensor(3); |
5123 | auto i0 = IrBuilder::create<Double>(); |
5124 | |
5125 | fusion->addInput(tv0); |
5126 | fusion->addInput(i0); |
5127 | |
5128 | auto i1 = add(i0, IrBuilder::create<Double>(1.0)); |
5129 | auto i2 = mul(i1, i1); |
5130 | auto i3 = add(i2, i1); |
5131 | |
5132 | // Branch 0 |
5133 | auto tv1 = sum(tv0, {0}); // 0 |
5134 | auto tv2 = add(tv1, i2); |
5135 | // Branch 1 |
5136 | auto tv3 = sum(tv2, {0}); // 1 |
5137 | auto tv4 = add(tv3, i3); |
5138 | |
5139 | auto tv5 = add(tv4, i0); |
5140 | |
5141 | fusion->addOutput(tv5); |
5142 | |
5143 | FusionExecutorCache executor_cache(std::move(fusion)); |
5144 | |
5145 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5146 | at::Tensor t0 = at::randn({16, 16, 16}, options); |
5147 | double s0 = 0.5; |
5148 | |
5149 | auto s1 = s0 + 1.0; |
5150 | auto s2 = s1 * s1; |
5151 | auto s3 = s2 + s1; |
5152 | auto t1 = t0.sum({0}); |
5153 | auto t2 = t1 + s2; |
5154 | auto t3 = sum(t2, {0}); |
5155 | auto t4 = t3 + s3; |
5156 | auto t5 = t4 + s0; |
5157 | |
5158 | auto outputs = executor_cache.runFusionWithInputs({t0, s0}); |
5159 | |
5160 | TORCH_CHECK( |
5161 | executor_cache.getMostRecentKernelRuntime()->isSegmented(), |
5162 | "segmentation didn't happen" ); |
5163 | TORCH_CHECK( |
5164 | executor_cache.getMostRecentKernelRuntime() |
5165 | ->fusionSegments() |
5166 | ->groups() |
5167 | .size() == 2, |
5168 | "segmentation didn't happen as expected" ); |
5169 | |
5170 | testValidate( |
5171 | executor_cache.fusion(), outputs, {t0, s0}, {t5}, __LINE__, __FILE__); |
5172 | } |
5173 | |
5174 | TEST_F(NVFuserTest, FusionBlockReduceInSerialLoop_CUDA) { |
5175 | Fusion fusion; |
5176 | FusionGuard fg(&fusion); |
5177 | |
5178 | constexpr int M = 10; |
5179 | constexpr int N = 20; |
5180 | constexpr int K = 20; |
5181 | |
5182 | auto tv0 = makeSymbolicTensor(3); |
5183 | auto tv1 = sum(tv0, {{1, 2}}); |
5184 | fusion.addInput(tv0); |
5185 | fusion.addOutput(tv1); |
5186 | |
5187 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
5188 | tv1->axis(0)->parallelize(ParallelType::BIDx); |
5189 | |
5190 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5191 | at::manual_seed(0); |
5192 | at::Tensor t0 = at::randn({M, N, K}, options); |
5193 | std::vector<IValue> aten_inputs = {t0}; |
5194 | |
5195 | FusionExecutor fe; |
5196 | fe.compileFusion(&fusion, aten_inputs); |
5197 | auto outputs = fe.runFusion(aten_inputs); |
5198 | at::Tensor aten_output = t0.sum({1, 2}); |
5199 | testValidate( |
5200 | &fusion, outputs, aten_inputs, {aten_output}, __LINE__, __FILE__); |
5201 | } |
5202 | |
5203 | TEST_F(NVFuserTest, FusionBlockWelfordInSerialLoop_CUDA) { |
5204 | Fusion fusion; |
5205 | FusionGuard fg(&fusion); |
5206 | |
5207 | constexpr int M = 10; |
5208 | constexpr int N = 20; |
5209 | constexpr int K = 20; |
5210 | |
5211 | auto tv0 = makeSymbolicTensor(3); |
5212 | auto tvs = Welford(tv0, {{1, 2}}); |
5213 | fusion.addInput(tv0); |
5214 | auto tv_avg = tvs.avg; |
5215 | auto tv_M2 = tvs.var_sum; |
5216 | auto tv_N = tvs.n; |
5217 | fusion.addOutput(tv_avg); |
5218 | fusion.addOutput(tv_M2); |
5219 | |
5220 | tv_avg->axis(-1)->parallelize(ParallelType::TIDx); |
5221 | tv_avg->axis(0)->parallelize(ParallelType::BIDx); |
5222 | |
5223 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5224 | at::manual_seed(0); |
5225 | at::Tensor t0 = at::randn({M, N, K}, options); |
5226 | std::vector<IValue> aten_inputs = {t0}; |
5227 | |
5228 | FusionExecutor fe; |
5229 | fe.compileFusion(&fusion, aten_inputs); |
5230 | auto outputs = fe.runFusion(aten_inputs); |
5231 | at::Tensor aten_avg = t0.mean({1, 2}); |
5232 | at::Tensor aten_M2 = t0.var({1, 2}, false) * N * K; |
5233 | testValidate( |
5234 | &fusion, outputs, aten_inputs, {aten_avg, aten_M2}, __LINE__, __FILE__); |
5235 | } |
5236 | |
5237 | // See Issue #716 |
5238 | TEST_F(NVFuserTest, FusionIOTensorTrivialReductionRepro_CUDA) { |
5239 | Fusion fusion; |
5240 | FusionGuard fg(&fusion); |
5241 | |
5242 | constexpr int M = 10; |
5243 | constexpr int N = 11; |
5244 | |
5245 | auto tv0 = makeSymbolicTensor(1); |
5246 | fusion.addInput(tv0); |
5247 | |
5248 | std::vector<int> reduction_axes = {1}; |
5249 | std::vector<bool> broadcast_mask = {false, true}; |
5250 | |
5251 | auto tv0_bcast = broadcast(tv0, broadcast_mask); |
5252 | auto path1_bcast = add(tv0_bcast, IrBuilder::create<Double>(1.0)); |
5253 | auto path1 = sum(path1_bcast, reduction_axes); |
5254 | fusion.addOutput(path1); |
5255 | |
5256 | auto p = path1->split(1, 1); |
5257 | path1->rFactor({1}); |
5258 | path1->axis(0)->parallelize(ParallelType::BIDx); |
5259 | tv0->computeAt(path1, 1); |
5260 | |
5261 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5262 | at::manual_seed(0); |
5263 | at::Tensor t0 = at::randn({M}, options); |
5264 | at::Tensor t0_ref = t0.clone(); |
5265 | std::vector<IValue> aten_inputs = {t0}; |
5266 | |
5267 | FusionExecutor fe; |
5268 | fe.compileFusion(&fusion, aten_inputs); |
5269 | |
5270 | // inplace op, we are adding t0 to itself |
5271 | auto outputs = fe.runFusion(aten_inputs, {t0}); |
5272 | |
5273 | TORCH_CHECK(outputs[0].allclose(t0_ref.add(1))); |
5274 | } |
5275 | |
5276 | TEST_F(NVFuserTest, FusionReductionPredicate_CUDA) { |
5277 | Fusion fusion; |
5278 | FusionGuard fg(&fusion); |
5279 | |
5280 | auto tv0 = makeSymbolicTensor(2); |
5281 | fusion.addInput(tv0); |
5282 | auto tv1 = sum(tv0, {0}); |
5283 | fusion.addOutput(tv1); |
5284 | |
5285 | auto tv2 = tv0->cacheAfter(); |
5286 | |
5287 | const int bdimx = 128; |
5288 | tv1->split(1, bdimx); |
5289 | tv1->split(1, 4); |
5290 | tv1->split(1, 1); |
5291 | |
5292 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
5293 | tv1->axis(2)->parallelize(ParallelType::Unroll); |
5294 | tv1->split(0, 10); |
5295 | tv0->computeAt(tv1, 4); |
5296 | |
5297 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
5298 | |
5299 | int numel_x = 650; |
5300 | int numel_y = 102; |
5301 | |
5302 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5303 | at::Tensor input = at::randn({numel_x, numel_y}, options); |
5304 | at::Tensor cg_output = at::empty({numel_y}, options); |
5305 | |
5306 | FusionExecutor fe; |
5307 | fe.compileFusion(&fusion, {input}); |
5308 | fe.runFusion({input}, {cg_output}); |
5309 | |
5310 | auto aten_output = input.to(at::kDouble).sum({0}); |
5311 | |
5312 | testValidate( |
5313 | &fusion, {cg_output}, {input}, {aten_output}, __LINE__, __FILE__); |
5314 | } |
5315 | |
5316 | TEST_F(NVFuserTest, FusionIssue728_CUDA) { |
5317 | Fusion fusion; |
5318 | FusionGuard fg(&fusion); |
5319 | |
5320 | auto tv0 = makeSymbolicTensor(1); |
5321 | fusion.addOutput(tv0); |
5322 | auto tv1 = makeSymbolicTensor(1); |
5323 | fusion.addOutput(tv1); |
5324 | auto tv2 = makeSymbolicTensor(1); |
5325 | fusion.addOutput(tv2); |
5326 | |
5327 | auto tv3 = add(tv0, IrBuilder::create<Double>(1)); |
5328 | auto tv4 = add(tv3, tv1); |
5329 | auto tv5 = add(tv4, IrBuilder::create<Double>(1)); |
5330 | auto tv6 = add(tv2, IrBuilder::create<Double>(1)); |
5331 | fusion.addOutput(tv5); |
5332 | fusion.addOutput(tv6); |
5333 | |
5334 | // tv0 -> tv3 -+ |
5335 | // tv1 --------+-> tv4 -> tv5 |
5336 | // |
5337 | // tv2 -> tv6 |
5338 | |
5339 | auto all_vals_under_tv3 = |
5340 | DependencyCheck::getAllValsBetween({tv3}, fusion.outputs()); |
5341 | std::unordered_set<Val*> included_tensors({tv3, tv4, tv5}); |
5342 | for (auto tv : included_tensors) { |
5343 | TORCH_CHECK( |
5344 | std::find(all_vals_under_tv3.begin(), all_vals_under_tv3.end(), tv) != |
5345 | all_vals_under_tv3.end(), |
5346 | "TV" , |
5347 | tv->name(), |
5348 | " not found" ); |
5349 | } |
5350 | for (auto tv : ir_utils::filterByType<TensorView>(fusion.vals())) { |
5351 | if (included_tensors.find(tv) == included_tensors.end()) { |
5352 | TORCH_CHECK( |
5353 | std::find(all_vals_under_tv3.begin(), all_vals_under_tv3.end(), tv) == |
5354 | all_vals_under_tv3.end(), |
5355 | "TV" , |
5356 | tv->name(), |
5357 | " should not be found" ); |
5358 | } |
5359 | } |
5360 | |
5361 | auto no_dependency = DependencyCheck::getAllValsBetween({}, fusion.outputs()); |
5362 | TORCH_CHECK(no_dependency.empty(), "No val should be returned" ); |
5363 | |
5364 | auto no_dep_path = DependencyCheck::getAllValsBetween({tv0, tv1}, {tv6}); |
5365 | TORCH_CHECK(no_dep_path.empty(), "No val should be returned" ); |
5366 | |
5367 | auto no_dep_path2 = DependencyCheck::getAllValsBetween({tv2}, {tv5}); |
5368 | TORCH_CHECK(no_dep_path2.empty(), "No val should be returned" ); |
5369 | |
5370 | auto just_tv3 = DependencyCheck::getAllValsBetween({tv3}, {tv3}); |
5371 | TORCH_CHECK( |
5372 | just_tv3.size() == 1 && *(just_tv3.begin()) == tv3, |
5373 | "Only tv3 should be included" ); |
5374 | } |
5375 | |
5376 | TEST_F(NVFuserTest, FusionIssue757_CUDA) { |
5377 | Fusion fusion; |
5378 | FusionGuard fg(&fusion); |
5379 | |
5380 | auto tv0 = makeSymbolicTensor(2); |
5381 | fusion.addInput(tv0); |
5382 | auto tv1 = sum(tv0, {1}); |
5383 | auto tv2 = broadcast(tv1, {false, true}); |
5384 | auto tv3 = makeSymbolicTensor(2); |
5385 | fusion.addInput(tv3); |
5386 | auto tv4 = add(tv2, tv3); |
5387 | fusion.addOutput(tv4); |
5388 | |
5389 | tv1->computeAt(tv4, -1); |
5390 | |
5391 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
5392 | tv4->axis(-1)->parallelize(ParallelType::TIDx); |
5393 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
5394 | |
5395 | int numel_x = 650; |
5396 | int numel_y = 102; |
5397 | |
5398 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5399 | at::Tensor t0 = at::randn({numel_x, numel_y}, options); |
5400 | at::Tensor t3 = at::randn({numel_x, numel_y}, options); |
5401 | std::vector<IValue> inputs = {t0, t3}; |
5402 | |
5403 | FusionExecutor fe; |
5404 | fe.compileFusion(&fusion, inputs); |
5405 | auto outputs = fe.runFusion(inputs); |
5406 | |
5407 | auto t1 = t0.sum({1}); |
5408 | auto t2 = t1.unsqueeze(-1).expand({numel_x, numel_y}); |
5409 | auto t4 = t2 + t3; |
5410 | |
5411 | testValidate(&fusion, outputs, inputs, {t4}, __LINE__, __FILE__); |
5412 | } |
5413 | |
5414 | // See issue #759 |
5415 | TEST_F(NVFuserTest, FusionPredicatedBlockBroadcast_CUDA) { |
5416 | Fusion fusion; |
5417 | FusionGuard fg(&fusion); |
5418 | |
5419 | auto tv0 = makeSymbolicTensor(2); |
5420 | fusion.addInput(tv0); |
5421 | auto tv1 = sum(tv0, {1}); |
5422 | auto tv2 = broadcast(tv1, {false, true}); |
5423 | auto tv3 = makeSymbolicTensor(2); |
5424 | fusion.addInput(tv3); |
5425 | auto tv4 = add(tv2, tv3); |
5426 | fusion.addOutput(tv4); |
5427 | |
5428 | tv4->split(0, 4); |
5429 | tv1->computeAt(tv4, -1); |
5430 | |
5431 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
5432 | tv2->axis(1)->parallelize(ParallelType::TIDy); |
5433 | tv4->axis(-1)->parallelize(ParallelType::TIDx); |
5434 | tv4->axis(1)->parallelize(ParallelType::TIDy); |
5435 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
5436 | |
5437 | int numel_x = 100; |
5438 | int numel_y = 101; |
5439 | |
5440 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5441 | at::Tensor t0 = at::randn({numel_x, numel_y}, options); |
5442 | at::Tensor t3 = at::randn({numel_x, numel_y}, options); |
5443 | std::vector<IValue> inputs = {t0, t3}; |
5444 | |
5445 | FusionExecutor fe; |
5446 | fe.compileFusion(&fusion, inputs); |
5447 | auto outputs = fe.runFusion(inputs); |
5448 | |
5449 | auto t1 = t0.sum({1}); |
5450 | auto t2 = t1.unsqueeze(-1).expand({numel_x, numel_y}); |
5451 | auto t4 = t2 + t3; |
5452 | |
5453 | testValidate(&fusion, outputs, inputs, {t4}, __LINE__, __FILE__); |
5454 | } |
5455 | |
5456 | TEST_F(NVFuserTest, FusionSegmentVerticalMerge_CUDA) { |
5457 | auto fusion = std::make_unique<Fusion>(); |
5458 | FusionGuard fg(fusion.get()); |
5459 | |
5460 | auto tv0 = makeSymbolicTensor(3); |
5461 | |
5462 | fusion->addInput(tv0); |
5463 | // {first kernel} |
5464 | auto tv1 = sum(tv0, {0}); |
5465 | auto tv2 = add(tv1, tv0); |
5466 | auto tv3 = sum(tv2, {0}); |
5467 | auto tv4 = add(tv3, tv0); |
5468 | auto tv5 = sum(tv4, {0}); |
5469 | auto tv6 = sum(tv5, {0}); |
5470 | // {second kernel} |
5471 | auto tv7 = add(tv6, tv5); |
5472 | auto tv8 = add(tv7, tv5); |
5473 | auto tv9 = sum(tv8, {0}); |
5474 | |
5475 | fusion->addOutput(tv9); |
5476 | |
5477 | SegmentCandidateFinderOptions segment_options; |
5478 | segment_options.run_herrmann_merge = false; |
5479 | segment_options.run_final_merge = false; |
5480 | |
5481 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5482 | at::Tensor t0 = at::randn({2, 2, 2}, options); |
5483 | |
5484 | KernelArgumentHolder args(KernelIndexMode::INT32); |
5485 | args.setDeviceIndex(0); |
5486 | args.push(t0); |
5487 | |
5488 | auto segmented_fusion = |
5489 | SegmentCandidateFinder::segment(fusion.get(), args, segment_options); |
5490 | |
5491 | TORCH_CHECK(segmented_fusion->groups().size() == 2); |
5492 | } |
5493 | |
5494 | TEST_F(NVFuserTest, FusionSegmentHorizontalMerge_CUDA) { |
5495 | auto fusion = std::make_unique<Fusion>(); |
5496 | FusionGuard fg(fusion.get()); |
5497 | |
5498 | auto tv0 = makeSymbolicTensor(3); |
5499 | auto i0 = IrBuilder::create<Double>(); |
5500 | |
5501 | fusion->addInput(tv0); |
5502 | fusion->addInput(i0); |
5503 | |
5504 | // Branch 0 {first kernel} |
5505 | auto tv1 = sum(tv0, {0}); |
5506 | auto tv2 = add(tv0, i0); |
5507 | auto tv3 = unaryOp(UnaryOpType::Rsqrt, tv2); |
5508 | auto tv4 = sum(tv3, {0}); |
5509 | |
5510 | // Branch 1 {first kernel} |
5511 | auto tv5 = unaryOp(UnaryOpType::Rsqrt, tv3); |
5512 | auto tv6 = sum(tv5, {0}); |
5513 | |
5514 | // Incompatible {second kernel} |
5515 | auto tv7 = sum(tv6, {0}); |
5516 | |
5517 | fusion->addOutput(tv1); |
5518 | fusion->addOutput(tv4); |
5519 | fusion->addOutput(tv7); |
5520 | |
5521 | SegmentCandidateFinderOptions segment_options; |
5522 | segment_options.run_herrmann_merge = false; |
5523 | segment_options.run_final_merge = false; |
5524 | |
5525 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5526 | at::Tensor t0 = at::randn({2, 2, 2}, options); |
5527 | |
5528 | KernelArgumentHolder args(KernelIndexMode::INT32); |
5529 | args.setDeviceIndex(0); |
5530 | args.push(t0); |
5531 | c10::IValue scalar = 1.0; |
5532 | args.push(scalar); |
5533 | |
5534 | auto segmented_fusion = |
5535 | SegmentCandidateFinder::segment(fusion.get(), args, segment_options); |
5536 | |
5537 | TORCH_CHECK(segmented_fusion->groups().size() == 2); |
5538 | } |
5539 | |
5540 | TEST_F(NVFuserTest, FusionSegmentMixReduction_CUDA) { |
5541 | auto fusion = std::make_unique<Fusion>(); |
5542 | FusionGuard fg(fusion.get()); |
5543 | |
5544 | auto tv0 = makeSymbolicTensor(3); |
5545 | |
5546 | fusion->addInput(tv0); |
5547 | |
5548 | // def of tv1 in kernel 1 through horizontal |
5549 | auto tv1 = sum(tv0, {0, 1}); |
5550 | // kernel 2 |
5551 | auto tv2 = sum(tv0, {2}); |
5552 | auto tv3 = broadcast(tv2, {false, false, true}); |
5553 | auto tv4 = add(tv0, tv3); |
5554 | auto tv5 = sum(tv4, {2}); |
5555 | // end of kernel 2 |
5556 | // kernel 1 |
5557 | auto tv6 = unaryOp(UnaryOpType::Rsqrt, tv0); |
5558 | auto tv7 = sum(tv6, {0, 1}); |
5559 | auto tv8 = sum(tv6, {0, 1}); |
5560 | |
5561 | fusion->addOutput(tv1); |
5562 | fusion->addOutput(tv5); |
5563 | fusion->addOutput(tv7); |
5564 | fusion->addOutput(tv8); |
5565 | |
5566 | SegmentCandidateFinderOptions segment_options; |
5567 | segment_options.run_herrmann_merge = false; |
5568 | segment_options.run_final_merge = false; |
5569 | |
5570 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5571 | at::Tensor t0 = at::randn({2, 2, 2}, options); |
5572 | |
5573 | KernelArgumentHolder args(KernelIndexMode::INT32); |
5574 | args.setDeviceIndex(0); |
5575 | args.push(t0); |
5576 | |
5577 | auto segmented_fusion = |
5578 | SegmentCandidateFinder::segment(fusion.get(), args, segment_options); |
5579 | |
5580 | TORCH_CHECK(segmented_fusion->groups().size() <= 2); |
5581 | } |
5582 | |
5583 | TEST_F(NVFuserTest, FusionSBAR_CUDA) { |
5584 | Fusion fusion; |
5585 | FusionGuard fg(&fusion); |
5586 | |
5587 | // N, H, W, C format |
5588 | std::vector<int64_t> input_shape{656, 7, 7, 64}; |
5589 | |
5590 | auto x = makeContigTensor(4); |
5591 | auto y = makeContigTensor(4); |
5592 | auto weight = makeContigTensor(1); |
5593 | auto bias = makeContigTensor(1); |
5594 | |
5595 | fusion.addInput(x); |
5596 | fusion.addInput(y); |
5597 | fusion.addInput(weight); |
5598 | fusion.addInput(bias); |
5599 | |
5600 | const size_t kNumberOfDims = x->nDims(); |
5601 | std::vector<bool> broadcast_mask(kNumberOfDims, false); |
5602 | for (const auto axis : c10::irange(kNumberOfDims - 1)) { |
5603 | broadcast_mask[axis] = true; |
5604 | } |
5605 | |
5606 | auto weight_bcast = broadcast(weight, broadcast_mask); |
5607 | auto scale = mul(x, weight_bcast); |
5608 | auto bias_bcast = broadcast(bias, broadcast_mask); |
5609 | auto scale_bias = add(scale, bias_bcast); |
5610 | auto scale_bias_add = add(scale_bias, y); |
5611 | auto scale_bias_add_relu = unaryOp(UnaryOpType::Relu, scale_bias_add); |
5612 | |
5613 | fusion.addOutput(scale_bias_add_relu); |
5614 | |
5615 | // inputs |
5616 | at::manual_seed(0); |
5617 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5618 | at::Tensor at_x = at::randn(input_shape, options); |
5619 | at::Tensor at_y = at::randn(input_shape, options); |
5620 | at::Tensor at_weight = at::ones({input_shape[3]}, options); |
5621 | at::Tensor at_bias = at::zeros({input_shape[3]}, options); |
5622 | |
5623 | // inputs |
5624 | std::vector<c10::IValue> inputs = {at_x, at_y, at_weight, at_bias}; |
5625 | |
5626 | // outputs |
5627 | std::vector<at::Tensor> outputs; |
5628 | |
5629 | auto lparams = schedulePointwise(&fusion, inputs); |
5630 | |
5631 | FusionExecutor executor; |
5632 | executor.compileFusion(&fusion, inputs, lparams); |
5633 | outputs = executor.runFusion(inputs, lparams); |
5634 | |
5635 | auto at_scale = at::mul(at_x, at_weight); |
5636 | auto at_scale_bias = at::add(at_scale, at_bias); |
5637 | auto pwise_add = at::add(at_scale_bias, at_y); |
5638 | auto output = at::relu(pwise_add); |
5639 | |
5640 | testValidate(&fusion, outputs, inputs, {output}, __LINE__, __FILE__); |
5641 | } |
5642 | |
5643 | TEST_F(NVFuserTest, FusionSingleElement_CUDA) { |
5644 | Fusion fusion; |
5645 | FusionGuard fg(&fusion); |
5646 | |
5647 | auto tv0 = makeSymbolicTensor(0); |
5648 | fusion.addInput(tv0); |
5649 | |
5650 | auto tv1 = add(tv0, IrBuilder::create<Double>(2.5)); |
5651 | |
5652 | auto tv2 = add(tv1, IrBuilder::create<Double>(3.5)); |
5653 | fusion.addOutput(tv2); |
5654 | |
5655 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5656 | at::Tensor input = at::randn({}, options); |
5657 | |
5658 | at::Tensor cg_output = at::empty({}, options); |
5659 | |
5660 | auto lparams = schedulePointwise(&fusion, {input}); |
5661 | |
5662 | FusionExecutor fe; |
5663 | fe.compileFusion(&fusion, {input}, lparams); |
5664 | fe.runFusion({input}, {cg_output}, lparams); |
5665 | |
5666 | auto aten_output = input.add(2.5).add(3.5); |
5667 | |
5668 | testValidate( |
5669 | &fusion, {cg_output}, {input}, {aten_output}, __LINE__, __FILE__); |
5670 | } |
5671 | |
5672 | TEST_F(NVFuserTest, FusionBNBackwardRepro_CUDA) { |
5673 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
5674 | Fusion& fusion = *fusion_ptr.get(); |
5675 | FusionGuard fg(&fusion); |
5676 | |
5677 | int batch = 4; |
5678 | int c = 4; |
5679 | int h = 4; |
5680 | int w = 4; |
5681 | int numDims = 4; |
5682 | |
5683 | auto input = makeSymbolicTensor(numDims); |
5684 | fusion.addInput(input); |
5685 | auto weight = makeSymbolicTensor(1); |
5686 | fusion.addInput(weight); |
5687 | auto running_mean = makeSymbolicTensor(1); |
5688 | fusion.addInput(running_mean); |
5689 | auto running_var = makeSymbolicTensor(1); |
5690 | fusion.addInput(running_var); |
5691 | auto save_mean = makeSymbolicTensor(1); |
5692 | fusion.addInput(save_mean); |
5693 | auto save_invstd = makeSymbolicTensor(1); |
5694 | fusion.addInput(save_invstd); |
5695 | |
5696 | auto grad_out_prev = makeSymbolicTensor(numDims); |
5697 | fusion.addInput(grad_out_prev); |
5698 | auto gt_0 = |
5699 | makeSymbolicTensor(numDims); // single tensor broadcasted is dangerous. |
5700 | fusion.addInput(gt_0); |
5701 | |
5702 | auto gt_bool = binaryOp(BinaryOpType::GT, gt_0, IrBuilder::create<Int>(1)); |
5703 | auto gt_float = castOp(DataType::Float, gt_bool); |
5704 | |
5705 | auto grad_out = mul(grad_out_prev, gt_float); |
5706 | |
5707 | Val* eps_ptr = IrBuilder::create<Double>(1e-5); |
5708 | |
5709 | auto grads = batch_norm_backward( |
5710 | input, |
5711 | grad_out, |
5712 | weight, |
5713 | running_mean, |
5714 | running_var, |
5715 | save_mean, |
5716 | save_invstd, |
5717 | true, |
5718 | eps_ptr, |
5719 | {true, true, true}); |
5720 | |
5721 | fusion.addOutput(grads.grad_input); |
5722 | fusion.addOutput(grads.grad_weight); |
5723 | fusion.addOutput(grads.grad_bias); |
5724 | |
5725 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5726 | at::Tensor input0 = at::randn({batch, c, h, w}, options); |
5727 | at::Tensor input1 = at::randn({c}, options); |
5728 | at::Tensor input2 = at::randn_like(input1); |
5729 | at::Tensor input3 = at::randn_like(input1); |
5730 | at::Tensor input4 = at::randn_like(input1); |
5731 | at::Tensor input5 = at::randn_like(input1); |
5732 | at::Tensor input6 = at::randn_like(input0); |
5733 | at::Tensor input7 = at::randn_like(input0); |
5734 | |
5735 | FusionExecutorCache fec(std::move(fusion_ptr)); |
5736 | std::vector<IValue> inputs = { |
5737 | input0, input1, input2, input3, input4, input5, input6, input7}; |
5738 | auto outputs = fec.runFusionWithInputs(inputs); |
5739 | } |
5740 | |
5741 | // TODO: We only changed inputs, merge this with the test above. |
5742 | TEST_F(NVFuserTest, FusionBNBackwardRepro2_CUDA) { |
5743 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
5744 | Fusion& fusion = *fusion_ptr.get(); |
5745 | FusionGuard fg(&fusion); |
5746 | |
5747 | int batch = 2; |
5748 | int c = 81; |
5749 | int h = 1; |
5750 | int w = 1; |
5751 | int numDims = 4; |
5752 | |
5753 | // auto input = makeSymbolicTensor(numDims); |
5754 | auto input = makeConcreteTensor({-1, -1, 1, 1}); |
5755 | fusion.addInput(input); |
5756 | auto weight = makeSymbolicTensor(1); |
5757 | fusion.addInput(weight); |
5758 | auto running_mean = makeSymbolicTensor(1); |
5759 | fusion.addInput(running_mean); |
5760 | auto running_var = makeSymbolicTensor(1); |
5761 | fusion.addInput(running_var); |
5762 | auto save_mean = makeSymbolicTensor(1); |
5763 | fusion.addInput(save_mean); |
5764 | auto save_invstd = makeSymbolicTensor(1); |
5765 | fusion.addInput(save_invstd); |
5766 | |
5767 | // auto grad_out_prev = makeSymbolicTensor(numDims); |
5768 | auto grad_out_prev = makeConcreteTensor({-1, -1, 1, 1}); |
5769 | fusion.addInput(grad_out_prev); |
5770 | // auto gt_0 = |
5771 | // makeSymbolicTensor(numDims); // single tensor broadcasted is dangerous. |
5772 | auto gt_0 = makeConcreteTensor({-1, -1, 1, 1}); |
5773 | fusion.addInput(gt_0); |
5774 | |
5775 | auto gt_bool = binaryOp(BinaryOpType::GT, gt_0, IrBuilder::create<Int>(1)); |
5776 | auto gt_float = castOp(DataType::Float, gt_bool); |
5777 | |
5778 | auto grad_out = mul(grad_out_prev, gt_float); |
5779 | |
5780 | Val* eps_ptr = IrBuilder::create<Double>(1e-5); |
5781 | |
5782 | auto grads = batch_norm_backward( |
5783 | input, |
5784 | grad_out, |
5785 | weight, |
5786 | running_mean, |
5787 | running_var, |
5788 | save_mean, |
5789 | save_invstd, |
5790 | true, |
5791 | eps_ptr, |
5792 | {true, true, true}); |
5793 | |
5794 | fusion.addOutput(grads.grad_input); |
5795 | fusion.addOutput(grads.grad_weight); |
5796 | fusion.addOutput(grads.grad_bias); |
5797 | |
5798 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5799 | at::Tensor input0 = at::randn({batch, c, h, w}, options); |
5800 | at::Tensor input1 = at::randn({c}, options); |
5801 | at::Tensor input2 = at::randn_like(input1); |
5802 | at::Tensor input3 = at::randn_like(input1); |
5803 | at::Tensor input4 = at::randn_like(input1); |
5804 | at::Tensor input5 = at::randn_like(input1); |
5805 | at::Tensor input6 = at::randn_like(input0); |
5806 | at::Tensor input7 = at::randn_like(input0); |
5807 | |
5808 | FusionExecutorCache fec(std::move(fusion_ptr)); |
5809 | std::vector<IValue> inputs = { |
5810 | input0, input1, input2, input3, input4, input5, input6, input7}; |
5811 | auto outputs = fec.runFusionWithInputs(inputs); |
5812 | } |
5813 | |
5814 | TEST_F(NVFuserTest, FusionBNRepro_CUDA) { |
5815 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
5816 | Fusion& fusion = *fusion_ptr.get(); |
5817 | FusionGuard fg(&fusion); |
5818 | |
5819 | const bool kTraining = true; |
5820 | const float kMomentum = 0.1; |
5821 | const float kEps = 1e-5; |
5822 | |
5823 | int batch = 14; |
5824 | int c = 65; |
5825 | int h = 7; |
5826 | int w = 7; |
5827 | int numDims = 4; |
5828 | |
5829 | auto input = makeSymbolicTensor(numDims); |
5830 | fusion.addInput(input); |
5831 | auto weight = makeSymbolicTensor(1); |
5832 | fusion.addInput(weight); |
5833 | auto bias = makeSymbolicTensor(1); |
5834 | fusion.addInput(bias); |
5835 | auto running_mean = makeSymbolicTensor(1); |
5836 | fusion.addInput(running_mean); |
5837 | auto running_var = makeSymbolicTensor(1); |
5838 | fusion.addInput(running_var); |
5839 | |
5840 | auto momentum_ptr = IrBuilder::create<Double>(kMomentum); |
5841 | auto eps_ptr = IrBuilder::create<Double>(kEps); |
5842 | |
5843 | auto result = batch_norm( |
5844 | input, |
5845 | weight, |
5846 | bias, |
5847 | running_mean, |
5848 | running_var, |
5849 | kTraining, |
5850 | momentum_ptr, |
5851 | eps_ptr); |
5852 | |
5853 | fusion.addOutput(result.output); |
5854 | fusion.addOutput(result.mean); |
5855 | fusion.addOutput(result.invstd); |
5856 | |
5857 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5858 | at::Tensor input1 = at::randn({batch, c, h, w}, options); |
5859 | at::Tensor input2 = at::randn({c}, options); |
5860 | at::Tensor input3 = at::randn_like(input2); |
5861 | at::Tensor input4 = at::randn_like(input2); |
5862 | at::Tensor input5 = at::randn_like(input2); |
5863 | |
5864 | auto input1_ref = input1.clone(); |
5865 | auto input2_ref = input2.clone(); |
5866 | auto input3_ref = input3.clone(); |
5867 | auto input4_ref = input4.clone(); |
5868 | auto input5_ref = input5.clone(); |
5869 | |
5870 | FusionExecutorCache fec(std::move(fusion_ptr)); |
5871 | std::vector<IValue> aten_inputs = {input1, input2, input3, input4, input5}; |
5872 | auto cg_outputs = fec.runFusionWithInputs(aten_inputs); |
5873 | |
5874 | auto at_results = at::native_batch_norm( |
5875 | input1_ref, |
5876 | input2_ref, |
5877 | input3_ref, |
5878 | input4_ref, |
5879 | input5_ref, |
5880 | kTraining, |
5881 | kMomentum, |
5882 | kEps); |
5883 | |
5884 | auto at_output = std::get<0>(at_results); |
5885 | auto at_mean = std::get<1>(at_results); |
5886 | auto at_invstd = std::get<2>(at_results); |
5887 | |
5888 | std::vector<at::Tensor> aten_outputs = {at_output, at_mean, at_invstd}; |
5889 | |
5890 | testValidate( |
5891 | &fusion, cg_outputs, aten_inputs, aten_outputs, __LINE__, __FILE__); |
5892 | } |
5893 | |
5894 | TEST_F(NVFuserTest, FusionBNRepro2_CUDA) { |
5895 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
5896 | Fusion& fusion = *fusion_ptr.get(); |
5897 | FusionGuard fg(&fusion); |
5898 | |
5899 | const bool kTraining = true; |
5900 | const float kMomentum = 0.1; |
5901 | const float kEps = 1e-5; |
5902 | |
5903 | int batch = 2; |
5904 | int c = 4; |
5905 | int h = 17; |
5906 | int w = 17; |
5907 | int numDims = 4; |
5908 | |
5909 | auto input = makeSymbolicTensor(numDims); |
5910 | fusion.addInput(input); |
5911 | |
5912 | Val* momentum_ptr = IrBuilder::create<Double>(kMomentum); |
5913 | Val* eps_ptr = IrBuilder::create<Double>(kEps); |
5914 | |
5915 | auto result = batch_norm( |
5916 | input, |
5917 | nullptr, |
5918 | nullptr, |
5919 | nullptr, |
5920 | nullptr, |
5921 | kTraining, |
5922 | momentum_ptr, |
5923 | eps_ptr); |
5924 | |
5925 | fusion.addOutput(result.output); |
5926 | fusion.addOutput(result.mean); |
5927 | fusion.addOutput(result.invstd); |
5928 | |
5929 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5930 | at::Tensor input1 = at::randn({batch, c, h, w}, options); |
5931 | |
5932 | auto input1_ref = input1.clone(); |
5933 | at::Tensor r_m; |
5934 | at::Tensor r_v; |
5935 | at::Tensor weight; |
5936 | at::Tensor bias; |
5937 | |
5938 | FusionExecutorCache fec(std::move(fusion_ptr)); |
5939 | std::vector<IValue> aten_inputs = {input1}; |
5940 | auto cg_outputs = fec.runFusionWithInputs(aten_inputs); |
5941 | |
5942 | auto at_results = at::native_batch_norm( |
5943 | input1_ref, r_m, r_v, weight, bias, kTraining, kMomentum, kEps); |
5944 | |
5945 | auto at_output = std::get<0>(at_results); |
5946 | auto at_mean = std::get<1>(at_results); |
5947 | auto at_invstd = std::get<2>(at_results); |
5948 | |
5949 | std::vector<at::Tensor> aten_outputs = {at_output, at_mean, at_invstd}; |
5950 | |
5951 | testValidate( |
5952 | &fusion, cg_outputs, aten_inputs, aten_outputs, __LINE__, __FILE__); |
5953 | } |
5954 | |
5955 | TEST_F(NVFuserTest, FusionZeroSizeTensorPW_CUDA) { |
5956 | Fusion fusion; |
5957 | FusionGuard fg(&fusion); |
5958 | |
5959 | auto tv0 = makeSymbolicTensor(1); |
5960 | fusion.addInput(tv0); |
5961 | |
5962 | auto tv1 = makeConcreteTensor({0}); |
5963 | fusion.addInput(tv1); |
5964 | |
5965 | auto tv2 = add(tv0, IrBuilder::create<Double>(2.5)); |
5966 | fusion.addOutput(tv2); |
5967 | |
5968 | // This test used to just have: |
5969 | // auto tv3 = makeConcreteTensor({0}); |
5970 | // and somehow that was running through our system fine, but size-0 tensors |
5971 | // are not supported, so making sure this fails. |
5972 | auto tv3 = set(tv1); |
5973 | fusion.addOutput(tv3); |
5974 | |
5975 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
5976 | |
5977 | at::Tensor input0 = at::randn({2}, options); |
5978 | at::Tensor input1 = at::randn({0}, options); |
5979 | at::Tensor cg_output2 = at::empty({2}, options); |
5980 | at::Tensor cg_output3 = at::empty({0}, options); |
5981 | |
5982 | // Fails at schedule pointwise because our (maybe only) size-0 check is in |
5983 | // binding input sizes which the scheduler ends up calling. |
5984 | // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto) |
5985 | ASSERT_ANY_THROW(schedulePointwise(&fusion, {input0, input1})); |
5986 | } |
5987 | |
5988 | TEST_F(NVFuserTest, FusionZeroSizeTensorReduction_CUDA) { |
5989 | Fusion fusion; |
5990 | FusionGuard fg(&fusion); |
5991 | |
5992 | auto tv0 = makeSymbolicTensor(2); |
5993 | fusion.addInput(tv0); |
5994 | |
5995 | auto tv1 = makeConcreteTensor({0}); |
5996 | fusion.addInput(tv1); |
5997 | |
5998 | auto tv2 = sum(tv0, {1}); |
5999 | fusion.addOutput(tv2); |
6000 | |
6001 | auto tv3 = makeConcreteTensor({0}); |
6002 | fusion.addOutput(tv3); |
6003 | |
6004 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6005 | |
6006 | at::Tensor input0 = at::randn({2, 4}, options); |
6007 | at::Tensor input1 = at::randn({0}, options); |
6008 | at::Tensor cg_output2 = at::empty({2}, options); |
6009 | at::Tensor cg_output3 = at::empty({0}, options); |
6010 | |
6011 | auto reduction_params = getReductionHeuristics(&fusion, {input0, input1}); |
6012 | TORCH_CHECK(reduction_params, "Reduction schedule was not generated!" ); |
6013 | scheduleReduction(&fusion, *reduction_params); |
6014 | TORCH_CHECK(reduction_params, "Reduction schedule was not generated!" ); |
6015 | |
6016 | auto lparams = reduction_params->lparams; |
6017 | FusionExecutor fe; |
6018 | fe.compileFusion(&fusion, {input0, input1}, lparams); |
6019 | auto cg_outputs = fe.runFusion({input0, input1}, lparams); |
6020 | auto aten_output2 = input0.sum({1}); |
6021 | at::Tensor aten_output3 = at::empty({0}, options); |
6022 | |
6023 | testValidate( |
6024 | &fusion, |
6025 | cg_outputs, |
6026 | {input0, input1}, |
6027 | {aten_output2, aten_output3}, |
6028 | __LINE__, |
6029 | __FILE__, |
6030 | "" , |
6031 | lparams); |
6032 | } |
6033 | |
6034 | TEST_F(NVFuserTest, FusionZeroSizeTensorNormalization_CUDA) { |
6035 | Fusion fusion; |
6036 | FusionGuard fg(&fusion); |
6037 | |
6038 | auto tv0 = makeSymbolicTensor(2); |
6039 | fusion.addInput(tv0); |
6040 | |
6041 | auto tv1 = makeConcreteTensor({0}); |
6042 | fusion.addInput(tv1); |
6043 | |
6044 | auto tv2 = sum(tv0, {0}); |
6045 | auto tv3 = broadcast(tv2, {true, false}); |
6046 | auto tv4 = add(tv0, tv3); |
6047 | fusion.addOutput(tv4); |
6048 | |
6049 | auto tv5 = makeConcreteTensor({0}); |
6050 | fusion.addOutput(tv5); |
6051 | |
6052 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6053 | |
6054 | at::Tensor input0 = at::randn({2, 4}, options); |
6055 | at::Tensor input1 = at::randn({0}, options); |
6056 | at::Tensor cg_output2 = at::empty({2, 4}, options); |
6057 | at::Tensor cg_output3 = at::empty({0}, options); |
6058 | |
6059 | auto reduction_params = getPersistentHeuristics(&fusion, {input0, input1}); |
6060 | TORCH_CHECK(reduction_params, "Reduction schedule was not generated!" ); |
6061 | schedulePersistentKernel(&fusion, *reduction_params); |
6062 | |
6063 | auto lparams = reduction_params->lparams; |
6064 | FusionExecutor fe; |
6065 | fe.compileFusion(&fusion, {input0, input1}, lparams); |
6066 | auto cg_outputs = fe.runFusion({input0, input1}, lparams); |
6067 | auto aten_output2 = input0.sum({0}).add(input0); |
6068 | at::Tensor aten_output3 = at::empty({0}, options); |
6069 | |
6070 | testValidate( |
6071 | &fusion, |
6072 | cg_outputs, |
6073 | {input0, input1}, |
6074 | {aten_output2, aten_output3}, |
6075 | __LINE__, |
6076 | __FILE__, |
6077 | "" , |
6078 | lparams); |
6079 | } |
6080 | |
6081 | TEST_F(NVFuserTest, FusionSegmentIoAlias_CUDA) { |
6082 | auto fusion = std::make_unique<Fusion>(); |
6083 | FusionGuard fg(fusion.get()); |
6084 | |
6085 | TensorView* tv0 = makeSymbolicTensor(2); |
6086 | TensorView* tv1 = makeSymbolicTensor(1); |
6087 | TensorView* tv2 = makeSymbolicTensor(2); |
6088 | |
6089 | fusion->addInput(tv0); |
6090 | fusion->addInput(tv1); |
6091 | fusion->addInput(tv2); |
6092 | |
6093 | TensorView* tv3 = add(tv0, IrBuilder::create<Double>(1)); // Group 0 |
6094 | TensorView* tv4 = |
6095 | max(tv3, {0}); // Group 0 (use max instead to avoid numerical issues) |
6096 | TensorView* tv5 = add(tv4, tv1); // Group 0 (Non Broadcast after reduce, |
6097 | // keeps normalization scheduler away) |
6098 | TensorView* tv6 = add(tv5, tv2); // Group 1 (Broadcast after reduce) |
6099 | |
6100 | // Note: test alias; |
6101 | fusion->aliasOutputToInput(tv6, tv0); |
6102 | // TODO: support output on aliased fusion #1488 |
6103 | // remove tv7 after #1488 |
6104 | // fusion->addOutput(tv6); |
6105 | TensorView* tv7 = add(tv6, IrBuilder::create<Double>(1)); // Group 0 |
6106 | fusion->addOutput(tv7); |
6107 | |
6108 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6109 | at::Tensor t0 = at::randn({128, 65}, options); |
6110 | at::Tensor t1 = at::randn({65}, options); |
6111 | at::Tensor t2 = at::randn({128, 65}, options); |
6112 | |
6113 | auto t3 = t0.add(1.0); |
6114 | auto t4 = std::get<0>(at::max(t3, 0)); |
6115 | auto t5 = t4.add(t1); |
6116 | auto t6 = t5.add(t2); |
6117 | auto t7 = t6.add(1.0); |
6118 | |
6119 | FusionExecutorCache executor_cache(std::move(fusion)); |
6120 | |
6121 | auto outputs = executor_cache.runFusionWithInputs({t0, t1, t2}); |
6122 | |
6123 | // TODO: support output on aliased fusion #1488 |
6124 | // validating aliasing |
6125 | // TORCH_INTERNAL_ASSERT(outputs[0].data_ptr() == t0.data_ptr()); |
6126 | |
6127 | TORCH_CHECK( |
6128 | executor_cache.getMostRecentKernelRuntime()->isSegmented(), |
6129 | "segmentation didn't happen" ); |
6130 | TORCH_CHECK( |
6131 | executor_cache.getMostRecentKernelRuntime() |
6132 | ->fusionSegments() |
6133 | ->groups() |
6134 | .size() == 2, |
6135 | "segmentation didn't happen as expected" ); |
6136 | |
6137 | testValidate( |
6138 | executor_cache.fusion(), outputs, {t0, t1, t2}, {t7}, __LINE__, __FILE__); |
6139 | } |
6140 | |
6141 | TEST_F(NVFuserTest, FusionWelford1Output_CUDA) { |
6142 | auto fusion_ptr = std::make_unique<Fusion>(); |
6143 | auto fusion = fusion_ptr.get(); |
6144 | FusionGuard fg(fusion); |
6145 | |
6146 | auto tv0 = makeSymbolicTensor(2); |
6147 | fusion->addInput(tv0); |
6148 | |
6149 | auto tvs = Welford(tv0, {1}); |
6150 | fusion->addOutput(tvs.var_sum); |
6151 | FusionExecutorCache executor_cache(std::move(fusion_ptr)); |
6152 | |
6153 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6154 | at::Tensor t0 = at::randn({128, 65}, options); |
6155 | auto outputs = executor_cache.runFusionWithInputs({t0}); |
6156 | |
6157 | auto t1 = t0.var({1}, false) * 65; |
6158 | testValidate(fusion, outputs, {t0}, {t1}, __LINE__, __FILE__); |
6159 | } |
6160 | |
6161 | TEST_F(NVFuserTest, FusionTranslate1Welford_CUDA) { |
6162 | auto fusion_ptr = std::make_unique<Fusion>(); |
6163 | auto fusion = fusion_ptr.get(); |
6164 | FusionGuard fg(fusion); |
6165 | |
6166 | auto tv0 = makeSymbolicTensor(2); |
6167 | fusion->addInput(tv0); |
6168 | |
6169 | auto tvs = Welford(tv0, {1}); |
6170 | auto tv_out = add(tv0, broadcast(tvs.avg, {false, true})); |
6171 | fusion->addOutput(tv_out); |
6172 | FusionExecutorCache executor_cache(std::move(fusion_ptr)); |
6173 | |
6174 | auto run_test = [&executor_cache, |
6175 | fusion](auto inner_size) -> FusionKernelRuntime* { |
6176 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6177 | at::Tensor t0 = at::randn({128, inner_size}, options); |
6178 | auto outputs = executor_cache.runFusionWithInputs({t0}); |
6179 | // Square sums does not fit well in the testValidate assumptions, |
6180 | // so we just compare the divided output here. |
6181 | testValidate( |
6182 | fusion, |
6183 | outputs, |
6184 | {t0}, |
6185 | {t0.add(t0.mean({1}).unsqueeze(1))}, |
6186 | __LINE__, |
6187 | __FILE__); |
6188 | |
6189 | return executor_cache.getMostRecentKernelRuntime(); |
6190 | }; |
6191 | |
6192 | // Run a translated welford |
6193 | auto runtime1 = run_test(64); |
6194 | // Check it was translated |
6195 | TORCH_CHECK( |
6196 | runtime1->fusionSegments()->groups().size() == 1 && |
6197 | runtime1->fusionSegments()->groups()[0]->exprs().size() > 2); |
6198 | |
6199 | // Run an un-translated welford |
6200 | auto runtime2 = run_test(65536); |
6201 | |
6202 | bool found_welford = false; |
6203 | for (auto group : runtime2->fusionSegments()->groups()) { |
6204 | for (auto expr : group->exprs()) { |
6205 | if (expr->isA<WelfordOp>()) { |
6206 | found_welford = true; |
6207 | } |
6208 | } |
6209 | } |
6210 | TORCH_CHECK(found_welford); |
6211 | } |
6212 | |
6213 | TEST_F(NVFuserTest, FusionTranslate2Welford_CUDA) { |
6214 | auto fusion_ptr = std::make_unique<Fusion>(); |
6215 | auto fusion = fusion_ptr.get(); |
6216 | FusionGuard fg(fusion); |
6217 | |
6218 | auto tv0 = makeSymbolicTensor(2); |
6219 | fusion->addInput(tv0); |
6220 | |
6221 | auto tvs1 = Welford(tv0, {1}); |
6222 | auto tv_out1 = add(tv0, broadcast(tvs1.avg, {false, true})); |
6223 | fusion->addOutput(tv_out1); |
6224 | |
6225 | auto tvs2 = Welford(tv0, {1}); |
6226 | auto tv_out2 = add(tv0, broadcast(tvs2.avg, {false, true})); |
6227 | fusion->addOutput(tv_out2); |
6228 | |
6229 | FusionExecutorCache executor_cache(std::move(fusion_ptr)); |
6230 | |
6231 | auto run_test = [&executor_cache, |
6232 | fusion](auto inner_size) -> FusionKernelRuntime* { |
6233 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6234 | at::Tensor t0 = at::randn({128, inner_size}, options); |
6235 | auto outputs = executor_cache.runFusionWithInputs({t0}); |
6236 | |
6237 | // Square sums does not fit well in the testValidate assumptions, |
6238 | // so we just compare the divided output here. |
6239 | auto out = t0.add(t0.mean({1}).unsqueeze(1)); |
6240 | testValidate(fusion, outputs, {t0}, {out, out}, __LINE__, __FILE__); |
6241 | |
6242 | return executor_cache.getMostRecentKernelRuntime(); |
6243 | }; |
6244 | |
6245 | // Run a translated welford |
6246 | auto runtime1 = run_test(64); |
6247 | // Check it was translated |
6248 | TORCH_CHECK( |
6249 | runtime1->fusionSegments()->groups().size() == 1 && |
6250 | runtime1->fusionSegments()->groups()[0]->exprs().size() > 4); |
6251 | |
6252 | // Run an un-translated welford |
6253 | auto runtime2 = run_test(65536); |
6254 | // // Check it was not translated |
6255 | bool found_welford = false; |
6256 | for (auto group : runtime2->fusionSegments()->groups()) { |
6257 | for (auto expr : group->exprs()) { |
6258 | if (expr->isA<WelfordOp>()) { |
6259 | found_welford = true; |
6260 | } |
6261 | } |
6262 | } |
6263 | TORCH_CHECK(found_welford); |
6264 | } |
6265 | |
6266 | TEST_F(NVFuserTest, FusionLargeWelfordNormalization_CUDA) { |
6267 | auto fusion_ptr = std::make_unique<Fusion>(); |
6268 | auto fusion = fusion_ptr.get(); |
6269 | FusionGuard fg(fusion); |
6270 | |
6271 | auto tv0 = makeSymbolicTensor(2); |
6272 | fusion->addInput(tv0); |
6273 | |
6274 | auto tvs1 = Welford(tv0, {1}); |
6275 | auto sum_of_tv0 = sum(tv0, {1}); |
6276 | |
6277 | fusion->addOutput(tvs1.var_sum); |
6278 | fusion->addOutput(sum_of_tv0); |
6279 | |
6280 | FusionExecutorCache executor_cache(std::move(fusion_ptr)); |
6281 | |
6282 | auto run_test = [&executor_cache, |
6283 | fusion](auto inner_size) -> FusionKernelRuntime* { |
6284 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6285 | at::Tensor t0 = at::randn({128, inner_size}, options); |
6286 | auto outputs = executor_cache.runFusionWithInputs({t0}); |
6287 | |
6288 | auto t1 = t0.var({1}, false) * inner_size; |
6289 | auto t2 = t0.sum({1}); |
6290 | testValidate(fusion, outputs, {t0}, {t1, t2}, __LINE__, __FILE__); |
6291 | |
6292 | return executor_cache.getMostRecentKernelRuntime(); |
6293 | }; |
6294 | |
6295 | auto runtime = run_test(65536); |
6296 | TORCH_CHECK(!runtime->isSegmented()); |
6297 | } |
6298 | |
6299 | TEST_F(NVFuserTest, FusionWelfordOuterPersistence_CUDA) { |
6300 | auto fusion_ptr = std::make_unique<Fusion>(); |
6301 | auto fusion = fusion_ptr.get(); |
6302 | FusionGuard fg(fusion); |
6303 | |
6304 | auto tv0 = makeSymbolicTensor(2); |
6305 | fusion->addInput(tv0); |
6306 | |
6307 | auto tvs1 = Welford(tv0, {1}); |
6308 | auto sum_of_tv0 = sum(tv0, {1}); |
6309 | auto sum_bcasted = broadcast(sum_of_tv0, {false, true}); |
6310 | auto avg_bcasted = broadcast(tvs1.avg, {false, true}); |
6311 | auto tv0_plus_sum = add(tv0, sum_bcasted); |
6312 | auto tv0_plus_avg = add(tv0, avg_bcasted); |
6313 | |
6314 | fusion->addOutput(tv0_plus_sum); |
6315 | fusion->addOutput(tv0_plus_avg); |
6316 | |
6317 | FusionExecutorCache executor_cache(std::move(fusion_ptr)); |
6318 | |
6319 | auto run_test = [&executor_cache, |
6320 | fusion](auto inner_size) -> FusionKernelRuntime* { |
6321 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6322 | at::Tensor t0 = at::randn({128, inner_size}, options); |
6323 | auto outputs = executor_cache.runFusionWithInputs({t0}); |
6324 | |
6325 | auto t1 = t0.to(c10::kDouble).mean({1}).unsqueeze(1) + t0; |
6326 | auto t2 = t0.to(c10::kDouble).sum({1}).unsqueeze(1) + t0; |
6327 | testValidate(fusion, outputs, {t0}, {t2, t1}, __LINE__, __FILE__); |
6328 | |
6329 | return executor_cache.getMostRecentKernelRuntime(); |
6330 | }; |
6331 | |
6332 | for (auto inner_size : {4096, 8192, 32768}) { |
6333 | auto runtime = run_test(inner_size); |
6334 | TORCH_CHECK(!runtime->isSegmented()); |
6335 | } |
6336 | } |
6337 | |
6338 | TEST_F(NVFuserTest, FusionSegmentIslands_CUDA) { |
6339 | auto fusion = std::make_unique<Fusion>(); |
6340 | FusionGuard fg(fusion.get()); |
6341 | |
6342 | auto tv0 = makeSymbolicTensor(2); |
6343 | auto tv1 = makeSymbolicTensor(2); |
6344 | fusion->addInput(tv0); |
6345 | fusion->addInput(tv1); |
6346 | |
6347 | auto tv2 = sum(tv0, {0}); |
6348 | auto tv3 = sum(tv1, {1}); |
6349 | fusion->addOutput(tv2); |
6350 | fusion->addOutput(tv3); |
6351 | |
6352 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6353 | at::Tensor t0 = at::randn({16, 16}, options); |
6354 | at::Tensor t1 = at::randn({16, 16}, options); |
6355 | |
6356 | FusionExecutorCache fusion_executor_cache(std::move(fusion)); |
6357 | fusion_executor_cache.runFusionWithInputs({t0, t1}); |
6358 | } |
6359 | |
6360 | TEST_F(NVFuserTest, FusionBackOffInnerBroadcast_CUDA) { |
6361 | auto fusion = std::make_unique<Fusion>(); |
6362 | FusionGuard fg(fusion.get()); |
6363 | |
6364 | auto tv0 = makeSymbolicTensor(1); |
6365 | auto tv1 = makeSymbolicTensor(2); |
6366 | auto tv2 = makeSymbolicTensor(4); |
6367 | fusion->addInput(tv0); |
6368 | fusion->addInput(tv1); |
6369 | |
6370 | auto tv3 = broadcast(tv0, {false, true, true, true}); |
6371 | auto tv4 = broadcast(tv1, {false, false, true, true}); |
6372 | auto tv5 = unaryOp(UnaryOpType::Rsqrt, tv2); |
6373 | |
6374 | auto tv6 = add(tv3, tv5); |
6375 | auto tv7 = add(tv4, tv5); |
6376 | auto tv8 = add(tv3, tv4); |
6377 | |
6378 | auto tv9 = add(tv6, tv7); |
6379 | auto tv10 = add(tv9, tv8); |
6380 | |
6381 | fusion->addOutput(tv10); |
6382 | |
6383 | tv0->computeAt(tv10, -2); |
6384 | tv1->computeAt(tv10, -2); |
6385 | tv2->computeAt(tv10, -2); |
6386 | |
6387 | TORCH_CHECK(tv3->getComputeAtPosition() == 1); |
6388 | TORCH_CHECK(tv4->getComputeAtPosition() == 2); |
6389 | TORCH_CHECK(tv5->getComputeAtPosition() == 3); |
6390 | |
6391 | TORCH_CHECK(tv6->getMaxProducerPosition() == 3); |
6392 | TORCH_CHECK(tv7->getMaxProducerPosition() == 3); |
6393 | TORCH_CHECK(tv8->getMaxProducerPosition() == 2); |
6394 | } |
6395 | |
6396 | TEST_F(NVFuserTest, FusionBackOffInnerBroadcast2_CUDA) { |
6397 | auto fusion = std::make_unique<Fusion>(); |
6398 | FusionGuard fg(fusion.get()); |
6399 | |
6400 | auto tv0 = makeSymbolicTensor(2); |
6401 | auto tv1 = makeSymbolicTensor(3); |
6402 | fusion->addInput(tv0); |
6403 | fusion->addInput(tv1); |
6404 | auto tv2 = broadcast(tv0, {false, false, true}); |
6405 | auto tv3 = add(tv2, tv1); |
6406 | |
6407 | fusion->addOutput(tv3); |
6408 | tv3->split(-2, 4); |
6409 | tv3->reorder({{-1, -2}}); |
6410 | tv0->computeAt(tv3, -2); |
6411 | tv1->computeAt(tv3, -2); |
6412 | TORCH_CHECK(tv2->getComputeAtPosition() == 2); |
6413 | TORCH_CHECK(tv3->getMaxProducerPosition() == 2); |
6414 | } |
6415 | |
6416 | TEST_F(NVFuserTest, FusionBackOffInnerBroadcast3_CUDA) { |
6417 | auto fusion = std::make_unique<Fusion>(); |
6418 | FusionGuard fg(fusion.get()); |
6419 | |
6420 | auto tv0 = makeSymbolicTensor(2); |
6421 | auto tv1 = makeSymbolicTensor(4); |
6422 | fusion->addInput(tv0); |
6423 | fusion->addInput(tv1); |
6424 | auto tv2 = broadcast(tv0, {false, false, true}); |
6425 | auto tv3 = broadcast(tv2, {false, true, false, false}); |
6426 | auto tv4 = add(tv3, tv1); |
6427 | |
6428 | fusion->addOutput(tv4); |
6429 | tv0->computeAt(tv4, -1); |
6430 | tv1->computeAt(tv4, -1); |
6431 | TORCH_CHECK(tv2->getComputeAtPosition() == 2); |
6432 | TORCH_CHECK(tv3->getMaxProducerPosition() == 3); |
6433 | } |
6434 | |
6435 | TEST_F(NVFuserTest, FusionSimpleWarp_CUDA) { |
6436 | auto fusion = std::make_unique<Fusion>(); |
6437 | FusionGuard fg(fusion.get()); |
6438 | |
6439 | auto tv0 = makeSymbolicTensor(2); |
6440 | fusion->addInput(tv0); |
6441 | |
6442 | auto tv1 = sum(tv0, {1}); |
6443 | auto tv2 = broadcast(tv1, {false, true}); |
6444 | auto tv3 = add(tv2, tv0); |
6445 | |
6446 | fusion->addOutput(tv3); |
6447 | |
6448 | tv1->split(1, 32); |
6449 | auto tv1_rf = tv1->rFactor({1}); |
6450 | TransformPropagatorWithCheck propagator(tv1_rf); |
6451 | MaxRootDomainInfoSpanningTree(tv1_rf).traverse(&propagator); |
6452 | tv1_rf->axis(-1)->parallelize(ParallelType::TIDx); |
6453 | tv1->axis(0)->parallelize(ParallelType::BIDx); |
6454 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
6455 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
6456 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
6457 | tv3->axis(0)->parallelize(ParallelType::BIDx); |
6458 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
6459 | tv0->computeAt(tv3, -1, ComputeAtMode::MostInlined); |
6460 | |
6461 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6462 | at::Tensor input1 = at::randn({16, 128}, options); |
6463 | |
6464 | auto at_output = input1.sum({1}, true).add(input1); |
6465 | |
6466 | FusionExecutor fe; |
6467 | fe.compileFusion(fusion.get(), {input1}); |
6468 | auto outputs = fe.runFusion({input1}); |
6469 | |
6470 | testValidate( |
6471 | fusion.get(), outputs, {input1}, {at_output}, __LINE__, __FILE__); |
6472 | } |
6473 | |
6474 | TEST_F(NVFuserTest, FusionSimpleWarpPad_CUDA) { |
6475 | auto fusion = std::make_unique<Fusion>(); |
6476 | FusionGuard fg(fusion.get()); |
6477 | |
6478 | auto tv0 = makeSymbolicTensor(2); |
6479 | |
6480 | fusion->addInput(tv0); |
6481 | |
6482 | auto tv1 = sum(tv0, {1}); |
6483 | auto tv2 = broadcast(tv1, {false, true}); |
6484 | auto tv3 = add(tv2, tv0); |
6485 | |
6486 | fusion->addOutput(tv3); |
6487 | |
6488 | // Schedule a persistent kernel |
6489 | auto tv0_cache = tv0->cacheAfter(); |
6490 | tv1->split(1, 8, false); |
6491 | auto tv1_rf = tv1->rFactor({1}); |
6492 | tv1_rf->axis(0)->parallelize(ParallelType::BIDx); |
6493 | tv1_rf->axis(-1)->parallelize(ParallelType::TIDx); |
6494 | tv1_rf->axis(-1)->padToMultipleOfWarp(32); |
6495 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
6496 | tv1->axis(-1)->padToMultipleOfWarp(32); |
6497 | TransformPropagatorWithCheck propagator(tv1_rf); |
6498 | MaxRootDomainInfoSpanningTree(tv1_rf).traverse(&propagator); |
6499 | tv0->axis(-1)->parallelize(ParallelType::TIDx); |
6500 | tv0->axis(-1)->padToMultipleOfWarp(32); |
6501 | tv0_cache->axis(-1)->parallelize(ParallelType::TIDx); |
6502 | tv0_cache->axis(-1)->padToMultipleOfWarp(32); |
6503 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
6504 | tv2->axis(-1)->padToMultipleOfWarp(32); |
6505 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
6506 | tv3->axis(-1)->padToMultipleOfWarp(32); |
6507 | |
6508 | tv0->computeAt(tv3, -1, ComputeAtMode::MostInlined); |
6509 | |
6510 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6511 | at::Tensor input1 = at::randn({16, 127}, options); |
6512 | |
6513 | auto at_output = input1.sum({1}, true).add(input1); |
6514 | |
6515 | FusionExecutor fe; |
6516 | fe.compileFusion(fusion.get(), {input1}); |
6517 | auto outputs = fe.runFusion({input1}); |
6518 | testValidate( |
6519 | fusion.get(), outputs, {input1}, {at_output}, __LINE__, __FILE__); |
6520 | } |
6521 | |
6522 | TEST_F(NVFuserTest, FusionWarpPadMergeSplit_CUDA) { |
6523 | auto fusion = std::make_unique<Fusion>(); |
6524 | FusionGuard fg(fusion.get()); |
6525 | |
6526 | auto tv0 = makeSymbolicTensor(3); |
6527 | |
6528 | fusion->addInput(tv0); |
6529 | |
6530 | auto tv1 = sum(tv0, {1, 2}); |
6531 | auto tv2 = broadcast(tv1, {false, true, true}); |
6532 | auto tv3 = add(tv2, tv0); |
6533 | |
6534 | fusion->addOutput(tv3); |
6535 | |
6536 | // Schedule a persistent kernel |
6537 | auto tv0_cache = tv0->cacheAfter(); |
6538 | tv1->merge(1); |
6539 | tv1->split(1, 8, false); |
6540 | |
6541 | auto tv1_rf = tv1->rFactor({1}); |
6542 | tv1_rf->axis(0)->parallelize(ParallelType::BIDx); |
6543 | tv1_rf->axis(-1)->parallelize(ParallelType::TIDx); |
6544 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
6545 | tv1->axis(-1)->padToMultipleOfWarp(); |
6546 | TransformPropagatorWithCheck propagator(tv1_rf); |
6547 | MaxRootDomainInfoSpanningTree(tv1_rf).traverse(&propagator); |
6548 | tv0->axis(-1)->parallelize(ParallelType::TIDx); |
6549 | tv0_cache->axis(-1)->parallelize(ParallelType::TIDx); |
6550 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
6551 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
6552 | |
6553 | tv0->computeAt(tv3, -1, ComputeAtMode::MostInlined); |
6554 | |
6555 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6556 | at::Tensor input1 = at::randn({16, 17, 128}, options); |
6557 | |
6558 | auto at_output = input1.sum({1, 2}, true).add(input1); |
6559 | |
6560 | FusionExecutor fe; |
6561 | fe.compileFusion(fusion.get(), {input1}); |
6562 | auto outputs = fe.runFusion({input1}); |
6563 | testValidate( |
6564 | fusion.get(), outputs, {input1}, {at_output}, __LINE__, __FILE__); |
6565 | } |
6566 | |
6567 | TEST_F(NVFuserTest, FusionSerialWarpReduction_CUDA) { |
6568 | auto fusion = std::make_unique<Fusion>(); |
6569 | FusionGuard fg(fusion.get()); |
6570 | |
6571 | auto tv0 = makeSymbolicTensor(3); |
6572 | |
6573 | fusion->addInput(tv0); |
6574 | |
6575 | auto tv1 = sum(tv0, {1, 2}); |
6576 | auto tv2 = broadcast(tv1, {false, true, true}); |
6577 | auto tv3 = add(tv2, tv0); |
6578 | |
6579 | fusion->addOutput(tv3); |
6580 | |
6581 | // Schedule a persistent kernel |
6582 | auto tv0_cache = tv0->cacheAfter(); |
6583 | tv1->merge(1); |
6584 | tv1->split(1, 8, false); |
6585 | |
6586 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
6587 | tv1->axis(-1)->padToMultipleOfWarp(); |
6588 | TransformPropagatorWithCheck propagator(tv1); |
6589 | MaxRootDomainInfoSpanningTree(tv1).traverse(&propagator); |
6590 | tv0->axis(-1)->parallelize(ParallelType::TIDx); |
6591 | tv0_cache->axis(-1)->parallelize(ParallelType::TIDx); |
6592 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
6593 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
6594 | |
6595 | tv0->computeAt(tv3, -1, ComputeAtMode::MostInlined); |
6596 | |
6597 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6598 | at::Tensor input1 = at::randn({16, 17, 128}, options); |
6599 | |
6600 | auto at_output = input1.sum({1, 2}, true).add(input1); |
6601 | |
6602 | FusionExecutor fe; |
6603 | fe.compileFusion(fusion.get(), {input1}); |
6604 | auto outputs = fe.runFusion({input1}); |
6605 | testValidate( |
6606 | fusion.get(), outputs, {input1}, {at_output}, __LINE__, __FILE__); |
6607 | } |
6608 | |
6609 | TEST_F(NVFuserTest, FusionTrivialWarpReduction_CUDA) { |
6610 | auto fusion = std::make_unique<Fusion>(); |
6611 | FusionGuard fg(fusion.get()); |
6612 | |
6613 | auto tv0 = makeConcreteTensor({17, 18, 128, 1}); |
6614 | |
6615 | fusion->addInput(tv0); |
6616 | |
6617 | auto tv1 = sum(tv0, {1, 2, 3}); |
6618 | auto tv2 = broadcast(tv1, {false, true, true, true}); |
6619 | auto tv3 = add(tv2, tv0); |
6620 | |
6621 | fusion->addOutput(tv3); |
6622 | |
6623 | // Schedule a persistent kernel |
6624 | auto tv0_cache = tv0->cacheAfter(); |
6625 | tv1->merge(1); |
6626 | tv1->split(1, 8, false); |
6627 | |
6628 | auto tv1_rf = tv1->rFactor({1}); |
6629 | tv1_rf->axis(0)->parallelize(ParallelType::BIDx); |
6630 | tv1_rf->axis(-2)->parallelize(ParallelType::TIDx); |
6631 | tv1->axis(-2)->parallelize(ParallelType::TIDx); |
6632 | tv1->axis(-2)->padToMultipleOfWarp(); |
6633 | TransformPropagatorWithCheck propagator(tv1_rf); |
6634 | MaxRootDomainInfoSpanningTree(tv1_rf).traverse(&propagator); |
6635 | tv0->axis(-2)->parallelize(ParallelType::TIDx); |
6636 | tv0_cache->axis(-2)->parallelize(ParallelType::TIDx); |
6637 | tv2->axis(-2)->parallelize(ParallelType::TIDx); |
6638 | tv3->axis(-2)->parallelize(ParallelType::TIDx); |
6639 | |
6640 | tv0->computeAt(tv3, -1, ComputeAtMode::MostInlined); |
6641 | |
6642 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6643 | at::Tensor input1 = at::randn({17, 18, 128, 1}, options); |
6644 | |
6645 | auto at_output = input1.sum({1, 2, 3}, true).add(input1); |
6646 | |
6647 | FusionExecutor fe; |
6648 | fe.compileFusion(fusion.get(), {input1}); |
6649 | auto outputs = fe.runFusion({input1}); |
6650 | testValidate( |
6651 | fusion.get(), outputs, {input1}, {at_output}, __LINE__, __FILE__); |
6652 | } |
6653 | |
6654 | TEST_F(NVFuserTest, FusionMultipleDimBinding_CUDA) { |
6655 | auto fusion = std::make_unique<Fusion>(); |
6656 | FusionGuard fg(fusion.get()); |
6657 | |
6658 | auto tv0 = makeSymbolicTensor(2); |
6659 | auto tv_add = makeSymbolicTensor(2); |
6660 | |
6661 | fusion->addInput(tv0); |
6662 | fusion->addInput(tv_add); |
6663 | |
6664 | auto tv1 = sum(tv0, {1}); |
6665 | auto tv2 = broadcast(tv1, {false, true}); |
6666 | auto tv3 = add(tv2, tv0); |
6667 | auto tv4 = add(tv0, tv_add); |
6668 | |
6669 | fusion->addOutput(tv3); |
6670 | fusion->addOutput(tv4); |
6671 | |
6672 | // Schedule a persistent kernel |
6673 | auto tv0_cache = tv0->cacheAfter(); |
6674 | tv1->split(1, 8, false); |
6675 | auto tv1_rf = tv1->rFactor({1}); |
6676 | tv1_rf->axis(0)->parallelize(ParallelType::BIDx); |
6677 | tv1_rf->axis(-1)->parallelize(ParallelType::TIDx); |
6678 | tv1_rf->axis(-1)->padToMultipleOfWarp(32); |
6679 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
6680 | tv1->axis(-1)->padToMultipleOfWarp(32); |
6681 | TransformPropagatorWithCheck propagator(tv1_rf); |
6682 | MaxRootDomainInfoSpanningTree(tv1_rf).traverse(&propagator); |
6683 | tv0->axis(-1)->parallelize(ParallelType::TIDx); |
6684 | tv0->axis(-1)->padToMultipleOfWarp(32); |
6685 | tv0_cache->axis(-1)->parallelize(ParallelType::TIDx); |
6686 | tv0_cache->axis(-1)->padToMultipleOfWarp(32); |
6687 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
6688 | tv2->axis(-1)->padToMultipleOfWarp(32); |
6689 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
6690 | tv3->axis(-1)->padToMultipleOfWarp(32); |
6691 | tv4->axis(-1)->parallelize(ParallelType::TIDx); |
6692 | tv4->axis(-1)->padToMultipleOfWarp(64); |
6693 | |
6694 | tv0->computeAt(tv3, -1, ComputeAtMode::MostInlined); |
6695 | |
6696 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6697 | at::Tensor input1 = at::randn({16, 128}, options); |
6698 | at::Tensor input2 = at::randn({16, 128}, options); |
6699 | |
6700 | auto at_output = input1.sum({1}, true).add(input1); |
6701 | |
6702 | FusionExecutor fe; |
6703 | fe.compileFusion(fusion.get(), {input1, input2}); |
6704 | auto outputs = fe.runFusion({input1, input2}); |
6705 | testValidate( |
6706 | fusion.get(), |
6707 | outputs, |
6708 | {input1, input2}, |
6709 | {at_output, input1 + input2}, |
6710 | __LINE__, |
6711 | __FILE__); |
6712 | } |
6713 | |
6714 | TEST_F(NVFuserTest, FusionPadNoWarpReduce_CUDA) { |
6715 | auto fusion = std::make_unique<Fusion>(); |
6716 | FusionGuard fg(fusion.get()); |
6717 | |
6718 | auto tv0 = makeSymbolicTensor(2); |
6719 | |
6720 | fusion->addInput(tv0); |
6721 | |
6722 | auto tv1 = sum(tv0, {1}); |
6723 | auto tv2 = broadcast(tv1, {false, true}); |
6724 | auto tv3 = add(tv2, tv0); |
6725 | |
6726 | fusion->addOutput(tv3); |
6727 | |
6728 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
6729 | tv1->axis(-1)->padToMultipleOfWarp(); |
6730 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
6731 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
6732 | |
6733 | tv1->axis(0)->parallelize(ParallelType::TIDy); |
6734 | tv2->axis(0)->parallelize(ParallelType::TIDy); |
6735 | tv3->axis(0)->parallelize(ParallelType::TIDy); |
6736 | |
6737 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6738 | at::Tensor input1 = at::randn({16, 31}, options); |
6739 | |
6740 | auto at_output = input1.sum({1}, true).add(input1); |
6741 | |
6742 | FusionExecutor fe; |
6743 | fe.compileFusion(fusion.get(), {input1}); |
6744 | auto outputs = fe.runFusion({input1}); |
6745 | testValidate( |
6746 | fusion.get(), outputs, {input1}, {at_output}, __LINE__, __FILE__); |
6747 | } |
6748 | |
6749 | TEST_F(NVFuserTest, FusionWarpMutipleThreadDim_CUDA) { |
6750 | auto fusion = std::make_unique<Fusion>(); |
6751 | FusionGuard fg(fusion.get()); |
6752 | |
6753 | auto tv0 = makeSymbolicTensor(2); |
6754 | fusion->addInput(tv0); |
6755 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
6756 | auto tv2 = sum(tv1, {1}); |
6757 | fusion->addOutput(tv2); |
6758 | |
6759 | tv2->split(1, 8); |
6760 | auto tv2_rf = tv2->rFactor({-1}); |
6761 | tv2_rf->axis(-1)->parallelize(ParallelType::TIDx); |
6762 | tv2_rf->axis(-1)->padToMultipleOfWarp(); |
6763 | |
6764 | TransformPropagatorWithCheck propagator(tv2_rf); |
6765 | MaxRootDomainInfoSpanningTree(tv2_rf).traverse(&propagator); |
6766 | |
6767 | tv0->axis(-1)->parallelize(ParallelType::TIDx); |
6768 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
6769 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
6770 | tv2->axis(1)->parallelize(ParallelType::TIDy); |
6771 | tv0->computeAt(tv2, 2); |
6772 | |
6773 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6774 | at::Tensor input1 = at::randn({16, 31}, options); |
6775 | |
6776 | auto at_output = (input1 + 1).sum({1}); |
6777 | |
6778 | FusionExecutor fe; |
6779 | fe.compileFusion(fusion.get(), {input1}); |
6780 | auto outputs = fe.runFusion({input1}); |
6781 | testValidate( |
6782 | fusion.get(), outputs, {input1}, {at_output}, __LINE__, __FILE__); |
6783 | } |
6784 | |
6785 | TEST_F(NVFuserTest, FusionWarpReduceUnrollOuterLoop_CUDA) { |
6786 | auto fusion = std::make_unique<Fusion>(); |
6787 | FusionGuard fg(fusion.get()); |
6788 | |
6789 | auto tv0 = makeSymbolicTensor(2); |
6790 | |
6791 | fusion->addInput(tv0); |
6792 | |
6793 | auto tv1 = sum(tv0, {1}); |
6794 | auto tv2 = broadcast(tv1, {false, true}); |
6795 | auto tv3 = add(tv2, tv0); |
6796 | |
6797 | fusion->addOutput(tv3); |
6798 | |
6799 | // Schedule a persistent kernel |
6800 | auto tv0_cache = tv0->cacheAfter(); |
6801 | tv1->split(1, 8, false); |
6802 | tv1->split(0, 4); |
6803 | auto tv1_rf = tv1->rFactor({2}); |
6804 | |
6805 | tv1_rf->axis(0)->parallelize(ParallelType::BIDx); |
6806 | tv1_rf->axis(1)->parallelize(ParallelType::Unroll); |
6807 | tv1_rf->axis(-1)->parallelize(ParallelType::TIDx); |
6808 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
6809 | tv1->axis(-1)->padToMultipleOfWarp(); |
6810 | tv1->axis(1)->parallelize(ParallelType::Unroll); |
6811 | TransformPropagatorWithCheck propagator(tv1_rf); |
6812 | MaxRootDomainInfoSpanningTree(tv1_rf).traverse(&propagator); |
6813 | tv0->axis(-1)->parallelize(ParallelType::TIDx); |
6814 | tv0->axis(1)->parallelize(ParallelType::Unroll); |
6815 | tv0_cache->axis(-1)->parallelize(ParallelType::TIDx); |
6816 | tv0_cache->axis(1)->parallelize(ParallelType::Unroll); |
6817 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
6818 | tv2->axis(1)->parallelize(ParallelType::Unroll); |
6819 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
6820 | tv3->axis(1)->parallelize(ParallelType::Unroll); |
6821 | |
6822 | tv0->computeAt(tv3, -1, ComputeAtMode::MostInlined); |
6823 | |
6824 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6825 | at::Tensor input1 = at::randn({16, 128}, options); |
6826 | |
6827 | auto at_output = input1.sum({1}, true).add(input1); |
6828 | |
6829 | FusionExecutor fe; |
6830 | fe.compileFusion(fusion.get(), {input1}); |
6831 | auto outputs = fe.runFusion({input1}); |
6832 | testValidate( |
6833 | fusion.get(), outputs, {input1}, {at_output}, __LINE__, __FILE__); |
6834 | } |
6835 | |
6836 | // Repro of issue #1579 |
6837 | TEST_F(NVFuserTest, FusionWarpReducePredication_CUDA) { |
6838 | Fusion fusion; |
6839 | FusionGuard fg(&fusion); |
6840 | |
6841 | std::vector<int64_t> shape1 = {1024}; |
6842 | std::vector<int64_t> shape2 = {50}; |
6843 | |
6844 | auto tv0 = makeConcreteTensor(shape1); |
6845 | fusion.addInput(tv0); |
6846 | auto tv1 = sum(tv0, {0}); |
6847 | fusion.addOutput(tv1); |
6848 | |
6849 | auto tv2 = makeConcreteTensor(shape2); |
6850 | fusion.addInput(tv2); |
6851 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
6852 | auto tv4 = sum(tv3, {0}); |
6853 | auto tv5 = add(tv4, IrBuilder::create<Double>(1)); |
6854 | fusion.addOutput(tv5); |
6855 | |
6856 | // Just to fill the smem buffer by a thread block of 1024 threads |
6857 | // with some values |
6858 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
6859 | |
6860 | // Make the tv4_rf reduction a warp reduction to trigger the |
6861 | // bug. Since the smem buffer is filled with some values due to the |
6862 | // reduction of tv1, those values would be used by predicated-out |
6863 | // threads. |
6864 | tv4->split(-1, 10); |
6865 | auto tv4_rf = tv4->rFactor({-1}); |
6866 | tv4_rf->axis(-1)->parallelize(ParallelType::TIDx); |
6867 | tv4_rf->axis(-1)->padToMultipleOfWarp(); |
6868 | |
6869 | tv4_rf->computeAt(tv4, 1); |
6870 | |
6871 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6872 | auto t0 = at::randn(shape1, options); |
6873 | auto t2 = at::randn(shape2, options); |
6874 | |
6875 | FusionExecutor fe; |
6876 | fe.compileFusion(&fusion, {t0, t2}); |
6877 | auto cg_outputs = fe.runFusion({t0, t2}); |
6878 | |
6879 | auto t1 = t0.sum({0}); |
6880 | auto t4 = (t2 + 1).sum({0}) + 1; |
6881 | |
6882 | testValidate(&fusion, cg_outputs, {t0, t2}, {t1, t4}, __LINE__, __FILE__); |
6883 | } |
6884 | |
6885 | TEST_F(NVFuserTest, FusionSegfaultReduction_CUDA) { |
6886 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
6887 | Fusion& fusion = *fusion_ptr.get(); |
6888 | FusionGuard fg(&fusion); |
6889 | |
6890 | int batch = 2; |
6891 | int c = 1; |
6892 | int h = 1; |
6893 | int w = 1; |
6894 | int numDims = 4; |
6895 | |
6896 | auto input = makeConcreteTensor({-1, 1, 1, 1}); |
6897 | fusion.addInput(input); |
6898 | auto bcast_bias = makeConcreteTensor({-1, 1, 1, 1}); |
6899 | fusion.addInput(bcast_bias); |
6900 | |
6901 | std::vector<int64_t> at_sum_axes; |
6902 | std::vector<int> outer_reduction_axes; |
6903 | std::vector<bool> outer_broadcast_mask(numDims, false); |
6904 | Val* N = IrBuilder::create<Double>(1); |
6905 | for (const auto axis : c10::irange(numDims)) { |
6906 | if (axis != 1) { |
6907 | outer_reduction_axes.push_back(axis); |
6908 | at_sum_axes.push_back(axis); |
6909 | outer_broadcast_mask[axis] = true; |
6910 | N = mul(N, input->domain()->domain()[axis]->extent()); |
6911 | } |
6912 | } |
6913 | |
6914 | auto output0 = mul(input, bcast_bias); |
6915 | fusion.addOutput(output0); |
6916 | auto output1 = sum(output0, outer_reduction_axes); |
6917 | fusion.addOutput(output1); |
6918 | |
6919 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6920 | at::Tensor input0 = at::randn({batch, c, h, w}, options); |
6921 | at::Tensor input1 = at::randn({batch, c, h, w}, options); |
6922 | |
6923 | auto at_output0 = input0.mul(input1); |
6924 | auto at_output1 = at_output0.sum(at_sum_axes); |
6925 | |
6926 | FusionExecutorCache fec(std::move(fusion_ptr)); |
6927 | std::vector<IValue> inputs = {input0, input1}; |
6928 | auto outputs = fec.runFusionWithInputs(inputs); |
6929 | |
6930 | testValidate( |
6931 | &fusion, outputs, inputs, {at_output0, at_output1}, __LINE__, __FILE__); |
6932 | } |
6933 | |
6934 | TEST_F(NVFuserTest, FusionPredicateElimination1_CUDA) { |
6935 | Fusion fusion; |
6936 | FusionGuard fg(&fusion); |
6937 | |
6938 | auto tv0 = makeSymbolicTensor(1); |
6939 | fusion.addInput(tv0); |
6940 | |
6941 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
6942 | auto tv2 = add(tv1, IrBuilder::create<Double>(2)); |
6943 | auto tv3 = add(tv2, IrBuilder::create<Double>(3)); |
6944 | |
6945 | fusion.addOutput(tv3); |
6946 | |
6947 | tv3->split(0, 32); |
6948 | tv0->computeAt(tv3, 1); |
6949 | |
6950 | tv2->axis(1)->parallelize(ParallelType::Unswitch); |
6951 | |
6952 | { |
6953 | GpuLower gpulw(&fusion); |
6954 | TORCH_CHECK(!PredicatedChecker::isPredicated(tv2, gpulw)); |
6955 | } |
6956 | |
6957 | tv2->axis(1)->parallelize(ParallelType::Serial); |
6958 | tv2->split(1, 5); |
6959 | |
6960 | { |
6961 | GpuLower gpulw(&fusion); |
6962 | TORCH_CHECK(PredicatedChecker::isPredicated(tv2, gpulw)); |
6963 | } |
6964 | } |
6965 | |
6966 | // Repro of issue #1571 |
6967 | TEST_F(NVFuserTest, FusionPredicateElimination2_CUDA) { |
6968 | Fusion fusion; |
6969 | FusionGuard fg(&fusion); |
6970 | |
6971 | std::vector<int64_t> shape({10, 11}); |
6972 | |
6973 | auto tv0 = makeConcreteTensor(shape); |
6974 | fusion.addInput(tv0); |
6975 | |
6976 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
6977 | auto tv2 = sum(tv1, {1}); |
6978 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
6979 | |
6980 | fusion.addOutput(tv3); |
6981 | |
6982 | tv1->split(1, 4); |
6983 | tv1->split(0, 4); |
6984 | tv2->split(1, 4); |
6985 | tv2->split(0, 4); |
6986 | |
6987 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
6988 | auto t0 = at::randn(shape, options); |
6989 | |
6990 | FusionExecutor fe; |
6991 | fe.compileFusion(&fusion, {t0}); |
6992 | auto cg_outputs = fe.runFusion({t0}); |
6993 | |
6994 | auto ref = (t0 + 1).sum({1}) + 1; |
6995 | |
6996 | testValidate(&fusion, cg_outputs, {t0}, {ref}, __LINE__, __FILE__); |
6997 | } |
6998 | |
6999 | TEST_F(NVFuserTest, FusionPredicateElimination3_CUDA) { |
7000 | Fusion fusion; |
7001 | FusionGuard fg(&fusion); |
7002 | |
7003 | auto tv0 = makeSymbolicTensor(1); |
7004 | fusion.addInput(tv0); |
7005 | |
7006 | auto tv1 = sum(tv0, {0}); |
7007 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
7008 | fusion.addOutput(tv2); |
7009 | |
7010 | auto tv3 = tv0->cacheAfter(); |
7011 | |
7012 | tv1->split(0, 10); |
7013 | tv1->split(0, 33); |
7014 | TransformPropagatorWithCheck propagator(tv1); |
7015 | MaxRootDomainInfoSpanningTree(tv1).traverse(&propagator); |
7016 | |
7017 | auto tv4 = tv1->rFactor({-1}); |
7018 | auto tv5 = tv1->rFactor({-1}); |
7019 | |
7020 | tv4->axis(0)->parallelize(ParallelType::BIDx); |
7021 | tv4->axis(1)->parallelize(ParallelType::TIDx); |
7022 | scheduler_utils::parallelizeAllLike(tv4); |
7023 | |
7024 | GpuLower gpulw(&fusion); |
7025 | |
7026 | // The fusion has three reductions: one within each thread, one |
7027 | // within each block, and another with the whole grid. All of them |
7028 | // should not need to be predicated as they use the same init value |
7029 | // and same reduction op. |
7030 | TORCH_CHECK(!PredicatedChecker::isPredicated(tv4, gpulw)); |
7031 | TORCH_CHECK(!PredicatedChecker::isPredicated(tv5, gpulw)); |
7032 | TORCH_CHECK(!PredicatedChecker::isPredicated(tv1, gpulw)); |
7033 | |
7034 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7035 | |
7036 | for (auto size : {1, 2, 999, 1001, 1234, 10000}) { |
7037 | auto t0 = at::randn({size}, options); |
7038 | |
7039 | FusionExecutor fe; |
7040 | fe.compileFusion(&fusion, {t0}); |
7041 | auto cg_outputs = fe.runFusion({t0}); |
7042 | |
7043 | auto ref = sum(t0) + 1; |
7044 | testValidate(&fusion, cg_outputs, {t0}, {ref}, __LINE__, __FILE__); |
7045 | } |
7046 | } |
7047 | |
7048 | TEST_F(NVFuserTest, FusionPredicateElimination4_CUDA) { |
7049 | Fusion fusion; |
7050 | FusionGuard fg(&fusion); |
7051 | |
7052 | auto tv0 = makeSymbolicTensor(2); |
7053 | fusion.addInput(tv0); |
7054 | |
7055 | auto tv1 = sum(tv0, {1}); |
7056 | |
7057 | auto tv2 = sum(tv1, {0}); |
7058 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
7059 | fusion.addOutput(tv3); |
7060 | |
7061 | auto tv4 = max(tv1, {0}); |
7062 | auto tv5 = add(tv4, IrBuilder::create<Double>(1)); |
7063 | fusion.addOutput(tv5); |
7064 | |
7065 | tv1->split(1, 7); |
7066 | tv1->split(0, 11); |
7067 | tv1->reorder({{1, 2}, {2, 1}}); |
7068 | TransformPropagatorWithCheck propagator(tv1); |
7069 | MaxRootDomainInfoSpanningTree(tv1).traverse(&propagator); |
7070 | |
7071 | tv1->axis(0)->parallelize(ParallelType::TIDy); |
7072 | tv1->axis(1)->parallelize(ParallelType::TIDx); |
7073 | scheduler_utils::parallelizeAllLike(tv1); |
7074 | |
7075 | GpuLower gpulw(&fusion); |
7076 | |
7077 | // tv2 uses the same op and init with tv1, so tv2 should be fine |
7078 | // without a predicate. However, tv4, while it uses the tv1 as its |
7079 | // input, the reduction op and init value is different from those of |
7080 | // tv1, so tv4 needs to be predicated. |
7081 | TORCH_CHECK(!PredicatedChecker::isPredicated(tv2, gpulw)); |
7082 | TORCH_CHECK(PredicatedChecker::isPredicated(tv4, gpulw)); |
7083 | |
7084 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7085 | |
7086 | std::vector<int64_t> sizes = {1, 2, 33, 34, 64, 99}; |
7087 | for (auto s0 : sizes) { |
7088 | for (auto s1 : sizes) { |
7089 | auto t0 = at::randn({s0, s1}, options); |
7090 | |
7091 | FusionExecutor fe; |
7092 | fe.compileFusion(&fusion, {t0}); |
7093 | auto cg_outputs = fe.runFusion({t0}); |
7094 | |
7095 | auto t1 = t0.sum({1}); |
7096 | auto t3 = t1.sum({0}) + 1; |
7097 | auto t5 = std::get<0>(t1.max(0)) + 1; |
7098 | |
7099 | testValidate(&fusion, cg_outputs, {t0}, {t3, t5}, __LINE__, __FILE__); |
7100 | } |
7101 | } |
7102 | } |
7103 | |
7104 | TEST_F(NVFuserTest, FusionPredicateElimination5_CUDA) { |
7105 | Fusion fusion; |
7106 | FusionGuard fg(&fusion); |
7107 | |
7108 | auto tv0 = makeSymbolicTensor(1); |
7109 | fusion.addInput(tv0); |
7110 | |
7111 | auto tv1 = set(tv0); |
7112 | auto tvs2 = Welford(tv1, {0}); |
7113 | auto tv3 = set(tvs2.avg); |
7114 | fusion.addOutput(tv3); |
7115 | |
7116 | tvs2.avg->split(0, 4); |
7117 | TransformPropagatorWithCheck propagator(tvs2.avg); |
7118 | MaxRootDomainInfoSpanningTree(tvs2.avg).traverse(&propagator); |
7119 | auto avg_rf = ir_utils::rfactorHelper(tvs2.avg, {1}); |
7120 | |
7121 | avg_rf->axis(0)->parallelize(ParallelType::TIDx); |
7122 | scheduler_utils::parallelizeAllLike(avg_rf); |
7123 | |
7124 | GpuLower gpulw(&fusion); |
7125 | |
7126 | // The first per-thread welford needs to be predicated as the N |
7127 | // input is different from its init value. The second welford op |
7128 | // does not need a predicate. |
7129 | TORCH_CHECK(PredicatedChecker::isPredicated(avg_rf, gpulw)); |
7130 | TORCH_CHECK(!PredicatedChecker::isPredicated(tvs2.avg, gpulw)); |
7131 | |
7132 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7133 | |
7134 | std::vector<int64_t> sizes = {1, 2, 33, 34, 64, 99}; |
7135 | for (auto s0 : sizes) { |
7136 | auto t0 = at::randn({s0}, options); |
7137 | |
7138 | FusionExecutor fe; |
7139 | fe.compileFusion(&fusion, {t0}); |
7140 | auto cg_outputs = fe.runFusion({t0}); |
7141 | |
7142 | auto ref = t0.mean({0}); |
7143 | |
7144 | testValidate(&fusion, cg_outputs, {t0}, {ref}, __LINE__, __FILE__); |
7145 | } |
7146 | } |
7147 | |
7148 | TEST_F(NVFuserTest, FusionPredicateElimination6_CUDA) { |
7149 | Fusion fusion; |
7150 | FusionGuard fg(&fusion); |
7151 | |
7152 | auto tv0 = makeConcreteTensor({2, 3}); |
7153 | fusion.addInput(tv0); |
7154 | |
7155 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
7156 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
7157 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
7158 | auto tv4 = add(tv3, IrBuilder::create<Double>(1)); |
7159 | fusion.addOutput(tv4); |
7160 | |
7161 | tv4->split(1, 5); |
7162 | TransformPropagatorWithCheck propagator(tv4); |
7163 | MaxRootDomainInfoSpanningTree(tv4).traverse(&propagator); |
7164 | |
7165 | tv4->reorder({{0, 1}, {1, 0}}); |
7166 | tv3->computeAt(tv4, 1); |
7167 | |
7168 | GpuLower gpulw(&fusion); |
7169 | |
7170 | // The expression for tv2 is a local-to-local expression. It |
7171 | // satisfies all the requirements of predicate elimination, except |
7172 | // for the on on split root domains. As the second root axis of tv2 |
7173 | // is split, its index exceeds its extent (i.e., 3 in this case) |
7174 | // without its predicate. |
7175 | TORCH_CHECK(PredicatedChecker::isPredicated(tv2, gpulw)); |
7176 | |
7177 | // Unlike tv2, tv3 is computed at tv4, so the second root axis does |
7178 | // have a zero domain. Its index should look like "i * 5 + j", where |
7179 | // i comes from the first root domain and j comes from the split |
7180 | // inner domain. |
7181 | TORCH_CHECK(!PredicatedChecker::isPredicated(tv3, gpulw)); |
7182 | |
7183 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7184 | auto t0 = at::randn({2, 3}, options); |
7185 | |
7186 | FusionExecutor fe; |
7187 | fe.compileFusion(&fusion, {t0}); |
7188 | auto cg_outputs = fe.runFusion({t0}); |
7189 | |
7190 | auto ref = t0 + 4; |
7191 | |
7192 | testValidate(&fusion, cg_outputs, {t0}, {ref}, __LINE__, __FILE__); |
7193 | } |
7194 | |
7195 | TEST_F(NVFuserTest, FusionPredicateElimination7_CUDA) { |
7196 | Fusion fusion; |
7197 | FusionGuard fg(&fusion); |
7198 | |
7199 | auto tv0 = makeSymbolicTensor(1); |
7200 | fusion.addInput(tv0); |
7201 | |
7202 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
7203 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
7204 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
7205 | fusion.addOutput(tv3); |
7206 | |
7207 | tv3->split(-1, 5); |
7208 | tv3->split(-1, 4); |
7209 | tv3->split(-1, 3); |
7210 | TransformPropagatorWithCheck propagator(tv3); |
7211 | MaxRootDomainInfoSpanningTree(tv3).traverse(&propagator); |
7212 | |
7213 | tv0->computeAt(tv3, 1); |
7214 | |
7215 | // The last split of tv2 is a non-divisible split, and omitting it |
7216 | // is invalid. |
7217 | GpuLower gpulw(&fusion); |
7218 | TORCH_CHECK(PredicatedChecker::isPredicated(tv2, gpulw)); |
7219 | |
7220 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7221 | auto t0 = at::randn({123}, options); |
7222 | |
7223 | FusionExecutor fe; |
7224 | fe.compileFusion(&fusion, {t0}); |
7225 | auto cg_outputs = fe.runFusion({t0}); |
7226 | |
7227 | auto ref = t0 + 3; |
7228 | |
7229 | testValidate(&fusion, cg_outputs, {t0}, {ref}, __LINE__, __FILE__); |
7230 | } |
7231 | |
7232 | TEST_F(NVFuserTest, FusionForceFp16Simple_CUDA) { |
7233 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
7234 | auto fusion = fusion_ptr.get(); |
7235 | FusionGuard fg(fusion); |
7236 | |
7237 | auto tv0 = makeSymbolicTensor(2); |
7238 | auto tv1 = makeSymbolicTensor(2); |
7239 | |
7240 | fusion->addInput(tv0); |
7241 | fusion->addInput(tv1); |
7242 | |
7243 | // Group 1 |
7244 | auto tv2 = sum(tv0, {1}); |
7245 | auto tv3 = broadcast(tv2, {false, true}); |
7246 | |
7247 | // Group 2 |
7248 | auto tv4 = add(tv3, tv1); // Edge: tv3: expect cast |
7249 | auto tv5 = castOp(DataType::Half, tv4); |
7250 | |
7251 | fusion->addOutput(tv5); |
7252 | |
7253 | FusionExecutorCache fec(std::move(fusion_ptr)); |
7254 | |
7255 | std::vector<int64_t> shape{15, 16}; |
7256 | |
7257 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7258 | auto in0 = at::randn(shape, options); |
7259 | auto in1 = at::randn(shape, options); |
7260 | fec.runFusionWithInputs({in0, in1}); |
7261 | |
7262 | // Check the segmented edge is fp16 |
7263 | auto segmented_fusion = fec.getMostRecentKernelRuntime()->fusionSegments(); |
7264 | for (auto edge : segmented_fusion->edges()) { |
7265 | auto edge_tv = edge->val->as<TensorView>(); |
7266 | TORCH_CHECK(edge_tv->getDataType() == DataType::Half); |
7267 | } |
7268 | } |
7269 | |
7270 | TEST_F(NVFuserTest, FusionForceBf16Simple_CUDA) { |
7271 | #if !defined(USE_ROCM) |
7272 | // requires ampere+ GPU |
7273 | if (!deviceMajorMinorCheck(8)) { |
7274 | GTEST_SKIP() << "skipping tests on pre-AMPERE GPUs" ; |
7275 | return; |
7276 | } |
7277 | |
7278 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
7279 | auto fusion = fusion_ptr.get(); |
7280 | FusionGuard fg(fusion); |
7281 | |
7282 | auto tv0 = makeSymbolicTensor(2); |
7283 | auto tv1 = makeSymbolicTensor(2); |
7284 | |
7285 | fusion->addInput(tv0); |
7286 | fusion->addInput(tv1); |
7287 | |
7288 | // Group 1 |
7289 | auto tv2 = sum(tv0, {1}); |
7290 | auto tv3 = broadcast(tv2, {false, true}); |
7291 | |
7292 | // Group 2 |
7293 | auto tv4 = add(tv3, tv1); // Edge: tv3: expect cast |
7294 | auto tv5 = castOp(DataType::BFloat16, tv4); |
7295 | |
7296 | fusion->addOutput(tv5); |
7297 | |
7298 | FusionExecutorCache fec(std::move(fusion_ptr)); |
7299 | |
7300 | std::vector<int64_t> shape{15, 16}; |
7301 | |
7302 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7303 | auto in0 = at::randn(shape, options); |
7304 | auto in1 = at::randn(shape, options); |
7305 | fec.runFusionWithInputs({in0, in1}); |
7306 | |
7307 | // Check the segmented edge is bf16 |
7308 | auto segmented_fusion = fec.getMostRecentKernelRuntime()->fusionSegments(); |
7309 | for (auto edge : segmented_fusion->edges()) { |
7310 | auto edge_tv = edge->val->as<TensorView>(); |
7311 | TORCH_CHECK(edge_tv->getDataType() == DataType::BFloat16); |
7312 | } |
7313 | #else |
7314 | GTEST_SKIP() << "requires cuda 11.0 or newer toolkit" ; |
7315 | #endif |
7316 | } |
7317 | |
7318 | TEST_F(NVFuserTest, FusionForceFp16NotAllCast_CUDA) { |
7319 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
7320 | auto fusion = fusion_ptr.get(); |
7321 | FusionGuard fg(fusion); |
7322 | |
7323 | auto tv0 = makeSymbolicTensor(3); |
7324 | auto tv1 = makeSymbolicTensor(3); |
7325 | |
7326 | fusion->addInput(tv0); |
7327 | fusion->addInput(tv1); |
7328 | |
7329 | // Group 1 |
7330 | auto tv3 = sum(tv0, {1}); |
7331 | auto tv4 = broadcast(tv3, {false, true, false}); |
7332 | auto tv5 = sum(tv0, {1}); |
7333 | |
7334 | // Group 2 |
7335 | auto tv6 = add(tv4, tv1); // edge tv4, expect cast |
7336 | auto tv7 = castOp(DataType::Half, tv6); |
7337 | |
7338 | // Group 3 |
7339 | auto tv8 = sum(tv5, {1}); // edge tv5, don't expect cast |
7340 | |
7341 | fusion->addOutput(tv7); |
7342 | fusion->addOutput(tv8); |
7343 | |
7344 | FusionExecutorCache fec(std::move(fusion_ptr)); |
7345 | |
7346 | std::vector<int64_t> shape{16, 16, 16}; |
7347 | |
7348 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7349 | auto in0 = at::randn(shape, options); |
7350 | auto in1 = at::randn(shape, options); |
7351 | fec.runFusionWithInputs({in0, in1}); |
7352 | |
7353 | auto segmented_fusion = fec.getMostRecentKernelRuntime()->fusionSegments(); |
7354 | auto complete_fusion = segmented_fusion->completeFusion(); |
7355 | |
7356 | // Check that the edge that wasn't fp16 is the producer of the |
7357 | // reduction op, i.e. tv8 = sum(tv5,{1});. |
7358 | for (auto edge : segmented_fusion->edges()) { |
7359 | auto edge_tv = edge->val->as<TensorView>(); |
7360 | if (edge_tv->getDataType() == DataType::Float) { |
7361 | auto consumer = *(complete_fusion->unordered_uses(edge_tv).begin()); |
7362 | TORCH_CHECK(consumer->isA<ReductionOp>()); |
7363 | } |
7364 | } |
7365 | } |
7366 | |
7367 | TEST_F(NVFuserTest, FusionForceBf16NotAllCast_CUDA) { |
7368 | #if !defined(USE_ROCM) |
7369 | // requires ampere+ GPU |
7370 | if (!deviceMajorMinorCheck(8)) { |
7371 | GTEST_SKIP() << "skipping tests on pre-AMPERE GPUs" ; |
7372 | return; |
7373 | } |
7374 | |
7375 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
7376 | auto fusion = fusion_ptr.get(); |
7377 | FusionGuard fg(fusion); |
7378 | |
7379 | auto tv0 = makeSymbolicTensor(3); |
7380 | auto tv1 = makeSymbolicTensor(3); |
7381 | |
7382 | fusion->addInput(tv0); |
7383 | fusion->addInput(tv1); |
7384 | |
7385 | // Group 1 |
7386 | auto tv3 = sum(tv0, {1}); |
7387 | auto tv4 = broadcast(tv3, {false, true, false}); |
7388 | auto tv5 = sum(tv0, {1}); |
7389 | |
7390 | // Group 2 |
7391 | auto tv6 = add(tv4, tv1); // edge tv4, expect cast |
7392 | auto tv7 = castOp(DataType::BFloat16, tv6); |
7393 | |
7394 | // Group 3 |
7395 | auto tv8 = sum(tv5, {1}); // edge tv5, don't expect cast |
7396 | |
7397 | fusion->addOutput(tv7); |
7398 | fusion->addOutput(tv8); |
7399 | |
7400 | FusionExecutorCache fec(std::move(fusion_ptr)); |
7401 | |
7402 | std::vector<int64_t> shape{16, 16, 16}; |
7403 | |
7404 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7405 | auto in0 = at::randn(shape, options); |
7406 | auto in1 = at::randn(shape, options); |
7407 | fec.runFusionWithInputs({in0, in1}); |
7408 | |
7409 | auto segmented_fusion = fec.getMostRecentKernelRuntime()->fusionSegments(); |
7410 | auto complete_fusion = segmented_fusion->completeFusion(); |
7411 | |
7412 | // Check that the edge that wasn't fp16 is the producer of the |
7413 | // reduction op, i.e. tv8 = sum(tv5,{1});. |
7414 | for (auto edge : segmented_fusion->edges()) { |
7415 | auto edge_tv = edge->val->as<TensorView>(); |
7416 | if (edge_tv->getDataType() == DataType::Float) { |
7417 | auto consumer = *(complete_fusion->unordered_uses(edge_tv).begin()); |
7418 | TORCH_CHECK(consumer->isA<ReductionOp>()); |
7419 | } |
7420 | } |
7421 | #else |
7422 | GTEST_SKIP() << "requires cuda 11.0 or newer toolkit" ; |
7423 | #endif |
7424 | } |
7425 | |
7426 | TEST_F(NVFuserTest, FusionBufferReuseBroadCastMultiVisit_CUDA) { |
7427 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
7428 | auto fusion = fusion_ptr.get(); |
7429 | FusionGuard fg(fusion); |
7430 | |
7431 | auto tv0 = makeConcreteTensor({2, 2}); |
7432 | auto tv1 = makeConcreteTensor({2, 2, 2}); |
7433 | |
7434 | fusion->addInput(tv0); |
7435 | fusion->addInput(tv1); |
7436 | |
7437 | auto tv2 = mul(tv0, IrBuilder::create<Double>(2)); |
7438 | auto tv3 = broadcast(tv2, {false, false, true}); |
7439 | auto tv4 = add(tv3, tv1); |
7440 | auto tv5 = mul(tv4, IrBuilder::create<Double>(3)); |
7441 | fusion->addOutput(tv5); |
7442 | |
7443 | // t4 cannot inner re-use t2, because there's a broadcast |
7444 | // between them. |
7445 | tv0->computeAt(tv5, 1, ComputeAtMode::BestEffort); |
7446 | tv3->computeAt(tv5, 2, ComputeAtMode::BestEffort); |
7447 | |
7448 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7449 | auto in0 = at::randn({2, 2}, options); |
7450 | auto in1 = at::randn({2, 2, 2}, options); |
7451 | |
7452 | auto at_output = ((in0 * 2).unsqueeze(2) + in1) * 3; |
7453 | FusionExecutor fe; |
7454 | fe.compileFusion(fusion, {in0, in1}); |
7455 | auto outputs = fe.runFusion({in0, in1}); |
7456 | |
7457 | testValidate(fusion, outputs, {in0, in1}, {at_output}, __LINE__, __FILE__); |
7458 | } |
7459 | |
7460 | TEST_F(NVFuserTest, FusionBufferReuseStressTest_CUDA) { |
7461 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
7462 | auto fusion = fusion_ptr.get(); |
7463 | FusionGuard fg(fusion); |
7464 | |
7465 | auto tv0 = makeConcreteTensor({2, 2}); |
7466 | auto tv1 = makeConcreteTensor({2, 2, 2}); |
7467 | |
7468 | fusion->addInput(tv0); |
7469 | fusion->addInput(tv1); |
7470 | |
7471 | auto tv2 = mul(tv0, IrBuilder::create<Double>(2)); |
7472 | auto tv3 = mul(tv0, IrBuilder::create<Double>(3)); |
7473 | auto tv4 = mul(tv2, tv3); |
7474 | // Broadcast buffer can be reused through outer sharing |
7475 | auto tv5 = broadcast(tv4, {true, false, false}); |
7476 | auto tv6 = mul(tv5, IrBuilder::create<Double>(5)); |
7477 | auto tv7 = mul(tv6, tv1); |
7478 | auto tv8 = mul(tv7, IrBuilder::create<Double>(7)); |
7479 | // tv9 shouldn't alias to avoid buffer over-subscription |
7480 | auto tv9 = broadcast(tv4, {true, false, false}); |
7481 | auto tv10 = mul(tv9, IrBuilder::create<Double>(9)); |
7482 | auto tv11 = add(tv5, tv9); |
7483 | fusion->addOutput(tv7); |
7484 | fusion->addOutput(tv11); |
7485 | |
7486 | tv0->computeAt(tv5, 1, ComputeAtMode::BestEffort); |
7487 | tv0->computeAt(tv9, 1, ComputeAtMode::BestEffort); |
7488 | |
7489 | tv5->computeAt(tv7, 1, ComputeAtMode::BestEffort); |
7490 | tv5->computeAt(tv11, 1, ComputeAtMode::BestEffort); |
7491 | tv9->computeAt(tv11, 1, ComputeAtMode::BestEffort); |
7492 | |
7493 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7494 | auto in0 = at::randn({2, 2}, options); |
7495 | auto in1 = at::randn({2, 2, 2}, options); |
7496 | auto t2 = in0 * 2; |
7497 | auto t3 = in0 * 3; |
7498 | auto t4 = t2 * t3; |
7499 | auto t5 = t4.unsqueeze(0); |
7500 | auto t6 = t5 * 5; |
7501 | auto t7 = t6 * in1; |
7502 | auto t8 = t7 * 7; |
7503 | auto t9 = t4.unsqueeze(0); |
7504 | auto t10 = t9 * 9; |
7505 | auto t11 = t5 + t9; |
7506 | FusionExecutor fe; |
7507 | fe.compileFusion(fusion, {in0, in1}); |
7508 | |
7509 | auto at_output = ((in0 * 2).unsqueeze(2) + in1) * 3; |
7510 | auto outputs = fe.runFusion({in0, in1}); |
7511 | |
7512 | testValidate(fusion, outputs, {in0, in1}, {t7, t11}, __LINE__, __FILE__); |
7513 | } |
7514 | |
7515 | TEST_F(NVFuserTest, FusionBufferReuseLargeBuffer_CUDA) { |
7516 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
7517 | auto fusion = fusion_ptr.get(); |
7518 | FusionGuard fg(fusion); |
7519 | |
7520 | auto tv0 = makeConcreteTensor({256, 512}); |
7521 | |
7522 | fusion->addInput(tv0); |
7523 | |
7524 | auto tv1 = mul(tv0, IrBuilder::create<Double>(2)); |
7525 | auto tv2 = mul(tv1, IrBuilder::create<Double>(2)); |
7526 | auto tv3 = mul(tv2, IrBuilder::create<Double>(2)); |
7527 | auto tv4 = mul(tv3, IrBuilder::create<Double>(2)); |
7528 | auto tv5 = mul(tv4, IrBuilder::create<Double>(2)); |
7529 | auto tv6 = mul(tv5, IrBuilder::create<Double>(2)); |
7530 | |
7531 | fusion->addOutput(tv6); |
7532 | |
7533 | tv0->computeAt(tv6, 1, ComputeAtMode::BestEffort); |
7534 | tv6->axis(0)->parallelize(ParallelType::TIDx); |
7535 | |
7536 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7537 | auto in0 = at::randn({256, 512}, options); |
7538 | |
7539 | FusionExecutor fe; |
7540 | fe.compileFusion(fusion, {in0}); |
7541 | auto outputs = fe.runFusion({in0}); |
7542 | |
7543 | auto at_out = in0.mul(2).mul(2).mul(2).mul(2).mul(2).mul(2); |
7544 | |
7545 | testValidate(fusion, outputs, {in0}, {at_out}, __LINE__, __FILE__); |
7546 | } |
7547 | |
7548 | TEST_F(NVFuserTest, FusionBufferReuseNo2hop_CUDA) { |
7549 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
7550 | auto fusion = fusion_ptr.get(); |
7551 | FusionGuard fg(fusion); |
7552 | |
7553 | auto tv0 = makeConcreteTensor({2, 2}); |
7554 | auto tv1 = makeConcreteTensor({2, 2, 2}); |
7555 | |
7556 | fusion->addInput(tv0); |
7557 | fusion->addInput(tv1); |
7558 | |
7559 | auto tv2 = mul(tv0, IrBuilder::create<Double>(2)); |
7560 | auto tv3 = broadcast(tv2, {false, false, true}); |
7561 | auto tv4 = add(tv3, tv1); // T4 to be inner aliased first, and |
7562 | // shouldn't outer alias on top |
7563 | auto tv5 = mul(tv4, IrBuilder::create<Double>(3)); |
7564 | auto tv6 = mul(tv5, IrBuilder::create<Double>(3)); |
7565 | fusion->addOutput(tv6); |
7566 | |
7567 | tv0->computeAt(tv6, 1, ComputeAtMode::BestEffort); |
7568 | tv4->computeAt(tv6, 2, ComputeAtMode::BestEffort); |
7569 | |
7570 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7571 | auto in0 = at::randn({2, 2}, options); |
7572 | auto in1 = at::randn({2, 2, 2}, options); |
7573 | FusionExecutor fe; |
7574 | fe.compileFusion(fusion, {in0, in1}); |
7575 | auto outputs = fe.runFusion({in0, in1}); |
7576 | |
7577 | auto at_out = (in0.mul(2.0).unsqueeze(2) + in1).mul(3.0).mul(3.0); |
7578 | |
7579 | testValidate(fusion, outputs, {in0, in1}, {at_out}, __LINE__, __FILE__); |
7580 | } |
7581 | |
7582 | TEST_F(NVFuserTest, FusionBufferReuseAllocationOrder_CUDA) { |
7583 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
7584 | auto fusion = fusion_ptr.get(); |
7585 | FusionGuard fg(fusion); |
7586 | |
7587 | auto tv0 = makeConcreteTensor({3, 3, 3}); |
7588 | |
7589 | fusion->addInput(tv0); |
7590 | |
7591 | auto tv1 = sum(tv0, {1}); |
7592 | auto tv2 = mul(tv1, IrBuilder::create<Double>(2)); |
7593 | auto tv3 = mul(tv2, IrBuilder::create<Double>(2)); |
7594 | |
7595 | fusion->addOutput(tv3); |
7596 | |
7597 | // In this case tv1 "reuses" allocation of tv2 |
7598 | // due to the switched allocation order |
7599 | tv1->computeAt(tv2, 1, ComputeAtMode::BestEffort); |
7600 | |
7601 | tv0->axis(0)->parallelize(ParallelType::TIDx); |
7602 | tv1->axis(0)->parallelize(ParallelType::TIDx); |
7603 | tv2->axis(0)->parallelize(ParallelType::TIDx); |
7604 | tv3->axis(0)->parallelize(ParallelType::TIDx); |
7605 | |
7606 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7607 | auto in0 = at::randn({3, 3, 3}, options); |
7608 | |
7609 | FusionExecutor fe; |
7610 | fe.compileFusion(fusion, {in0}); |
7611 | auto outputs = fe.runFusion({in0}); |
7612 | |
7613 | auto at_out = in0.sum(1).mul(2).mul(2); |
7614 | |
7615 | testValidate(fusion, outputs, {in0}, {at_out}, __LINE__, __FILE__); |
7616 | } |
7617 | |
7618 | TEST_F(NVFuserTest, FusionBufferReuseLiveInterval_CUDA) { |
7619 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
7620 | auto fusion = fusion_ptr.get(); |
7621 | FusionGuard fg(fusion); |
7622 | |
7623 | auto tv0 = makeConcreteTensor({16, 16}); |
7624 | |
7625 | fusion->addInput(tv0); |
7626 | |
7627 | auto tv1 = mul(tv0, IrBuilder::create<Double>(3)); |
7628 | auto tv2 = mul(tv1, IrBuilder::create<Double>(2)); |
7629 | auto tv3 = mul(tv2, IrBuilder::create<Double>(2)); |
7630 | // tv1 used till here, cannot be reused by tv2 or tv3 |
7631 | auto tv4 = mul(tv3, tv1); |
7632 | |
7633 | fusion->addOutput(tv4); |
7634 | |
7635 | tv0->computeAt(tv4, 1); |
7636 | |
7637 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7638 | auto in0 = at::randn({16, 16}, options); |
7639 | |
7640 | FusionExecutor fe; |
7641 | fe.compileFusion(fusion, {in0}); |
7642 | auto cg_outputs = fe.runFusion({in0}); |
7643 | |
7644 | auto at_t0 = in0 * 3.0; |
7645 | auto at_out = at_t0 * 2.0 * 2.0 * at_t0; |
7646 | |
7647 | testValidate(fusion, cg_outputs, {in0}, {at_out}, __LINE__, __FILE__); |
7648 | } |
7649 | |
7650 | TEST_F(NVFuserTest, FusionBufferReuseNoAcrossBroadcast_CUDA) { |
7651 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
7652 | auto fusion = fusion_ptr.get(); |
7653 | FusionGuard fg(fusion); |
7654 | |
7655 | auto tv0 = makeConcreteTensor({2, 2}); |
7656 | auto tv1 = makeConcreteTensor({2, 2, 2}); |
7657 | |
7658 | fusion->addInput(tv0); |
7659 | fusion->addInput(tv1); |
7660 | |
7661 | auto tv2 = mul(tv0, IrBuilder::create<Double>(2)); |
7662 | auto tv3 = mul(tv0, IrBuilder::create<Double>(3)); |
7663 | auto tv4 = mul(tv2, tv3); |
7664 | auto tv5 = broadcast(tv4, {false, false, true}); |
7665 | auto tv6 = mul(tv5, tv1); |
7666 | auto tv7 = mul(tv6, IrBuilder::create<Double>(7)); |
7667 | fusion->addOutput(tv7); |
7668 | |
7669 | // tv6 shouldn't re-use t2 or t3 because of |
7670 | // the broadcast in between |
7671 | tv0->computeAt(tv4, 1, ComputeAtMode::BestEffort); |
7672 | tv4->computeAt(tv7, 2, ComputeAtMode::BestEffort); |
7673 | |
7674 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7675 | auto in0 = at::randn({2, 2}, options); |
7676 | auto in1 = at::randn({2, 2, 2}, options); |
7677 | FusionExecutor fe; |
7678 | fe.compileFusion(fusion, {in0, in1}); |
7679 | auto outputs = fe.runFusion({in0, in1}); |
7680 | |
7681 | auto t2 = in0 * 2; |
7682 | auto t3 = in0 * 3; |
7683 | auto t4 = t2 * t3; |
7684 | auto t5 = t4.unsqueeze(2); |
7685 | auto t6 = t5 * in1; |
7686 | auto t7 = t6 * 7; |
7687 | testValidate(fusion, outputs, {in0, in1}, {t7}, __LINE__, __FILE__); |
7688 | } |
7689 | |
7690 | TEST_F(NVFuserTest, FusionIssue970_CUDA) { |
7691 | Fusion fusion; |
7692 | FusionGuard fg(&fusion); |
7693 | |
7694 | const int nelm = 10; |
7695 | |
7696 | // tv3 = tv0 + sum(tv0) |
7697 | auto tv0 = makeConcreteTensor({nelm, nelm}); |
7698 | fusion.addInput(tv0); |
7699 | auto tv1 = sum(tv0, {1}); |
7700 | auto tv2 = broadcast(tv1, {false, true}); |
7701 | auto tv3 = add(tv2, tv0); |
7702 | fusion.addOutput(tv3); |
7703 | |
7704 | tv1->split(1, 4); |
7705 | |
7706 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7707 | auto options_int = at::TensorOptions().dtype(at::kLong).device(at::kCUDA, 0); |
7708 | at::manual_seed(0); |
7709 | at::Tensor t0 = at::randn({nelm, nelm}, options); |
7710 | |
7711 | FusionExecutor fe; |
7712 | fe.compileFusion(&fusion, {t0}); |
7713 | auto outputs = fe.runFusion({t0}); |
7714 | |
7715 | auto ref = sum(t0, {1}).unsqueeze(-1).expand({nelm, nelm}) + t0; |
7716 | |
7717 | testValidate(&fusion, outputs, {t0}, {ref}, __LINE__, __FILE__); |
7718 | } |
7719 | |
7720 | // Reproducer of #1016 |
7721 | TEST_F(NVFuserTest, FusionIssue1016_CUDA) { |
7722 | Fusion fusion; |
7723 | FusionGuard fg(&fusion); |
7724 | |
7725 | auto tv0 = makeSymbolicTensor(2); |
7726 | fusion.addInput(tv0); |
7727 | |
7728 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
7729 | auto tv2 = add(tv1, IrBuilder::create<Double>(2)); |
7730 | |
7731 | fusion.addOutput(tv2); |
7732 | |
7733 | tv1->setMemoryType(MemoryType::Shared); |
7734 | |
7735 | tv2->split(-1, 8); |
7736 | |
7737 | int numel_x = 10; |
7738 | int numel_y = 11; |
7739 | |
7740 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7741 | at::Tensor t0 = at::randn({numel_x, numel_y}, options); |
7742 | std::vector<IValue> inputs = {t0}; |
7743 | |
7744 | FusionExecutor fe; |
7745 | fe.compileFusion(&fusion, inputs); |
7746 | auto outputs = fe.runFusion(inputs); |
7747 | |
7748 | auto ref = t0 + 1 + 2; |
7749 | |
7750 | testValidate(&fusion, outputs, {t0}, {ref}, __LINE__, __FILE__); |
7751 | } |
7752 | |
7753 | // Reproducer of #1021 |
7754 | TEST_F(NVFuserTest, FusionIssue1021_CUDA) { |
7755 | Fusion fusion; |
7756 | FusionGuard fg(&fusion); |
7757 | |
7758 | auto tv0 = makeSymbolicTensor(1); |
7759 | fusion.addInput(tv0); |
7760 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
7761 | auto tv2 = broadcast(tv1, {false, true}); |
7762 | fusion.addOutput(tv2); |
7763 | |
7764 | auto tv3 = tv2->cacheBefore(); |
7765 | |
7766 | tv2->split(0, 2); |
7767 | |
7768 | tv1->computeAt(tv2, 1); |
7769 | |
7770 | tv2->axis(0)->parallelize(ParallelType::TIDx); |
7771 | tv2->axis(1)->parallelize(ParallelType::Vectorize); |
7772 | |
7773 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7774 | at::Tensor t0 = at::randn({10}, options); |
7775 | std::vector<IValue> inputs = {t0}; |
7776 | |
7777 | FusionExecutor fe; |
7778 | fe.compileFusion(&fusion, inputs); |
7779 | auto outputs = fe.runFusion(inputs); |
7780 | |
7781 | auto ref = (t0 + 1).unsqueeze(-1); |
7782 | |
7783 | testValidate(&fusion, outputs, inputs, {ref}, __LINE__, __FILE__); |
7784 | } |
7785 | |
7786 | // Reproducer of issue #1053 |
7787 | TEST_F(NVFuserTest, FusionNonUniqueThreadDim_CUDA) { |
7788 | auto fusion = std::make_unique<Fusion>(); |
7789 | FusionGuard fg(fusion.get()); |
7790 | |
7791 | auto tv0 = makeSymbolicTensor(1); |
7792 | fusion->addInput(tv0); |
7793 | auto tv1 = sum(tv0, {0}); |
7794 | fusion->addOutput(tv1); |
7795 | |
7796 | auto tv2 = add(tv0, IrBuilder::create<Double>(1)); |
7797 | fusion->addOutput(tv2); |
7798 | |
7799 | tv1->split(0, 8); |
7800 | auto tv1_rf = tv1->rFactor({-1}); |
7801 | |
7802 | tv1_rf->computeAt(tv1, 1); |
7803 | |
7804 | tv1_rf->axis(-1)->parallelize(ParallelType::TIDx); |
7805 | |
7806 | tv2->axis(0)->parallelize(ParallelType::TIDx); |
7807 | |
7808 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7809 | at::Tensor input1 = at::randn({32}, options); |
7810 | |
7811 | auto at_tv1 = (input1).sum({0}); |
7812 | auto at_tv2 = input1 + 1; |
7813 | |
7814 | FusionExecutor fe; |
7815 | fe.compileFusion(fusion.get(), {input1}); |
7816 | auto outputs = fe.runFusion({input1}); |
7817 | testValidate( |
7818 | fusion.get(), outputs, {input1}, {at_tv1, at_tv2}, __LINE__, __FILE__); |
7819 | } |
7820 | |
7821 | TEST_F(NVFuserTest, FusionParallelDimensionMap1_CUDA) { |
7822 | auto fusion = std::make_unique<Fusion>(); |
7823 | FusionGuard fg(fusion.get()); |
7824 | |
7825 | auto tv0 = makeSymbolicTensor(1); |
7826 | fusion->addInput(tv0); |
7827 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
7828 | auto tv2 = add(tv0, IrBuilder::create<Double>(1)); |
7829 | fusion->addOutput(tv1); |
7830 | fusion->addOutput(tv2); |
7831 | |
7832 | tv1->split(0, 8, false); |
7833 | tv1->axis(1)->parallelize(ParallelType::TIDx); |
7834 | tv2->split(0, 8, false); |
7835 | tv2->axis(1)->parallelize(ParallelType::TIDx); |
7836 | |
7837 | // The extents of tv1 and tv2 axes are equal even though their |
7838 | // actual values are not statically known |
7839 | GpuLower gpulw(fusion.get()); |
7840 | const auto& pdmap = gpulw.parallelDimensionMap(); |
7841 | for (const auto i : c10::irange(tv1->domain()->domain().size())) { |
7842 | auto dom1 = tv1->domain()->domain()[i]; |
7843 | auto dom2 = tv2->domain()->domain()[i]; |
7844 | TORCH_INTERNAL_ASSERT(pdmap.equalDim(dom1->extent(), dom2->extent())); |
7845 | } |
7846 | |
7847 | TORCH_CHECK(pdmap.isExact(ParallelType::TIDx)); |
7848 | TORCH_CHECK( |
7849 | pdmap.get(ParallelType::TIDx)->isA<NamedScalar>() && |
7850 | pdmap.get(ParallelType::TIDx)->as<NamedScalar>()->name() == "blockDim.x" ); |
7851 | |
7852 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7853 | at::Tensor input1 = at::randn({32}, options); |
7854 | |
7855 | FusionExecutor fe; |
7856 | fe.compileFusion(fusion.get(), {input1}); |
7857 | auto outputs = fe.runFusion({input1}); |
7858 | |
7859 | testValidate( |
7860 | fusion.get(), |
7861 | outputs, |
7862 | {input1}, |
7863 | {input1 + 1, input1 + 1}, |
7864 | __LINE__, |
7865 | __FILE__); |
7866 | } |
7867 | |
7868 | TEST_F(NVFuserTest, FusionParallelDimensionMap2_CUDA) { |
7869 | auto fusion = std::make_unique<Fusion>(); |
7870 | FusionGuard fg(fusion.get()); |
7871 | |
7872 | auto tv0 = makeSymbolicTensor(1); |
7873 | fusion->addInput(tv0); |
7874 | auto tv1 = makeSymbolicTensor(2); |
7875 | fusion->addInput(tv1); |
7876 | auto tv2 = broadcast(tv0, {false, true}); |
7877 | auto tv3 = add(tv1, tv2); |
7878 | fusion->addOutput(tv3); |
7879 | |
7880 | tv3->split(-1, 8, false); |
7881 | tv2->computeAt(tv3, -1); |
7882 | |
7883 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
7884 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
7885 | |
7886 | GpuLower gpulw(fusion.get()); |
7887 | const auto& pdmap = gpulw.parallelDimensionMap(); |
7888 | TORCH_CHECK(pdmap.isExact(ParallelType::TIDx)); |
7889 | TORCH_CHECK( |
7890 | pdmap.get(ParallelType::TIDx)->isA<NamedScalar>() && |
7891 | pdmap.get(ParallelType::TIDx)->as<NamedScalar>()->name() == "blockDim.x" ); |
7892 | |
7893 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7894 | at::Tensor input1 = at::randn({11}, options); |
7895 | at::Tensor input2 = at::randn({11, 13}, options); |
7896 | |
7897 | FusionExecutor fe; |
7898 | fe.compileFusion(fusion.get(), {input1, input2}); |
7899 | auto outputs = fe.runFusion({input1, input2}); |
7900 | |
7901 | auto ref = input1.unsqueeze(-1) + input2; |
7902 | |
7903 | testValidate( |
7904 | fusion.get(), outputs, {input1, input2}, {ref}, __LINE__, __FILE__); |
7905 | } |
7906 | |
7907 | // Mix symbolic and concrete tensors |
7908 | TEST_F(NVFuserTest, FusionParallelDimensionMap3_CUDA) { |
7909 | auto fusion = std::make_unique<Fusion>(); |
7910 | FusionGuard fg(fusion.get()); |
7911 | |
7912 | auto tv0 = makeSymbolicTensor(1); |
7913 | fusion->addInput(tv0); |
7914 | |
7915 | auto tv2 = add(tv0, IrBuilder::create<Double>(1)); |
7916 | fusion->addOutput(tv2); |
7917 | auto tv3 = add(tv0, IrBuilder::create<Double>(1)); |
7918 | fusion->addOutput(tv3); |
7919 | |
7920 | tv2->split(0, 10); |
7921 | tv3->split(0, 20); |
7922 | |
7923 | auto tv4 = add(tv0, IrBuilder::create<Double>(1)); |
7924 | fusion->addOutput(tv4); |
7925 | auto tv5 = add(tv0, IrBuilder::create<Double>(1)); |
7926 | fusion->addOutput(tv5); |
7927 | |
7928 | // Not mapped but equal extent |
7929 | tv4->split(0, 10); |
7930 | tv5->split(0, 10); |
7931 | |
7932 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
7933 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
7934 | |
7935 | tv4->axis(-1)->parallelize(ParallelType::TIDy); |
7936 | tv5->axis(-1)->parallelize(ParallelType::TIDy); |
7937 | |
7938 | GpuLower gpulw(fusion.get()); |
7939 | const auto& pdmap = gpulw.parallelDimensionMap(); |
7940 | TORCH_CHECK(!pdmap.isExact(ParallelType::TIDx)); |
7941 | TORCH_CHECK( |
7942 | pdmap.get(ParallelType::TIDx)->isA<NamedScalar>() && |
7943 | pdmap.get(ParallelType::TIDx)->as<NamedScalar>()->name() == "blockDim.x" ); |
7944 | TORCH_CHECK(pdmap.isExact(ParallelType::TIDy)); |
7945 | TORCH_CHECK( |
7946 | pdmap.get(ParallelType::TIDy)->isConst() && |
7947 | pdmap.get(ParallelType::TIDy)->as<Int>()->value().value() == 10); |
7948 | |
7949 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
7950 | at::Tensor input1 = at::randn({13}, options); |
7951 | |
7952 | FusionExecutor fe; |
7953 | fe.compileFusion(fusion.get(), {input1}); |
7954 | auto outputs = fe.runFusion({input1}); |
7955 | |
7956 | testValidate( |
7957 | fusion.get(), |
7958 | outputs, |
7959 | {input1}, |
7960 | {input1 + 1, input1 + 1, input1 + 1, input1 + 1}, |
7961 | __LINE__, |
7962 | __FILE__); |
7963 | } |
7964 | |
7965 | // Parallelizing merged broadcast domains |
7966 | TEST_F(NVFuserTest, FusionParallelDimensionMap4_CUDA) { |
7967 | Fusion fusion; |
7968 | FusionGuard fg(&fusion); |
7969 | |
7970 | auto tv0 = makeSymbolicTensor(1); |
7971 | fusion.addInput(tv0); |
7972 | auto tv1 = makeSymbolicTensor(2); |
7973 | fusion.addInput(tv1); |
7974 | auto tv2 = add(tv0, IrBuilder::create<Double>(1)); |
7975 | auto tv3 = broadcast(tv2, {true, false}); |
7976 | auto tv4 = add(tv3, tv1); |
7977 | fusion.addOutput(tv4); |
7978 | |
7979 | tv4->split(1, 4); |
7980 | tv4->reorder({{1, 2}, {2, 1}}); |
7981 | tv4->merge(0); |
7982 | tv0->computeAt(tv4, 1); |
7983 | tv1->computeAt(tv4, 1); |
7984 | |
7985 | // TIDx is mapped to tv4.axis(0) as well as tv2.axis(0), so it's not |
7986 | // exact. |
7987 | tv4->axis(0)->parallelize(ParallelType::TIDx); |
7988 | |
7989 | tv2->setMemoryType(MemoryType::Shared); |
7990 | tv3->setMemoryType(MemoryType::Shared); |
7991 | |
7992 | GpuLower gpulw(&fusion); |
7993 | const auto& pdmap = gpulw.parallelDimensionMap(); |
7994 | TORCH_CHECK(!pdmap.isExact(ParallelType::TIDx)); |
7995 | TORCH_CHECK( |
7996 | pdmap.get(ParallelType::TIDx)->isA<NamedScalar>() && |
7997 | pdmap.get(ParallelType::TIDx)->as<NamedScalar>()->name() == "blockDim.x" ); |
7998 | |
7999 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8000 | at::Tensor input1 = at::randn({13}, options); |
8001 | at::Tensor input2 = at::randn({15, 13}, options); |
8002 | |
8003 | FusionExecutor fe; |
8004 | fe.compileFusion(&fusion, {input1, input2}); |
8005 | auto outputs = fe.runFusion({input1, input2}); |
8006 | |
8007 | auto ref = (input1 + 1).unsqueeze(0) + input2; |
8008 | |
8009 | testValidate(&fusion, outputs, {input1, input2}, {ref}, __LINE__, __FILE__); |
8010 | } |
8011 | |
8012 | TEST_F(NVFuserTest, FusionParallelDimensionMap5_CUDA) { |
8013 | Fusion fusion; |
8014 | FusionGuard fg(&fusion); |
8015 | |
8016 | auto tv0 = makeSymbolicTensor(1); |
8017 | fusion.addInput(tv0); |
8018 | auto tv1 = makeSymbolicTensor(2); |
8019 | fusion.addInput(tv1); |
8020 | auto tv3 = broadcast(tv0, {false, true}); |
8021 | auto tv4 = add(tv3, tv1); |
8022 | fusion.addOutput(tv4); |
8023 | |
8024 | tv4->split(1, 4); |
8025 | tv0->computeAt(tv4, -1); |
8026 | tv1->computeAt(tv4, -1); |
8027 | |
8028 | tv4->axis(-1)->parallelize(ParallelType::TIDx); |
8029 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
8030 | tv4->axis(-2)->parallelize(ParallelType::TIDy); |
8031 | tv3->axis(-2)->parallelize(ParallelType::TIDy); |
8032 | |
8033 | GpuLower gpulw(&fusion); |
8034 | const auto& pdmap = gpulw.parallelDimensionMap(); |
8035 | TORCH_CHECK(pdmap.isExact(ParallelType::TIDx)); |
8036 | TORCH_CHECK(pdmap.isExact(ParallelType::TIDy)); |
8037 | TORCH_CHECK( |
8038 | pdmap.get(ParallelType::TIDx)->isConst() && |
8039 | pdmap.get(ParallelType::TIDx)->as<Int>()->value().value() == 4); |
8040 | TORCH_CHECK( |
8041 | pdmap.get(ParallelType::TIDy)->isA<NamedScalar>() && |
8042 | pdmap.get(ParallelType::TIDy)->as<NamedScalar>()->name() == "blockDim.y" ); |
8043 | |
8044 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8045 | at::Tensor input1 = at::randn({13}, options); |
8046 | at::Tensor input2 = at::randn({13, 15}, options); |
8047 | |
8048 | FusionExecutor fe; |
8049 | fe.compileFusion(&fusion, {input1, input2}); |
8050 | auto outputs = fe.runFusion({input1, input2}); |
8051 | |
8052 | auto ref = (input1).unsqueeze(-1) + input2; |
8053 | |
8054 | testValidate(&fusion, outputs, {input1, input2}, {ref}, __LINE__, __FILE__); |
8055 | } |
8056 | |
8057 | TEST_F(NVFuserTest, FusionSegmenterCombineReductionsCycleRepro_CUDA) { |
8058 | auto fusion_ptr = std::make_unique<Fusion>(); |
8059 | auto& fusion = *fusion_ptr.get(); |
8060 | FusionGuard fg(&fusion); |
8061 | |
8062 | auto t0 = makeSymbolicTensor(3, DataType::Float); |
8063 | auto t1 = makeSymbolicTensor(3, DataType::Half); |
8064 | auto t3 = makeSymbolicTensor(3, DataType::Half); |
8065 | auto t5 = makeSymbolicTensor(3, DataType::Half); |
8066 | auto t7 = makeSymbolicTensor(1, DataType::Half); |
8067 | auto t11 = makeSymbolicTensor(3, DataType::Half); |
8068 | auto t13 = makeSymbolicTensor(3, DataType::Half); |
8069 | auto t15 = makeSymbolicTensor(3, DataType::Half); |
8070 | auto t17 = makeSymbolicTensor(3, DataType::Half); |
8071 | auto d56 = IrBuilder::create<Double>(); |
8072 | |
8073 | fusion.addInput(t0); |
8074 | fusion.addInput(t1); |
8075 | fusion.addInput(t3); |
8076 | fusion.addInput(t5); |
8077 | fusion.addInput(t7); |
8078 | fusion.addInput(t11); |
8079 | fusion.addInput(t13); |
8080 | fusion.addInput(t15); |
8081 | fusion.addInput(t17); |
8082 | fusion.addInput(d56); |
8083 | |
8084 | auto t2 = castOp(DataType::Float, t1); |
8085 | auto t4 = castOp(DataType::Float, t3); |
8086 | auto t22 = sub(t2, t4); |
8087 | auto t6 = castOp(DataType::Float, t5); |
8088 | auto t23 = mul(t22, t6); |
8089 | auto t16 = castOp(DataType::Float, t15); |
8090 | auto t18 = castOp(DataType::Float, t17); |
8091 | auto t19 = add(t16, t18); |
8092 | auto t14 = castOp(DataType::Float, t13); |
8093 | auto t20 = add(t19, t14); |
8094 | auto t12 = castOp(DataType::Float, t11); |
8095 | auto t21 = add(t20, t12); |
8096 | auto t8 = castOp(DataType::Float, t7); |
8097 | auto t24 = broadcast(t8, {true, true, false}); |
8098 | auto t25 = mul(t21, t24); |
8099 | auto t27 = sum(t25, {2}); |
8100 | auto t28 = broadcast(t27, {false, false, true}); |
8101 | auto t29 = mul(t25, t23); |
8102 | auto t30 = sum(t29, {2}); |
8103 | auto t31 = broadcast(t30, {false, false, true}); |
8104 | auto d59 = |
8105 | mul(t1->getRootDomain()[2]->extent(), IrBuilder::create<Double>(1)); |
8106 | auto t26 = mul(d59, t25); |
8107 | auto txx = mul(t26, IrBuilder::create<Double>(1)); |
8108 | auto t33 = sub(txx, t28); |
8109 | auto d70 = unaryOp(UnaryOpType::Reciprocal, d59); |
8110 | auto t35 = mul(d70, t6); |
8111 | auto t39 = sum(t21, {0, 1}); |
8112 | auto t47 = castOp(DataType::Half, t39); |
8113 | auto t37 = mul(t21, t23); |
8114 | auto t38 = sum(t37, {0, 1}); |
8115 | auto t46 = castOp(DataType::Half, t38); |
8116 | auto t32 = mul(t23, t31); |
8117 | auto t34 = sub(t33, t32); |
8118 | auto t36 = mul(t35, t34); |
8119 | auto t45 = castOp(DataType::Half, t36); |
8120 | auto t40 = mul(t36, t0); |
8121 | auto t41 = mul(t40, d56); |
8122 | auto t44 = castOp(DataType::Half, t41); |
8123 | auto t42 = sum(t41, {0, 1}); |
8124 | auto t43 = castOp(DataType::Half, t42); |
8125 | |
8126 | fusion.addOutput(t43); |
8127 | fusion.addOutput(t44); |
8128 | fusion.addOutput(t45); |
8129 | fusion.addOutput(t46); |
8130 | fusion.addOutput(t47); |
8131 | |
8132 | auto options_half = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0); |
8133 | auto options_float = |
8134 | at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8135 | at::Tensor at_t0 = at::randn({128, 64, 1024}, options_float); |
8136 | at::Tensor at_t1 = at::randn({128, 64, 1024}, options_half); |
8137 | at::Tensor at_t3 = at::randn({128, 64, 1024}, options_half); |
8138 | at::Tensor at_t5 = at::randn({128, 64, 1024}, options_half); |
8139 | at::Tensor at_t7 = at::randn({1024}, options_half); |
8140 | at::Tensor at_t11 = at::randn({128, 64, 1024}, options_half); |
8141 | at::Tensor at_t13 = at::randn({128, 64, 1024}, options_half); |
8142 | at::Tensor at_t15 = at::randn({128, 64, 1024}, options_half); |
8143 | at::Tensor at_t17 = at::randn({128, 64, 1024}, options_half); |
8144 | double at_d56 = 1.1111; |
8145 | |
8146 | std::vector<at::Tensor> aten_inputs = { |
8147 | at_t0, at_t1, at_t3, at_t5, at_t7, at_t11, at_t13, at_t15, at_t17}; |
8148 | |
8149 | c10::IValue val = at_d56; |
8150 | |
8151 | KernelArgumentHolder args(KernelIndexMode::INT32); |
8152 | args.setDeviceIndex(0); |
8153 | args.push(aten_inputs); |
8154 | args.push(val); |
8155 | |
8156 | for (auto _ : c10::irange(5)) { |
8157 | auto segmented_fusion = |
8158 | SegmentCandidateFinder::segment(fusion_ptr.get(), args); |
8159 | } |
8160 | } |
8161 | |
8162 | TEST_F(NVFuserTest, FusionSerialAndParallelIndexing_CUDA) { |
8163 | Fusion fusion; |
8164 | FusionGuard fg(&fusion); |
8165 | |
8166 | auto tv0 = makeSymbolicTensor(1); |
8167 | fusion.addInput(tv0); |
8168 | |
8169 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
8170 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
8171 | fusion.addOutput(tv2); |
8172 | |
8173 | auto tv3 = add(tv0, IrBuilder::create<Double>(1)); |
8174 | auto tv4 = add(tv3, IrBuilder::create<Double>(1)); |
8175 | fusion.addOutput(tv4); |
8176 | |
8177 | auto tv5 = add(tv0, IrBuilder::create<Double>(1)); |
8178 | auto tv6 = add(tv5, IrBuilder::create<Double>(1)); |
8179 | fusion.addOutput(tv6); |
8180 | |
8181 | // Case 1: local memory tensor computed serially and used by |
8182 | // parallel threads |
8183 | tv2->split(-1, 4); |
8184 | tv1->computeAt(tv2, -2); |
8185 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
8186 | |
8187 | // Case 2: shared memory tensor computed serially and used by BID |
8188 | tv4->split(-1, 4); |
8189 | tv3->computeAt(tv4, -2); |
8190 | tv4->axis(-1)->parallelize(ParallelType::BIDx); |
8191 | tv3->setMemoryType(MemoryType::Shared); |
8192 | |
8193 | // Case 3: shared memory tensor computed by TID and used by BID |
8194 | tv6->split(-1, 4); |
8195 | tv5->computeAt(tv6, -2); |
8196 | tv6->axis(-1)->parallelize(ParallelType::BIDx); |
8197 | tv5->axis(-1)->parallelize(ParallelType::TIDx); |
8198 | tv5->setMemoryType(MemoryType::Shared); |
8199 | |
8200 | const int nx = 11; |
8201 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8202 | at::Tensor t0 = at::randn({nx}, options); |
8203 | std::vector<IValue> aten_inputs = {t0}; |
8204 | |
8205 | FusionExecutor fe; |
8206 | fe.compileFusion(&fusion, aten_inputs); |
8207 | auto outputs = fe.runFusion(aten_inputs); |
8208 | |
8209 | auto ref = t0 + 2; |
8210 | |
8211 | testValidate( |
8212 | &fusion, outputs, aten_inputs, {ref, ref, ref}, __LINE__, __FILE__); |
8213 | } |
8214 | |
8215 | // Repro of issue #1105 |
8216 | TEST_F(NVFuserTest, FusionWARSyncAliasedSmem_CUDA) { |
8217 | Fusion fusion; |
8218 | FusionGuard fg(&fusion); |
8219 | |
8220 | auto tv0 = makeSymbolicTensor(1); |
8221 | fusion.addInput(tv0); |
8222 | |
8223 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
8224 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
8225 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
8226 | |
8227 | fusion.addOutput(tv3); |
8228 | |
8229 | tv1->setMemoryType(MemoryType::Shared); |
8230 | tv2->setMemoryType(MemoryType::Shared); |
8231 | |
8232 | tv3->split(0, 4); |
8233 | tv0->computeAt(tv3, 1); |
8234 | |
8235 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
8236 | tv2->axis(-1)->parallelize(ParallelType::TIDy); |
8237 | tv3->axis(-1)->parallelize(ParallelType::TIDz); |
8238 | |
8239 | // Make sure a WAR sync is inserted at the end of the outer loop |
8240 | GpuLower gpulw(&fusion); |
8241 | for (const auto& kir_node : gpulw.kernel()->topLevelExprs()) { |
8242 | if (auto loop = dynamic_cast<kir::ForLoop*>(kir_node)) { |
8243 | const auto& body = loop->body().exprs(); |
8244 | TORCH_CHECK(!body.empty()); |
8245 | auto last_expr = dynamic_cast<kir::BlockSync*>(body.back()); |
8246 | TORCH_CHECK(last_expr != nullptr, "Invalid expr found" ); |
8247 | TORCH_CHECK(last_expr->isWarHazardSync(), "Not a sync for WAR hazard" ); |
8248 | } |
8249 | } |
8250 | |
8251 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8252 | at::Tensor t0 = at::randn({17}, options); |
8253 | std::vector<IValue> aten_inputs = {t0}; |
8254 | |
8255 | FusionExecutor fe; |
8256 | fe.compileFusion(&fusion, aten_inputs); |
8257 | auto outputs = fe.runFusion(aten_inputs); |
8258 | |
8259 | auto ref1 = t0 + 3; |
8260 | |
8261 | testValidate(&fusion, outputs, aten_inputs, {ref1}, __LINE__, __FILE__); |
8262 | } |
8263 | |
8264 | TEST_F(NVFuserTest, FusionIssue1099_CUDA) { |
8265 | Fusion fusion; |
8266 | FusionGuard fg(&fusion); |
8267 | |
8268 | auto tv0 = makeSymbolicTensor(1); |
8269 | fusion.addInput(tv0); |
8270 | |
8271 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
8272 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
8273 | fusion.addOutput(tv2); |
8274 | |
8275 | auto tv3 = makeSymbolicTensor(1); |
8276 | fusion.addInput(tv3); |
8277 | |
8278 | // Just to make TIDx/y/z non-exact |
8279 | auto tv4 = add(tv3, IrBuilder::create<Double>(1)); |
8280 | auto tv5 = add(tv4, IrBuilder::create<Double>(1)); |
8281 | auto tv6 = add(tv5, IrBuilder::create<Double>(1)); |
8282 | fusion.addOutput(tv6); |
8283 | |
8284 | tv2->split(0, 4); |
8285 | tv0->computeAt(tv2, 1); |
8286 | |
8287 | tv0->axis(-1)->parallelize(ParallelType::TIDx); |
8288 | tv1->axis(-1)->parallelize(ParallelType::TIDy); |
8289 | tv2->axis(-1)->parallelize(ParallelType::TIDz); |
8290 | tv2->axis(0)->parallelize(ParallelType::BIDx); |
8291 | |
8292 | tv1->setMemoryType(MemoryType::Shared); |
8293 | |
8294 | tv4->split(0, 5); |
8295 | tv4->axis(-1)->parallelize(ParallelType::TIDx); |
8296 | tv4->setMemoryType(MemoryType::Shared); |
8297 | tv5->split(0, 6); |
8298 | tv5->axis(-1)->parallelize(ParallelType::TIDy); |
8299 | tv5->setMemoryType(MemoryType::Shared); |
8300 | tv6->split(0, 7); |
8301 | tv6->axis(-1)->parallelize(ParallelType::TIDz); |
8302 | |
8303 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8304 | at::Tensor t0 = at::randn({17}, options); |
8305 | at::Tensor t3 = at::randn({19}, options); |
8306 | std::vector<IValue> aten_inputs = {t0, t3}; |
8307 | |
8308 | FusionExecutor fe; |
8309 | fe.compileFusion(&fusion, aten_inputs); |
8310 | auto outputs = fe.runFusion(aten_inputs); |
8311 | |
8312 | auto ref_t2 = t0 + 2; |
8313 | auto ref_t3 = t3 + 3; |
8314 | |
8315 | testValidate( |
8316 | &fusion, outputs, aten_inputs, {ref_t2, ref_t3}, __LINE__, __FILE__); |
8317 | } |
8318 | |
8319 | // Repro of issue #1080 |
8320 | TEST_F(NVFuserTest, FusionUnswitchPredicate_CUDA) { |
8321 | Fusion fusion; |
8322 | FusionGuard fg(&fusion); |
8323 | |
8324 | auto tv0 = makeSymbolicTensor(2); |
8325 | fusion.addInput(tv0); |
8326 | |
8327 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
8328 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
8329 | fusion.addOutput(tv2); |
8330 | |
8331 | tv2->split(0, 4); |
8332 | tv0->computeAt(tv2, 2); |
8333 | |
8334 | tv2->split(-1, 8); |
8335 | tv1->split(-1, 8); |
8336 | |
8337 | tv2->axis(1)->parallelize(ParallelType::Unswitch); |
8338 | |
8339 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
8340 | tv2->axis(-2)->parallelize(ParallelType::TIDy); |
8341 | |
8342 | // swap TIDx and TIDy |
8343 | tv1->axis(-1)->parallelize(ParallelType::TIDy); |
8344 | tv1->axis(-2)->parallelize(ParallelType::TIDx); |
8345 | |
8346 | tv1->setMemoryType(MemoryType::Shared); |
8347 | |
8348 | const int nx = 4; |
8349 | const int ny = 10; |
8350 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8351 | at::Tensor t0 = at::randn({nx, ny}, options); |
8352 | std::vector<IValue> aten_inputs = {t0}; |
8353 | |
8354 | FusionExecutor fe; |
8355 | fe.compileFusion(&fusion, aten_inputs); |
8356 | auto outputs = fe.runFusion(aten_inputs); |
8357 | |
8358 | auto ref = t0 + 2; |
8359 | |
8360 | testValidate(&fusion, outputs, aten_inputs, {ref}, __LINE__, __FILE__); |
8361 | } |
8362 | |
8363 | TEST_F(NVFuserTest, FusionIssue1189_CUDA) { |
8364 | Fusion fusion; |
8365 | FusionGuard fg(&fusion); |
8366 | |
8367 | auto tv0 = makeConcreteTensor({16, 16}); |
8368 | auto tv1 = makeConcreteTensor({16, 16}); |
8369 | |
8370 | auto tv0b = broadcast(tv0, {false, false, true}); |
8371 | auto tv1b = broadcast(tv1, {false, false, true}); |
8372 | |
8373 | fusion.addInput(tv0b); |
8374 | fusion.addInput(tv1b); |
8375 | |
8376 | auto tv2 = add(tv0b, tv1b); |
8377 | auto tv3 = sum(tv2, {1}); |
8378 | fusion.addOutput(tv3); |
8379 | |
8380 | auto parallelize = [](auto tv) { |
8381 | tv->axis(0)->parallelize(ParallelType::TIDx); |
8382 | tv->axis(1)->parallelize(ParallelType::BIDx); |
8383 | tv->axis(2)->parallelize(ParallelType::BIDy); |
8384 | }; |
8385 | |
8386 | parallelize(tv0b); |
8387 | parallelize(tv1b); |
8388 | parallelize(tv2); |
8389 | parallelize(tv3); |
8390 | |
8391 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8392 | at::Tensor t0 = at::randn({16, 16, 1}, options); |
8393 | at::Tensor t1 = at::randn({16, 16, 1}, options); |
8394 | |
8395 | FusionExecutor fe; |
8396 | fe.compileFusion(&fusion, {t0, t1}); |
8397 | auto outputs = fe.runFusion({t0, t1}); |
8398 | |
8399 | auto ref = (t0 + t1).sum({1}); |
8400 | |
8401 | testValidate(&fusion, outputs, {t0, t1}, {ref}, __LINE__, __FILE__); |
8402 | } |
8403 | |
8404 | TEST_F(NVFuserTest, FusionIssue1052_CUDA) { |
8405 | Fusion fusion; |
8406 | FusionGuard fg(&fusion); |
8407 | |
8408 | auto tv0 = makeSymbolicTensor(1); |
8409 | fusion.addInput(tv0); |
8410 | auto tv1 = makeSymbolicTensor(1); |
8411 | fusion.addInput(tv1); |
8412 | |
8413 | auto tv2 = add(tv0, IrBuilder::create<Double>(1)); |
8414 | fusion.addOutput(tv2); |
8415 | |
8416 | auto tv3 = add(tv1, IrBuilder::create<Double>(1)); |
8417 | fusion.addOutput(tv3); |
8418 | |
8419 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
8420 | tv3->axis(-1)->parallelize(ParallelType::TIDx); |
8421 | |
8422 | scheduler_utils::parallelizeAllLike(tv2, {tv0}); |
8423 | scheduler_utils::parallelizeAllLike(tv3, {tv1}); |
8424 | |
8425 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8426 | at::Tensor t0 = at::randn({10}, options); |
8427 | at::Tensor t1 = at::randn({100}, options); |
8428 | std::vector<IValue> aten_inputs = {t0, t1}; |
8429 | |
8430 | FusionExecutor fe; |
8431 | fe.compileFusion(&fusion, aten_inputs); |
8432 | auto outputs = fe.runFusion(aten_inputs); |
8433 | |
8434 | auto ref_t2 = t0 + 1; |
8435 | auto ref_t3 = t1 + 1; |
8436 | |
8437 | testValidate( |
8438 | &fusion, outputs, aten_inputs, {ref_t2, ref_t3}, __LINE__, __FILE__); |
8439 | } |
8440 | |
8441 | // Repro of issue #1115 |
8442 | TEST_F(NVFuserTest, FusionPointwiseBroadcast_CUDA) { |
8443 | Fusion fusion; |
8444 | FusionGuard fg(&fusion); |
8445 | |
8446 | std::vector<int64_t> input_shape{3, 17, 80}; |
8447 | std::vector<int64_t> output_shape{3, 17, 1, 80}; |
8448 | |
8449 | TensorView* x = makeSymbolicTensor(input_shape.size()); |
8450 | TensorView* bias = makeSymbolicTensor(input_shape.size()); |
8451 | fusion.addInput(x); |
8452 | fusion.addInput(bias); |
8453 | |
8454 | auto x_add_bias = add(x, bias); |
8455 | auto x_bcast = broadcast(x_add_bias, {false, false, true, false}); |
8456 | auto y = gelu(x_bcast); |
8457 | fusion.addOutput(y); |
8458 | |
8459 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8460 | at::Tensor at_x = at::randn(input_shape, options); |
8461 | at::Tensor at_bias = at::randn(input_shape, options); |
8462 | std::vector<IValue> aten_inputs = {at_x, at_bias}; |
8463 | |
8464 | schedulePointwise(&fusion, aten_inputs); |
8465 | |
8466 | FusionExecutor fe; |
8467 | fe.compileFusion(&fusion, aten_inputs); |
8468 | auto outputs = fe.runFusion(aten_inputs); |
8469 | |
8470 | auto at_x_add_bias = at_x + at_bias; |
8471 | auto at_x_view = at::native::view(at_x_add_bias, output_shape); |
8472 | auto aten_y = at::gelu(at_x_view); |
8473 | |
8474 | testValidate(&fusion, outputs, aten_inputs, {aten_y}, __LINE__, __FILE__); |
8475 | } |
8476 | |
8477 | TEST_F(NVFuserTest, FusionPointwiseVectorize_CUDA) { |
8478 | Fusion fusion; |
8479 | FusionGuard fg(&fusion); |
8480 | |
8481 | const int size = 1024 * 64; |
8482 | |
8483 | TensorView* x = makeContigTensor(1); |
8484 | fusion.addInput(x); |
8485 | auto y = sin(x); |
8486 | fusion.addOutput(y); |
8487 | |
8488 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8489 | |
8490 | // PyTorch's CUDA caching allocator should always return aligned pointer for |
8491 | // freshly allocated tensor |
8492 | at::Tensor at_x = at::randn({size}, options); |
8493 | |
8494 | schedulePointwise(&fusion, {at_x}); |
8495 | |
8496 | for (auto x_consumer : ir_utils::consumerTvsOf(x)) { |
8497 | bool found_vec_in_input = false; |
8498 | for (auto id : x_consumer->domain()->domain()) { |
8499 | if (isParallelTypeVectorize(id->getParallelType())) { |
8500 | found_vec_in_input = true; |
8501 | break; |
8502 | } |
8503 | } |
8504 | TORCH_CHECK(found_vec_in_input, "Expect input to be vectorized" ); |
8505 | } |
8506 | |
8507 | for (auto id : y->domain()->domain()) { |
8508 | if (isParallelTypeVectorize(id->getParallelType())) { |
8509 | return; |
8510 | } |
8511 | } |
8512 | TORCH_CHECK(false, "Expect output to be vectorized" ); |
8513 | } |
8514 | |
8515 | TEST_F(NVFuserTest, FusionSmemAliasSerial_CUDA) { |
8516 | Fusion fusion; |
8517 | FusionGuard fg(&fusion); |
8518 | |
8519 | auto tv0 = makeSymbolicTensor(1); |
8520 | fusion.addInput(tv0); |
8521 | |
8522 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
8523 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
8524 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
8525 | |
8526 | fusion.addOutput(tv3); |
8527 | |
8528 | // Just set the dimension of TIDx |
8529 | auto tv4 = makeSymbolicTensor(1); |
8530 | fusion.addInput(tv4); |
8531 | auto tv5 = add(tv4, IrBuilder::create<Double>(1)); |
8532 | fusion.addOutput(tv5); |
8533 | |
8534 | tv1->setMemoryType(MemoryType::Shared); |
8535 | tv2->setMemoryType(MemoryType::Shared); |
8536 | |
8537 | tv5->axis(0)->parallelize(ParallelType::TIDx); |
8538 | |
8539 | // tv1 and tv2 are on shared memory and are not parallelized with |
8540 | // TIDx. They should be predicated as they are redundant and can |
8541 | // interfere with smem aliasing (issue #1100). |
8542 | |
8543 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8544 | at::Tensor t0 = at::randn({10}, options); |
8545 | at::Tensor t4 = at::randn({1024}, options); |
8546 | std::vector<IValue> aten_inputs = {t0, t4}; |
8547 | |
8548 | FusionExecutor fe; |
8549 | fe.compileFusion(&fusion, aten_inputs); |
8550 | auto outputs = fe.runFusion(aten_inputs); |
8551 | |
8552 | auto ref1 = t0 + 3; |
8553 | auto ref2 = t4 + 1; |
8554 | |
8555 | testValidate(&fusion, outputs, aten_inputs, {ref1, ref2}, __LINE__, __FILE__); |
8556 | } |
8557 | |
8558 | TEST_F(NVFuserTest, FusionGridReductionWithNonExactParallelDimensions_CUDA) { |
8559 | Fusion fusion; |
8560 | FusionGuard fg(&fusion); |
8561 | |
8562 | auto tv0 = makeSymbolicTensor(1); |
8563 | fusion.addInput(tv0); |
8564 | |
8565 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
8566 | fusion.addOutput(tv1); |
8567 | |
8568 | auto tv2 = makeSymbolicTensor(1); |
8569 | fusion.addInput(tv2); |
8570 | auto tv3 = sum(tv2, {0}); |
8571 | fusion.addOutput(tv3); |
8572 | |
8573 | tv1->axis(0)->parallelize(ParallelType::TIDx); |
8574 | tv3->axis(0)->parallelize(ParallelType::BIDx); |
8575 | |
8576 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8577 | at::Tensor t0 = at::randn({17}, options); |
8578 | at::Tensor t2 = at::randn({19}, options); |
8579 | std::vector<IValue> aten_inputs = {t0, t2}; |
8580 | |
8581 | FusionExecutor fe; |
8582 | fe.compileFusion(&fusion, aten_inputs); |
8583 | auto outputs = fe.runFusion(aten_inputs); |
8584 | |
8585 | auto ref1 = t0 + 1; |
8586 | auto ref2 = sum(t2); |
8587 | |
8588 | testValidate(&fusion, outputs, aten_inputs, {ref1, ref2}, __LINE__, __FILE__); |
8589 | } |
8590 | |
8591 | TEST_F(NVFuserTest, FusionGridWelfordWithNonExactParallelDimensions_CUDA) { |
8592 | Fusion fusion; |
8593 | FusionGuard fg(&fusion); |
8594 | |
8595 | auto tv0 = makeSymbolicTensor(1); |
8596 | fusion.addInput(tv0); |
8597 | |
8598 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
8599 | fusion.addOutput(tv1); |
8600 | |
8601 | auto tv2 = makeSymbolicTensor(1); |
8602 | fusion.addInput(tv2); |
8603 | auto tv3 = Welford(tv2, {0}).avg; |
8604 | fusion.addOutput(tv3); |
8605 | |
8606 | tv1->axis(0)->parallelize(ParallelType::TIDx); |
8607 | tv3->axis(0)->parallelize(ParallelType::BIDx); |
8608 | |
8609 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8610 | at::Tensor t0 = at::randn({17}, options); |
8611 | at::Tensor t2 = at::randn({19}, options); |
8612 | std::vector<IValue> aten_inputs = {t0, t2}; |
8613 | |
8614 | FusionExecutor fe; |
8615 | fe.compileFusion(&fusion, aten_inputs); |
8616 | auto outputs = fe.runFusion(aten_inputs); |
8617 | |
8618 | auto ref1 = t0 + 1; |
8619 | auto ref2 = mean(t2, {0}); |
8620 | |
8621 | testValidate(&fusion, outputs, aten_inputs, {ref1, ref2}, __LINE__, __FILE__); |
8622 | } |
8623 | |
8624 | TEST_F(NVFuserTest, FusionGridReductionWithNonExactParallelDimensions2_CUDA) { |
8625 | Fusion fusion; |
8626 | FusionGuard fg(&fusion); |
8627 | |
8628 | auto tv0 = makeSymbolicTensor(2); |
8629 | fusion.addInput(tv0); |
8630 | |
8631 | auto tv1 = sum(tv0, {0, 1}); |
8632 | fusion.addOutput(tv1); |
8633 | |
8634 | auto tv2 = makeSymbolicTensor(3); |
8635 | fusion.addInput(tv2); |
8636 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
8637 | fusion.addOutput(tv3); |
8638 | |
8639 | auto tv4 = makeSymbolicTensor(3); |
8640 | fusion.addInput(tv4); |
8641 | auto tv5 = add(tv4, IrBuilder::create<Double>(1)); |
8642 | fusion.addOutput(tv5); |
8643 | |
8644 | tv1->axis(0)->parallelize(ParallelType::BIDx); |
8645 | tv1->axis(1)->parallelize(ParallelType::TIDx); |
8646 | |
8647 | tv3->axis(0)->parallelize(ParallelType::TIDx); |
8648 | tv3->axis(1)->parallelize(ParallelType::TIDy); |
8649 | tv3->axis(2)->parallelize(ParallelType::TIDz); |
8650 | |
8651 | tv5->axis(0)->parallelize(ParallelType::BIDx); |
8652 | tv5->axis(1)->parallelize(ParallelType::BIDy); |
8653 | tv5->axis(2)->parallelize(ParallelType::BIDz); |
8654 | |
8655 | // TODO: This needs a fix for issue #1102. |
8656 | // Also, need to allow predicated grid reductions. |
8657 | #if 0 |
8658 | FusionExecutor fe; |
8659 | fe.compileFusion(&fusion); |
8660 | |
8661 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8662 | at::Tensor t0 = at::randn({2, 3}, options); |
8663 | at::Tensor t2 = at::randn({5, 6, 7}, options); |
8664 | at::Tensor t4 = at::randn({8, 9, 10}, options); |
8665 | std::vector<IValue> aten_inputs = {t0, t2, t4}; |
8666 | auto outputs = fe.runFusion(aten_inputs); |
8667 | |
8668 | auto ref1 = t0.sum(at::IntArrayRef{0, 1}); |
8669 | auto ref2 = t2 + 1; |
8670 | auto ref3 = t4 + 1; |
8671 | |
8672 | testValidate( |
8673 | &fusion, outputs, aten_inputs, {ref1, ref2, ref3}, __LINE__, __FILE__); |
8674 | #endif |
8675 | } |
8676 | |
8677 | TEST_F(NVFuserTest, FusionGridWelfordWithNonExactParallelDimensions2_CUDA) { |
8678 | Fusion fusion; |
8679 | FusionGuard fg(&fusion); |
8680 | |
8681 | auto tv0 = makeSymbolicTensor(2); |
8682 | fusion.addInput(tv0); |
8683 | |
8684 | auto tvs = Welford(tv0, {0, 1}); |
8685 | fusion.addOutput(tvs.avg); |
8686 | |
8687 | auto tv2 = makeSymbolicTensor(3); |
8688 | fusion.addInput(tv2); |
8689 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
8690 | fusion.addOutput(tv3); |
8691 | |
8692 | auto tv4 = makeSymbolicTensor(3); |
8693 | fusion.addInput(tv4); |
8694 | auto tv5 = add(tv4, IrBuilder::create<Double>(1)); |
8695 | fusion.addOutput(tv5); |
8696 | |
8697 | tvs.avg->axis(0)->parallelize(ParallelType::BIDx); |
8698 | tvs.avg->axis(1)->parallelize(ParallelType::TIDx); |
8699 | |
8700 | tv3->axis(0)->parallelize(ParallelType::TIDx); |
8701 | tv3->axis(1)->parallelize(ParallelType::TIDy); |
8702 | tv3->axis(2)->parallelize(ParallelType::TIDz); |
8703 | |
8704 | tv5->axis(0)->parallelize(ParallelType::BIDx); |
8705 | tv5->axis(1)->parallelize(ParallelType::BIDy); |
8706 | tv5->axis(2)->parallelize(ParallelType::BIDz); |
8707 | |
8708 | // TODO: needs a fix for issue #1102 |
8709 | // Also, need to allow predicated grid reductions. |
8710 | #if 0 |
8711 | FusionExecutor fe; |
8712 | fe.compileFusion(&fusion); |
8713 | |
8714 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8715 | at::Tensor t0 = at::randn({2, 3}, options); |
8716 | at::Tensor t2 = at::randn({5, 6, 7}, options); |
8717 | at::Tensor t4 = at::randn({8, 9, 10}, options); |
8718 | std::vector<IValue> aten_inputs = {t0, t2, t4}; |
8719 | auto outputs = fe.runFusion(aten_inputs); |
8720 | |
8721 | auto ref1 = t0.mean(at::IntArrayRef{0, 1}); |
8722 | auto ref2 = t2 + 1; |
8723 | auto ref3 = t4 + 1; |
8724 | |
8725 | testValidate( |
8726 | &fusion, outputs, aten_inputs, {ref1, ref2, ref3}, __LINE__, __FILE__); |
8727 | #endif |
8728 | } |
8729 | |
8730 | // Repro of issue #1102 |
8731 | TEST_F(NVFuserTest, FusionPredicateParallelizedDomains_CUDA) { |
8732 | Fusion fusion; |
8733 | FusionGuard fg(&fusion); |
8734 | |
8735 | auto tv0 = makeSymbolicTensor(1); |
8736 | fusion.addInput(tv0); |
8737 | |
8738 | // Just to make TIDx/y/z non-exact |
8739 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
8740 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
8741 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
8742 | fusion.addOutput(tv3); |
8743 | |
8744 | auto tv4 = makeSymbolicTensor(1); |
8745 | fusion.addInput(tv4); |
8746 | |
8747 | auto tv5 = add(tv4, IrBuilder::create<Double>(1)); |
8748 | auto tv6 = add(tv5, IrBuilder::create<Double>(1)); |
8749 | auto tv7 = add(tv6, IrBuilder::create<Double>(1)); |
8750 | auto tv8 = add(tv7, IrBuilder::create<Double>(1)); |
8751 | auto tv9 = sum(tv8, {0}); |
8752 | fusion.addOutput(tv9); |
8753 | |
8754 | tv1->split(0, 5); |
8755 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
8756 | tv1->setMemoryType(MemoryType::Shared); |
8757 | tv2->split(0, 6); |
8758 | tv2->axis(-1)->parallelize(ParallelType::TIDy); |
8759 | tv2->setMemoryType(MemoryType::Shared); |
8760 | tv3->split(0, 7); |
8761 | tv3->axis(-1)->parallelize(ParallelType::TIDz); |
8762 | |
8763 | tv9->split(0, 4); |
8764 | tv4->computeAt(tv9, 1); |
8765 | |
8766 | tv4->axis(-1)->parallelize(ParallelType::TIDx); |
8767 | tv5->axis(-1)->parallelize(ParallelType::TIDy); |
8768 | tv6->axis(-1)->parallelize(ParallelType::TIDz); |
8769 | tv7->axis(-1)->parallelize(ParallelType::TIDz); |
8770 | tv8->axis(-1)->parallelize(ParallelType::TIDz); |
8771 | tv9->axis(-1)->parallelize(ParallelType::TIDz); |
8772 | tv9->axis(0)->parallelize(ParallelType::BIDx); |
8773 | |
8774 | tv5->setMemoryType(MemoryType::Shared); |
8775 | |
8776 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8777 | at::Tensor t0 = at::randn({17}, options); |
8778 | at::Tensor t4 = at::randn({19}, options); |
8779 | std::vector<IValue> aten_inputs = {t0, t4}; |
8780 | |
8781 | FusionExecutor fe; |
8782 | fe.compileFusion(&fusion, aten_inputs); |
8783 | auto outputs = fe.runFusion(aten_inputs); |
8784 | |
8785 | auto ref1 = t0 + 3; |
8786 | auto ref2 = sum(t4 + 4); |
8787 | |
8788 | testValidate(&fusion, outputs, aten_inputs, {ref1, ref2}, __LINE__, __FILE__); |
8789 | } |
8790 | |
8791 | // Repro of #1102 and #1129 |
8792 | TEST_F(NVFuserTest, FusionSmemPredicateUnswitch_CUDA) { |
8793 | if (!deviceMajorMinorCheck(7)) { |
8794 | GTEST_SKIP() << "skipping tests on pre-Volta GPUs" ; |
8795 | return; |
8796 | } |
8797 | Fusion fusion; |
8798 | FusionGuard fg(&fusion); |
8799 | |
8800 | auto tv0 = makeSymbolicTensor(1); |
8801 | fusion.addInput(tv0); |
8802 | auto tv1 = makeSymbolicTensor(1); |
8803 | fusion.addInput(tv1); |
8804 | |
8805 | auto tv2 = add(tv0, IrBuilder::create<Double>(1)); |
8806 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
8807 | auto tv4 = add(tv3, IrBuilder::create<Double>(1)); |
8808 | auto tv5 = add(tv4, IrBuilder::create<Double>(1)); |
8809 | fusion.addOutput(tv5); |
8810 | |
8811 | // Just to make TIDx/y/z non-exact |
8812 | auto tvx = add(tv1, IrBuilder::create<Double>(1)); |
8813 | auto tvy = add(tvx, IrBuilder::create<Double>(1)); |
8814 | auto tvz = add(tvy, IrBuilder::create<Double>(1)); |
8815 | fusion.addOutput(tvz); |
8816 | |
8817 | tv5->split(0, 4); |
8818 | tv0->computeAt(tv5, 1); |
8819 | |
8820 | tv0->axis(-1)->parallelize(ParallelType::TIDx); |
8821 | tv2->axis(-1)->parallelize(ParallelType::TIDy); |
8822 | tv3->axis(-1)->parallelize(ParallelType::TIDz); |
8823 | tv4->axis(-1)->parallelize(ParallelType::TIDx); |
8824 | tv5->axis(-1)->parallelize(ParallelType::TIDy); |
8825 | tv5->axis(0)->parallelize(ParallelType::Unswitch); |
8826 | |
8827 | tvx->split(0, 5); |
8828 | tvx->axis(-1)->parallelize(ParallelType::TIDx); |
8829 | tvy->split(0, 6); |
8830 | tvy->axis(-1)->parallelize(ParallelType::TIDy); |
8831 | tvz->split(0, 7); |
8832 | tvz->axis(-1)->parallelize(ParallelType::TIDz); |
8833 | |
8834 | for (auto tv : {tv2, tv3, tv4, tvx, tvy}) { |
8835 | tv->setMemoryType(MemoryType::Shared); |
8836 | } |
8837 | |
8838 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8839 | at::Tensor t0 = at::randn({17}, options); |
8840 | at::Tensor t1 = at::randn({19}, options); |
8841 | std::vector<IValue> aten_inputs = {t0, t1}; |
8842 | |
8843 | FusionExecutor fe; |
8844 | fe.compileFusion(&fusion, aten_inputs); |
8845 | auto outputs = fe.runFusion(aten_inputs); |
8846 | |
8847 | auto ref1 = t0 + 4; |
8848 | auto ref2 = t1 + 3; |
8849 | |
8850 | testValidate(&fusion, outputs, aten_inputs, {ref1, ref2}, __LINE__, __FILE__); |
8851 | } |
8852 | |
8853 | // Repro of issue #1136 |
8854 | TEST_F(NVFuserTest, FusionFloatPow_CUDA) { |
8855 | Fusion fusion; |
8856 | FusionGuard fg(&fusion); |
8857 | |
8858 | auto tv0 = makeSymbolicTensor(1); |
8859 | fusion.addInput(tv0); |
8860 | |
8861 | auto tv1 = binaryOp(BinaryOpType::Pow, tv0, IrBuilder::create<Int>(4)); |
8862 | // To check if pow(tv0, 2) is replaced with tv0 * tv0 |
8863 | auto tv2 = binaryOp(BinaryOpType::Pow, tv0, IrBuilder::create<Int>(2)); |
8864 | // To check if pow(tv0, 2.0) is replaced with tv0 * tv0 |
8865 | auto tv3 = binaryOp(BinaryOpType::Pow, tv0, IrBuilder::create<Double>(2)); |
8866 | auto tv4 = binaryOp(BinaryOpType::Pow, tv0, IrBuilder::create<Int>(3)); |
8867 | auto tv5 = binaryOp(BinaryOpType::Pow, tv0, IrBuilder::create<Double>(3)); |
8868 | auto s = binaryOp( |
8869 | BinaryOpType::Pow, |
8870 | IrBuilder::create<Double>(3), |
8871 | IrBuilder::create<Double>(3)); |
8872 | auto tv6 = add(tv0, s); |
8873 | |
8874 | fusion.addOutput(tv1); |
8875 | fusion.addOutput(tv2); |
8876 | fusion.addOutput(tv3); |
8877 | fusion.addOutput(tv4); |
8878 | fusion.addOutput(tv5); |
8879 | fusion.addOutput(tv6); |
8880 | |
8881 | tv1->split(0, 32); |
8882 | tv1->axis(0)->parallelize(ParallelType::BIDx); |
8883 | tv1->axis(1)->parallelize(ParallelType::TIDx); |
8884 | |
8885 | TransformPropagatorWithCheck propagator(tv1); |
8886 | MaxRootDomainInfoSpanningTree(tv1).traverse(&propagator); |
8887 | scheduler_utils::parallelizeAllLike(tv1, {tv2, tv3, tv4, tv5, tv6}); |
8888 | |
8889 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
8890 | at::Tensor t0 = at::randn({1000}, options); |
8891 | // Negative inputs cause nan in Fuesr as use_fast_math is enabled |
8892 | t0 = abs(t0); |
8893 | std::vector<IValue> aten_inputs = {t0}; |
8894 | |
8895 | FusionExecutor fe; |
8896 | fe.compileFusion(&fusion, aten_inputs); |
8897 | auto outputs = fe.runFusion(aten_inputs); |
8898 | |
8899 | auto p4 = at::pow(t0, 4); |
8900 | auto p2 = at::pow(t0, 2); |
8901 | auto p3 = at::pow(t0, 3); |
8902 | auto t6 = t0 + std::pow(3, 3); |
8903 | |
8904 | testValidate( |
8905 | &fusion, |
8906 | outputs, |
8907 | aten_inputs, |
8908 | {p4, p2, p2, p3, p3, t6}, |
8909 | __LINE__, |
8910 | __FILE__); |
8911 | } |
8912 | |
8913 | TEST_F(NVFuserTest, FusionIssue1127_CUDA) { |
8914 | Fusion fusion; |
8915 | FusionGuard fg(&fusion); |
8916 | |
8917 | const int numel = 4; |
8918 | |
8919 | auto tv0 = makeConcreteTensor({numel}); |
8920 | fusion.addInput(tv0); |
8921 | |
8922 | auto tv1 = sum(tv0, {0}); |
8923 | auto tv2 = broadcast(tv1, {true}); |
8924 | |
8925 | auto tv3 = makeConcreteTensor({numel, numel}); |
8926 | fusion.addInput(tv3); |
8927 | |
8928 | auto tv4 = sum(tv3, {1}); |
8929 | |
8930 | auto tv5 = add(tv2, tv4); |
8931 | fusion.addOutput(tv5); |
8932 | |
8933 | tv1->axis(0)->parallelize(ParallelType::TIDx); |
8934 | tv2->axis(0)->parallelize(ParallelType::TIDx); |
8935 | tv4->axis(1)->parallelize(ParallelType::TIDx); |
8936 | tv5->axis(0)->parallelize(ParallelType::TIDx); |
8937 | |
8938 | // Lowering should fail since tv5 is predicated and paralellized with TIDx. |
8939 | // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto) |
8940 | ASSERT_ANY_THROW(fusion.printKernel()); |
8941 | } |
8942 | |
8943 | TEST_F(NVFuserTest, FusionChannelsLastParser_CUDA) { |
8944 | // This test may not pass if using a custom block sync as there may |
8945 | // be additional calls. Skip the test as it's not specifically |
8946 | // relevant with block synchronizatin. |
8947 | if (std::getenv("PYTORCH_NVFUSER_USE_BLOCK_SYNC_ATOMIC" )) { |
8948 | return; |
8949 | } |
8950 | auto g = std::make_shared<Graph>(); |
8951 | const auto graph0_string = R"IR( |
8952 | graph(%0 : Half(8, 4, 10, 16, strides=[640, 1, 64, 4]), |
8953 | %1 : Half(8, 4, 10, 16, strides=[640, 160, 16, 1])): |
8954 | %o.1 : Half(8, 4, 10, 16, strides=[640, 1, 64, 4]) = aten::mul(%0, %1) # sum_dyn.py:5:6 |
8955 | %3 : Half(8, 4, 10, 16, strides=[640, 1, 64, 4]) = aten::relu(%o.1) # sum_dyn.py:6:9 |
8956 | return (%3))IR" ; |
8957 | parseIR(graph0_string, g.get()); |
8958 | |
8959 | // strides are not yet supported in the irparser. |
8960 | { |
8961 | auto val = g->block()->inputs()[0]; |
8962 | val->setType(val->type()->castRaw<TensorType>()->withSizesStrides( |
8963 | {8, 4, 10, 16}, {640, 1, 64, 4})); |
8964 | } |
8965 | |
8966 | { |
8967 | auto val = g->block()->inputs()[1]; |
8968 | val->setType(val->type()->castRaw<TensorType>()->withSizesStrides( |
8969 | {8, 4, 10, 16}, {640, 160, 16, 1})); |
8970 | } |
8971 | |
8972 | for (auto node : g->block()->nodes()) { |
8973 | for (auto val : node->outputs()) { |
8974 | if (val->isCompleteTensor()) |
8975 | val->setType(val->type()->castRaw<TensorType>()->withSizesStrides( |
8976 | {8, 4, 10, 16}, {640, 1, 64, 4})); |
8977 | } |
8978 | } |
8979 | |
8980 | auto fusion = parseJitIR(g); |
8981 | FusionGuard fg(fusion.get()); |
8982 | auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0); |
8983 | at::Tensor input0 = |
8984 | at::randn({2, 2, 2, 16}, options).clone(c10::MemoryFormat::ChannelsLast); |
8985 | at::Tensor input1 = at::randn({2, 2, 2, 16}, options); |
8986 | auto lparams = schedulePointwise(fusion.get(), {input0, input1}); |
8987 | |
8988 | // CONSIDER: |
8989 | // 1. this can be moved to a dedicated "golden" file |
8990 | // 2. use a fuzzy compare (ignore non-significant whitespaces for example) |
8991 | const std::string expected_kernel = R"( |
8992 | __global__ void CUDAGeneratedKernel(Tensor<__half, 4> T0, Tensor<__half, 4> T2, Tensor<__half, 4> T7) { |
8993 | int64_t i165; |
8994 | i165 = (((nvfuser_index_t)blockIdx.x) * 128) + ((nvfuser_index_t)threadIdx.x); |
8995 | if ((i165 < (T0.size[0] * (T0.size[1] * (T0.size[2] * T0.size[3]))))) { |
8996 | __half T9[1]; |
8997 | T9[0] = 0; |
8998 | T9[0] |
8999 | = T2[((((((nvfuser_index_t)blockIdx.x) * 128) + ((nvfuser_index_t)threadIdx.x)) / (T0.size[1] * (T0.size[2] * T0.size[3]))) * ((T0.size[2] * T0.size[1]) * T0.size[3])) + ((((((((nvfuser_index_t)blockIdx.x) * 128) + ((nvfuser_index_t)threadIdx.x)) % (T0.size[1] * (T0.size[2] * T0.size[3]))) % (T0.size[2] * T0.size[3])) % T0.size[3]) * (T0.size[2] * T0.size[1])) + (((((((nvfuser_index_t)blockIdx.x) * 128) + ((nvfuser_index_t)threadIdx.x)) % (T0.size[1] * (T0.size[2] * T0.size[3]))) / (T0.size[2] * T0.size[3])) * T0.size[2]) + (((((((nvfuser_index_t)blockIdx.x) * 128) + ((nvfuser_index_t)threadIdx.x)) % (T0.size[1] * (T0.size[2] * T0.size[3]))) % (T0.size[2] * T0.size[3])) / T0.size[3])]; |
9000 | __half T8[1]; |
9001 | T8[0] = 0; |
9002 | T8[0] |
9003 | = T0[i165]; |
9004 | float T3[1]; |
9005 | T3[0] |
9006 | = __half2float(T9[0]); |
9007 | float T4[1]; |
9008 | T4[0] |
9009 | = T3[0]; |
9010 | float T1[1]; |
9011 | T1[0] |
9012 | = __half2float(T8[0]); |
9013 | float T5[1]; |
9014 | T5[0] |
9015 | = T1[0] |
9016 | * T4[0]; |
9017 | float T6[1]; |
9018 | T6[0] |
9019 | = relu(T5[0]); |
9020 | __half T10[1]; |
9021 | T10[0] |
9022 | = __float2half(T6[0]); |
9023 | T7[i165] |
9024 | = T10[0]; |
9025 | } |
9026 | } |
9027 | )" ; |
9028 | |
9029 | const std::string actual_kernel = |
9030 | "\n" + codegen::generateCudaKernel(GpuLower(fusion.get()).kernel()); |
9031 | |
9032 | if (expected_kernel.size() != actual_kernel.size() || |
9033 | expected_kernel.compare(actual_kernel) != 0) { |
9034 | std::cerr |
9035 | << " Codegen mismatch, codegen possibly changed, or is incorrect. " |
9036 | << " \n ========= EXPECTED ========= \n" |
9037 | << expected_kernel << "\n========= ACTUAL ========== \n" |
9038 | << actual_kernel << "\n=================" << std::endl; |
9039 | auto it = std::mismatch( |
9040 | expected_kernel.begin(), |
9041 | expected_kernel.end(), |
9042 | actual_kernel.begin(), |
9043 | actual_kernel.end()); |
9044 | std::string actual_mismatched_snippet(it.second, actual_kernel.end()); |
9045 | actual_mismatched_snippet = actual_mismatched_snippet.substr(0, 10); |
9046 | std::string expected_mismatched_snippet(it.first, expected_kernel.end()); |
9047 | expected_mismatched_snippet = expected_mismatched_snippet.substr(0, 10); |
9048 | std::cerr << "First mismatch found at: " << actual_mismatched_snippet |
9049 | << ", expected: " << expected_mismatched_snippet << std::endl; |
9050 | TORCH_CHECK(false); |
9051 | } |
9052 | |
9053 | // TODO: runFusion hits assertion. I'm probably doing something wrong here. |
9054 | // FusionExecutor fe; |
9055 | // fe.compileFusion(fusion.get()); |
9056 | // auto outputs = fe.runFusion({input0, input1}, lparams); |
9057 | // at::Tensor output_ref = (input0 * input1).relu(); |
9058 | // TORCH_CHECK(output_ref.equal(outputs[0])); |
9059 | } |
9060 | |
9061 | TEST_F(NVFuserTest, FusionThreadPredicateUnswitch_CUDA) { |
9062 | Fusion fusion; |
9063 | FusionGuard fg(&fusion); |
9064 | |
9065 | auto tv0 = makeConcreteTensor({10, 1024}); |
9066 | fusion.addInput(tv0); |
9067 | |
9068 | auto tv1 = sum(tv0, {1}); |
9069 | auto tv2 = add(tv1, IrBuilder::create<Double>(1)); |
9070 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
9071 | |
9072 | fusion.addOutput(tv3); |
9073 | |
9074 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
9075 | tv2->computeAt(tv3, -1); |
9076 | tv3->axis(0)->parallelize(ParallelType::Unswitch); |
9077 | |
9078 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
9079 | at::Tensor t0 = at::randn({10, 1024}, options); |
9080 | std::vector<IValue> aten_inputs = {t0}; |
9081 | |
9082 | FusionExecutor fe; |
9083 | fe.compileFusion(&fusion, aten_inputs); |
9084 | auto outputs = fe.runFusion(aten_inputs); |
9085 | |
9086 | auto ref = sum(t0, {1}) + 2; |
9087 | |
9088 | testValidate(&fusion, outputs, aten_inputs, {ref}, __LINE__, __FILE__); |
9089 | } |
9090 | |
9091 | TEST_F(NVFuserTest, FusionNonContigOutputs_CUDA) { |
9092 | Fusion fusion; |
9093 | FusionGuard fg(&fusion); |
9094 | |
9095 | auto tv0 = makeSymbolicTensor(1); |
9096 | fusion.addInput(tv0); |
9097 | |
9098 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
9099 | fusion.addOutput(tv1); |
9100 | |
9101 | tv1->setContiguity(false); |
9102 | |
9103 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
9104 | at::Tensor at_input = at::randn({10}, options); |
9105 | at::Tensor at_output = at::empty_strided({10}, {2}, options); |
9106 | |
9107 | FusionExecutor fe; |
9108 | fe.compileFusion(&fusion, {at_input}); |
9109 | auto returned_outputs = fe.runFusion({at_input}, {at_output}); |
9110 | |
9111 | // Returned outputs should only contain one tensor that is the same |
9112 | // as the output tensor given to runFusion |
9113 | TORCH_CHECK(returned_outputs.size() == 1); |
9114 | TORCH_CHECK(returned_outputs[0].is_same(at_output)); |
9115 | TORCH_CHECK(!returned_outputs[0].is_contiguous()); |
9116 | |
9117 | auto at_ref = at_input + 1; |
9118 | |
9119 | testValidate(&fusion, {at_output}, {at_input}, {at_ref}, __LINE__, __FILE__); |
9120 | } |
9121 | |
9122 | TEST_F(NVFuserTest, FusionTestWarpSoftMax_CUDA) { |
9123 | Fusion fusion; |
9124 | FusionGuard fg(&fusion); |
9125 | |
9126 | // Setup softmax fusion |
9127 | auto input = makeContigTensor(2); |
9128 | fusion.addInput(input); |
9129 | auto output = softmax(input, 1); |
9130 | fusion.addOutput(output); |
9131 | |
9132 | // Setup runtime input |
9133 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
9134 | at::Tensor aten_input = at::randn({8, 16 * 197}, options); |
9135 | std::vector<c10::IValue> aten_inputs({aten_input}); |
9136 | |
9137 | // Schedule through magic scheduler |
9138 | SchedulerRuntimeInfo runtime_info(&fusion, aten_inputs, true); |
9139 | TORCH_CHECK(SchedulerEntry::canSchedule( |
9140 | ScheduleHeuristic::Persistent, &fusion, runtime_info)); |
9141 | auto scheduler = SchedulerEntry::makeEntry( |
9142 | ScheduleHeuristic::Persistent, &fusion, runtime_info); |
9143 | scheduler->schedule(&fusion); |
9144 | |
9145 | // Modify the schedule to use warp reduction |
9146 | auto used_vals = fusion.usedMathVals(); |
9147 | for (auto tv : ir_utils::filterByType<TensorView>(used_vals)) { |
9148 | for (IterDomain* id : tv->domain()->domain()) { |
9149 | if (id->getParallelType() == ParallelType::TIDx) { |
9150 | id->padToMultipleOfWarp(); |
9151 | } |
9152 | } |
9153 | } |
9154 | |
9155 | // Test result |
9156 | FusionExecutor fe; |
9157 | fe.compileFusion(&fusion, aten_inputs); |
9158 | auto outputs = fe.runFusion(aten_inputs); |
9159 | auto ref_output = at::_softmax(aten_input, 1, false); |
9160 | testValidate(&fusion, outputs, aten_inputs, {ref_output}, __LINE__, __FILE__); |
9161 | } |
9162 | |
9163 | TEST_F(NVFuserTest, FusionIssue1133_CUDA) { |
9164 | if (!deviceMajorMinorCheck(7)) { |
9165 | GTEST_SKIP() << "skipping tests on pre-Volta GPUs" ; |
9166 | return; |
9167 | } |
9168 | Fusion fusion; |
9169 | FusionGuard fg(&fusion); |
9170 | |
9171 | auto tv0 = makeSymbolicTensor(2); |
9172 | fusion.addInput(tv0); |
9173 | |
9174 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
9175 | auto tv2 = sum(tv1, {1}); |
9176 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
9177 | |
9178 | fusion.addOutput(tv3); |
9179 | |
9180 | tv0->computeAt(tv3, 1); |
9181 | |
9182 | const int split_factor = 32; |
9183 | |
9184 | tv2->split(-1, split_factor); |
9185 | tv1->computeAt(tv2, -2); |
9186 | |
9187 | tv1->axis(-1)->parallelize(ParallelType::TIDx); |
9188 | tv2->axis(-1)->parallelize(ParallelType::TIDx); |
9189 | |
9190 | tv3->axis(0)->parallelize(ParallelType::Unswitch); |
9191 | |
9192 | tv1->setMemoryType(MemoryType::Shared); |
9193 | tv2->setMemoryType(MemoryType::Shared); |
9194 | |
9195 | // Both tv1 and tv2 should be allocated at the top-level scope |
9196 | GpuLower gpulw(&fusion); |
9197 | bool tv1_validated = false; |
9198 | bool tv2_validated = false; |
9199 | for (const auto& kir_node : gpulw.kernel()->topLevelExprs()) { |
9200 | if (auto alloc = dynamic_cast<kir::Allocate*>(kir_node)) { |
9201 | auto size = alloc->size(); |
9202 | if (!(alloc->buffer()->name() == 1 || alloc->buffer()->name() == 2)) { |
9203 | // There should be no allocation other than those for tv1 and tv2 |
9204 | TORCH_CHECK(false, "Invalid allocation detected" ); |
9205 | } |
9206 | TORCH_CHECK(size->isA<Int>(), "Invalid allocation size" ); |
9207 | TORCH_CHECK(size->as<Int>()->isConst(), "Allocation not constant" ); |
9208 | auto size_int = size->as<Int>()->value().value(); |
9209 | if (alloc->buffer()->name() == 1) { |
9210 | TORCH_CHECK( |
9211 | size_int == split_factor, |
9212 | "Invalid allocation size: " , |
9213 | size->as<Int>()->value().value()); |
9214 | tv1_validated = true; |
9215 | } else { |
9216 | TORCH_CHECK( |
9217 | size_int == 1, |
9218 | "Invalid allocation size: " , |
9219 | size->as<Int>()->value().value()); |
9220 | tv2_validated = true; |
9221 | } |
9222 | } |
9223 | } |
9224 | |
9225 | TORCH_CHECK(tv1_validated, "Failed to validate tv1 allocation" ); |
9226 | TORCH_CHECK(tv2_validated, "Failed to validate tv2 allocation" ); |
9227 | |
9228 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
9229 | at::Tensor t0 = at::randn({99, 101}, options); |
9230 | std::vector<IValue> aten_inputs = {t0}; |
9231 | |
9232 | FusionExecutor fe; |
9233 | fe.compileFusion(&fusion, aten_inputs); |
9234 | auto outputs = fe.runFusion(aten_inputs); |
9235 | |
9236 | auto ref = (t0 + 1).sum({1}) + 1; |
9237 | |
9238 | testValidate(&fusion, outputs, aten_inputs, {ref}, __LINE__, __FILE__); |
9239 | } |
9240 | |
9241 | TEST_F(NVFuserTest, FusionRfactorContigIDs_CUDA) { |
9242 | Fusion fusion; |
9243 | FusionGuard fg(&fusion); |
9244 | |
9245 | auto tv0 = makeSymbolicTensor(2); |
9246 | fusion.addInput(tv0); |
9247 | |
9248 | auto tv1 = sum(tv0, {1}); |
9249 | fusion.addOutput(tv1); |
9250 | |
9251 | tv1->split(1, 32); |
9252 | |
9253 | auto tv2 = tv1->rFactor({1}); |
9254 | |
9255 | // This merged domain is not contiguous. |
9256 | tv2->merge(0, 2); |
9257 | |
9258 | tv2->setMemoryType(MemoryType::Shared); |
9259 | |
9260 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
9261 | at::Tensor t0 = at::randn({99, 101}, options); |
9262 | std::vector<IValue> aten_inputs = {t0}; |
9263 | |
9264 | FusionExecutor fe; |
9265 | fe.compileFusion(&fusion, aten_inputs); |
9266 | auto outputs = fe.runFusion(aten_inputs); |
9267 | |
9268 | auto ref = t0.sum({1}); |
9269 | |
9270 | testValidate(&fusion, outputs, aten_inputs, {ref}, __LINE__, __FILE__); |
9271 | } |
9272 | |
9273 | TEST_F(NVFuserTest, FusionPersistentBufferCalculation1_CUDA) { |
9274 | Fusion fusion; |
9275 | FusionGuard fg(&fusion); |
9276 | |
9277 | auto tv0 = makeSymbolicTensor(2); |
9278 | fusion.addInput(tv0); |
9279 | |
9280 | auto tv1 = set(tv0); |
9281 | auto tv2 = sum(tv1, {1}); |
9282 | auto tv3 = broadcast(tv2, {false, true}); |
9283 | auto tv4 = set(tv1); |
9284 | auto tv5 = add(tv3, tv4); |
9285 | fusion.addOutput(tv5); |
9286 | |
9287 | auto persistent_buffer_info = scheduler_utils::persistentBuffers(&fusion); |
9288 | |
9289 | auto isTvWithinVec = [](std::vector<TensorView*>& vec, TensorView* tv) { |
9290 | return std::find(vec.begin(), vec.end(), tv) != vec.end(); |
9291 | }; |
9292 | |
9293 | auto tvEntryInVecVec = [](std::vector<std::vector<TensorView*>>& vec_o_vec, |
9294 | std::vector<TensorView*>& buffer_vec, |
9295 | TensorView* tv) { |
9296 | auto buffer_it = std::find(buffer_vec.begin(), buffer_vec.end(), tv); |
9297 | return vec_o_vec.begin() + std::distance(buffer_vec.begin(), buffer_it); |
9298 | }; |
9299 | |
9300 | auto& buffers = persistent_buffer_info.persistent_buffers; |
9301 | auto& resolution = persistent_buffer_info.persistent_buffer_resolution_points; |
9302 | auto& projectable = persistent_buffer_info.projectable_persistent_buffers; |
9303 | auto& projectable_inputs = persistent_buffer_info.projectable_buffer_inputs; |
9304 | |
9305 | TORCH_INTERNAL_ASSERT(buffers.size() == 1); |
9306 | TORCH_INTERNAL_ASSERT(resolution.size() == 1 && resolution[0].size() == 1); |
9307 | TORCH_INTERNAL_ASSERT(projectable.size() == 1); |
9308 | TORCH_INTERNAL_ASSERT(projectable_inputs.size() == 1); |
9309 | |
9310 | TORCH_INTERNAL_ASSERT(isTvWithinVec(buffers, tv1)); |
9311 | TORCH_INTERNAL_ASSERT(isTvWithinVec(projectable, tv1)); |
9312 | TORCH_INTERNAL_ASSERT(isTvWithinVec(projectable_inputs, tv0)); |
9313 | |
9314 | auto tv1_resolution_it = tvEntryInVecVec(resolution, buffers, tv1); |
9315 | TORCH_INTERNAL_ASSERT(tv1_resolution_it != resolution.end()) |
9316 | |
9317 | TORCH_INTERNAL_ASSERT(isTvWithinVec(*tv1_resolution_it, tv5)); |
9318 | |
9319 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
9320 | at::Tensor aten_t0 = at::randn({99, 101}, options); |
9321 | |
9322 | // Schedule through magic scheduler |
9323 | SchedulerRuntimeInfo runtime_info(&fusion, {aten_t0}, true); |
9324 | auto persistent_buffer_size = |
9325 | persistentBufferSize(&fusion, runtime_info, persistent_buffer_info); |
9326 | |
9327 | TORCH_INTERNAL_ASSERT( |
9328 | persistent_buffer_size.persistent_buffer_size == |
9329 | static_cast<int64_t>(aten_t0.size(1) * dataTypeSize(DataType::Float))); |
9330 | TORCH_INTERNAL_ASSERT( |
9331 | persistent_buffer_size.projected_persistent_buffer_size == |
9332 | static_cast<int64_t>(aten_t0.size(1) * dataTypeSize(DataType::Float))); |
9333 | } |
9334 | |
9335 | TEST_F(NVFuserTest, FusionPersistentBufferCalculation2_CUDA) { |
9336 | Fusion fusion; |
9337 | FusionGuard fg(&fusion); |
9338 | |
9339 | auto tv0 = makeSymbolicTensor(2, DataType::Half); |
9340 | fusion.addInput(tv0); |
9341 | |
9342 | auto tv1 = castOp(DataType::Float, tv0); |
9343 | auto tv2 = sum(tv1, {1}); |
9344 | auto tv3 = broadcast(tv2, {false, true}); |
9345 | auto tv4 = set(tv1); |
9346 | auto tv5 = add(tv3, tv4); |
9347 | auto tv6 = castOp(DataType::Half, tv5); |
9348 | fusion.addOutput(tv6); |
9349 | |
9350 | auto persistent_buffer_info = scheduler_utils::persistentBuffers(&fusion); |
9351 | |
9352 | auto isTvWithinVec = [](std::vector<TensorView*>& vec, TensorView* tv) { |
9353 | return std::find(vec.begin(), vec.end(), tv) != vec.end(); |
9354 | }; |
9355 | |
9356 | auto tvEntryInVecVec = [](std::vector<std::vector<TensorView*>>& vec_o_vec, |
9357 | std::vector<TensorView*>& buffer_vec, |
9358 | TensorView* tv) { |
9359 | auto buffer_it = std::find(buffer_vec.begin(), buffer_vec.end(), tv); |
9360 | return vec_o_vec.begin() + std::distance(buffer_vec.begin(), buffer_it); |
9361 | }; |
9362 | |
9363 | auto& buffers = persistent_buffer_info.persistent_buffers; |
9364 | auto& resolution = persistent_buffer_info.persistent_buffer_resolution_points; |
9365 | auto& projectable = persistent_buffer_info.projectable_persistent_buffers; |
9366 | auto& projectable_inputs = persistent_buffer_info.projectable_buffer_inputs; |
9367 | |
9368 | TORCH_INTERNAL_ASSERT(buffers.size() == 1); |
9369 | TORCH_INTERNAL_ASSERT(resolution.size() == 1 && resolution[0].size() == 1); |
9370 | TORCH_INTERNAL_ASSERT(projectable.size() == 1); |
9371 | TORCH_INTERNAL_ASSERT(projectable_inputs.size() == 1); |
9372 | |
9373 | TORCH_INTERNAL_ASSERT(isTvWithinVec(buffers, tv1)); |
9374 | TORCH_INTERNAL_ASSERT(isTvWithinVec(projectable, tv1)); |
9375 | TORCH_INTERNAL_ASSERT(isTvWithinVec(projectable_inputs, tv0)); |
9376 | |
9377 | auto tv1_resolution_it = tvEntryInVecVec(resolution, buffers, tv1); |
9378 | TORCH_INTERNAL_ASSERT(tv1_resolution_it != resolution.end()) |
9379 | |
9380 | TORCH_INTERNAL_ASSERT(isTvWithinVec(*tv1_resolution_it, tv5)); |
9381 | |
9382 | auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0); |
9383 | at::Tensor aten_t0 = at::randn({99, 101}, options); |
9384 | |
9385 | // Schedule through magic scheduler |
9386 | SchedulerRuntimeInfo runtime_info(&fusion, {aten_t0}, true); |
9387 | auto persistent_buffer_size = |
9388 | persistentBufferSize(&fusion, runtime_info, persistent_buffer_info); |
9389 | |
9390 | TORCH_INTERNAL_ASSERT( |
9391 | persistent_buffer_size.persistent_buffer_size == |
9392 | static_cast<int64_t>(aten_t0.size(1) * dataTypeSize(DataType::Float))); |
9393 | TORCH_INTERNAL_ASSERT( |
9394 | persistent_buffer_size.projected_persistent_buffer_size == |
9395 | static_cast<int64_t>(aten_t0.size(1) * dataTypeSize(DataType::Half))); |
9396 | } |
9397 | |
9398 | TEST_F(NVFuserTest, FusionPersistentBufferCalculation3_CUDA) { |
9399 | Fusion fusion; |
9400 | FusionGuard fg(&fusion); |
9401 | |
9402 | auto tv0 = makeSymbolicTensor(2, DataType::Half); |
9403 | fusion.addInput(tv0); |
9404 | |
9405 | auto tv1 = castOp(DataType::Float, tv0); |
9406 | auto tv2 = set(tv1); |
9407 | auto tv3 = sum(tv2, {1}); |
9408 | auto tv4 = broadcast(tv3, {false, true}); |
9409 | |
9410 | auto tv5 = makeSymbolicTensor(2, DataType::Half); |
9411 | fusion.addInput(tv5); |
9412 | |
9413 | auto tv6 = castOp(DataType::Float, tv5); |
9414 | |
9415 | auto tv7 = add(tv6, tv4); |
9416 | auto tv8 = set(tv1); |
9417 | auto tv9 = add(tv7, tv8); |
9418 | auto tv10 = sum(tv9, {1}); |
9419 | auto tv11 = broadcast(tv10, {false, true}); |
9420 | auto tv12 = set(tv7); |
9421 | auto tv13 = add(tv12, tv11); |
9422 | |
9423 | fusion.addOutput(tv13); |
9424 | |
9425 | auto persistent_buffer_info = scheduler_utils::persistentBuffers(&fusion); |
9426 | |
9427 | auto isTvWithinVec = [](std::vector<TensorView*>& vec, TensorView* tv) { |
9428 | return std::find(vec.begin(), vec.end(), tv) != vec.end(); |
9429 | }; |
9430 | |
9431 | auto tvEntryInVecVec = [](std::vector<std::vector<TensorView*>>& vec_o_vec, |
9432 | std::vector<TensorView*>& buffer_vec, |
9433 | TensorView* tv) { |
9434 | auto buffer_it = std::find(buffer_vec.begin(), buffer_vec.end(), tv); |
9435 | return vec_o_vec.begin() + std::distance(buffer_vec.begin(), buffer_it); |
9436 | }; |
9437 | |
9438 | auto& buffers = persistent_buffer_info.persistent_buffers; |
9439 | auto& resolution = persistent_buffer_info.persistent_buffer_resolution_points; |
9440 | auto& projectable = persistent_buffer_info.projectable_persistent_buffers; |
9441 | auto& projectable_inputs = persistent_buffer_info.projectable_buffer_inputs; |
9442 | |
9443 | TORCH_INTERNAL_ASSERT(buffers.size() == 2); |
9444 | TORCH_INTERNAL_ASSERT( |
9445 | resolution.size() == 2 && resolution[0].size() == 1 && |
9446 | resolution[1].size() == 1); |
9447 | TORCH_INTERNAL_ASSERT(projectable.size() == 1); |
9448 | TORCH_INTERNAL_ASSERT(projectable_inputs.size() == 1); |
9449 | |
9450 | TORCH_INTERNAL_ASSERT( |
9451 | isTvWithinVec(buffers, tv1) && isTvWithinVec(buffers, tv7)); |
9452 | TORCH_INTERNAL_ASSERT( |
9453 | isTvWithinVec(projectable, tv1) && !isTvWithinVec(projectable, tv7)); |
9454 | |
9455 | TORCH_INTERNAL_ASSERT(isTvWithinVec(projectable_inputs, tv0)); |
9456 | |
9457 | auto tv1_resolution_it = tvEntryInVecVec(resolution, buffers, tv1); |
9458 | TORCH_INTERNAL_ASSERT(tv1_resolution_it != resolution.end()) |
9459 | TORCH_INTERNAL_ASSERT(isTvWithinVec(*tv1_resolution_it, tv9)); |
9460 | |
9461 | auto tv7_resolution_it = tvEntryInVecVec(resolution, buffers, tv7); |
9462 | TORCH_INTERNAL_ASSERT(tv7_resolution_it != resolution.end()) |
9463 | TORCH_INTERNAL_ASSERT(isTvWithinVec(*tv7_resolution_it, tv13)); |
9464 | |
9465 | auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0); |
9466 | at::Tensor aten_t0 = at::randn({99, 101}, options); |
9467 | at::Tensor aten_t5 = at::randn({99, 101}, options); |
9468 | |
9469 | // Schedule through magic scheduler |
9470 | SchedulerRuntimeInfo runtime_info(&fusion, {aten_t0, aten_t5}, true); |
9471 | auto persistent_buffer_size = |
9472 | persistentBufferSize(&fusion, runtime_info, persistent_buffer_info); |
9473 | |
9474 | TORCH_INTERNAL_ASSERT( |
9475 | persistent_buffer_size.persistent_buffer_size == |
9476 | static_cast<int64_t>( |
9477 | aten_t0.size(1) * dataTypeSize(DataType::Float) * 2)); |
9478 | TORCH_INTERNAL_ASSERT( |
9479 | persistent_buffer_size.projected_persistent_buffer_size == |
9480 | static_cast<int64_t>( |
9481 | aten_t0.size(1) * |
9482 | (dataTypeSize(DataType::Half) + dataTypeSize(DataType::Float)))); |
9483 | } |
9484 | |
9485 | TEST_F(NVFuserTest, FusionPersistentBufferCalculation4_CUDA) { |
9486 | Fusion fusion; |
9487 | FusionGuard fg(&fusion); |
9488 | |
9489 | auto tv0 = makeSymbolicTensor(2, DataType::Half); |
9490 | fusion.addInput(tv0); |
9491 | |
9492 | auto tv1 = castOp(DataType::Float, tv0); |
9493 | auto tv2 = set(tv1); |
9494 | auto tv3 = sum(tv2, {1}); |
9495 | auto tv4 = broadcast(tv3, {false, true}); |
9496 | auto tv5 = set(tv1); |
9497 | auto tv6 = add(tv4, tv5); |
9498 | auto tv7 = set(tv2); |
9499 | auto tv8 = add(tv7, tv6); |
9500 | auto tv9 = castOp(DataType::Half, tv8); |
9501 | |
9502 | fusion.addOutput(tv9); |
9503 | |
9504 | auto persistent_buffer_info = scheduler_utils::persistentBuffers(&fusion); |
9505 | |
9506 | auto isTvWithinVec = [](std::vector<TensorView*>& vec, TensorView* tv) { |
9507 | return std::find(vec.begin(), vec.end(), tv) != vec.end(); |
9508 | }; |
9509 | |
9510 | auto tvEntryInVecVec = [](std::vector<std::vector<TensorView*>>& vec_o_vec, |
9511 | std::vector<TensorView*>& buffer_vec, |
9512 | TensorView* tv) { |
9513 | auto buffer_it = std::find(buffer_vec.begin(), buffer_vec.end(), tv); |
9514 | return vec_o_vec.begin() + std::distance(buffer_vec.begin(), buffer_it); |
9515 | }; |
9516 | |
9517 | auto& buffers = persistent_buffer_info.persistent_buffers; |
9518 | auto& resolution = persistent_buffer_info.persistent_buffer_resolution_points; |
9519 | auto& projectable = persistent_buffer_info.projectable_persistent_buffers; |
9520 | auto& projectable_inputs = persistent_buffer_info.projectable_buffer_inputs; |
9521 | |
9522 | TORCH_INTERNAL_ASSERT(buffers.size() == 2); |
9523 | TORCH_INTERNAL_ASSERT( |
9524 | resolution.size() == 2 && resolution[0].size() == 1 && |
9525 | resolution[1].size() == 1); |
9526 | |
9527 | TORCH_INTERNAL_ASSERT(projectable.size() == 2); |
9528 | TORCH_INTERNAL_ASSERT(projectable_inputs.size() == 1); |
9529 | |
9530 | TORCH_INTERNAL_ASSERT( |
9531 | isTvWithinVec(buffers, tv1) && isTvWithinVec(buffers, tv2)); |
9532 | TORCH_INTERNAL_ASSERT( |
9533 | isTvWithinVec(projectable, tv1) && isTvWithinVec(projectable, tv2)); |
9534 | |
9535 | TORCH_INTERNAL_ASSERT(isTvWithinVec(projectable_inputs, tv0)); |
9536 | |
9537 | auto tv1_resolution_it = tvEntryInVecVec(resolution, buffers, tv1); |
9538 | TORCH_INTERNAL_ASSERT(tv1_resolution_it != resolution.end()) |
9539 | TORCH_INTERNAL_ASSERT(isTvWithinVec(*tv1_resolution_it, tv6)); |
9540 | |
9541 | auto tv2_resolution_it = tvEntryInVecVec(resolution, buffers, tv2); |
9542 | TORCH_INTERNAL_ASSERT(tv2_resolution_it != resolution.end()) |
9543 | TORCH_INTERNAL_ASSERT(isTvWithinVec(*tv2_resolution_it, tv8)); |
9544 | |
9545 | auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0); |
9546 | at::Tensor aten_t0 = at::randn({99, 101}, options); |
9547 | |
9548 | // Schedule through magic scheduler |
9549 | SchedulerRuntimeInfo runtime_info(&fusion, {aten_t0}, true); |
9550 | auto persistent_buffer_size = |
9551 | persistentBufferSize(&fusion, runtime_info, persistent_buffer_info); |
9552 | |
9553 | TORCH_INTERNAL_ASSERT( |
9554 | persistent_buffer_size.persistent_buffer_size == |
9555 | static_cast<int64_t>( |
9556 | aten_t0.size(1) * dataTypeSize(DataType::Float) * 2)); |
9557 | |
9558 | TORCH_INTERNAL_ASSERT( |
9559 | persistent_buffer_size.projected_persistent_buffer_size == |
9560 | static_cast<int64_t>(aten_t0.size(1) * dataTypeSize(DataType::Half))); |
9561 | } |
9562 | |
9563 | TEST_F(NVFuserTest, FusionPersistentBufferProjection_CUDA) { |
9564 | std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>(); |
9565 | Fusion& fusion = *fusion_ptr.get(); |
9566 | FusionGuard fg(&fusion); |
9567 | |
9568 | auto tv0 = makeSymbolicTensor(2, DataType::Half); |
9569 | fusion.addInput(tv0); |
9570 | |
9571 | auto tv1 = castOp(DataType::Float, tv0); |
9572 | auto tv2 = set(tv1); |
9573 | auto tv3 = sum(tv2, {1}); |
9574 | auto tv4 = broadcast(tv3, {false, true}); |
9575 | auto tv5 = set(tv1); |
9576 | auto tv6 = add(tv4, tv5); |
9577 | auto tv7 = set(tv2); |
9578 | auto tv8 = add(tv7, tv6); |
9579 | auto tv9 = castOp(DataType::Half, tv8); |
9580 | |
9581 | fusion.addOutput(tv9); |
9582 | |
9583 | reduction_scheduler_utils::projectPersistentBuffers(&fusion); |
9584 | |
9585 | auto tv5_producers = ir_utils::producerTvsOf(tv5); |
9586 | auto tv7_producers = ir_utils::producerTvsOf(tv7); |
9587 | |
9588 | // Projection should have broken these dependencies |
9589 | |
9590 | TORCH_INTERNAL_ASSERT( |
9591 | std::find(tv5_producers.begin(), tv5_producers.end(), tv1) == |
9592 | tv5_producers.end()); |
9593 | TORCH_INTERNAL_ASSERT( |
9594 | std::find(tv7_producers.begin(), tv7_producers.end(), tv2) == |
9595 | tv7_producers.end()); |
9596 | |
9597 | auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0); |
9598 | at::Tensor aten_t0 = at::randn({99, 101}, options); |
9599 | |
9600 | FusionExecutorCache fec(std::move(fusion_ptr)); |
9601 | auto cg_outputs = fec.runFusionWithInputs({aten_t0}); |
9602 | |
9603 | auto aten_t1 = aten_t0.to(c10::kDouble); |
9604 | auto aten_t3 = aten_t1.sum({1}); |
9605 | auto aten_t4 = aten_t3.unsqueeze(1); |
9606 | auto aten_t7 = aten_t4.add(aten_t1).add(aten_t1); |
9607 | |
9608 | testValidate(&fusion, cg_outputs, {aten_t0}, {aten_t7}, __LINE__, __FILE__); |
9609 | } |
9610 | |
9611 | TEST_F(NVFuserTest, FusionIssue1223_CUDA) { |
9612 | if (!deviceMajorMinorCheck(7)) { |
9613 | GTEST_SKIP() << "skipping tests on pre-Volta GPUs" ; |
9614 | return; |
9615 | } |
9616 | Fusion fusion; |
9617 | FusionGuard fg(&fusion); |
9618 | |
9619 | auto tv0 = makeContigTensor(2); |
9620 | fusion.addInput(tv0); |
9621 | |
9622 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
9623 | auto tv2 = sum(tv1, {0, 1}); |
9624 | fusion.addOutput(tv2); |
9625 | |
9626 | auto tv3 = add(tv0, IrBuilder::create<Double>(0)); |
9627 | fusion.addOutput(tv3); |
9628 | |
9629 | tv2->split(0, 4); |
9630 | tv2->split(1, 1, false); |
9631 | tv2->split(-1, 4); |
9632 | |
9633 | tv2->axis(1)->parallelize(ParallelType::Unswitch); |
9634 | tv2->axis(-3)->parallelize(ParallelType::TIDx); |
9635 | tv2->axis(-1)->parallelize(ParallelType::TIDy); |
9636 | |
9637 | tv1->computeAt(tv2, -1); |
9638 | |
9639 | // Make TIDx and TIDy non-exact |
9640 | tv3->split(0, 32); |
9641 | tv3->split(-1, 32); |
9642 | tv3->axis(1)->parallelize(ParallelType::TIDx); |
9643 | tv3->axis(3)->parallelize(ParallelType::TIDy); |
9644 | |
9645 | // The second axis of both tv1 and tv2 are fully unswitched, so they |
9646 | // don't need to predicate the parallel type usage of TIDy, whereas |
9647 | // the first axis is only partially unswitched, i.e., part of its |
9648 | // split output domains is outside the unswitched axis, so the first |
9649 | // axis, which uses TIDx, needs to predicate the parallel |
9650 | // dimension. Previously, as reported in issue #1223, unswitched |
9651 | // expressions didn't predicate parallel dimensions. It should be |
9652 | // fixed by PR #1222. |
9653 | |
9654 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
9655 | at::Tensor at_t0 = at::ones({11, 10}, options); |
9656 | |
9657 | FusionExecutor fe; |
9658 | fe.compileFusion(&fusion, {at_t0}); |
9659 | auto cg_outputs = fe.runFusion({at_t0}); |
9660 | |
9661 | auto at_t1 = (at_t0 + 1).sum(); |
9662 | |
9663 | testValidate( |
9664 | &fusion, cg_outputs, {at_t0}, {at_t1, at_t0}, __LINE__, __FILE__); |
9665 | } |
9666 | |
9667 | // See #1247 and #1250 |
9668 | TEST_F(NVFuserTest, FusionRfactorPredication1_CUDA) { |
9669 | Fusion fusion; |
9670 | FusionGuard fg(&fusion); |
9671 | |
9672 | auto tv0 = makeContigTensor(1); |
9673 | fusion.addInput(tv0); |
9674 | |
9675 | auto tv1 = add(tv0, IrBuilder::create<Double>(1)); |
9676 | auto tv2 = min(tv1, {0}); |
9677 | |
9678 | fusion.addOutput(tv2); |
9679 | |
9680 | // Make TIDx non-exact |
9681 | auto tv3 = makeContigTensor(1); |
9682 | fusion.addInput(tv3); |
9683 | |
9684 | auto tv4 = add(tv3, IrBuilder::create<Double>(1)); |
9685 | fusion.addOutput(tv4); |
9686 | |
9687 | tv2->split(0, 4); |
9688 | auto tv5 = tv2->rFactor({1}); |
9689 | |
9690 | tv0->computeAt(tv2, 1); |
9691 | |
9692 | tv2->axis(0)->parallelize(ParallelType::TIDx); |
9693 | |
9694 | tv4->axis(0)->parallelize(ParallelType::TIDx); |
9695 | |
9696 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
9697 | at::Tensor at_t0 = at::randn({9}, options); |
9698 | at_t0 = at::abs(at_t0); |
9699 | at::Tensor at_t3 = at::randn({128}, options); |
9700 | |
9701 | FusionExecutor fe; |
9702 | fe.compileFusion(&fusion, {at_t0, at_t3}); |
9703 | auto cg_outputs = fe.runFusion({at_t0, at_t3}); |
9704 | |
9705 | auto at_t2 = (at_t0 + 1).min(); |
9706 | auto at_t4 = at_t3 + 1; |
9707 | |
9708 | testValidate( |
9709 | &fusion, cg_outputs, {at_t0, at_t3}, {at_t2, at_t4}, __LINE__, __FILE__); |
9710 | } |
9711 | |
9712 | TEST_F(NVFuserTest, FusionRfactorPredication2_CUDA) { |
9713 | Fusion fusion; |
9714 | FusionGuard fg(&fusion); |
9715 | |
9716 | auto tv0 = makeContigTensor(1); |
9717 | fusion.addInput(tv0); |
9718 | |
9719 | auto tv1 = min(tv0, {0}); |
9720 | fusion.addOutput(tv1); |
9721 | |
9722 | // Make TIDx non-exact |
9723 | auto tv2 = makeContigTensor(1); |
9724 | fusion.addInput(tv2); |
9725 | |
9726 | auto tv3 = add(tv2, IrBuilder::create<Double>(1)); |
9727 | fusion.addOutput(tv3); |
9728 | |
9729 | tv1->split(0, 4); |
9730 | auto tv4 = tv1->rFactor({0}); |
9731 | |
9732 | tv1->split(0, 3); |
9733 | |
9734 | // tv0->computeAt(tv1, 3); |
9735 | tv4->reorder({{0, 1}}); |
9736 | tv4->split(0, 3); |
9737 | tv4->setMemoryType(MemoryType::Shared); |
9738 | |
9739 | // tv0: [I] |
9740 | // tv4: [4/3, 3, I/4] |
9741 | // tv1: [4/3, 3] |
9742 | |
9743 | tv1->axis(0)->parallelize(ParallelType::TIDx); |
9744 | scheduler_utils::parallelizeAllLike(tv1, {tv4}); |
9745 | |
9746 | tv3->axis(0)->parallelize(ParallelType::TIDx); |
9747 | |
9748 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
9749 | at::manual_seed(0); |
9750 | at::Tensor at_t0 = at::randn({9}, options); |
9751 | at_t0 = at::abs(at_t0); |
9752 | at::Tensor at_t3 = at::randn({128}, options); |
9753 | |
9754 | FusionExecutor fe; |
9755 | fe.compileFusion(&fusion, {at_t0, at_t3}); |
9756 | auto cg_outputs = fe.runFusion({at_t0, at_t3}); |
9757 | |
9758 | auto at_t2 = std::get<0>(at_t0.min(0)); |
9759 | auto at_t4 = at_t3 + 1; |
9760 | |
9761 | testValidate( |
9762 | &fusion, cg_outputs, {at_t0, at_t3}, {at_t2, at_t4}, __LINE__, __FILE__); |
9763 | } |
9764 | |
9765 | TEST_F(NVFuserTest, FusionRfactorIndirectRoot_CUDA) { |
9766 | // https://github.com/csarofeen/pytorch/issues/1692 |
9767 | Fusion fusion; |
9768 | FusionGuard fg(&fusion); |
9769 | |
9770 | auto tv0 = makeSymbolicTensor(3); |
9771 | fusion.addInput(tv0); |
9772 | |
9773 | auto tv1 = sum(tv0, {1, 2}); |
9774 | fusion.addOutput(tv1); |
9775 | |
9776 | tv1->split(2, 4); |
9777 | tv1->split(1, 3); |
9778 | tv1->merge(2, 3); |
9779 | auto rf = tv1->rFactor({-1}); |
9780 | |
9781 | tv1->split(0, 256); |
9782 | tv1->axis(0)->parallelize(ParallelType::BIDx); |
9783 | tv1->axis(1)->parallelize(ParallelType::TIDx); |
9784 | rf->computeAt(tv1, -1); |
9785 | |
9786 | auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0); |
9787 | at::manual_seed(0); |
9788 | |
9789 | auto at_in = at::randn({6, 6, 6}, options); |
9790 | auto at_out = at_in.sum({1, 2}); |
9791 | |
9792 | FusionExecutor fe; |
9793 | fe.compileFusion(&fusion, {at_in}); |
9794 | auto cg_outputs = fe.runFusion({at_in}); |
9795 | |
9796 | testValidate(&fusion, cg_outputs, {at_in}, {at_out}, __LINE__, __FILE__); |
9797 | } |
9798 | |
9799 | } // namespace jit |
9800 | } // namespace torch |
9801 | #endif // #if defined(USE_CUDA) |
9802 | |