1 | #include <c10/core/Device.h> |
2 | #include <c10/core/DeviceType.h> |
3 | #include <gtest/gtest.h> |
4 | #include <test/cpp/lazy/test_lazy_ops_util.h> |
5 | #include <torch/csrc/lazy/core/debug_util.h> |
6 | #include <torch/csrc/lazy/core/helpers.h> |
7 | #include <torch/csrc/lazy/core/ir_builder.h> |
8 | #include <torch/csrc/lazy/core/lazy_graph_executor.h> |
9 | #include <torch/csrc/lazy/core/metrics.h> |
10 | #include <torch/csrc/lazy/core/permutation_util.h> |
11 | #include <torch/csrc/lazy/ts_backend/dynamic_ir.h> |
12 | #include <torch/csrc/lazy/ts_backend/ts_backend_impl.h> |
13 | #include <torch/torch.h> |
14 | #include <iostream> |
15 | |
16 | namespace torch { |
17 | namespace lazy { |
18 | |
19 | // Lazy Tensor is disabled in FBCODE until addressing non-virtual methods (e.g. |
20 | // sizes) in TensorImpl |
21 | #ifndef FBCODE_CAFFE2 |
22 | |
23 | namespace { |
24 | // This registers the torchscript backend, without which lazy device won't work. |
25 | // FIXME: This registers the backend for the whole test binary. We should |
26 | // probably do it and undo it in the test fixture below. |
27 | static bool inline init_backend() { |
28 | torch::lazy::InitTorchScriptBackend(); |
29 | return true; |
30 | } |
31 | static const bool backend_initialized = init_backend(); |
32 | |
33 | } // namespace |
34 | |
35 | class LazyTsTest : public ::testing::Test { |
36 | protected: |
37 | void SetUp() override; |
38 | |
39 | void TearDown() override; |
40 | |
41 | static void CommonSetup() {} |
42 | |
43 | void ExpectCounterNotChanged( |
44 | const std::string& counter_regex, |
45 | const std::unordered_set<std::string>* ignore_set) {} |
46 | |
47 | void ExpectCounterChanged( |
48 | const std::string& counter_regex, |
49 | const std::unordered_set<std::string>* ignore_set) {} |
50 | |
51 | void ResetCounters() {} |
52 | |
53 | private: |
54 | void MakeEndSnapshot() {} |
55 | }; |
56 | |
57 | class LazyOpsTestBase : public LazyTsTest { |
58 | protected: |
59 | static void SetUpTestCase() {} |
60 | }; |
61 | |
62 | void LazyTsTest::SetUp() { |
63 | (void)backend_initialized; // avoid unused parameter warning |
64 | at::manual_seed(42); |
65 | torch::lazy::LazyGraphExecutor::Get()->SetRngSeed( |
66 | torch::lazy::BackendDevice(), 42); |
67 | } |
68 | |
69 | void LazyTsTest::TearDown() {} |
70 | |
71 | namespace { |
72 | using torch::lazy::DebugUtil; |
73 | |
74 | class LazyOpsTest : public LazyOpsTestBase {}; |
75 | |
76 | static inline bool IsCuda() { |
77 | return torch::lazy::getBackend()->EagerFallbackDeviceType() == at::kCUDA; |
78 | } |
79 | |
80 | static inline at::DeviceType DefaultDevice() { |
81 | return torch::lazy::getBackend()->EagerFallbackDeviceType(); |
82 | } |
83 | |
84 | } // namespace |
85 | |
86 | TEST_F(LazyOpsTest, TestScalarTensor) { |
87 | torch::Tensor scalar_tensor = torch::scalar_tensor( |
88 | 1., torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
89 | ForEachDevice([&](const torch::Device& device) { |
90 | torch::Tensor lazy_scalar_tensor = torch::scalar_tensor( |
91 | 1., torch::TensorOptions(torch::kFloat).device(torch::kLazy)); |
92 | AllClose(scalar_tensor, lazy_scalar_tensor); |
93 | }); |
94 | } |
95 | |
96 | TEST_F(LazyOpsTest, TestClone) { |
97 | ForEachDevice([&](const torch::Device& device) { |
98 | torch::Tensor a = torch::rand( |
99 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
100 | torch::Tensor lazy_a = CopyToDevice(a, device); |
101 | torch::Tensor lazy_b = lazy_a.clone(); |
102 | AllClose(a, lazy_b); |
103 | lazy_a.add_(1.0); |
104 | AllClose(a, lazy_b); |
105 | }); |
106 | } |
107 | |
108 | TEST_F(LazyOpsTest, TestTo) { |
109 | ForEachDevice([&](const torch::Device& device) { |
110 | torch::Tensor a = torch::rand( |
111 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
112 | torch::Tensor lazy_a = CopyToDevice(a, device); |
113 | AllClose(a, lazy_a); |
114 | }); |
115 | } |
116 | |
117 | TEST_F(LazyOpsTest, TestIsFloatingPoint) { |
118 | ForEachDevice([&](const torch::Device& device) { |
119 | torch::Tensor a = torch::rand( |
120 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
121 | torch::Tensor lazy_a = CopyToDevice(a, device); |
122 | bool is_float = torch::is_floating_point(a); |
123 | bool lazy_is_float = torch::is_floating_point(lazy_a); |
124 | EXPECT_EQ(is_float, lazy_is_float); |
125 | }); |
126 | } |
127 | |
128 | TEST_F(LazyOpsTest, TestIsSigned) { |
129 | ForEachDevice([&](const torch::Device& device) { |
130 | torch::Tensor a = torch::rand( |
131 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
132 | torch::Tensor lazy_a = CopyToDevice(a, device); |
133 | bool is_signed = torch::is_signed(a); |
134 | bool lazy_is_signed = torch::is_signed(lazy_a); |
135 | EXPECT_EQ(is_signed, lazy_is_signed); |
136 | }); |
137 | } |
138 | |
139 | TEST_F(LazyOpsTest, TestCastByte) { |
140 | torch::Tensor a = |
141 | torch::rand( |
142 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
143 | 100.0; |
144 | torch::Tensor b = torch::_cast_Byte(a); |
145 | ForEachDevice([&](const torch::Device& device) { |
146 | torch::Tensor lazy_a = CopyToDevice(a, device); |
147 | torch::Tensor lazy_b = torch::_cast_Byte(lazy_a); |
148 | AllEqual(b, lazy_b); |
149 | }); |
150 | } |
151 | |
152 | TEST_F(LazyOpsTest, TestCastChar) { |
153 | torch::Tensor a = |
154 | torch::rand( |
155 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
156 | 100.0; |
157 | torch::Tensor b = torch::_cast_Char(a); |
158 | ForEachDevice([&](const torch::Device& device) { |
159 | torch::Tensor lazy_a = CopyToDevice(a, device); |
160 | torch::Tensor lazy_b = torch::_cast_Char(lazy_a); |
161 | AllEqual(b, lazy_b); |
162 | }); |
163 | } |
164 | |
165 | TEST_F(LazyOpsTest, TestCastShort) { |
166 | torch::Tensor a = |
167 | torch::rand( |
168 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
169 | 100.0; |
170 | torch::Tensor b = torch::_cast_Short(a); |
171 | ForEachDevice([&](const torch::Device& device) { |
172 | torch::Tensor lazy_a = CopyToDevice(a, device); |
173 | torch::Tensor lazy_b = torch::_cast_Short(lazy_a); |
174 | AllEqual(b, lazy_b); |
175 | }); |
176 | } |
177 | |
178 | TEST_F(LazyOpsTest, TestCastInt) { |
179 | torch::Tensor a = |
180 | torch::rand( |
181 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
182 | 100.0; |
183 | torch::Tensor b = torch::_cast_Int(a); |
184 | ForEachDevice([&](const torch::Device& device) { |
185 | torch::Tensor lazy_a = CopyToDevice(a, device); |
186 | torch::Tensor lazy_b = torch::_cast_Int(lazy_a); |
187 | AllEqual(b, lazy_b); |
188 | }); |
189 | } |
190 | |
191 | TEST_F(LazyOpsTest, TestCastLong) { |
192 | torch::Tensor a = |
193 | torch::rand( |
194 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
195 | 100.0; |
196 | torch::Tensor b = torch::_cast_Long(a); |
197 | ForEachDevice([&](const torch::Device& device) { |
198 | torch::Tensor lazy_a = CopyToDevice(a, device); |
199 | torch::Tensor lazy_b = torch::_cast_Long(lazy_a); |
200 | AllEqual(b, lazy_b); |
201 | }); |
202 | } |
203 | |
204 | TEST_F(LazyOpsTest, TestCastFloat) { |
205 | torch::Tensor a = |
206 | torch::rand( |
207 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
208 | 100.0; |
209 | torch::Tensor b = torch::_cast_Float(a); |
210 | ForEachDevice([&](const torch::Device& device) { |
211 | torch::Tensor lazy_a = CopyToDevice(a, device); |
212 | torch::Tensor lazy_b = torch::_cast_Float(lazy_a); |
213 | AllEqual(b, lazy_b); |
214 | }); |
215 | } |
216 | |
217 | TEST_F(LazyOpsTest, TestRetainType) { |
218 | torch::Tensor lazy_a = torch::zeros( |
219 | {2, 2}, torch::TensorOptions(torch::kByte).device(torch::kLazy)); |
220 | torch::Tensor lazy_b = torch::ones( |
221 | {2, 2}, torch::TensorOptions(torch::kByte).device(torch::kLazy)); |
222 | torch::Tensor lazy_c = lazy_a + lazy_b; |
223 | EXPECT_EQ(lazy_c.scalar_type(), torch::ScalarType::Byte); |
224 | } |
225 | |
226 | TEST_F(LazyOpsTest, TestLogicalTypeWithInterop) { |
227 | torch::Tensor query = torch::rand( |
228 | {2, 12, 20, 64}, |
229 | torch::TensorOptions(torch::kFloat).device(torch::kLazy)); |
230 | torch::Tensor key = torch::rand( |
231 | {2, 12, 64, 20}, |
232 | torch::TensorOptions(torch::kFloat).device(torch::kLazy)); |
233 | torch::Tensor scores = |
234 | torch::matmul(query, key) / |
235 | torch::scalar_tensor( |
236 | 8, torch::TensorOptions(torch::kDouble).device(torch::kLazy)); |
237 | torch::Tensor p_attn = torch::softmax(scores, /*dim=*/-1); |
238 | EXPECT_EQ(p_attn.scalar_type(), torch::ScalarType::Float); |
239 | } |
240 | |
241 | TEST_F(LazyOpsTest, TestAdd) { |
242 | torch::Tensor a = torch::rand( |
243 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
244 | torch::Tensor b = torch::rand( |
245 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
246 | torch::Tensor c = torch::add(a, b); |
247 | ForEachDevice([&](const torch::Device& device) { |
248 | torch::Tensor lazy_a = CopyToDevice(a, device); |
249 | torch::Tensor lazy_b = CopyToDevice(b, device); |
250 | torch::Tensor lazy_c = torch::add(lazy_a, lazy_b); |
251 | AllClose(c, lazy_c); |
252 | }); |
253 | } |
254 | |
255 | TEST_F(LazyOpsTest, TestAddHalf) { |
256 | torch::Tensor a = torch::rand( |
257 | {2, 2}, torch::TensorOptions(torch::kHalf).device(DefaultDevice())); |
258 | torch::Tensor b = torch::rand( |
259 | {2, 2}, torch::TensorOptions(torch::kHalf).device(DefaultDevice())); |
260 | torch::Tensor c = torch::add(a, b); |
261 | ForEachDevice([&](const torch::Device& device) { |
262 | torch::Tensor lazy_a = CopyToDevice(a, device); |
263 | torch::Tensor lazy_b = CopyToDevice(b, device); |
264 | torch::Tensor lazy_c = torch::add(lazy_a, lazy_b); |
265 | AllClose(c, lazy_c); |
266 | }); |
267 | } |
268 | |
269 | TEST_F(LazyOpsTest, TestAddMixedPrecision) { |
270 | torch::Tensor a = torch::rand( |
271 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
272 | torch::Tensor b = torch::rand( |
273 | {2, 2}, torch::TensorOptions(torch::kHalf).device(DefaultDevice())); |
274 | torch::Tensor c = torch::add(a, b); |
275 | ForEachDevice([&](const torch::Device& device) { |
276 | torch::Tensor lazy_a = CopyToDevice(a, device); |
277 | torch::Tensor lazy_b = CopyToDevice(b, device); |
278 | torch::Tensor lazy_c = torch::add(lazy_a, lazy_b); |
279 | AllClose(c, lazy_c); |
280 | }); |
281 | } |
282 | |
283 | TEST_F(LazyOpsTest, TestAddInPlace) { |
284 | ForEachDevice([&](const torch::Device& device) { |
285 | torch::Tensor a = torch::rand( |
286 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
287 | torch::Tensor lazy_a = CopyToDevice(a, device); |
288 | torch::Tensor b = torch::rand( |
289 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
290 | torch::Tensor lazy_b = CopyToDevice(b, device); |
291 | torch::Tensor c = a.add_(b); |
292 | torch::Tensor lazy_c = lazy_a.add_(lazy_b); |
293 | AllClose(a, lazy_a); |
294 | AllClose(c, lazy_c); |
295 | }); |
296 | } |
297 | |
298 | TEST_F(LazyOpsTest, TestAddScalar) { |
299 | torch::Tensor a = torch::rand( |
300 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
301 | torch::Scalar b(1); |
302 | torch::Tensor c = torch::add(a, b); |
303 | ForEachDevice([&](const torch::Device& device) { |
304 | torch::Tensor lazy_a = CopyToDevice(a, device); |
305 | torch::Tensor lazy_c = torch::add(lazy_a, b); |
306 | AllClose(c, lazy_c); |
307 | }); |
308 | } |
309 | |
310 | TEST_F(LazyOpsTest, TestAddScalarInPlace) { |
311 | torch::Scalar b(1); |
312 | ForEachDevice([&](const torch::Device& device) { |
313 | torch::Tensor a = torch::rand( |
314 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
315 | torch::Tensor lazy_a = CopyToDevice(a, device); |
316 | torch::Tensor c = a.add_(b); |
317 | torch::Tensor lazy_c = lazy_a.add_(b); |
318 | AllClose(a, lazy_a); |
319 | AllClose(c, lazy_c); |
320 | }); |
321 | } |
322 | |
323 | TEST_F(LazyOpsTest, TestAddZeroSizeDim) { |
324 | torch::Tensor a = torch::rand( |
325 | {0, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
326 | torch::Tensor b = torch::rand( |
327 | {1, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
328 | torch::Tensor c = torch::add(a, b); |
329 | ForEachDevice([&](const torch::Device& device) { |
330 | torch::Tensor lazy_a = CopyToDevice(a, device); |
331 | torch::Tensor lazy_b = CopyToDevice(b, device); |
332 | torch::Tensor lazy_c = torch::add(lazy_a, lazy_b); |
333 | AllClose(c, lazy_c); |
334 | }); |
335 | } |
336 | |
337 | TEST_F(LazyOpsTest, TestSub) { |
338 | torch::Tensor a = torch::rand( |
339 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
340 | torch::Tensor b = torch::rand( |
341 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
342 | torch::Tensor c = torch::sub(a, b); |
343 | ForEachDevice([&](const torch::Device& device) { |
344 | torch::Tensor lazy_a = CopyToDevice(a, device); |
345 | torch::Tensor lazy_b = CopyToDevice(b, device); |
346 | torch::Tensor lazy_c = torch::sub(lazy_a, lazy_b); |
347 | AllClose(c, lazy_c); |
348 | }); |
349 | } |
350 | |
351 | TEST_F(LazyOpsTest, TestSubInPlace) { |
352 | ForEachDevice([&](const torch::Device& device) { |
353 | torch::Tensor a = torch::rand( |
354 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
355 | torch::Tensor lazy_a = CopyToDevice(a, device); |
356 | torch::Tensor b = torch::rand( |
357 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
358 | torch::Tensor lazy_b = CopyToDevice(b, device); |
359 | torch::Tensor c = a.sub_(b); |
360 | torch::Tensor lazy_c = lazy_a.sub_(lazy_b); |
361 | AllClose(a, lazy_a); |
362 | AllClose(c, lazy_c); |
363 | }); |
364 | } |
365 | |
366 | TEST_F(LazyOpsTest, TestSubScalar) { |
367 | torch::Tensor a = torch::rand( |
368 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
369 | torch::Scalar b(1); |
370 | torch::Tensor c = torch::sub(a, b); |
371 | ForEachDevice([&](const torch::Device& device) { |
372 | torch::Tensor lazy_a = CopyToDevice(a, device); |
373 | torch::Tensor lazy_c = torch::sub(lazy_a, b); |
374 | AllClose(c, lazy_c); |
375 | }); |
376 | } |
377 | |
378 | TEST_F(LazyOpsTest, TestSubScalarInPlace) { |
379 | torch::Scalar b(1); |
380 | ForEachDevice([&](const torch::Device& device) { |
381 | torch::Tensor a = torch::rand( |
382 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
383 | torch::Tensor lazy_a = CopyToDevice(a, device); |
384 | torch::Tensor c = a.sub_(b); |
385 | torch::Tensor lazy_c = lazy_a.sub_(b); |
386 | AllClose(a, lazy_a); |
387 | AllClose(c, lazy_c); |
388 | }); |
389 | } |
390 | |
391 | TEST_F(LazyOpsTest, TestMul) { |
392 | torch::Tensor a = torch::rand( |
393 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
394 | torch::Tensor b = torch::rand( |
395 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
396 | torch::Tensor c = torch::mul(a, b); |
397 | ForEachDevice([&](const torch::Device& device) { |
398 | torch::Tensor lazy_a = CopyToDevice(a, device); |
399 | torch::Tensor lazy_b = CopyToDevice(b, device); |
400 | torch::Tensor lazy_c = torch::mul(lazy_a, lazy_b); |
401 | AllClose(c, lazy_c); |
402 | }); |
403 | } |
404 | |
405 | TEST_F(LazyOpsTest, TestMulInPlace) { |
406 | ForEachDevice([&](const torch::Device& device) { |
407 | torch::Tensor a = torch::rand( |
408 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
409 | torch::Tensor lazy_a = CopyToDevice(a, device); |
410 | torch::Tensor b = torch::rand( |
411 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
412 | torch::Tensor lazy_b = CopyToDevice(b, device); |
413 | torch::Tensor c = a.mul_(b); |
414 | torch::Tensor lazy_c = lazy_a.mul_(lazy_b); |
415 | AllClose(a, lazy_a); |
416 | AllClose(c, lazy_c); |
417 | }); |
418 | } |
419 | |
420 | TEST_F(LazyOpsTest, TestMulScalar) { |
421 | torch::Tensor a = torch::rand( |
422 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
423 | torch::Scalar b(3); |
424 | torch::Tensor c = torch::mul(a, b); |
425 | ForEachDevice([&](const torch::Device& device) { |
426 | torch::Tensor lazy_a = CopyToDevice(a, device); |
427 | torch::Tensor lazy_c = torch::mul(lazy_a, b); |
428 | AllClose(c, lazy_c); |
429 | }); |
430 | } |
431 | |
432 | TEST_F(LazyOpsTest, TestMulScalarInPlace) { |
433 | torch::Scalar b(3); |
434 | ForEachDevice([&](const torch::Device& device) { |
435 | torch::Tensor a = torch::rand( |
436 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
437 | torch::Tensor lazy_a = CopyToDevice(a, device); |
438 | torch::Tensor c = a.mul_(b); |
439 | torch::Tensor lazy_c = lazy_a.mul_(b); |
440 | AllClose(a, lazy_a); |
441 | AllClose(c, lazy_c); |
442 | }); |
443 | } |
444 | |
445 | TEST_F(LazyOpsTest, TestDiv) { |
446 | for (torch::ScalarType scalar_type1 : |
447 | {torch::kFloat, |
448 | torch::kByte, |
449 | torch::kChar, |
450 | torch::kShort, |
451 | torch::kInt, |
452 | torch::kLong}) { |
453 | torch::Tensor a = isFloatingType(scalar_type1) |
454 | ? torch::rand({3, 4}, torch::TensorOptions(scalar_type1)) |
455 | : torch::randint(0, 100, {3, 4}, torch::TensorOptions(scalar_type1)); |
456 | for (torch::ScalarType scalar_type2 : |
457 | {torch::kFloat, |
458 | torch::kByte, |
459 | torch::kChar, |
460 | torch::kShort, |
461 | torch::kInt, |
462 | torch::kLong}) { |
463 | torch::Tensor b = isFloatingType(scalar_type2) |
464 | ? torch::rand({3, 4}, torch::TensorOptions(scalar_type2)) |
465 | : torch::randint(1, 100, {3, 4}, torch::TensorOptions(scalar_type2)); |
466 | torch::Tensor c = torch::div(a, b); |
467 | ForEachDevice([&](const torch::Device& device) { |
468 | torch::Tensor lazy_a = CopyToDevice(a, device); |
469 | torch::Tensor lazy_b = CopyToDevice(b, device); |
470 | torch::Tensor lazy_c = torch::div(lazy_a, lazy_b); |
471 | AllClose(c, lazy_c); |
472 | }); |
473 | } |
474 | } |
475 | } |
476 | |
477 | TEST_F(LazyOpsTest, TestDivWithRoundingMode) { |
478 | c10::optional<c10::string_view> rounding_modes[] = { |
479 | "trunc" , "floor" , c10::nullopt}; |
480 | for (const auto& rounding_mode : rounding_modes) { |
481 | for (torch::ScalarType scalar_type1 : |
482 | {torch::kFloat, |
483 | torch::kByte, |
484 | torch::kChar, |
485 | torch::kShort, |
486 | torch::kInt, |
487 | torch::kLong}) { |
488 | int lower_bound = (scalar_type1 == torch::kByte) ? 0 : -100; |
489 | torch::Tensor a = isFloatingType(scalar_type1) |
490 | ? torch::rand({3, 4}, torch::TensorOptions(scalar_type1)) |
491 | : torch::randint( |
492 | lower_bound, 50, {3, 4}, torch::TensorOptions(scalar_type1)); |
493 | for (torch::ScalarType scalar_type2 : |
494 | {torch::kFloat, |
495 | torch::kByte, |
496 | torch::kChar, |
497 | torch::kShort, |
498 | torch::kInt, |
499 | torch::kLong}) { |
500 | torch::Tensor b = isFloatingType(scalar_type2) |
501 | ? torch::rand({3, 4}, torch::TensorOptions(scalar_type2)) |
502 | : torch::randint( |
503 | 51, 100, {3, 4}, torch::TensorOptions(scalar_type2)); |
504 | torch::Tensor c = torch::div(a, b, rounding_mode); |
505 | ForEachDevice([&](const torch::Device& device) { |
506 | torch::Tensor lazy_a = CopyToDevice(a, device); |
507 | torch::Tensor lazy_b = CopyToDevice(b, device); |
508 | torch::Tensor lazy_c = torch::div(lazy_a, lazy_b, rounding_mode); |
509 | AllClose(c, lazy_c); |
510 | }); |
511 | } |
512 | } |
513 | } |
514 | } |
515 | |
516 | TEST_F(LazyOpsTest, TestDivInPlace) { |
517 | for (torch::ScalarType scalar_type1 : {torch::kFloat}) { |
518 | torch::Tensor a = isFloatingType(scalar_type1) |
519 | ? torch::rand({3, 4}, torch::TensorOptions(scalar_type1)) |
520 | : torch::randint(0, 100, {3, 4}, torch::TensorOptions(scalar_type1)); |
521 | for (torch::ScalarType scalar_type2 : {torch::kFloat}) { |
522 | torch::Tensor b = isFloatingType(scalar_type2) |
523 | ? torch::rand({3, 4}, torch::TensorOptions(scalar_type2)) |
524 | : torch::randint(1, 100, {3, 4}, torch::TensorOptions(scalar_type2)); |
525 | ForEachDevice([&](const torch::Device& device) { |
526 | torch::Tensor lazy_a = CopyToDevice(a, device); |
527 | torch::Tensor c = a.div_(b); |
528 | torch::Tensor lazy_b = CopyToDevice(b, device); |
529 | torch::Tensor lazy_c = lazy_a.div_(lazy_b); |
530 | ; |
531 | AllClose(c, lazy_c); |
532 | }); |
533 | } |
534 | } |
535 | } |
536 | |
537 | TEST_F(LazyOpsTest, TestDivInPlaceWithRoundingMode) { |
538 | c10::optional<c10::string_view> rounding_modes[] = { |
539 | "trunc" , "floor" , c10::nullopt}; |
540 | for (const auto& rounding_mode : rounding_modes) { |
541 | for (torch::ScalarType scalar_type1 : {torch::kFloat}) { |
542 | torch::Tensor a = isFloatingType(scalar_type1) |
543 | ? torch::rand({3, 4}, torch::TensorOptions(scalar_type1)) |
544 | : torch::randint( |
545 | -100, 100, {3, 4}, torch::TensorOptions(scalar_type1)); |
546 | for (torch::ScalarType scalar_type2 : {torch::kFloat}) { |
547 | torch::Tensor b = isFloatingType(scalar_type2) |
548 | ? torch::rand({3, 4}, torch::TensorOptions(scalar_type2)) |
549 | : torch::randint( |
550 | 1, 100, {3, 4}, torch::TensorOptions(scalar_type2)); |
551 | ForEachDevice([&](const torch::Device& device) { |
552 | torch::Tensor lazy_a = CopyToDevice(a, device); |
553 | torch::Tensor c = a.div_(b, rounding_mode); |
554 | torch::Tensor lazy_b = CopyToDevice(b, device); |
555 | torch::Tensor lazy_c = lazy_a.div_(lazy_b, rounding_mode); |
556 | AllClose(c, lazy_c); |
557 | }); |
558 | } |
559 | } |
560 | } |
561 | } |
562 | |
563 | TEST_F(LazyOpsTest, TestDivScalar) { |
564 | for (torch::ScalarType scalar_type : |
565 | {torch::kFloat, |
566 | torch::kByte, |
567 | torch::kChar, |
568 | torch::kShort, |
569 | torch::kInt, |
570 | torch::kLong}) { |
571 | torch::Tensor a = isFloatingType(scalar_type) |
572 | ? torch::rand( |
573 | {3, 4}, torch::TensorOptions(scalar_type).device(DefaultDevice())) |
574 | : torch::randint( |
575 | 1, |
576 | 100, |
577 | {3, 4}, |
578 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
579 | for (bool is_float : {true, false}) { |
580 | torch::Scalar b = is_float ? torch::Scalar(3.0) : torch::Scalar(3); |
581 | torch::Tensor c = torch::div(a, b); |
582 | ForEachDevice([&](const torch::Device& device) { |
583 | torch::Tensor lazy_a = CopyToDevice(a, device); |
584 | torch::Tensor lazy_c = torch::div(lazy_a, b); |
585 | AllClose(c, lazy_c); |
586 | }); |
587 | } |
588 | } |
589 | } |
590 | |
591 | TEST_F(LazyOpsTest, TestDivScalarInPlace) { |
592 | for (torch::ScalarType scalar_type : {torch::kFloat}) { |
593 | torch::Tensor a = isFloatingType(scalar_type) |
594 | ? torch::rand( |
595 | {3, 4}, torch::TensorOptions(scalar_type).device(DefaultDevice())) |
596 | : torch::randint( |
597 | 1, |
598 | 100, |
599 | {3, 4}, |
600 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
601 | for (bool is_float : {true, false}) { |
602 | torch::Scalar b = is_float ? torch::Scalar(3.0) : torch::Scalar(3); |
603 | ForEachDevice([&](const torch::Device& device) { |
604 | torch::Tensor lazy_a = CopyToDevice(a, device); |
605 | torch::Tensor c = a.div_(b); |
606 | torch::Tensor lazy_c = lazy_a.div_(b); |
607 | AllClose(c, lazy_c); |
608 | }); |
609 | } |
610 | } |
611 | } |
612 | |
613 | TEST_F(LazyOpsTest, TestDivOut) { |
614 | for (torch::ScalarType scalar_type : {torch::kFloat, torch::kDouble}) { |
615 | torch::Tensor a = torch::rand( |
616 | {3, 4}, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
617 | torch::Tensor b = torch::rand( |
618 | {3, 4}, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
619 | torch::Tensor c = torch::empty( |
620 | {3, 4}, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
621 | torch::div_out(c, a, b); |
622 | ForEachDevice([&](const torch::Device& device) { |
623 | torch::Tensor lazy_a = CopyToDevice(a, device); |
624 | torch::Tensor lazy_b = CopyToDevice(b, device); |
625 | torch::Tensor lazy_c = torch::empty({3, 4}, lazy_b.options()); |
626 | torch::div_out(lazy_c, lazy_a, lazy_b); |
627 | AllClose(c, lazy_c); |
628 | }); |
629 | } |
630 | } |
631 | |
632 | TEST_F(LazyOpsTest, TestRsubScalar) { |
633 | torch::Tensor input = torch::rand( |
634 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
635 | torch::Scalar other(1.5); |
636 | torch::Scalar alpha(2.5); |
637 | torch::Tensor result = torch::rsub(input, other, alpha); |
638 | ForEachDevice([&](const torch::Device& device) { |
639 | torch::Tensor lazy_input = CopyToDevice(input, device); |
640 | torch::Tensor lazy_result = torch::rsub(lazy_input, other, alpha); |
641 | AllClose(result, lazy_result); |
642 | }); |
643 | } |
644 | |
645 | TEST_F(LazyOpsTest, TestNe) { |
646 | torch::Tensor a = torch::rand( |
647 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
648 | torch::Tensor b = torch::rand( |
649 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
650 | torch::Tensor c = torch::ne(a, b); |
651 | ForEachDevice([&](const torch::Device& device) { |
652 | torch::Tensor lazy_a = CopyToDevice(a, device); |
653 | torch::Tensor lazy_b = CopyToDevice(b, device); |
654 | torch::Tensor lazy_c = torch::ne(lazy_a, lazy_b); |
655 | AllEqual(c, lazy_c); |
656 | }); |
657 | } |
658 | |
659 | TEST_F(LazyOpsTest, TestNeInplace) { |
660 | torch::Tensor a = torch::rand( |
661 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
662 | torch::Tensor a_copy = a.clone(); |
663 | torch::Tensor b = a.clone(); |
664 | b[0] += 1; |
665 | a.ne_(b); |
666 | ForEachDevice([&](const torch::Device& device) { |
667 | torch::Tensor lazy_a = CopyToDevice(a_copy, device); |
668 | torch::Tensor lazy_b = CopyToDevice(b, device); |
669 | lazy_a.ne_(lazy_b); |
670 | AllClose(a, lazy_a); |
671 | }); |
672 | } |
673 | |
674 | TEST_F(LazyOpsTest, TestEq) { |
675 | torch::Tensor a = torch::rand( |
676 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
677 | torch::Tensor b = a.clone(); |
678 | torch::Tensor c = torch::eq(a, b); |
679 | ForEachDevice([&](const torch::Device& device) { |
680 | torch::Tensor lazy_a = CopyToDevice(a, device); |
681 | torch::Tensor lazy_b = CopyToDevice(b, device); |
682 | torch::Tensor lazy_c = torch::eq(lazy_a, lazy_b); |
683 | AllEqual(c, lazy_c); |
684 | }); |
685 | } |
686 | |
687 | TEST_F(LazyOpsTest, TestEqInplace) { |
688 | torch::Tensor a = torch::rand( |
689 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
690 | torch::Tensor b = a.clone(); |
691 | b[0] += 1; |
692 | torch::Tensor a_copy = a.clone(); |
693 | a.eq_(b); |
694 | ForEachDevice([&](const torch::Device& device) { |
695 | torch::Tensor lazy_a = CopyToDevice(a_copy, device); |
696 | torch::Tensor lazy_b = CopyToDevice(b, device); |
697 | lazy_a.eq_(lazy_b); |
698 | AllClose(lazy_a, a); |
699 | }); |
700 | } |
701 | |
702 | TEST_F(LazyOpsTest, TestGe) { |
703 | torch::Tensor a = torch::rand( |
704 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
705 | torch::Tensor b = a.clone(); |
706 | torch::Tensor c = torch::ge(a, b); |
707 | ForEachDevice([&](const torch::Device& device) { |
708 | torch::Tensor lazy_a = CopyToDevice(a, device); |
709 | torch::Tensor lazy_b = CopyToDevice(b, device); |
710 | torch::Tensor lazy_c = torch::ge(lazy_a, lazy_b); |
711 | AllEqual(c, lazy_c); |
712 | }); |
713 | } |
714 | |
715 | TEST_F(LazyOpsTest, TestGeInplace) { |
716 | torch::Tensor a = torch::rand( |
717 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
718 | torch::Tensor b = a.clone(); |
719 | b[0] += 1; |
720 | b[1] -= 1; |
721 | torch::Tensor a_copy = a.clone(); |
722 | a.ge_(b); |
723 | ForEachDevice([&](const torch::Device& device) { |
724 | torch::Tensor lazy_a = CopyToDevice(a_copy, device); |
725 | torch::Tensor lazy_b = CopyToDevice(b, device); |
726 | lazy_a.ge_(lazy_b); |
727 | AllClose(lazy_a, a); |
728 | }); |
729 | } |
730 | |
731 | TEST_F(LazyOpsTest, TestLe) { |
732 | torch::Tensor a = torch::rand( |
733 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
734 | torch::Tensor b = a.clone(); |
735 | torch::Tensor c = torch::le(a, b); |
736 | ForEachDevice([&](const torch::Device& device) { |
737 | torch::Tensor lazy_a = CopyToDevice(a, device); |
738 | torch::Tensor lazy_b = CopyToDevice(b, device); |
739 | torch::Tensor lazy_c = torch::le(lazy_a, lazy_b); |
740 | AllEqual(c, lazy_c); |
741 | }); |
742 | } |
743 | |
744 | TEST_F(LazyOpsTest, TestLeInplace) { |
745 | torch::Tensor a = torch::rand( |
746 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
747 | torch::Tensor b = a.clone(); |
748 | b[0] += 1; |
749 | b[1] -= 1; |
750 | torch::Tensor a_copy = a.clone(); |
751 | a.le_(b); |
752 | ForEachDevice([&](const torch::Device& device) { |
753 | torch::Tensor lazy_a = CopyToDevice(a_copy, device); |
754 | torch::Tensor lazy_b = CopyToDevice(b, device); |
755 | lazy_a.le_(lazy_b); |
756 | AllClose(lazy_a, a); |
757 | }); |
758 | } |
759 | |
760 | TEST_F(LazyOpsTest, TestGt) { |
761 | torch::Tensor a = torch::rand( |
762 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
763 | torch::Tensor b = torch::add(a.clone(), torch::ones_like(a)); |
764 | torch::Tensor c = torch::gt(b, a); |
765 | ForEachDevice([&](const torch::Device& device) { |
766 | torch::Tensor lazy_a = CopyToDevice(a, device); |
767 | torch::Tensor lazy_b = CopyToDevice(b, device); |
768 | torch::Tensor lazy_c = torch::gt(lazy_b, lazy_a); |
769 | AllEqual(c, lazy_c); |
770 | }); |
771 | } |
772 | |
773 | TEST_F(LazyOpsTest, TestGtInplace) { |
774 | torch::Tensor a = torch::rand( |
775 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
776 | torch::Tensor b = a.clone(); |
777 | b[0] += 1; |
778 | b[1] -= 1; |
779 | torch::Tensor a_copy = a.clone(); |
780 | a.gt_(b); |
781 | ForEachDevice([&](const torch::Device& device) { |
782 | torch::Tensor lazy_a = CopyToDevice(a_copy, device); |
783 | torch::Tensor lazy_b = CopyToDevice(b, device); |
784 | lazy_a.gt_(lazy_b); |
785 | AllClose(lazy_a, a); |
786 | }); |
787 | } |
788 | |
789 | TEST_F(LazyOpsTest, TestLt) { |
790 | torch::Tensor a = torch::rand( |
791 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
792 | torch::Tensor b = torch::add(a.clone(), torch::ones_like(a)); |
793 | torch::Tensor c = torch::lt(a, b); |
794 | ForEachDevice([&](const torch::Device& device) { |
795 | torch::Tensor lazy_a = CopyToDevice(a, device); |
796 | torch::Tensor lazy_b = CopyToDevice(b, device); |
797 | torch::Tensor lazy_c = torch::lt(lazy_a, lazy_b); |
798 | AllEqual(c, lazy_c); |
799 | }); |
800 | } |
801 | |
802 | TEST_F(LazyOpsTest, TestLtInplace) { |
803 | torch::Tensor a = torch::rand( |
804 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
805 | torch::Tensor b = a.clone(); |
806 | b[0] += 1; |
807 | b[1] -= 1; |
808 | torch::Tensor a_copy = a.clone(); |
809 | a.lt_(b); |
810 | ForEachDevice([&](const torch::Device& device) { |
811 | torch::Tensor lazy_a = CopyToDevice(a_copy, device); |
812 | torch::Tensor lazy_b = CopyToDevice(b, device); |
813 | lazy_a.lt_(lazy_b); |
814 | AllClose(lazy_a, a); |
815 | }); |
816 | } |
817 | |
818 | TEST_F(LazyOpsTest, TestNeScalar) { |
819 | torch::Tensor input = torch::ones({2, 3}); |
820 | torch::Scalar other(float(0)); |
821 | torch::Tensor result = torch::ne(input, other); |
822 | ForEachDevice([&](const torch::Device& device) { |
823 | torch::Tensor lazy_input = CopyToDevice(input, device); |
824 | torch::Tensor lazy_result = torch::ne(lazy_input, other); |
825 | AllEqual(result, lazy_result); |
826 | }); |
827 | } |
828 | |
829 | TEST_F(LazyOpsTest, TestEqScalar) { |
830 | torch::Tensor input = torch::ones({2, 3}); |
831 | torch::Scalar other(float(1)); |
832 | torch::Tensor result = torch::eq(input, other); |
833 | ForEachDevice([&](const torch::Device& device) { |
834 | torch::Tensor lazy_input = CopyToDevice(input, device); |
835 | torch::Tensor lazy_result = torch::eq(lazy_input, other); |
836 | AllEqual(result, lazy_result); |
837 | }); |
838 | } |
839 | |
840 | TEST_F(LazyOpsTest, TestGeScalar) { |
841 | torch::Tensor input = torch::ones({2, 3}); |
842 | torch::Scalar other(float(1)); |
843 | torch::Tensor result = torch::ge(input, other); |
844 | ForEachDevice([&](const torch::Device& device) { |
845 | torch::Tensor lazy_input = CopyToDevice(input, device); |
846 | torch::Tensor lazy_result = torch::ge(lazy_input, other); |
847 | AllEqual(result, lazy_result); |
848 | }); |
849 | } |
850 | |
851 | TEST_F(LazyOpsTest, TestGeScalarInplace) { |
852 | torch::Tensor input = torch::arange( |
853 | -1., |
854 | 1.5, |
855 | 0.5, |
856 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
857 | torch::Scalar other(float(0)); |
858 | torch::Tensor input_copy = input.clone(); |
859 | input.ge_(other); |
860 | ForEachDevice([&](const torch::Device& device) { |
861 | torch::Tensor lazy_input = CopyToDevice(input_copy, device); |
862 | lazy_input.ge_(other); |
863 | AllClose(lazy_input, input); |
864 | }); |
865 | } |
866 | |
867 | TEST_F(LazyOpsTest, TestLeScalar) { |
868 | torch::Tensor input = torch::ones({2, 3}); |
869 | torch::Scalar other(float(1)); |
870 | torch::Tensor result = torch::le(input, other); |
871 | ForEachDevice([&](const torch::Device& device) { |
872 | torch::Tensor lazy_input = CopyToDevice(input, device); |
873 | torch::Tensor lazy_result = torch::le(lazy_input, other); |
874 | AllEqual(result, lazy_result); |
875 | }); |
876 | } |
877 | |
878 | TEST_F(LazyOpsTest, TestLeScalarInplace) { |
879 | torch::Tensor input = torch::arange( |
880 | -1., |
881 | 1.5, |
882 | 0.5, |
883 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
884 | torch::Scalar other(float(0)); |
885 | torch::Tensor input_copy = input.clone(); |
886 | input.le_(other); |
887 | ForEachDevice([&](const torch::Device& device) { |
888 | torch::Tensor lazy_input = CopyToDevice(input_copy, device); |
889 | lazy_input.le_(other); |
890 | AllClose(lazy_input, input); |
891 | }); |
892 | } |
893 | |
894 | TEST_F(LazyOpsTest, TestGtScalar) { |
895 | torch::Tensor input = torch::ones({2, 3}); |
896 | torch::Scalar other(float(0.5)); |
897 | torch::Tensor result = torch::gt(input, other); |
898 | ForEachDevice([&](const torch::Device& device) { |
899 | torch::Tensor lazy_input = CopyToDevice(input, device); |
900 | torch::Tensor lazy_result = torch::gt(lazy_input, other); |
901 | AllEqual(result, lazy_result); |
902 | }); |
903 | } |
904 | |
905 | TEST_F(LazyOpsTest, TestGtScalarInplace) { |
906 | torch::Tensor input = torch::arange( |
907 | -1., |
908 | 1.5, |
909 | 0.5, |
910 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
911 | torch::Scalar other(float(0)); |
912 | torch::Tensor input_copy = input.clone(); |
913 | input.gt_(other); |
914 | ForEachDevice([&](const torch::Device& device) { |
915 | torch::Tensor lazy_input = CopyToDevice(input_copy, device); |
916 | lazy_input.gt_(other); |
917 | AllClose(lazy_input, input); |
918 | }); |
919 | } |
920 | |
921 | TEST_F(LazyOpsTest, TestLtScalar) { |
922 | torch::Tensor input = torch::ones({2, 3}); |
923 | torch::Scalar other(float(1.5)); |
924 | torch::Tensor result = torch::lt(input, other); |
925 | ForEachDevice([&](const torch::Device& device) { |
926 | torch::Tensor lazy_input = CopyToDevice(input, device); |
927 | torch::Tensor lazy_result = torch::lt(lazy_input, other); |
928 | AllEqual(result, lazy_result); |
929 | }); |
930 | } |
931 | |
932 | TEST_F(LazyOpsTest, TestLtScalarInplace) { |
933 | torch::Tensor input = torch::arange( |
934 | -1., |
935 | 1.5, |
936 | 0.5, |
937 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
938 | torch::Scalar other(float(0)); |
939 | torch::Tensor input_copy = input.clone(); |
940 | input.lt_(other); |
941 | ForEachDevice([&](const torch::Device& device) { |
942 | torch::Tensor lazy_input = CopyToDevice(input_copy, device); |
943 | lazy_input.lt_(other); |
944 | AllClose(lazy_input, input); |
945 | }); |
946 | } |
947 | |
948 | TEST_F(LazyOpsTest, TestIntegerAdd) { |
949 | std::vector<torch::ScalarType> types( |
950 | {torch::kByte, torch::kChar, torch::kShort, torch::kInt, torch::kLong}); |
951 | |
952 | ForEachDevice([&](const torch::Device& device) { |
953 | for (auto type : types) { |
954 | torch::Tensor a = |
955 | torch::randint(0, 63, {2, 2}, torch::TensorOptions(type)); |
956 | torch::Tensor b = |
957 | torch::randint(0, 63, {2, 2}, torch::TensorOptions(type)); |
958 | torch::Scalar one = |
959 | isIntegralType(type, false) ? torch::Scalar(1) : torch::Scalar(1.0); |
960 | torch::Tensor c = torch::add(b, one); |
961 | |
962 | torch::Tensor lazy_a = CopyToDevice(a, device); |
963 | torch::Tensor lazy_b = CopyToDevice(b, device); |
964 | torch::Tensor lazy_c = torch::add(lazy_b, one); |
965 | |
966 | AllEqual(c, lazy_c); |
967 | } |
968 | }); |
969 | } |
970 | |
971 | TEST_F(LazyOpsTest, TestSVD) { |
972 | static const int dims[] = {4, 7}; |
973 | for (auto m : dims) { |
974 | for (auto n : dims) { |
975 | torch::Tensor a = torch::rand( |
976 | {m, n}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
977 | auto b = torch::svd(a, /*some=*/true, /*compute_uv=*/true); |
978 | ForEachDevice([&](const torch::Device& device) { |
979 | torch::Tensor lazy_a = CopyToDevice(a, device); |
980 | auto lazy_b = torch::svd(lazy_a, /*some=*/true, /*compute_uv=*/true); |
981 | // The U and V matrices might have different sign for column vectors, so |
982 | // cannot be compared if not by absolute value. |
983 | AllClose( |
984 | std::get<0>(b).abs(), |
985 | std::get<0>(lazy_b).abs(), |
986 | /*rtol=*/1e-3, |
987 | /*atol=*/1e-4); |
988 | torch::Tensor diag = std::get<1>(b); |
989 | torch::Tensor lazy_diag = std::get<1>(lazy_b); |
990 | ASSERT_EQ(diag.sizes(), lazy_diag.sizes()); |
991 | AllClose( |
992 | diag, |
993 | lazy_diag, |
994 | /*rtol=*/1e-3, |
995 | /*atol=*/1e-4); |
996 | AllClose( |
997 | std::get<2>(b).abs(), |
998 | std::get<2>(lazy_b).abs(), |
999 | /*rtol=*/1e-3, |
1000 | /*atol=*/1e-4); |
1001 | }); |
1002 | } |
1003 | } |
1004 | } |
1005 | |
1006 | TEST_F(LazyOpsTest, TestQR) { |
1007 | static const int dims[] = {4, 7}; |
1008 | for (auto m : dims) { |
1009 | for (auto n : dims) { |
1010 | torch::Tensor a = torch::rand( |
1011 | {m, n}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1012 | auto b = torch::qr(a); |
1013 | ForEachDevice([&](const torch::Device& device) { |
1014 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1015 | auto lazy_b = torch::qr(lazy_a); |
1016 | AllClose( |
1017 | std::get<0>(b).abs(), |
1018 | std::get<0>(lazy_b).abs(), |
1019 | /*rtol=*/1e-3, |
1020 | /*atol=*/1e-4); |
1021 | AllClose( |
1022 | std::get<1>(b).abs(), |
1023 | std::get<1>(lazy_b).abs(), |
1024 | /*rtol=*/1e-3, |
1025 | /*atol=*/1e-4); |
1026 | }); |
1027 | } |
1028 | } |
1029 | } |
1030 | |
1031 | TEST_F(LazyOpsTest, TestCholesky) { |
1032 | static const int dims[] = {4, 7}; |
1033 | for (auto m : dims) { |
1034 | for (bool upper : {true, false}) { |
1035 | torch::Tensor a = torch::rand( |
1036 | {3, m, m}, |
1037 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1038 | torch::Tensor pd_a = |
1039 | torch::matmul(a, torch::transpose(a, 1, 2)) + |
1040 | torch::eye( |
1041 | m, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1042 | auto b = torch::cholesky(pd_a, upper); |
1043 | ForEachDevice([&](const torch::Device& device) { |
1044 | torch::Tensor lazy_a = CopyToDevice(pd_a, device); |
1045 | auto lazy_b = torch::cholesky(lazy_a, upper); |
1046 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-4); |
1047 | }); |
1048 | } |
1049 | } |
1050 | } |
1051 | |
1052 | TEST_F(LazyOpsTest, TestLogDet) { |
1053 | static const int dims[] = {4, 7}; |
1054 | for (auto m : dims) { |
1055 | torch::Tensor a = torch::rand( |
1056 | {3, m, m}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1057 | torch::Tensor pd_a = torch::matmul(a, torch::transpose(a, 1, 2)) + |
1058 | torch::eye(m, |
1059 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1060 | torch::Tensor b = torch::logdet(pd_a); |
1061 | ForEachDevice([&](const torch::Device& device) { |
1062 | torch::Tensor lazy_a = CopyToDevice(pd_a, device); |
1063 | torch::Tensor lazy_b = torch::logdet(lazy_a); |
1064 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-4); |
1065 | }); |
1066 | } |
1067 | } |
1068 | |
1069 | TEST_F(LazyOpsTest, TestTriangularSolve) { |
1070 | static const int dims[] = {4, 7}; |
1071 | for (bool batched_a : {true, false}) { |
1072 | for (bool batched_b : {true, false}) { |
1073 | for (auto m : dims) { |
1074 | for (auto n : dims) { |
1075 | for (bool upper : {true, false}) { |
1076 | for (bool transpose : {true, false}) { |
1077 | for (bool unitriangular : {true, false}) { |
1078 | torch::Tensor a = torch::randn( |
1079 | {m, m}, |
1080 | torch::TensorOptions(torch::kFloat) |
1081 | .device(DefaultDevice())); |
1082 | torch::Tensor b = torch::randn( |
1083 | {m, n}, |
1084 | torch::TensorOptions(torch::kFloat) |
1085 | .device(DefaultDevice())); |
1086 | a = batched_a ? a.expand({3, m, m}).clone() : a; |
1087 | b = batched_b ? b.expand({3, m, n}).clone() : b; |
1088 | auto result = torch::triangular_solve( |
1089 | b, |
1090 | a, |
1091 | /*upper=*/upper, |
1092 | /*transpose=*/transpose, |
1093 | /*unitriangular=*/unitriangular); |
1094 | ForEachDevice([&](const torch::Device& device) { |
1095 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1096 | torch::Tensor lazy_b = CopyToDevice(b, device); |
1097 | auto lazy_result = torch::triangular_solve( |
1098 | lazy_b, |
1099 | lazy_a, |
1100 | /*upper=*/upper, |
1101 | /*transpose=*/transpose, |
1102 | /*unitriangular=*/unitriangular); |
1103 | AllClose( |
1104 | std::get<0>(result), |
1105 | std::get<0>(lazy_result), |
1106 | /*rtol=*/1e-3, |
1107 | /*atol=*/1e-4); |
1108 | AllClose( |
1109 | std::get<1>(result), |
1110 | std::get<1>(lazy_result), |
1111 | /*rtol=*/1e-3, |
1112 | /*atol=*/1e-4); |
1113 | }); |
1114 | } |
1115 | } |
1116 | } |
1117 | } |
1118 | } |
1119 | } |
1120 | } |
1121 | } |
1122 | |
1123 | TEST_F(LazyOpsTest, TestKthValue) { |
1124 | torch::Tensor a = torch::rand( |
1125 | {4, 5, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1126 | for (int k = 1; k <= 3; ++k) { |
1127 | int rank = a.dim(); |
1128 | for (int dim = -rank; dim < rank; ++dim) { |
1129 | for (bool keepdim : {false, true}) { |
1130 | auto b = torch::kthvalue(a, k, dim, keepdim); |
1131 | ForEachDevice([&](const torch::Device& device) { |
1132 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1133 | auto lazy_b = torch::kthvalue(lazy_a, k, dim, keepdim); |
1134 | AllClose(std::get<0>(b), std::get<0>(lazy_b)); |
1135 | AllEqual(std::get<1>(b), std::get<1>(lazy_b)); |
1136 | }); |
1137 | } |
1138 | } |
1139 | } |
1140 | } |
1141 | |
1142 | TEST_F(LazyOpsTest, TestTopK) { |
1143 | torch::Tensor a = torch::rand( |
1144 | {4, 5, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1145 | for (int k = 1; k <= 3; ++k) { |
1146 | int rank = a.dim(); |
1147 | for (int dim = -rank; dim < rank; ++dim) { |
1148 | for (bool largest : {false, true}) { |
1149 | auto b = torch::topk(a, k, dim, largest, /*sorted=*/true); |
1150 | ForEachDevice([&](const torch::Device& device) { |
1151 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1152 | auto lazy_b = torch::topk(lazy_a, k, dim, largest, /*sorted=*/true); |
1153 | AllClose(std::get<0>(b), std::get<0>(lazy_b)); |
1154 | AllEqual(std::get<1>(b), std::get<1>(lazy_b)); |
1155 | }); |
1156 | } |
1157 | } |
1158 | } |
1159 | } |
1160 | |
1161 | TEST_F(LazyOpsTest, TestSort) { |
1162 | torch::Tensor a = torch::rand( |
1163 | {4, 5, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1164 | for (int k = 1; k <= 3; ++k) { |
1165 | for (int dim = 0; dim < 3; ++dim) { |
1166 | for (bool descending : {false, true}) { |
1167 | auto b = torch::sort(a, dim, descending); |
1168 | ForEachDevice([&](const torch::Device& device) { |
1169 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1170 | auto lazy_b = torch::sort(lazy_a, dim, descending); |
1171 | AllClose(std::get<0>(b), std::get<0>(lazy_b)); |
1172 | AllEqual(std::get<1>(b), std::get<1>(lazy_b)); |
1173 | }); |
1174 | } |
1175 | } |
1176 | } |
1177 | } |
1178 | |
1179 | TEST_F(LazyOpsTest, TestSortDescWithMinValue) { |
1180 | std::vector<int8_t> values{-128, 100}; |
1181 | torch::Tensor input = |
1182 | torch::tensor(values, torch::TensorOptions(torch::kChar)); |
1183 | auto output = torch::sort(input, /*dim=*/0, /*descending=*/true); |
1184 | ForEachDevice([&](const torch::Device& device) { |
1185 | torch::Tensor lazy_input = CopyToDevice(input, device); |
1186 | auto lazy_output = torch::sort(lazy_input, /*dim=*/0, /*descending=*/true); |
1187 | AllEqual(std::get<0>(output), std::get<0>(lazy_output)); |
1188 | AllEqual(std::get<1>(output), std::get<1>(lazy_output)); |
1189 | }); |
1190 | } |
1191 | |
1192 | TEST_F(LazyOpsTest, TestArgSort) { |
1193 | torch::Tensor a = torch::rand( |
1194 | {4, 5, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1195 | for (int k = 1; k <= 3; ++k) { |
1196 | for (int dim = 0; dim < 3; ++dim) { |
1197 | for (bool descending : {false, true}) { |
1198 | torch::Tensor b = torch::argsort(a, dim, descending); |
1199 | ForEachDevice([&](const torch::Device& device) { |
1200 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1201 | torch::Tensor lazy_b = torch::argsort(lazy_a, dim, descending); |
1202 | AllEqual(b, lazy_b); |
1203 | }); |
1204 | } |
1205 | } |
1206 | } |
1207 | } |
1208 | |
1209 | TEST_F(LazyOpsTest, TestMin) { |
1210 | torch::Tensor a = torch::rand( |
1211 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1212 | torch::Tensor b = torch::rand( |
1213 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1214 | torch::Tensor c = torch::min(a, b); |
1215 | ForEachDevice([&](const torch::Device& device) { |
1216 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1217 | torch::Tensor lazy_b = CopyToDevice(b, device); |
1218 | torch::Tensor lazy_c = torch::min(lazy_a, lazy_b); |
1219 | AllClose(c, lazy_c); |
1220 | }); |
1221 | } |
1222 | |
1223 | TEST_F(LazyOpsTest, TestMax) { |
1224 | torch::Tensor a = torch::rand( |
1225 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1226 | torch::Tensor b = torch::rand( |
1227 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1228 | torch::Tensor c = torch::max(a, b); |
1229 | ForEachDevice([&](const torch::Device& device) { |
1230 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1231 | torch::Tensor lazy_b = CopyToDevice(b, device); |
1232 | torch::Tensor lazy_c = torch::max(lazy_a, lazy_b); |
1233 | AllClose(c, lazy_c); |
1234 | }); |
1235 | } |
1236 | |
1237 | TEST_F(LazyOpsTest, TestUnaryMin) { |
1238 | torch::Tensor input = torch::rand( |
1239 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1240 | torch::Tensor output = torch::min(input); |
1241 | ForEachDevice([&](const torch::Device& device) { |
1242 | torch::Tensor lazy_input = CopyToDevice(input, device); |
1243 | torch::Tensor lazy_output = torch::min(lazy_input); |
1244 | AllClose(output, lazy_output); |
1245 | }); |
1246 | } |
1247 | |
1248 | TEST_F(LazyOpsTest, TestUnaryMax) { |
1249 | torch::Tensor input = torch::rand( |
1250 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1251 | torch::Tensor output = torch::max(input); |
1252 | ForEachDevice([&](const torch::Device& device) { |
1253 | torch::Tensor lazy_input = CopyToDevice(input, device); |
1254 | torch::Tensor lazy_output = torch::max(lazy_input); |
1255 | AllClose(output, lazy_output); |
1256 | }); |
1257 | } |
1258 | |
1259 | TEST_F(LazyOpsTest, TestAll) { |
1260 | for (torch::ScalarType scalar_type : |
1261 | {torch::kFloat, |
1262 | torch::kByte, |
1263 | torch::kChar, |
1264 | torch::kShort, |
1265 | torch::kInt, |
1266 | torch::kLong}) { |
1267 | torch::Tensor a = isFloatingType(scalar_type) |
1268 | ? torch::rand( |
1269 | {3, 4}, torch::TensorOptions(scalar_type).device(DefaultDevice())) |
1270 | : torch::randint( |
1271 | 100, |
1272 | {3, 4}, |
1273 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
1274 | torch::Tensor b = torch::all(a); |
1275 | ForEachDevice([&](const torch::Device& device) { |
1276 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1277 | torch::Tensor lazy_b = torch::all(lazy_a); |
1278 | EqualValues(b, lazy_b); |
1279 | }); |
1280 | } |
1281 | } |
1282 | |
1283 | TEST_F(LazyOpsTest, TestAllDim) { |
1284 | torch::Tensor a = torch::randint( |
1285 | 0, |
1286 | 5, |
1287 | {2, 3, 4}, |
1288 | torch::TensorOptions(torch::kByte).device(DefaultDevice())); |
1289 | int rank = a.dim(); |
1290 | for (int dim = -rank; dim < rank; ++dim) { |
1291 | torch::Tensor b = torch::all(a, dim, /*keepdim=*/false); |
1292 | ForEachDevice([&](const torch::Device& device) { |
1293 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1294 | torch::Tensor lazy_b = torch::all(lazy_a, dim, /*keepdim=*/false); |
1295 | EqualValues(b, lazy_b); |
1296 | }); |
1297 | } |
1298 | } |
1299 | |
1300 | TEST_F(LazyOpsTest, TestAllDimKeep) { |
1301 | torch::Tensor a = torch::randint( |
1302 | 0, |
1303 | 5, |
1304 | {2, 3, 4}, |
1305 | torch::TensorOptions(torch::kByte).device(DefaultDevice())); |
1306 | int rank = a.dim(); |
1307 | for (int dim = -rank; dim < rank; ++dim) { |
1308 | torch::Tensor b = torch::all(a, dim, /*keepdim=*/true); |
1309 | ForEachDevice([&](const torch::Device& device) { |
1310 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1311 | torch::Tensor lazy_b = torch::all(lazy_a, dim, /*keepdim=*/true); |
1312 | EqualValues(b, lazy_b); |
1313 | }); |
1314 | } |
1315 | } |
1316 | |
1317 | TEST_F(LazyOpsTest, TestAmax) { |
1318 | torch::Tensor input = torch::rand( |
1319 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1320 | int rank = input.dim(); |
1321 | for (bool keepdim : {false, true}) { |
1322 | for (int dim = -rank; dim < rank; ++dim) { |
1323 | torch::Tensor values = torch::amax(input, {dim}, /*keepdim=*/keepdim); |
1324 | ForEachDevice([&](const torch::Device& device) { |
1325 | torch::Tensor lazy_input = CopyToDevice(input, device); |
1326 | torch::Tensor lazy_values = |
1327 | torch::amax(lazy_input, {dim}, /*keepdim=*/keepdim); |
1328 | AllClose(values, lazy_values); |
1329 | }); |
1330 | } |
1331 | for (int dim1 = -rank; dim1 < rank; ++dim1) { |
1332 | for (int dim2 = -rank; dim2 < rank; ++dim2) { |
1333 | if ((dim1 == dim2) || (dim1 == rank + dim2) || (dim2 == rank + dim1)) |
1334 | continue; |
1335 | torch::Tensor values = |
1336 | torch::amax(input, {dim1, dim2}, /*keepdim=*/keepdim); |
1337 | ForEachDevice([&](const torch::Device& device) { |
1338 | torch::Tensor lazy_input = CopyToDevice(input, device); |
1339 | torch::Tensor lazy_values = |
1340 | torch::amax(lazy_input, {dim1, dim2}, /*keepdim=*/keepdim); |
1341 | AllClose(values, lazy_values); |
1342 | }); |
1343 | } |
1344 | } |
1345 | } |
1346 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
1347 | ExpectCounterChanged("xla::amax" , GetIgnoredCounters()); |
1348 | } |
1349 | |
1350 | TEST_F(LazyOpsTest, TestAmin) { |
1351 | torch::Tensor input = torch::rand( |
1352 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1353 | int rank = input.dim(); |
1354 | for (bool keepdim : {false, true}) { |
1355 | for (int dim = -rank; dim < rank; ++dim) { |
1356 | torch::Tensor values = torch::amin(input, {dim}, /*keepdim=*/keepdim); |
1357 | ForEachDevice([&](const torch::Device& device) { |
1358 | torch::Tensor lazy_input = CopyToDevice(input, device); |
1359 | torch::Tensor lazy_values = |
1360 | torch::amin(lazy_input, {dim}, /*keepdim=*/keepdim); |
1361 | AllClose(values, lazy_values); |
1362 | }); |
1363 | } |
1364 | for (int dim1 = -rank; dim1 < rank; ++dim1) { |
1365 | for (int dim2 = -rank; dim2 < rank; ++dim2) { |
1366 | if ((dim1 == dim2) || (dim1 == rank + dim2) || (dim2 == rank + dim1)) |
1367 | continue; |
1368 | torch::Tensor values = |
1369 | torch::amin(input, {dim1, dim2}, /*keepdim=*/keepdim); |
1370 | ForEachDevice([&](const torch::Device& device) { |
1371 | torch::Tensor lazy_input = CopyToDevice(input, device); |
1372 | torch::Tensor lazy_values = |
1373 | torch::amin(lazy_input, {dim1, dim2}, /*keepdim=*/keepdim); |
1374 | AllClose(values, lazy_values); |
1375 | }); |
1376 | } |
1377 | } |
1378 | } |
1379 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
1380 | ExpectCounterChanged("xla::amin" , GetIgnoredCounters()); |
1381 | } |
1382 | |
1383 | TEST_F(LazyOpsTest, TestAny) { |
1384 | for (torch::ScalarType scalar_type : |
1385 | {torch::kFloat, |
1386 | torch::kByte, |
1387 | torch::kChar, |
1388 | torch::kShort, |
1389 | torch::kInt, |
1390 | torch::kLong}) { |
1391 | torch::Tensor a = isFloatingType(scalar_type) |
1392 | ? torch::rand( |
1393 | {3, 4}, torch::TensorOptions(scalar_type).device(DefaultDevice())) |
1394 | : torch::randint( |
1395 | 100, |
1396 | {3, 4}, |
1397 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
1398 | torch::Tensor b = torch::any(a); |
1399 | ForEachDevice([&](const torch::Device& device) { |
1400 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1401 | torch::Tensor lazy_b = torch::any(lazy_a); |
1402 | EqualValues(b, lazy_b); |
1403 | }); |
1404 | } |
1405 | } |
1406 | |
1407 | TEST_F(LazyOpsTest, TestAnyDim) { |
1408 | torch::Tensor a = torch::randint( |
1409 | 0, |
1410 | 5, |
1411 | {2, 3, 4}, |
1412 | torch::TensorOptions(torch::kByte).device(DefaultDevice())); |
1413 | int rank = a.dim(); |
1414 | for (int dim = -rank; dim < rank; ++dim) { |
1415 | torch::Tensor b = torch::any(a, dim, /*keepdim=*/false); |
1416 | ForEachDevice([&](const torch::Device& device) { |
1417 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1418 | torch::Tensor lazy_b = torch::any(lazy_a, dim, /*keepdim=*/false); |
1419 | EqualValues(b, lazy_b); |
1420 | }); |
1421 | } |
1422 | } |
1423 | |
1424 | TEST_F(LazyOpsTest, TestAnyDimKeep) { |
1425 | torch::Tensor a = torch::randint( |
1426 | 0, |
1427 | 5, |
1428 | {2, 3, 4}, |
1429 | torch::TensorOptions(torch::kByte).device(DefaultDevice())); |
1430 | int rank = a.dim(); |
1431 | for (int dim = -rank; dim < rank; ++dim) { |
1432 | torch::Tensor b = torch::any(a, dim, /*keepdim=*/true); |
1433 | ForEachDevice([&](const torch::Device& device) { |
1434 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1435 | torch::Tensor lazy_b = torch::any(lazy_a, dim, /*keepdim=*/true); |
1436 | EqualValues(b, lazy_b); |
1437 | }); |
1438 | } |
1439 | } |
1440 | |
1441 | TEST_F(LazyOpsTest, TestMean) { |
1442 | torch::Tensor a = torch::rand( |
1443 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1444 | torch::Tensor b = torch::mean(a); |
1445 | ForEachDevice([&](const torch::Device& device) { |
1446 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1447 | torch::Tensor lazy_b = torch::mean(lazy_a); |
1448 | ASSERT_EQ(b.sizes(), lazy_b.sizes()); |
1449 | AllClose(b, lazy_b); |
1450 | }); |
1451 | } |
1452 | |
1453 | TEST_F(LazyOpsTest, TestMeanCast) { |
1454 | torch::Tensor a = torch::rand( |
1455 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1456 | torch::Tensor b = torch::mean(a, torch::kDouble); |
1457 | ForEachDevice([&](const torch::Device& device) { |
1458 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1459 | torch::Tensor lazy_b = torch::mean(lazy_a, torch::kDouble); |
1460 | AllClose(b, lazy_b); |
1461 | }); |
1462 | } |
1463 | |
1464 | TEST_F(LazyOpsTest, TestMeanInDim) { |
1465 | torch::Tensor a = torch::rand( |
1466 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1467 | int rank = a.dim(); |
1468 | for (int dim = -rank; dim < rank; ++dim) { |
1469 | torch::Tensor b = torch::mean(a, {dim}); |
1470 | ForEachDevice([&](const torch::Device& device) { |
1471 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1472 | torch::Tensor lazy_b = torch::mean(lazy_a, {dim}); |
1473 | AllClose(b, lazy_b); |
1474 | }); |
1475 | } |
1476 | } |
1477 | |
1478 | TEST_F(LazyOpsTest, TestMeanInDims) { |
1479 | torch::Tensor a = torch::rand( |
1480 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1481 | for (auto dims : std::vector<std::vector<int64_t>>{{0, 1}, {-3, -2}}) { |
1482 | torch::Tensor b = torch::mean(a, dims); |
1483 | ForEachDevice([&](const torch::Device& device) { |
1484 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1485 | torch::Tensor lazy_b = torch::mean(lazy_a, dims); |
1486 | AllClose(b, lazy_b); |
1487 | }); |
1488 | } |
1489 | } |
1490 | |
1491 | TEST_F(LazyOpsTest, TestMeanInDimsKeepCast) { |
1492 | torch::Tensor a = torch::rand( |
1493 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1494 | for (auto dims : std::vector<std::vector<int64_t>>{{0, 1}, {-3, -2}}) { |
1495 | torch::Tensor b = torch::mean(a, dims, true, torch::kDouble); |
1496 | ForEachDevice([&](const torch::Device& device) { |
1497 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1498 | torch::Tensor lazy_b = torch::mean(lazy_a, dims, true, torch::kDouble); |
1499 | AllClose(b, lazy_b); |
1500 | }); |
1501 | } |
1502 | } |
1503 | |
1504 | TEST_F(LazyOpsTest, TestMeanInDimOut) { |
1505 | torch::Tensor a = torch::rand( |
1506 | {4, 4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1507 | int rank = a.dim(); |
1508 | for (int dim = -rank; dim < rank; ++dim) { |
1509 | torch::Tensor b = torch::empty( |
1510 | {4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1511 | torch::mean_out(b, a, {dim}); |
1512 | ForEachDevice([&](const torch::Device& device) { |
1513 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1514 | torch::Tensor lazy_b = torch::empty({4, 4}, lazy_a.options()); |
1515 | torch::mean_out(lazy_b, lazy_a, {dim}); |
1516 | AllClose(b, lazy_b); |
1517 | }); |
1518 | } |
1519 | } |
1520 | |
1521 | TEST_F(LazyOpsTest, TestStd) { |
1522 | torch::Tensor a = torch::rand( |
1523 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1524 | for (auto unbiased : {true, false}) { |
1525 | torch::Tensor b = torch::std(a, unbiased); |
1526 | ForEachDevice([&](const torch::Device& device) { |
1527 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1528 | torch::Tensor lazy_b = torch::std(lazy_a, unbiased); |
1529 | AllClose(b, lazy_b); |
1530 | }); |
1531 | } |
1532 | } |
1533 | |
1534 | TEST_F(LazyOpsTest, TestStdInDim) { |
1535 | torch::Tensor a = torch::rand( |
1536 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1537 | int rank = a.dim(); |
1538 | for (auto unbiased : {true, false}) { |
1539 | for (auto keepdim : {true, false}) { |
1540 | for (int dim = -rank; dim < rank; ++dim) { |
1541 | torch::Tensor b = torch::std(a, {dim}, unbiased, keepdim); |
1542 | ForEachDevice([&](const torch::Device& device) { |
1543 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1544 | torch::Tensor lazy_b = torch::std(lazy_a, {dim}, unbiased, keepdim); |
1545 | AllClose(b, lazy_b); |
1546 | }); |
1547 | } |
1548 | } |
1549 | } |
1550 | } |
1551 | |
1552 | TEST_F(LazyOpsTest, TestStdWithCorrection) { |
1553 | torch::Tensor a = torch::rand( |
1554 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1555 | // int rank = a.dim(); |
1556 | c10::optional<int64_t> corrections[] = {1, 2, c10::nullopt}; |
1557 | for (const auto& correction : corrections) { |
1558 | for (auto keepdim : {true, false}) { |
1559 | for (const auto& dim : |
1560 | std::vector<std::vector<int64_t>>{{0, 1}, {-3, -2}}) { |
1561 | torch::Tensor b = torch::std(a, dim, correction, keepdim); |
1562 | ForEachDevice([&](const torch::Device& device) { |
1563 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1564 | torch::Tensor lazy_b = torch::std(lazy_a, dim, correction, keepdim); |
1565 | AllClose(b, lazy_b); |
1566 | }); |
1567 | } |
1568 | } |
1569 | } |
1570 | } |
1571 | |
1572 | TEST_F(LazyOpsTest, TestStdMeanWithCorrection) { |
1573 | torch::Tensor a = torch::rand( |
1574 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1575 | // int rank = a.dim(); |
1576 | c10::optional<int64_t> corrections[] = {1, 2, c10::nullopt}; |
1577 | for (const auto& correction : corrections) { |
1578 | for (auto keepdim : {true, false}) { |
1579 | for (const auto& dim : |
1580 | std::vector<std::vector<int64_t>>{{0, 1}, {-3, -2}}) { |
1581 | auto b = torch::std_mean(a, dim, correction, keepdim); |
1582 | ForEachDevice([&](const torch::Device& device) { |
1583 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1584 | auto lazy_b = torch::std_mean(lazy_a, dim, correction, keepdim); |
1585 | AllClose(std::get<0>(b), std::get<0>(lazy_b)); |
1586 | AllClose(std::get<1>(b), std::get<1>(lazy_b)); |
1587 | }); |
1588 | } |
1589 | } |
1590 | } |
1591 | } |
1592 | |
1593 | TEST_F(LazyOpsTest, TestSum) { |
1594 | torch::Tensor a = torch::rand( |
1595 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1596 | torch::Tensor b = torch::sum(a); |
1597 | ForEachDevice([&](const torch::Device& device) { |
1598 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1599 | torch::Tensor lazy_b = torch::sum(lazy_a); |
1600 | AllClose(b, lazy_b); |
1601 | }); |
1602 | } |
1603 | |
1604 | TEST_F(LazyOpsTest, TestSumCast) { |
1605 | torch::Tensor a = torch::rand( |
1606 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1607 | torch::Tensor b = torch::sum(a, torch::kDouble); |
1608 | ForEachDevice([&](const torch::Device& device) { |
1609 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1610 | torch::Tensor lazy_b = torch::sum(lazy_a, torch::kDouble); |
1611 | AllClose(b, lazy_b); |
1612 | }); |
1613 | } |
1614 | |
1615 | TEST_F(LazyOpsTest, TestSumU8) { |
1616 | torch::Tensor a = torch::ones( |
1617 | {256}, torch::TensorOptions(torch::kByte).device(DefaultDevice())); |
1618 | torch::Tensor b = torch::sum(a); |
1619 | ForEachDevice([&](const torch::Device& device) { |
1620 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1621 | torch::Tensor lazy_b = torch::sum(lazy_a); |
1622 | AllEqual(b, lazy_b); |
1623 | }); |
1624 | } |
1625 | |
1626 | TEST_F(LazyOpsTest, TestSumInDim) { |
1627 | torch::Tensor a = torch::rand( |
1628 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1629 | int rank = a.dim(); |
1630 | for (int dim = -rank; dim < rank; ++dim) { |
1631 | torch::Tensor b = torch::sum(a, {dim}); |
1632 | ForEachDevice([&](const torch::Device& device) { |
1633 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1634 | torch::Tensor lazy_b = torch::sum(lazy_a, {dim}); |
1635 | AllClose(b, lazy_b); |
1636 | }); |
1637 | } |
1638 | } |
1639 | |
1640 | TEST_F(LazyOpsTest, TestSumInDims) { |
1641 | torch::Tensor a = torch::rand( |
1642 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1643 | for (auto dims : std::vector<std::vector<int64_t>>{{0, 1}, {-3, -2}}) { |
1644 | torch::Tensor b = torch::sum(a, dims); |
1645 | ForEachDevice([&](const torch::Device& device) { |
1646 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1647 | torch::Tensor lazy_b = torch::sum(lazy_a, dims); |
1648 | AllClose(b, lazy_b); |
1649 | }); |
1650 | } |
1651 | } |
1652 | |
1653 | TEST_F(LazyOpsTest, TestSumInDimsKeep) { |
1654 | torch::Tensor a = torch::rand( |
1655 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1656 | for (auto dims : std::vector<std::vector<int64_t>>{{0, 1}, {-3, -2}}) { |
1657 | torch::Tensor b = torch::sum(a, dims, /*keepdim=*/true); |
1658 | ForEachDevice([&](const torch::Device& device) { |
1659 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1660 | torch::Tensor lazy_b = torch::sum(lazy_a, dims, /*keepdim=*/true); |
1661 | AllClose(b, lazy_b); |
1662 | }); |
1663 | } |
1664 | } |
1665 | |
1666 | TEST_F(LazyOpsTest, TestSumInDimsKeepCast) { |
1667 | torch::Tensor a = torch::rand( |
1668 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1669 | for (auto dims : std::vector<std::vector<int64_t>>{{0, 1}, {-3, -2}}) { |
1670 | torch::Tensor b = torch::sum(a, dims, /*keepdim=*/true, torch::kDouble); |
1671 | ForEachDevice([&](const torch::Device& device) { |
1672 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1673 | torch::Tensor lazy_b = |
1674 | torch::sum(lazy_a, dims, /*keepdim=*/true, torch::kDouble); |
1675 | AllClose(b, lazy_b); |
1676 | }); |
1677 | } |
1678 | } |
1679 | |
1680 | TEST_F(LazyOpsTest, TestVar) { |
1681 | torch::Tensor a = torch::rand( |
1682 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1683 | for (bool unbiased : {true, false}) { |
1684 | torch::Tensor b = torch::var(a, unbiased); |
1685 | ForEachDevice([&](const torch::Device& device) { |
1686 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1687 | torch::Tensor lazy_b = torch::var(lazy_a, unbiased); |
1688 | AllClose(b, lazy_b); |
1689 | }); |
1690 | } |
1691 | } |
1692 | |
1693 | TEST_F(LazyOpsTest, TestVarWithDim) { |
1694 | torch::Tensor a = torch::rand( |
1695 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1696 | for (auto dims : std::vector<std::vector<int64_t>>{{0, 1}, {-3, -2}}) { |
1697 | for (bool keepDim : {true, false}) { |
1698 | for (bool unbiased : {true, false}) { |
1699 | torch::Tensor b = torch::var(a, dims, unbiased, keepDim); |
1700 | ForEachDevice([&](const torch::Device& device) { |
1701 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1702 | torch::Tensor lazy_b = torch::var(lazy_a, dims, unbiased, keepDim); |
1703 | AllClose(b, lazy_b); |
1704 | }); |
1705 | } |
1706 | } |
1707 | } |
1708 | } |
1709 | |
1710 | TEST_F(LazyOpsTest, TestVarWithCorrection) { |
1711 | torch::Tensor a = torch::rand( |
1712 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1713 | c10::optional<int64_t> corrections[] = {1, 2, c10::nullopt}; |
1714 | for (const auto& dim : std::vector<std::vector<int64_t>>{{0, 1}, {-3, -2}}) { |
1715 | for (bool keepDim : {true, false}) { |
1716 | for (const auto& correction : corrections) { |
1717 | torch::Tensor b = torch::var(a, dim, correction, keepDim); |
1718 | ForEachDevice([&](const torch::Device& device) { |
1719 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1720 | torch::Tensor lazy_b = torch::var(lazy_a, dim, correction, keepDim); |
1721 | AllClose(b, lazy_b); |
1722 | }); |
1723 | } |
1724 | } |
1725 | } |
1726 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
1727 | ExpectCounterChanged("lazy::var" , GetIgnoredCounters()); |
1728 | } |
1729 | |
1730 | TEST_F(LazyOpsTest, TestVarMeanWithCorrection) { |
1731 | torch::Tensor a = torch::rand( |
1732 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1733 | c10::optional<int64_t> corrections[] = {1, 2, c10::nullopt}; |
1734 | for (const auto& dim : std::vector<std::vector<int64_t>>{{0, 1}, {-3, -2}}) { |
1735 | for (const auto& correction : corrections) { |
1736 | for (auto keepdim : {true, false}) { |
1737 | auto b = torch::var_mean(a, dim, correction, keepdim); |
1738 | ForEachDevice([&](const torch::Device& device) { |
1739 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1740 | auto lazy_b = torch::var_mean(lazy_a, dim, correction, keepdim); |
1741 | AllClose(std::get<0>(b), std::get<0>(lazy_b)); |
1742 | AllClose(std::get<1>(b), std::get<1>(lazy_b)); |
1743 | }); |
1744 | } |
1745 | } |
1746 | } |
1747 | } |
1748 | |
1749 | TEST_F(LazyOpsTest, TestMaxInDim) { |
1750 | torch::Tensor input = torch::rand( |
1751 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1752 | int rank = input.dim(); |
1753 | for (int dim = -rank; dim < rank; ++dim) { |
1754 | for (bool keepdim : {false, true}) { |
1755 | auto values_indices = torch::max(input, dim, /*keepdim=*/keepdim); |
1756 | ForEachDevice([&](const torch::Device& device) { |
1757 | torch::Tensor lazy_input = CopyToDevice(input, device); |
1758 | auto lazy_values_indices = |
1759 | torch::max(lazy_input, dim, /*keepdim=*/keepdim); |
1760 | AllClose(std::get<0>(values_indices), std::get<0>(lazy_values_indices)); |
1761 | AllEqual(std::get<1>(values_indices), std::get<1>(lazy_values_indices)); |
1762 | }); |
1763 | } |
1764 | } |
1765 | } |
1766 | |
1767 | TEST_F(LazyOpsTest, TestMinInDim) { |
1768 | torch::Tensor input = torch::rand( |
1769 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1770 | int rank = input.dim(); |
1771 | for (int dim = -rank; dim < rank; ++dim) { |
1772 | for (bool keepdim : {false, true}) { |
1773 | auto values_indices = torch::min(input, dim, /*keepdim=*/keepdim); |
1774 | ForEachDevice([&](const torch::Device& device) { |
1775 | torch::Tensor lazy_input = CopyToDevice(input, device); |
1776 | auto lazy_values_indices = |
1777 | torch::min(lazy_input, dim, /*keepdim=*/keepdim); |
1778 | AllClose(std::get<0>(values_indices), std::get<0>(lazy_values_indices)); |
1779 | AllEqual(std::get<1>(values_indices), std::get<1>(lazy_values_indices)); |
1780 | }); |
1781 | } |
1782 | } |
1783 | } |
1784 | |
1785 | TEST_F(LazyOpsTest, TestNorm) { |
1786 | torch::Tensor a = torch::rand( |
1787 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1788 | torch::Tensor b = torch::norm(a); |
1789 | ForEachDevice([&](const torch::Device& device) { |
1790 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1791 | torch::Tensor lazy_b = torch::norm(lazy_a); |
1792 | AllClose(b, lazy_b); |
1793 | }); |
1794 | } |
1795 | |
1796 | TEST_F(LazyOpsTest, TestNormInDim) { |
1797 | torch::Tensor a = torch::rand( |
1798 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1799 | for (int dim : {1, -2}) { |
1800 | torch::Tensor b = torch::norm(a, 2, {dim}, /*keepdim=*/false); |
1801 | ForEachDevice([&](const torch::Device& device) { |
1802 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1803 | torch::Tensor lazy_b = torch::norm(lazy_a, 2, {dim}, /*keepdim=*/false); |
1804 | AllClose(b, lazy_b); |
1805 | }); |
1806 | } |
1807 | } |
1808 | |
1809 | TEST_F(LazyOpsTest, TestNormInDims) { |
1810 | torch::Tensor a = torch::rand( |
1811 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1812 | for (auto dims : std::vector<std::vector<int64_t>>{{1, 2}, {-2, -1}}) { |
1813 | torch::Tensor b = torch::norm(a, 2, dims, /*keepdim=*/false); |
1814 | ForEachDevice([&](const torch::Device& device) { |
1815 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1816 | torch::Tensor lazy_b = torch::norm(lazy_a, 2, dims, /*keepdim=*/false); |
1817 | AllClose(b, lazy_b); |
1818 | }); |
1819 | } |
1820 | } |
1821 | |
1822 | TEST_F(LazyOpsTest, TestNormInDimsKeep) { |
1823 | torch::Tensor a = torch::rand( |
1824 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1825 | for (auto dims : std::vector<std::vector<int64_t>>{{1, 2}, {-2, -1}}) { |
1826 | torch::Tensor b = torch::norm(a, 2, dims, /*keepdim=*/true); |
1827 | ForEachDevice([&](const torch::Device& device) { |
1828 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1829 | torch::Tensor lazy_b = torch::norm(lazy_a, 2, dims, /*keepdim=*/true); |
1830 | AllClose(b, lazy_b); |
1831 | }); |
1832 | } |
1833 | } |
1834 | |
1835 | TEST_F(LazyOpsTest, TestNormalTwoTensor) { |
1836 | at::Tensor mean = at::zeros({10, 10, 10}, at::dtype(at::kFloat)); |
1837 | at::Tensor std = at::ones({10, 10, 10}, at::dtype(at::kFloat)); |
1838 | ForEachDevice([&](const torch::Device& device) { |
1839 | at::Tensor lazy_mean = CopyToDevice(mean, device); |
1840 | at::Tensor lazy_std = CopyToDevice(std, device); |
1841 | at::Tensor lazy_normal = at::normal(lazy_mean, lazy_std); |
1842 | double res_mean = lazy_normal.mean().item().toDouble(); |
1843 | double res_std = lazy_normal.std().item().toDouble(); |
1844 | EXPECT_GT(res_mean, -0.06); |
1845 | EXPECT_LT(res_mean, 0.06); |
1846 | EXPECT_GT(res_std, 0.94); |
1847 | EXPECT_LT(res_std, 1.06); |
1848 | }); |
1849 | } |
1850 | |
1851 | TEST_F(LazyOpsTest, TestNormalDoubleMean) { |
1852 | at::Tensor std = at::ones({10, 10, 10}, at::dtype(at::kFloat)); |
1853 | ForEachDevice([&](const torch::Device& device) { |
1854 | at::Tensor lazy_std = CopyToDevice(std, device); |
1855 | at::Tensor lazy_normal = at::normal(0, lazy_std); |
1856 | double res_mean = lazy_normal.mean().item().toDouble(); |
1857 | double res_std = lazy_normal.std().item().toDouble(); |
1858 | EXPECT_GT(res_mean, -0.06); |
1859 | EXPECT_LT(res_mean, 0.06); |
1860 | EXPECT_GT(res_std, 0.94); |
1861 | EXPECT_LT(res_std, 1.06); |
1862 | }); |
1863 | } |
1864 | |
1865 | TEST_F(LazyOpsTest, TestNormalDoubleStd) { |
1866 | at::Tensor mean = at::zeros({10, 10, 10}, at::dtype(at::kFloat)); |
1867 | ForEachDevice([&](const torch::Device& device) { |
1868 | at::Tensor lazy_mean = CopyToDevice(mean, device); |
1869 | at::Tensor lazy_normal = at::normal(lazy_mean, 1); |
1870 | double res_mean = lazy_normal.mean().item().toDouble(); |
1871 | double res_std = lazy_normal.std().item().toDouble(); |
1872 | EXPECT_GT(res_mean, -0.06); |
1873 | EXPECT_LT(res_mean, 0.06); |
1874 | EXPECT_GT(res_std, 0.94); |
1875 | EXPECT_LT(res_std, 1.06); |
1876 | }); |
1877 | } |
1878 | |
1879 | TEST_F(LazyOpsTest, TestNormalInPlace) { |
1880 | at::Tensor a = at::zeros({10, 10, 10}, at::dtype(at::kFloat)); |
1881 | ForEachDevice([&](const torch::Device& device) { |
1882 | at::Tensor lazy_a = CopyToDevice(a, device); |
1883 | lazy_a.normal_(/*mean=*/0, /*std=*/1); |
1884 | double res_mean = lazy_a.mean().item().toDouble(); |
1885 | double res_std = lazy_a.std().item().toDouble(); |
1886 | EXPECT_GT(res_mean, -0.06); |
1887 | EXPECT_LT(res_mean, 0.06); |
1888 | EXPECT_GT(res_std, 0.94); |
1889 | EXPECT_LT(res_std, 1.06); |
1890 | }); |
1891 | } |
1892 | |
1893 | TEST_F(LazyOpsTest, TestUniformInPlace) { |
1894 | const double eps = 1e-3; |
1895 | at::Tensor a = at::zeros({10, 10, 10}, at::dtype(at::kFloat)); |
1896 | ForEachDevice([&](const torch::Device& device) { |
1897 | at::Tensor lazy_a = CopyToDevice(a, device); |
1898 | lazy_a.uniform_(/*from=*/0, /*to=*/1); |
1899 | at::Tensor cpu_a = ToCpuTensor(lazy_a); |
1900 | double res_min = cpu_a.min().item().toDouble(); |
1901 | double res_max = cpu_a.max().item().toDouble(); |
1902 | EXPECT_GT(res_min, 0.0 - eps); |
1903 | EXPECT_LT(res_max, 1.0 + eps); |
1904 | }); |
1905 | } |
1906 | |
1907 | TEST_F(LazyOpsTest, TestRandomInPlace) { |
1908 | for (auto dtype : |
1909 | {torch::kFloat, |
1910 | torch::kDouble, |
1911 | torch::kByte, |
1912 | torch::kChar, |
1913 | torch::kShort, |
1914 | torch::kInt, |
1915 | torch::kLong}) { |
1916 | const double eps = 0.2; |
1917 | torch::Tensor a = torch::zeros({10, 10, 10}, torch::TensorOptions(dtype)); |
1918 | ForEachDevice([&](const torch::Device& device) { |
1919 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1920 | lazy_a.random_(/*from=*/0, /*to=*/10); |
1921 | double res_mean = lazy_a.sum().item().toDouble() / a.numel(); |
1922 | double res_min = lazy_a.min().item().toDouble(); |
1923 | double res_max = lazy_a.max().item().toDouble(); |
1924 | EXPECT_GT(res_mean, 4.5 - eps); |
1925 | EXPECT_LT(res_mean, 4.5 + eps); |
1926 | EXPECT_EQ(res_min, 0.0); |
1927 | EXPECT_EQ(res_max, 9.0); |
1928 | }); |
1929 | } |
1930 | } |
1931 | |
1932 | TEST_F(LazyOpsTest, TestRandomInPlaceDefaultFrom) { |
1933 | for (auto dtype : |
1934 | {torch::kFloat, |
1935 | torch::kDouble, |
1936 | torch::kByte, |
1937 | torch::kChar, |
1938 | torch::kShort, |
1939 | torch::kInt, |
1940 | torch::kLong}) { |
1941 | const double eps = 0.2; |
1942 | torch::Tensor a = torch::zeros({10, 10, 10}, torch::TensorOptions(dtype)); |
1943 | ForEachDevice([&](const torch::Device& device) { |
1944 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1945 | lazy_a.random_(/*to=*/10); |
1946 | double res_mean = lazy_a.sum().item().toDouble() / a.numel(); |
1947 | double res_min = lazy_a.min().item().toDouble(); |
1948 | double res_max = lazy_a.max().item().toDouble(); |
1949 | EXPECT_GT(res_mean, 4.5 - eps); |
1950 | EXPECT_LT(res_mean, 4.5 + eps); |
1951 | EXPECT_EQ(res_min, 0.0); |
1952 | EXPECT_EQ(res_max, 9.0); |
1953 | }); |
1954 | } |
1955 | } |
1956 | |
1957 | TEST_F(LazyOpsTest, TestRandomInPlaceDefault) { |
1958 | for (auto dtype : |
1959 | {torch::kFloat, |
1960 | torch::kDouble, |
1961 | torch::kByte, |
1962 | torch::kChar, |
1963 | torch::kShort, |
1964 | torch::kInt, |
1965 | torch::kLong}) { |
1966 | auto input = torch::zeros({10}, torch::TensorOptions(dtype)); |
1967 | ForEachDevice([&](const torch::Device& device) { |
1968 | auto lazyInput = CopyToDevice(input, device); |
1969 | lazyInput.random_(); |
1970 | auto output = ToCpuTensor(lazyInput); |
1971 | EXPECT_TRUE(torch::all(output.ne(input)).item<bool>()); |
1972 | }); |
1973 | } |
1974 | } |
1975 | |
1976 | TEST_F(LazyOpsTest, TestNormGeneral) { |
1977 | torch::Tensor a = torch::randn( |
1978 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1979 | torch::Tensor b = torch::norm(a, 3.5); |
1980 | ForEachDevice([&](const torch::Device& device) { |
1981 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1982 | torch::Tensor lazy_b = torch::norm(lazy_a, 3.5); |
1983 | AllClose(b, lazy_b); |
1984 | }); |
1985 | } |
1986 | |
1987 | TEST_F(LazyOpsTest, TestNormNuclear) { |
1988 | torch::Tensor a = torch::rand( |
1989 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
1990 | torch::Tensor b = torch::norm(a, 1); |
1991 | ForEachDevice([&](const torch::Device& device) { |
1992 | torch::Tensor lazy_a = CopyToDevice(a, device); |
1993 | torch::Tensor lazy_b = torch::norm(lazy_a, 1); |
1994 | AllClose(b, lazy_b); |
1995 | }); |
1996 | } |
1997 | |
1998 | TEST_F(LazyOpsTest, TestFrobeniusNormInDim) { |
1999 | torch::Tensor a = torch::rand( |
2000 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2001 | for (int dim : {1, -2}) { |
2002 | torch::Tensor b = torch::frobenius_norm(a, {dim}, /*keepdim=*/false); |
2003 | ForEachDevice([&](const torch::Device& device) { |
2004 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2005 | torch::Tensor lazy_b = |
2006 | torch::frobenius_norm(lazy_a, {dim}, /*keepdim=*/false); |
2007 | AllClose(b, lazy_b); |
2008 | }); |
2009 | } |
2010 | } |
2011 | |
2012 | TEST_F(LazyOpsTest, TestFrobeniusNormInDims) { |
2013 | torch::Tensor a = torch::rand( |
2014 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2015 | for (auto dims : std::vector<std::vector<int64_t>>{{1, 2}, {-2, -1}}) { |
2016 | torch::Tensor b = torch::frobenius_norm(a, dims, /*keepdim=*/false); |
2017 | ForEachDevice([&](const torch::Device& device) { |
2018 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2019 | torch::Tensor lazy_b = |
2020 | torch::frobenius_norm(lazy_a, dims, /*keepdim=*/false); |
2021 | AllClose(b, lazy_b); |
2022 | }); |
2023 | } |
2024 | } |
2025 | |
2026 | TEST_F(LazyOpsTest, TestGroupNorm) { |
2027 | int num_channels = 6; |
2028 | torch::Tensor input = torch::rand( |
2029 | {20, num_channels, 10, 10}, |
2030 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2031 | torch::Tensor weight = torch::rand( |
2032 | {num_channels}, |
2033 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2034 | torch::Tensor bias = torch::rand( |
2035 | {num_channels}, |
2036 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2037 | double eps = 1e-05; |
2038 | for (int num_groups : {3, 6, 1}) { |
2039 | torch::Tensor output = torch::group_norm( |
2040 | input, |
2041 | num_groups, |
2042 | weight, |
2043 | bias, |
2044 | eps, |
2045 | /*cudnn_enabled=*/false); |
2046 | ForEachDevice([&](const torch::Device& device) { |
2047 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2048 | torch::Tensor lazy_weight = CopyToDevice(weight, device); |
2049 | torch::Tensor lazy_bias = CopyToDevice(bias, device); |
2050 | torch::Tensor lazy_output = torch::group_norm( |
2051 | lazy_input, |
2052 | num_groups, |
2053 | lazy_weight, |
2054 | lazy_bias, |
2055 | eps, |
2056 | /*cudnn_enabled=*/false); |
2057 | AllClose(output, lazy_output, /*rtol=*/1e-3, /*atol=*/1e-5); |
2058 | }); |
2059 | } |
2060 | } |
2061 | |
2062 | TEST_F(LazyOpsTest, TestGroupNormBackward) { |
2063 | int num_channels = 6; |
2064 | torch::Tensor input = torch::rand( |
2065 | {2, num_channels, 5, 5}, |
2066 | torch::TensorOptions(torch::kFloat) |
2067 | .device(DefaultDevice()) |
2068 | .requires_grad(true)); |
2069 | torch::Tensor weight = torch::rand( |
2070 | {num_channels}, |
2071 | torch::TensorOptions(torch::kFloat) |
2072 | .device(DefaultDevice()) |
2073 | .requires_grad(true)); |
2074 | torch::Tensor bias = torch::rand( |
2075 | {num_channels}, |
2076 | torch::TensorOptions(torch::kFloat) |
2077 | .device(DefaultDevice()) |
2078 | .requires_grad(true)); |
2079 | double eps = 1e-05; |
2080 | for (bool undef_weight : {true, false}) { |
2081 | for (int num_groups : {3, 6, 1}) { |
2082 | auto testfn = |
2083 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
2084 | return torch::group_norm( |
2085 | /*input=*/inputs[0], |
2086 | num_groups, |
2087 | inputs[1], |
2088 | inputs[2], |
2089 | /*eps=*/eps, |
2090 | /*cudnn_enabled=*/false); |
2091 | }; |
2092 | torch::Tensor undef; |
2093 | ForEachDevice([&](const torch::Device& device) { |
2094 | TestBackward( |
2095 | {input, undef_weight ? undef : weight, undef_weight ? undef : bias}, |
2096 | device, |
2097 | testfn, |
2098 | /*rtol=*/1e-3, |
2099 | /*atol=*/1e-3, |
2100 | /*derivative_level=*/2); |
2101 | }); |
2102 | } |
2103 | } |
2104 | } |
2105 | |
2106 | TEST_F(LazyOpsTest, TestInstanceNorm) { |
2107 | int batch = 5; |
2108 | int num_channels = 20; |
2109 | torch::Tensor input = torch::rand( |
2110 | {batch, num_channels, 10, 10}, |
2111 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2112 | torch::Tensor weight = torch::rand( |
2113 | {num_channels}, |
2114 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2115 | torch::Tensor bias = torch::rand( |
2116 | {num_channels}, |
2117 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2118 | torch::Tensor running_mean = torch::zeros( |
2119 | {num_channels}, |
2120 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2121 | torch::Tensor running_var = torch::ones( |
2122 | {num_channels}, |
2123 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2124 | double momentum = 0.1; |
2125 | double eps = 1e-05; |
2126 | torch::Tensor output = torch::instance_norm( |
2127 | input, |
2128 | weight, |
2129 | bias, |
2130 | running_mean, |
2131 | running_var, |
2132 | /*use_input_stats=*/true, |
2133 | momentum, |
2134 | eps, |
2135 | /*cudnn_enabled=*/false); |
2136 | ForEachDevice([&](const torch::Device& device) { |
2137 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2138 | torch::Tensor lazy_weight = CopyToDevice(weight, device); |
2139 | torch::Tensor lazy_bias = CopyToDevice(bias, device); |
2140 | torch::Tensor lazy_running_mean = CopyToDevice(running_mean, device); |
2141 | torch::Tensor lazy_running_var = CopyToDevice(running_var, device); |
2142 | torch::Tensor lazy_output = torch::instance_norm( |
2143 | lazy_input, |
2144 | lazy_weight, |
2145 | lazy_bias, |
2146 | lazy_running_mean, |
2147 | lazy_running_var, |
2148 | /*use_input_stats=*/true, |
2149 | momentum, |
2150 | eps, |
2151 | /*cudnn_enabled=*/false); |
2152 | AllClose(output, lazy_output, /*rtol=*/1e-3, /*atol=*/1e-5); |
2153 | }); |
2154 | } |
2155 | |
2156 | TEST_F(LazyOpsTest, TestLayerNorm) { |
2157 | torch::Tensor input = torch::rand( |
2158 | {20, 10, 10, 10}, |
2159 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2160 | double eps = 1e-05; |
2161 | torch::Tensor undef; |
2162 | for (bool undef_weight : {true, false}) { |
2163 | for (int64_t normalized_size : {2, 3}) { |
2164 | std::vector<int64_t> normalized_shape(normalized_size, 10); |
2165 | torch::Tensor weight = torch::rand( |
2166 | normalized_shape, |
2167 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2168 | torch::Tensor bias = torch::rand( |
2169 | normalized_shape, |
2170 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2171 | torch::Tensor output = torch::layer_norm( |
2172 | input, |
2173 | normalized_shape, |
2174 | undef_weight ? undef : weight, |
2175 | undef_weight ? undef : bias, |
2176 | eps, |
2177 | /*cudnn_enabled=*/false); |
2178 | ForEachDevice([&](const torch::Device& device) { |
2179 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2180 | torch::Tensor lazy_weight = |
2181 | undef_weight ? undef : CopyToDevice(weight, device); |
2182 | torch::Tensor lazy_bias = |
2183 | undef_weight ? undef : CopyToDevice(bias, device); |
2184 | torch::Tensor lazy_output = torch::layer_norm( |
2185 | lazy_input, |
2186 | normalized_shape, |
2187 | lazy_weight, |
2188 | lazy_bias, |
2189 | eps, |
2190 | /*cudnn_enabled=*/false); |
2191 | AllClose(output, lazy_output, /*rtol=*/1e-3, /*atol=*/1e-5); |
2192 | }); |
2193 | } |
2194 | } |
2195 | } |
2196 | |
2197 | TEST_F(LazyOpsTest, TestLayerNormBackward) { |
2198 | torch::Tensor input = torch::rand( |
2199 | {2, 3, 3, 3}, |
2200 | torch::TensorOptions(torch::kFloat) |
2201 | .device(DefaultDevice()) |
2202 | .requires_grad(true)); |
2203 | double eps = 1e-05; |
2204 | for (bool undef_weight : {true, false}) { |
2205 | for (int64_t normalized_size : {2, 3}) { |
2206 | std::vector<int64_t> normalized_shape(normalized_size, 3); |
2207 | auto testfn = |
2208 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
2209 | return torch::layer_norm( |
2210 | /*input=*/inputs[0], |
2211 | normalized_shape, |
2212 | inputs[1], |
2213 | inputs[2], |
2214 | /*eps=*/eps, |
2215 | /*cudnn_enabled=*/false); |
2216 | }; |
2217 | torch::Tensor weight = torch::rand( |
2218 | normalized_shape, |
2219 | torch::TensorOptions(torch::kFloat) |
2220 | .device(DefaultDevice()) |
2221 | .requires_grad(true)); |
2222 | torch::Tensor bias = torch::rand( |
2223 | normalized_shape, |
2224 | torch::TensorOptions(torch::kFloat) |
2225 | .device(DefaultDevice()) |
2226 | .requires_grad(true)); |
2227 | torch::Tensor undef; |
2228 | ForEachDevice([&](const torch::Device& device) { |
2229 | TestBackward( |
2230 | {input, undef_weight ? undef : weight, undef_weight ? undef : bias}, |
2231 | device, |
2232 | testfn, |
2233 | /*rtol=*/1e-3, |
2234 | /*atol=*/1e-4, |
2235 | /*derivative_level=*/2); |
2236 | }); |
2237 | } |
2238 | } |
2239 | } |
2240 | |
2241 | TEST_F(LazyOpsTest, TestNuclearNorm) { |
2242 | torch::Tensor a = torch::rand( |
2243 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2244 | torch::Tensor b = torch::nuclear_norm(a); |
2245 | ForEachDevice([&](const torch::Device& device) { |
2246 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2247 | torch::Tensor lazy_b = torch::nuclear_norm(lazy_a); |
2248 | AllClose(b, lazy_b); |
2249 | }); |
2250 | } |
2251 | |
2252 | TEST_F(LazyOpsTest, TestPairwiseDistance) { |
2253 | torch::Tensor x1 = torch::rand( |
2254 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2255 | torch::Tensor x2 = torch::rand( |
2256 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2257 | double eps = 1e-6; |
2258 | for (bool keepdim : {false, true}) { |
2259 | for (double p : {1, 2, 3, 4}) { |
2260 | ForEachDevice([&](const torch::Device& device) { |
2261 | torch::Tensor output = |
2262 | torch::pairwise_distance(x1, x2, p, eps, keepdim); |
2263 | torch::Tensor lazy_x1 = CopyToDevice(x1, device); |
2264 | torch::Tensor lazy_x2 = CopyToDevice(x2, device); |
2265 | torch::Tensor lazy_output = |
2266 | torch::pairwise_distance(lazy_x1, lazy_x2, p, eps, keepdim); |
2267 | AllClose(output, lazy_output, /*rtol=*/1e-5, /*atol=*/1e-5); |
2268 | }); |
2269 | } |
2270 | } |
2271 | } |
2272 | |
2273 | TEST_F(LazyOpsTest, TestCosineSimilarity) { |
2274 | torch::Tensor x1 = torch::rand( |
2275 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2276 | torch::Tensor x2 = torch::rand( |
2277 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2278 | double eps = 1e-8; |
2279 | int rank = x1.dim(); |
2280 | for (int dim = -rank; dim < rank; ++dim) { |
2281 | ForEachDevice([&](const torch::Device& device) { |
2282 | torch::Tensor output = torch::cosine_similarity(x1, x2, dim, eps); |
2283 | torch::Tensor lazy_x1 = CopyToDevice(x1, device); |
2284 | torch::Tensor lazy_x2 = CopyToDevice(x2, device); |
2285 | torch::Tensor lazy_output = |
2286 | torch::cosine_similarity(lazy_x1, lazy_x2, dim, eps); |
2287 | AllClose(output, lazy_output); |
2288 | }); |
2289 | } |
2290 | } |
2291 | |
2292 | TEST_F(LazyOpsTest, TestCosineEmbeddingLoss) { |
2293 | torch::Tensor input1 = torch::rand( |
2294 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2295 | torch::Tensor input2 = torch::rand( |
2296 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2297 | torch::Tensor target = torch::rand( |
2298 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2299 | for (torch::Reduction::Reduction reduction : |
2300 | {torch::Reduction::Mean, torch::Reduction::Sum}) { |
2301 | for (double margin : {0., 0.2}) { |
2302 | ForEachDevice([&](const torch::Device& device) { |
2303 | torch::Tensor output = torch::cosine_embedding_loss( |
2304 | input1, input2, target, margin, reduction); |
2305 | torch::Tensor lazy_input1 = CopyToDevice(input1, device); |
2306 | torch::Tensor lazy_input2 = CopyToDevice(input2, device); |
2307 | torch::Tensor lazy_target = CopyToDevice(target, device); |
2308 | torch::Tensor lazy_output = torch::cosine_embedding_loss( |
2309 | lazy_input1, lazy_input2, lazy_target, margin, reduction); |
2310 | AllClose(output, lazy_output); |
2311 | }); |
2312 | } |
2313 | } |
2314 | } |
2315 | |
2316 | TEST_F(LazyOpsTest, TestHingeEmbeddingLoss) { |
2317 | torch::Tensor input = torch::rand( |
2318 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2319 | torch::Tensor target = torch::rand( |
2320 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2321 | for (torch::Reduction::Reduction reduction : |
2322 | {torch::Reduction::Mean, torch::Reduction::Sum}) { |
2323 | for (double margin : {0., 0.2}) { |
2324 | ForEachDevice([&](const torch::Device& device) { |
2325 | torch::Tensor output = |
2326 | torch::hinge_embedding_loss(input, target, margin, reduction); |
2327 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2328 | torch::Tensor lazy_target = CopyToDevice(target, device); |
2329 | torch::Tensor lazy_output = torch::hinge_embedding_loss( |
2330 | lazy_input, lazy_target, margin, reduction); |
2331 | AllClose(output, lazy_output); |
2332 | }); |
2333 | } |
2334 | } |
2335 | } |
2336 | |
2337 | TEST_F(LazyOpsTest, TestTripletMarginLoss) { |
2338 | torch::Tensor anchor = torch::rand( |
2339 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2340 | torch::Tensor positive = torch::abs(torch::rand( |
2341 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice()))); |
2342 | torch::Tensor negative = torch::neg(torch::abs(torch::rand( |
2343 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())))); |
2344 | double eps = 1e-6; |
2345 | for (double margin : {0., 0.2}) { |
2346 | for (double p : {1, 2, 3, 4}) { |
2347 | for (bool swap : {false, true}) { |
2348 | for (torch::Reduction::Reduction reduction : |
2349 | {torch::Reduction::Mean, torch::Reduction::Sum}) { |
2350 | ForEachDevice([&](const torch::Device& device) { |
2351 | torch::Tensor output = torch::triplet_margin_loss( |
2352 | anchor, positive, negative, margin, p, eps, swap, reduction); |
2353 | torch::Tensor lazy_anchor = CopyToDevice(anchor, device); |
2354 | torch::Tensor lazy_positive = CopyToDevice(positive, device); |
2355 | torch::Tensor lazy_negative = CopyToDevice(negative, device); |
2356 | torch::Tensor lazy_output = torch::triplet_margin_loss( |
2357 | lazy_anchor, |
2358 | lazy_positive, |
2359 | lazy_negative, |
2360 | margin, |
2361 | p, |
2362 | eps, |
2363 | swap, |
2364 | reduction); |
2365 | AllClose(output, lazy_output); |
2366 | }); |
2367 | } |
2368 | } |
2369 | } |
2370 | } |
2371 | } |
2372 | |
2373 | TEST_F(LazyOpsTest, TestBinaryCrossEntropy) { |
2374 | int batch = 10; |
2375 | int classes = 5; |
2376 | torch::Tensor input = torch::rand( |
2377 | {batch, classes}, |
2378 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2379 | torch::Tensor target = torch::rand( |
2380 | {batch, classes}, |
2381 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2382 | torch::Tensor weight = torch::rand( |
2383 | {batch, classes}, |
2384 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2385 | torch::Tensor undef; |
2386 | for (torch::Reduction::Reduction reduction : |
2387 | {torch::Reduction::Mean, |
2388 | torch::Reduction::Sum, |
2389 | torch::Reduction::None}) { |
2390 | for (bool undef_weight : {false, true}) { |
2391 | ForEachDevice([&](const torch::Device& device) { |
2392 | torch::Tensor output = torch::binary_cross_entropy( |
2393 | input, target, undef_weight ? undef : weight, reduction); |
2394 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2395 | torch::Tensor lazy_target = CopyToDevice(target, device); |
2396 | torch::Tensor lazy_weight = |
2397 | undef_weight ? undef : CopyToDevice(weight, device); |
2398 | torch::Tensor lazy_output = torch::binary_cross_entropy( |
2399 | lazy_input, lazy_target, lazy_weight, reduction); |
2400 | AllClose(output, lazy_output, /*rtol=*/1e-4, /*atol=*/1e-5); |
2401 | }); |
2402 | } |
2403 | } |
2404 | } |
2405 | |
2406 | TEST_F(LazyOpsTest, TestMarginRankingLoss) { |
2407 | torch::Tensor input1 = torch::rand( |
2408 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2409 | torch::Tensor input2 = torch::rand( |
2410 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2411 | torch::Tensor target = torch::rand( |
2412 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2413 | for (torch::Reduction::Reduction reduction : |
2414 | {torch::Reduction::Mean, torch::Reduction::Sum}) { |
2415 | for (double margin : {0., 0.2}) { |
2416 | ForEachDevice([&](const torch::Device& device) { |
2417 | torch::Tensor output = torch::margin_ranking_loss( |
2418 | input1, input2, target, margin, reduction); |
2419 | torch::Tensor lazy_input1 = CopyToDevice(input1, device); |
2420 | torch::Tensor lazy_input2 = CopyToDevice(input2, device); |
2421 | torch::Tensor lazy_target = CopyToDevice(target, device); |
2422 | torch::Tensor lazy_output = torch::margin_ranking_loss( |
2423 | lazy_input1, lazy_input2, lazy_target, margin, reduction); |
2424 | AllClose(output, lazy_output); |
2425 | }); |
2426 | } |
2427 | } |
2428 | } |
2429 | |
2430 | TEST_F(LazyOpsTest, TestBCEWithLogits) { |
2431 | int batch = 10; |
2432 | int classes = 5; |
2433 | torch::Tensor input = torch::rand( |
2434 | {batch, classes}, |
2435 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2436 | torch::Tensor target = torch::rand( |
2437 | {batch, classes}, |
2438 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2439 | torch::Tensor weight = torch::rand( |
2440 | {classes}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2441 | torch::Tensor pos_weight = torch::rand( |
2442 | {classes}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2443 | torch::Tensor undef; |
2444 | for (torch::Reduction::Reduction reduction : |
2445 | {torch::Reduction::Mean, torch::Reduction::Sum}) { |
2446 | for (bool undef_weight : {false, true}) { |
2447 | for (bool undef_pos_weight : {false, true}) { |
2448 | ForEachDevice([&](const torch::Device& device) { |
2449 | torch::Tensor output = torch::binary_cross_entropy_with_logits( |
2450 | input, |
2451 | target, |
2452 | undef_weight ? undef : weight, |
2453 | undef_pos_weight ? undef : pos_weight, |
2454 | reduction); |
2455 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2456 | torch::Tensor lazy_target = CopyToDevice(target, device); |
2457 | torch::Tensor lazy_weight = |
2458 | undef_weight ? undef : CopyToDevice(weight, device); |
2459 | torch::Tensor lazy_pos_weight = |
2460 | undef_pos_weight ? undef : CopyToDevice(pos_weight, device); |
2461 | torch::Tensor lazy_output = torch::binary_cross_entropy_with_logits( |
2462 | lazy_input, lazy_target, lazy_weight, lazy_pos_weight, reduction); |
2463 | }); |
2464 | } |
2465 | } |
2466 | } |
2467 | } |
2468 | |
2469 | TEST_F(LazyOpsTest, TestKlDiv) { |
2470 | torch::Tensor input = torch::rand( |
2471 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2472 | torch::Tensor target = torch::rand( |
2473 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2474 | for (bool log_target : {true, false}) { |
2475 | for (torch::Reduction::Reduction reduction : |
2476 | {torch::Reduction::Mean, torch::Reduction::Sum}) { |
2477 | ForEachDevice([&](const torch::Device& device) { |
2478 | torch::Tensor output = |
2479 | torch::kl_div(input, target, reduction, log_target); |
2480 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2481 | torch::Tensor lazy_target = CopyToDevice(target, device); |
2482 | torch::Tensor lazy_output = |
2483 | torch::kl_div(lazy_input, lazy_target, reduction, log_target); |
2484 | AllClose(output, lazy_output); |
2485 | }); |
2486 | } |
2487 | } |
2488 | } |
2489 | |
2490 | TEST_F(LazyOpsTest, TestProd) { |
2491 | torch::Tensor a = torch::rand( |
2492 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2493 | torch::Tensor b = torch::prod(a); |
2494 | ForEachDevice([&](const torch::Device& device) { |
2495 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2496 | torch::Tensor lazy_b = torch::prod(lazy_a); |
2497 | AllClose(b, lazy_b); |
2498 | }); |
2499 | } |
2500 | |
2501 | TEST_F(LazyOpsTest, TestProdCast) { |
2502 | torch::Tensor a = torch::rand( |
2503 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2504 | torch::Tensor b = torch::prod(a, torch::kDouble); |
2505 | ForEachDevice([&](const torch::Device& device) { |
2506 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2507 | torch::Tensor lazy_b = torch::prod(lazy_a, torch::kDouble); |
2508 | AllClose(b, lazy_b); |
2509 | }); |
2510 | } |
2511 | |
2512 | TEST_F(LazyOpsTest, TestProdInDim) { |
2513 | torch::Tensor a = torch::rand( |
2514 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2515 | int rank = a.dim(); |
2516 | for (int dim = -rank; dim < rank; ++dim) { |
2517 | torch::Tensor b = torch::prod(a, dim); |
2518 | ForEachDevice([&](const torch::Device& device) { |
2519 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2520 | torch::Tensor lazy_b = torch::prod(lazy_a, dim); |
2521 | AllClose(b, lazy_b); |
2522 | }); |
2523 | } |
2524 | } |
2525 | |
2526 | TEST_F(LazyOpsTest, TestProdInDimKeepCast) { |
2527 | torch::Tensor a = torch::rand( |
2528 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2529 | int rank = a.dim(); |
2530 | for (int dim = -rank; dim < rank; ++dim) { |
2531 | torch::Tensor b = torch::prod(a, dim, /*keepdim=*/true, torch::kDouble); |
2532 | ForEachDevice([&](const torch::Device& device) { |
2533 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2534 | torch::Tensor lazy_b = |
2535 | torch::prod(lazy_a, dim, /*keepdim=*/true, torch::kDouble); |
2536 | AllClose(b, lazy_b); |
2537 | }); |
2538 | } |
2539 | } |
2540 | |
2541 | TEST_F(LazyOpsTest, TestProdInDimKeep) { |
2542 | torch::Tensor a = torch::rand( |
2543 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2544 | int rank = a.dim(); |
2545 | for (int dim = -rank; dim < rank; ++dim) { |
2546 | torch::Tensor b = torch::prod(a, dim, /*keepdim=*/true); |
2547 | ForEachDevice([&](const torch::Device& device) { |
2548 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2549 | torch::Tensor lazy_b = torch::prod(lazy_a, dim, /*keepdim=*/true); |
2550 | AllClose(b, lazy_b); |
2551 | }); |
2552 | } |
2553 | } |
2554 | |
2555 | TEST_F(LazyOpsTest, TestCumSum) { |
2556 | torch::Tensor input = torch::rand( |
2557 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2558 | int rank = input.dim(); |
2559 | for (int dim = -rank; dim < rank; ++dim) { |
2560 | torch::Tensor result = torch::cumsum(input, dim); |
2561 | ForEachDevice([&](const torch::Device& device) { |
2562 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2563 | torch::Tensor lazy_result = torch::cumsum(lazy_input, dim); |
2564 | AllClose(result, lazy_result); |
2565 | }); |
2566 | } |
2567 | } |
2568 | |
2569 | TEST_F(LazyOpsTest, TestCumSumCast) { |
2570 | torch::Tensor input = torch::rand( |
2571 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2572 | int rank = input.dim(); |
2573 | for (int dim = -rank; dim < rank; ++dim) { |
2574 | torch::Tensor result = torch::cumsum(input, dim, torch::kDouble); |
2575 | ForEachDevice([&](const torch::Device& device) { |
2576 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2577 | torch::Tensor lazy_result = |
2578 | torch::cumsum(lazy_input, dim, torch::kDouble); |
2579 | AllClose(result, lazy_result); |
2580 | }); |
2581 | } |
2582 | } |
2583 | |
2584 | TEST_F(LazyOpsTest, TestCumSumLong) { |
2585 | torch::Tensor input = torch::randint( |
2586 | 1000, |
2587 | {4, 3, 4}, |
2588 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
2589 | int rank = input.dim(); |
2590 | for (int dim = -rank; dim < rank; ++dim) { |
2591 | torch::Tensor result = torch::cumsum(input, dim); |
2592 | ForEachDevice([&](const torch::Device& device) { |
2593 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2594 | torch::Tensor lazy_result = torch::cumsum(lazy_input, dim); |
2595 | AllEqual(result, lazy_result); |
2596 | }); |
2597 | } |
2598 | } |
2599 | |
2600 | TEST_F(LazyOpsTest, TestCumSumCastLong) { |
2601 | torch::Tensor input = torch::rand( |
2602 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2603 | int rank = input.dim(); |
2604 | for (int dim = -rank; dim < rank; ++dim) { |
2605 | torch::Tensor result = torch::cumsum(input, dim, torch::kLong); |
2606 | ForEachDevice([&](const torch::Device& device) { |
2607 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2608 | torch::Tensor lazy_result = torch::cumsum(lazy_input, dim, torch::kLong); |
2609 | AllEqual(result, lazy_result); |
2610 | }); |
2611 | } |
2612 | } |
2613 | |
2614 | TEST_F(LazyOpsTest, TestCumProd) { |
2615 | torch::Tensor input = torch::rand( |
2616 | {4, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2617 | int rank = input.dim(); |
2618 | for (int dim = -rank; dim < rank; ++dim) { |
2619 | torch::Tensor result = torch::cumprod(input, dim); |
2620 | ForEachDevice([&](const torch::Device& device) { |
2621 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2622 | torch::Tensor lazy_result = torch::cumprod(lazy_input, dim); |
2623 | AllClose(result, lazy_result); |
2624 | }); |
2625 | } |
2626 | } |
2627 | |
2628 | TEST_F(LazyOpsTest, TestCumProdCast) { |
2629 | torch::Tensor input = torch::mul( |
2630 | torch::rand( |
2631 | {4, 3, 4}, |
2632 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())), |
2633 | 10); |
2634 | int rank = input.dim(); |
2635 | for (int dim = -rank; dim < rank; ++dim) { |
2636 | torch::Tensor result = torch::cumprod(input, dim, torch::kDouble); |
2637 | ForEachDevice([&](const torch::Device& device) { |
2638 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2639 | torch::Tensor lazy_result = |
2640 | torch::cumprod(lazy_input, dim, torch::kDouble); |
2641 | AllClose(result, lazy_result); |
2642 | }); |
2643 | } |
2644 | } |
2645 | |
2646 | TEST_F(LazyOpsTest, TestCumProdLong) { |
2647 | torch::Tensor input = torch::randint( |
2648 | 7, {2, 3}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
2649 | int rank = input.dim(); |
2650 | for (int dim = -rank; dim < rank; ++dim) { |
2651 | torch::Tensor result = torch::cumsum(input, dim); |
2652 | ForEachDevice([&](const torch::Device& device) { |
2653 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2654 | torch::Tensor lazy_result = torch::cumsum(lazy_input, dim); |
2655 | AllEqual(result, lazy_result); |
2656 | }); |
2657 | } |
2658 | } |
2659 | |
2660 | TEST_F(LazyOpsTest, TestCumProdCastLong) { |
2661 | torch::Tensor input = |
2662 | torch::rand( |
2663 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
2664 | 7; |
2665 | int rank = input.dim(); |
2666 | for (int dim = -rank; dim < rank; ++dim) { |
2667 | torch::Tensor result = torch::cumsum(input, dim, torch::kLong); |
2668 | ForEachDevice([&](const torch::Device& device) { |
2669 | torch::Tensor lazy_input = CopyToDevice(input, device); |
2670 | torch::Tensor lazy_result = torch::cumsum(lazy_input, dim, torch::kLong); |
2671 | AllEqual(result, lazy_result); |
2672 | }); |
2673 | } |
2674 | } |
2675 | |
2676 | TEST_F(LazyOpsTest, TestArgMin) { |
2677 | torch::Tensor a = torch::rand( |
2678 | {4, 4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2679 | torch::Tensor b = torch::argmin(a, c10::nullopt, /*keepdim=*/false); |
2680 | ForEachDevice([&](const torch::Device& device) { |
2681 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2682 | torch::Tensor lazy_b = |
2683 | torch::argmin(lazy_a, c10::nullopt, /*keepdim=*/false); |
2684 | AllEqual(b, lazy_b); |
2685 | }); |
2686 | } |
2687 | |
2688 | TEST_F(LazyOpsTest, TestArgMinDim) { |
2689 | torch::Tensor a = torch::rand( |
2690 | {4, 4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2691 | for (int dim : {1, -2}) { |
2692 | torch::Tensor b = torch::argmin(a, dim, /*keepdim=*/false); |
2693 | ForEachDevice([&](const torch::Device& device) { |
2694 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2695 | torch::Tensor lazy_b = torch::argmin(lazy_a, dim, /*keepdim=*/false); |
2696 | AllEqual(b, lazy_b); |
2697 | }); |
2698 | } |
2699 | } |
2700 | |
2701 | TEST_F(LazyOpsTest, TestArgMinDimKeep) { |
2702 | torch::Tensor a = torch::rand( |
2703 | {4, 4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2704 | for (int dim : {1, -2}) { |
2705 | torch::Tensor b = torch::argmin(a, dim, /*keepdim=*/true); |
2706 | ForEachDevice([&](const torch::Device& device) { |
2707 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2708 | torch::Tensor lazy_b = torch::argmin(lazy_a, dim, /*keepdim=*/true); |
2709 | AllEqual(b, lazy_b); |
2710 | }); |
2711 | } |
2712 | } |
2713 | |
2714 | TEST_F(LazyOpsTest, TestArgMinSameValue) { |
2715 | torch::Tensor a = torch::ones( |
2716 | {4, 4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2717 | torch::Tensor b = torch::argmin(a); |
2718 | ForEachDevice([&](const torch::Device& device) { |
2719 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2720 | torch::Tensor lazy_b = torch::argmin(lazy_a); |
2721 | AllEqual(b, lazy_b); |
2722 | }); |
2723 | } |
2724 | |
2725 | TEST_F(LazyOpsTest, TestArgMinWrapper) { |
2726 | torch::Tensor a = torch::rand( |
2727 | {4, 4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2728 | for (int dim : {1, -2}) { |
2729 | torch::Tensor b = torch::argmin(a, dim, /*keepdim=*/false); |
2730 | ForEachDevice([&](const torch::Device& device) { |
2731 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2732 | torch::Tensor lazy_b = torch::argmin(lazy_a, dim, /*keepdim=*/false); |
2733 | AllEqual(b, lazy_b); |
2734 | }); |
2735 | } |
2736 | } |
2737 | |
2738 | TEST_F(LazyOpsTest, TestArgMax) { |
2739 | torch::Tensor a = torch::rand( |
2740 | {4, 4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2741 | torch::Tensor b = torch::argmax(a, c10::nullopt, /*keepdim=*/false); |
2742 | ForEachDevice([&](const torch::Device& device) { |
2743 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2744 | torch::Tensor lazy_b = |
2745 | torch::argmax(lazy_a, c10::nullopt, /*keepdim=*/false); |
2746 | AllEqual(b, lazy_b); |
2747 | }); |
2748 | } |
2749 | |
2750 | TEST_F(LazyOpsTest, TestArgMaxDim) { |
2751 | torch::Tensor a = torch::rand( |
2752 | {4, 4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2753 | for (int dim : {1, -2}) { |
2754 | torch::Tensor b = torch::argmax(a, dim, /*keepdim=*/false); |
2755 | ForEachDevice([&](const torch::Device& device) { |
2756 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2757 | torch::Tensor lazy_b = torch::argmax(lazy_a, dim, /*keepdim=*/false); |
2758 | AllEqual(b, lazy_b); |
2759 | }); |
2760 | } |
2761 | } |
2762 | |
2763 | TEST_F(LazyOpsTest, TestArgMaxDimKeep) { |
2764 | torch::Tensor a = torch::rand( |
2765 | {4, 4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2766 | for (int dim : {1, -2}) { |
2767 | torch::Tensor b = torch::argmax(a, dim, /*keepdim=*/true); |
2768 | ForEachDevice([&](const torch::Device& device) { |
2769 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2770 | torch::Tensor lazy_b = torch::argmax(lazy_a, dim, /*keepdim=*/true); |
2771 | AllEqual(b, lazy_b); |
2772 | }); |
2773 | } |
2774 | } |
2775 | |
2776 | TEST_F(LazyOpsTest, TestArgMaxSameValue) { |
2777 | torch::Tensor a = torch::ones( |
2778 | {4, 4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2779 | torch::Tensor b = torch::argmax(a, c10::nullopt, /*keepdim=*/false); |
2780 | ForEachDevice([&](const torch::Device& device) { |
2781 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2782 | torch::Tensor lazy_b = |
2783 | torch::argmax(lazy_a, c10::nullopt, /*keepdim=*/false); |
2784 | AllEqual(b, lazy_b); |
2785 | }); |
2786 | } |
2787 | |
2788 | TEST_F(LazyOpsTest, TestArgMaxWrapper) { |
2789 | torch::Tensor a = torch::rand( |
2790 | {4, 4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2791 | for (int dim : {1, -2}) { |
2792 | torch::Tensor b = torch::argmax(a, dim, /*keepdim=*/false); |
2793 | ForEachDevice([&](const torch::Device& device) { |
2794 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2795 | torch::Tensor lazy_b = torch::argmax(lazy_a, dim, /*keepdim=*/false); |
2796 | AllEqual(b, lazy_b); |
2797 | }); |
2798 | } |
2799 | } |
2800 | |
2801 | TEST_F(LazyOpsTest, TestAsin) { |
2802 | torch::Tensor a = torch::rand( |
2803 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2804 | torch::Tensor b = torch::asin(a); |
2805 | ForEachDevice([&](const torch::Device& device) { |
2806 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2807 | torch::Tensor lazy_b = torch::asin(lazy_a); |
2808 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2809 | }); |
2810 | } |
2811 | |
2812 | TEST_F(LazyOpsTest, TestAsinh) { |
2813 | torch::Tensor a = torch::rand( |
2814 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2815 | torch::Tensor b = torch::asinh(a); |
2816 | ForEachDevice([&](const torch::Device& device) { |
2817 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2818 | torch::Tensor lazy_b = torch::asinh(lazy_a); |
2819 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2820 | }); |
2821 | } |
2822 | |
2823 | TEST_F(LazyOpsTest, TestAsinhInPlace) { |
2824 | torch::Tensor a = torch::rand( |
2825 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2826 | ForEachDevice([&](const torch::Device& device) { |
2827 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2828 | torch::Tensor b = torch::asinh_(a); |
2829 | torch::Tensor lazy_b = torch::asinh_(lazy_a); |
2830 | AllClose(a, lazy_a, /*rtol=*/1e-3, /*atol=*/1e-5); |
2831 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2832 | }); |
2833 | } |
2834 | |
2835 | TEST_F(LazyOpsTest, TestSin) { |
2836 | torch::Tensor a = torch::rand( |
2837 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2838 | torch::Tensor b = torch::sin(a); |
2839 | ForEachDevice([&](const torch::Device& device) { |
2840 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2841 | torch::Tensor lazy_b = torch::sin(lazy_a); |
2842 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2843 | }); |
2844 | } |
2845 | |
2846 | TEST_F(LazyOpsTest, TestSinh) { |
2847 | torch::Tensor a = torch::rand( |
2848 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2849 | torch::Tensor b = torch::sinh(a); |
2850 | ForEachDevice([&](const torch::Device& device) { |
2851 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2852 | torch::Tensor lazy_b = torch::sinh(lazy_a); |
2853 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2854 | }); |
2855 | } |
2856 | |
2857 | TEST_F(LazyOpsTest, TestAcos) { |
2858 | torch::Tensor a = torch::rand( |
2859 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2860 | torch::Tensor b = torch::acos(a); |
2861 | ForEachDevice([&](const torch::Device& device) { |
2862 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2863 | torch::Tensor lazy_b = torch::acos(lazy_a); |
2864 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2865 | }); |
2866 | } |
2867 | |
2868 | TEST_F(LazyOpsTest, TestAcosh) { |
2869 | torch::Tensor a = |
2870 | torch::rand( |
2871 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
2872 | 100; |
2873 | torch::Tensor b = torch::acosh(a); |
2874 | ForEachDevice([&](const torch::Device& device) { |
2875 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2876 | torch::Tensor lazy_b = torch::acosh(lazy_a); |
2877 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2878 | }); |
2879 | } |
2880 | |
2881 | TEST_F(LazyOpsTest, TestAcoshInPlace) { |
2882 | torch::Tensor a = |
2883 | torch::rand( |
2884 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
2885 | 100; |
2886 | ForEachDevice([&](const torch::Device& device) { |
2887 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2888 | torch::Tensor b = torch::acosh_(a); |
2889 | torch::Tensor lazy_b = torch::acosh_(lazy_a); |
2890 | AllClose(a, lazy_a, /*rtol=*/1e-3, /*atol=*/1e-5); |
2891 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2892 | }); |
2893 | } |
2894 | |
2895 | TEST_F(LazyOpsTest, TestCos) { |
2896 | torch::Tensor a = torch::rand( |
2897 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2898 | torch::Tensor b = torch::cos(a); |
2899 | ForEachDevice([&](const torch::Device& device) { |
2900 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2901 | torch::Tensor lazy_b = torch::cos(lazy_a); |
2902 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2903 | }); |
2904 | } |
2905 | |
2906 | TEST_F(LazyOpsTest, TestCosh) { |
2907 | torch::Tensor a = torch::rand( |
2908 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2909 | torch::Tensor b = torch::cosh(a); |
2910 | ForEachDevice([&](const torch::Device& device) { |
2911 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2912 | torch::Tensor lazy_b = torch::cosh(lazy_a); |
2913 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2914 | }); |
2915 | } |
2916 | |
2917 | TEST_F(LazyOpsTest, TestAtan) { |
2918 | torch::Tensor a = torch::rand( |
2919 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2920 | torch::Tensor b = torch::atan(a); |
2921 | ForEachDevice([&](const torch::Device& device) { |
2922 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2923 | torch::Tensor lazy_b = torch::atan(lazy_a); |
2924 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2925 | }); |
2926 | } |
2927 | |
2928 | TEST_F(LazyOpsTest, TestAtanh) { |
2929 | torch::Tensor a = torch::rand( |
2930 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2931 | torch::Tensor b = torch::atanh(a); |
2932 | ForEachDevice([&](const torch::Device& device) { |
2933 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2934 | torch::Tensor lazy_b = torch::atanh(lazy_a); |
2935 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2936 | }); |
2937 | } |
2938 | |
2939 | TEST_F(LazyOpsTest, TestAtanhInPlace) { |
2940 | torch::Tensor a = torch::rand( |
2941 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2942 | ForEachDevice([&](const torch::Device& device) { |
2943 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2944 | torch::Tensor b = torch::atanh_(a); |
2945 | torch::Tensor lazy_b = torch::atanh_(lazy_a); |
2946 | AllClose(a, lazy_a, /*rtol=*/1e-3, /*atol=*/1e-5); |
2947 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2948 | }); |
2949 | } |
2950 | |
2951 | TEST_F(LazyOpsTest, TestAtan2) { |
2952 | torch::Tensor a = torch::randn( |
2953 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2954 | torch::Tensor b = torch::randn( |
2955 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2956 | torch::Tensor c = torch::atan2(a, b); |
2957 | ForEachDevice([&](const torch::Device& device) { |
2958 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2959 | torch::Tensor lazy_b = CopyToDevice(b, device); |
2960 | torch::Tensor lazy_c = torch::atan2(lazy_a, lazy_b); |
2961 | AllClose(c, lazy_c, /*rtol=*/1e-3, /*atol=*/1e-5); |
2962 | }); |
2963 | } |
2964 | |
2965 | TEST_F(LazyOpsTest, TestTan) { |
2966 | torch::Tensor a = torch::rand( |
2967 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2968 | torch::Tensor b = torch::tan(a); |
2969 | ForEachDevice([&](const torch::Device& device) { |
2970 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2971 | torch::Tensor lazy_b = torch::tan(lazy_a); |
2972 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2973 | }); |
2974 | } |
2975 | |
2976 | TEST_F(LazyOpsTest, TestTanh) { |
2977 | torch::Tensor a = torch::rand( |
2978 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2979 | torch::Tensor b = torch::tanh(a); |
2980 | ForEachDevice([&](const torch::Device& device) { |
2981 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2982 | torch::Tensor lazy_b = torch::tanh(lazy_a); |
2983 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
2984 | }); |
2985 | } |
2986 | |
2987 | TEST_F(LazyOpsTest, TestClampMinMax) { |
2988 | torch::Tensor a = torch::rand( |
2989 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
2990 | torch::Scalar min_val(0.311); |
2991 | torch::Scalar max_val(0.409); |
2992 | torch::Tensor b = torch::clamp(a, min_val, max_val); |
2993 | ForEachDevice([&](const torch::Device& device) { |
2994 | torch::Tensor lazy_a = CopyToDevice(a, device); |
2995 | torch::Tensor lazy_b = torch::clamp(lazy_a, min_val, max_val); |
2996 | AllClose(b, lazy_b); |
2997 | }); |
2998 | } |
2999 | |
3000 | TEST_F(LazyOpsTest, TestClampMin) { |
3001 | torch::Tensor a = torch::rand( |
3002 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3003 | torch::Scalar min_val(0.311); |
3004 | torch::Tensor b = torch::clamp(a, min_val, c10::nullopt); |
3005 | ForEachDevice([&](const torch::Device& device) { |
3006 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3007 | torch::Tensor lazy_b = torch::clamp(lazy_a, min_val, c10::nullopt); |
3008 | AllClose(b, lazy_b); |
3009 | }); |
3010 | } |
3011 | |
3012 | TEST_F(LazyOpsTest, TestClampMax) { |
3013 | torch::Tensor a = torch::rand( |
3014 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3015 | torch::Scalar max_val(0.409); |
3016 | torch::Tensor b = torch::clamp(a, c10::nullopt, max_val); |
3017 | ForEachDevice([&](const torch::Device& device) { |
3018 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3019 | torch::Tensor lazy_b = torch::clamp(lazy_a, c10::nullopt, max_val); |
3020 | AllClose(b, lazy_b); |
3021 | }); |
3022 | } |
3023 | |
3024 | TEST_F(LazyOpsTest, TestClampMinExplicit) { |
3025 | torch::Tensor a = torch::rand( |
3026 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3027 | torch::Scalar min_val(0.311); |
3028 | torch::Tensor b = torch::clamp_min(a, min_val); |
3029 | ForEachDevice([&](const torch::Device& device) { |
3030 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3031 | torch::Tensor lazy_b = torch::clamp_min(lazy_a, min_val); |
3032 | AllClose(b, lazy_b); |
3033 | }); |
3034 | } |
3035 | |
3036 | TEST_F(LazyOpsTest, TestClampMaxExplicit) { |
3037 | torch::Tensor a = torch::rand( |
3038 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3039 | torch::Scalar max_val(0.409); |
3040 | torch::Tensor b = torch::clamp_max(a, max_val); |
3041 | ForEachDevice([&](const torch::Device& device) { |
3042 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3043 | torch::Tensor lazy_b = torch::clamp_max(lazy_a, max_val); |
3044 | AllClose(b, lazy_b); |
3045 | }); |
3046 | } |
3047 | |
3048 | TEST_F(LazyOpsTest, TestClampMinExplicitInPlace) { |
3049 | torch::Tensor a = torch::rand( |
3050 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3051 | torch::Scalar min_val(0.311); |
3052 | ForEachDevice([&](const torch::Device& device) { |
3053 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3054 | torch::Tensor b = torch::clamp_min_(a, min_val); |
3055 | torch::Tensor lazy_b = torch::clamp_min_(lazy_a, min_val); |
3056 | AllClose(a, lazy_a); |
3057 | AllClose(b, lazy_b); |
3058 | }); |
3059 | } |
3060 | |
3061 | TEST_F(LazyOpsTest, TestClampMaxExplicitInPlace) { |
3062 | torch::Tensor a = torch::rand( |
3063 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3064 | torch::Scalar max_val(0.409); |
3065 | ForEachDevice([&](const torch::Device& device) { |
3066 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3067 | torch::Tensor b = torch::clamp_max_(a, max_val); |
3068 | torch::Tensor lazy_b = torch::clamp_max_(lazy_a, max_val); |
3069 | AllClose(a, lazy_a); |
3070 | AllClose(b, lazy_b); |
3071 | }); |
3072 | } |
3073 | |
3074 | TEST_F(LazyOpsTest, TestCeil) { |
3075 | torch::Tensor a = |
3076 | torch::randn( |
3077 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
3078 | 100.0; |
3079 | torch::Tensor b = torch::ceil(a); |
3080 | ForEachDevice([&](const torch::Device& device) { |
3081 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3082 | torch::Tensor lazy_b = torch::ceil(lazy_a); |
3083 | AllClose(b, lazy_b); |
3084 | }); |
3085 | } |
3086 | |
3087 | TEST_F(LazyOpsTest, TestFloor) { |
3088 | torch::Tensor a = |
3089 | torch::randn( |
3090 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
3091 | 100.0; |
3092 | torch::Tensor b = torch::floor(a); |
3093 | ForEachDevice([&](const torch::Device& device) { |
3094 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3095 | torch::Tensor lazy_b = torch::floor(lazy_a); |
3096 | AllClose(b, lazy_b); |
3097 | }); |
3098 | } |
3099 | |
3100 | TEST_F(LazyOpsTest, TestRound) { |
3101 | torch::Tensor a = torch::cat( |
3102 | {torch::randn( |
3103 | {8}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
3104 | 100.0, |
3105 | // Special case: 0.5, -0.5. lazy::Round impl rounds to -1/1 whereas |
3106 | // lazy::RoundToEven properly implements bankers rounding. |
3107 | torch::tensor( |
3108 | {-0.5, 0.5}, |
3109 | torch::TensorOptions(torch::kFloat).device(DefaultDevice()))}, |
3110 | 0); |
3111 | torch::Tensor b = torch::round(a); |
3112 | ForEachDevice([&](const torch::Device& device) { |
3113 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3114 | torch::Tensor lazy_b = torch::round(lazy_a); |
3115 | AllClose(b, lazy_b); |
3116 | }); |
3117 | } |
3118 | |
3119 | TEST_F(LazyOpsTest, TestTrunc) { |
3120 | torch::Tensor a = |
3121 | torch::randn( |
3122 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
3123 | 100.0; |
3124 | torch::Tensor b = torch::trunc(a); |
3125 | ForEachDevice([&](const torch::Device& device) { |
3126 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3127 | torch::Tensor lazy_b = torch::trunc(lazy_a); |
3128 | AllClose(b, lazy_b); |
3129 | }); |
3130 | } |
3131 | |
3132 | TEST_F(LazyOpsTest, TestFrac) { |
3133 | torch::Tensor a = |
3134 | torch::randn( |
3135 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
3136 | 100.0; |
3137 | torch::Tensor b = torch::frac(a); |
3138 | ForEachDevice([&](const torch::Device& device) { |
3139 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3140 | torch::Tensor lazy_b = torch::frac(lazy_a); |
3141 | AllClose(b, lazy_b); |
3142 | }); |
3143 | } |
3144 | |
3145 | TEST_F(LazyOpsTest, TestNeg) { |
3146 | torch::Tensor a = torch::rand( |
3147 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3148 | torch::Tensor b = torch::neg(a); |
3149 | ForEachDevice([&](const torch::Device& device) { |
3150 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3151 | torch::Tensor lazy_b = torch::neg(lazy_a); |
3152 | AllClose(b, lazy_b); |
3153 | }); |
3154 | } |
3155 | |
3156 | TEST_F(LazyOpsTest, TestBitwiseNot) { |
3157 | std::vector<torch::ScalarType> types( |
3158 | {torch::kByte, torch::kChar, torch::kShort, torch::kInt, torch::kLong}); |
3159 | |
3160 | ForEachDevice([&](const torch::Device& device) { |
3161 | for (auto type : types) { |
3162 | torch::Tensor a = |
3163 | torch::randint(0, 63, {2, 2}, torch::TensorOptions(type)); |
3164 | torch::Tensor b = torch::bitwise_not(a); |
3165 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3166 | torch::Tensor lazy_b = torch::bitwise_not(lazy_a); |
3167 | AllEqual(b, lazy_b); |
3168 | } |
3169 | }); |
3170 | } |
3171 | |
3172 | TEST_F(LazyOpsTest, TestBitwiseNotInPlace) { |
3173 | std::vector<torch::ScalarType> types( |
3174 | {torch::kByte, torch::kChar, torch::kShort, torch::kInt, torch::kLong}); |
3175 | |
3176 | ForEachDevice([&](const torch::Device& device) { |
3177 | for (auto type : types) { |
3178 | torch::Tensor a = |
3179 | torch::randint(0, 63, {2, 2}, torch::TensorOptions(type)); |
3180 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3181 | a.bitwise_not_(); |
3182 | lazy_a.bitwise_not_(); |
3183 | AllEqual(a, lazy_a); |
3184 | } |
3185 | }); |
3186 | } |
3187 | |
3188 | TEST_F(LazyOpsTest, TestSign) { |
3189 | torch::Tensor a = |
3190 | torch::randn( |
3191 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
3192 | 100.0; |
3193 | torch::Tensor b = torch::sign(a); |
3194 | ForEachDevice([&](const torch::Device& device) { |
3195 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3196 | torch::Tensor lazy_b = torch::sign(lazy_a); |
3197 | AllClose(b, lazy_b); |
3198 | }); |
3199 | } |
3200 | |
3201 | TEST_F(LazyOpsTest, TestSignByte) { |
3202 | torch::Tensor a = torch::randint( |
3203 | 256, {2, 2}, torch::TensorOptions(torch::kByte).device(DefaultDevice())); |
3204 | torch::Tensor b = torch::sign(a); |
3205 | ForEachDevice([&](const torch::Device& device) { |
3206 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3207 | torch::Tensor lazy_b = torch::sign(lazy_a); |
3208 | AllEqual(b, lazy_b); |
3209 | }); |
3210 | } |
3211 | |
3212 | TEST_F(LazyOpsTest, TestAbs) { |
3213 | torch::Tensor a = torch::randn( |
3214 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3215 | torch::Tensor b = torch::abs(a); |
3216 | ForEachDevice([&](const torch::Device& device) { |
3217 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3218 | torch::Tensor lazy_b = torch::abs(lazy_a); |
3219 | AllClose(b, lazy_b); |
3220 | }); |
3221 | } |
3222 | |
3223 | TEST_F(LazyOpsTest, TestAbsByte) { |
3224 | torch::Tensor a = torch::randint( |
3225 | 256, {2, 2}, torch::TensorOptions(torch::kByte).device(DefaultDevice())); |
3226 | torch::Tensor b = torch::abs(a); |
3227 | ForEachDevice([&](const torch::Device& device) { |
3228 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3229 | torch::Tensor lazy_b = torch::abs(lazy_a); |
3230 | AllEqual(b, lazy_b); |
3231 | }); |
3232 | } |
3233 | |
3234 | TEST_F(LazyOpsTest, TestEmptyLike) { |
3235 | torch::Tensor a = torch::rand( |
3236 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3237 | torch::Tensor b = torch::empty_like(a); |
3238 | ForEachDevice([&](const torch::Device& device) { |
3239 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3240 | torch::Tensor lazy_b = torch::empty_like(lazy_a); |
3241 | EXPECT_EQ(b.sizes(), lazy_b.sizes()); |
3242 | }); |
3243 | } |
3244 | |
3245 | TEST_F(LazyOpsTest, TestEmptyLikeOptions) { |
3246 | torch::Tensor a = torch::rand( |
3247 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3248 | torch::Tensor b = torch::empty_like( |
3249 | a, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3250 | ForEachDevice([&](const torch::Device& device) { |
3251 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3252 | torch::Tensor lazy_b = torch::empty_like( |
3253 | lazy_a, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3254 | EXPECT_EQ(b.sizes(), lazy_b.sizes()); |
3255 | }); |
3256 | } |
3257 | |
3258 | TEST_F(LazyOpsTest, TestEmpty) { |
3259 | torch::Tensor a = torch::zeros( |
3260 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3261 | ForEachDevice([&](const torch::Device& device) { |
3262 | torch::Tensor lazy_a = torch::empty( |
3263 | {2, 2}, torch::TensorOptions(torch::kFloat).device(device)); |
3264 | EXPECT_EQ(a.sizes(), lazy_a.sizes()); |
3265 | }); |
3266 | } |
3267 | |
3268 | TEST_F(LazyOpsTest, TestZeroInPlace) { |
3269 | torch::Tensor input = torch::ones( |
3270 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3271 | |
3272 | ForEachDevice([&](const torch::Device& device) { |
3273 | torch::Tensor lazyInput = CopyToDevice(input, device); |
3274 | auto& output = torch::zero_(input); |
3275 | auto& lazyOutput = torch::zero_(lazyInput); |
3276 | AllClose(output, lazyOutput); |
3277 | }); |
3278 | } |
3279 | |
3280 | TEST_F(LazyOpsTest, TestZerosLike) { |
3281 | torch::Tensor a = torch::rand( |
3282 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3283 | torch::Tensor b = torch::zeros_like(a); |
3284 | ForEachDevice([&](const torch::Device& device) { |
3285 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3286 | torch::Tensor lazy_b = torch::zeros_like(lazy_a); |
3287 | AllClose(a, lazy_a); |
3288 | }); |
3289 | } |
3290 | |
3291 | TEST_F(LazyOpsTest, TestZerosLikeOptions) { |
3292 | torch::Tensor a = torch::rand( |
3293 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3294 | torch::Tensor b = torch::zeros_like( |
3295 | a, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3296 | ForEachDevice([&](const torch::Device& device) { |
3297 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3298 | torch::Tensor lazy_b = torch::zeros_like( |
3299 | lazy_a, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3300 | AllClose(a, lazy_a); |
3301 | }); |
3302 | } |
3303 | |
3304 | TEST_F(LazyOpsTest, TestZeros) { |
3305 | torch::Tensor a = torch::zeros( |
3306 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3307 | ForEachDevice([&](const torch::Device& device) { |
3308 | torch::Tensor lazy_a = torch::zeros( |
3309 | {2, 2}, torch::TensorOptions(torch::kFloat).device(device)); |
3310 | AllClose(a, lazy_a); |
3311 | }); |
3312 | } |
3313 | |
3314 | TEST_F(LazyOpsTest, TestOnes) { |
3315 | torch::Tensor a = torch::ones( |
3316 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3317 | ForEachDevice([&](const torch::Device& device) { |
3318 | torch::Tensor lazy_a = |
3319 | torch::ones({2, 2}, torch::TensorOptions(torch::kFloat).device(device)); |
3320 | AllClose(a, lazy_a); |
3321 | }); |
3322 | } |
3323 | |
3324 | TEST_F(LazyOpsTest, TestOnesLike) { |
3325 | torch::Tensor a = torch::rand( |
3326 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3327 | torch::Tensor b = torch::ones_like(a); |
3328 | ForEachDevice([&](const torch::Device& device) { |
3329 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3330 | torch::Tensor lazy_b = torch::ones_like(lazy_a); |
3331 | AllClose(a, lazy_a); |
3332 | }); |
3333 | } |
3334 | |
3335 | TEST_F(LazyOpsTest, TestOnesLikeOptions) { |
3336 | torch::Tensor a = torch::rand( |
3337 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3338 | torch::Tensor b = torch::ones_like( |
3339 | a, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3340 | ForEachDevice([&](const torch::Device& device) { |
3341 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3342 | torch::Tensor lazy_b = torch::ones_like( |
3343 | lazy_a, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3344 | AllClose(a, lazy_a); |
3345 | }); |
3346 | } |
3347 | |
3348 | TEST_F(LazyOpsTest, TestFull) { |
3349 | torch::Tensor a = torch::full( |
3350 | {2, 2}, |
3351 | 3.1165, |
3352 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3353 | ForEachDevice([&](const torch::Device& device) { |
3354 | torch::Tensor lazy_a = torch::full( |
3355 | {2, 2}, 3.1165, torch::TensorOptions(torch::kFloat).device(device)); |
3356 | AllClose(a, lazy_a); |
3357 | }); |
3358 | } |
3359 | |
3360 | TEST_F(LazyOpsTest, TestFullLike) { |
3361 | torch::Tensor a = torch::rand( |
3362 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3363 | torch::Tensor b = torch::full_like(a, 3.1165); |
3364 | ForEachDevice([&](const torch::Device& device) { |
3365 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3366 | torch::Tensor lazy_b = torch::full_like(lazy_a, 3.1165); |
3367 | AllClose(a, lazy_a); |
3368 | }); |
3369 | } |
3370 | |
3371 | TEST_F(LazyOpsTest, TestFullLikeOptions) { |
3372 | torch::Tensor a = torch::rand( |
3373 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3374 | torch::Tensor b = torch::full_like( |
3375 | a, 3.1165, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3376 | ForEachDevice([&](const torch::Device& device) { |
3377 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3378 | torch::Tensor lazy_b = torch::full_like( |
3379 | lazy_a, |
3380 | 3.1165, |
3381 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3382 | AllClose(a, lazy_a); |
3383 | }); |
3384 | } |
3385 | |
3386 | TEST_F(LazyOpsTest, TestARange) { |
3387 | for (auto& ranges : std::vector<std::vector<float>>{ |
3388 | {0.0, 100.0, 0.5}, {0.0, -100.0, -0.5}}) { |
3389 | torch::Tensor a = torch::arange( |
3390 | ranges[0], |
3391 | ranges[1], |
3392 | ranges[2], |
3393 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3394 | ForEachDevice([&](const torch::Device& device) { |
3395 | torch::Tensor lazy_a = torch::arange( |
3396 | ranges[0], |
3397 | ranges[1], |
3398 | ranges[2], |
3399 | torch::TensorOptions(torch::kFloat).device(device)); |
3400 | AllClose(a, lazy_a); |
3401 | }); |
3402 | } |
3403 | } |
3404 | |
3405 | TEST_F(LazyOpsTest, TestARangeOut) { |
3406 | torch::Tensor a = torch::randn( |
3407 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3408 | for (auto& ranges : std::vector<std::vector<float>>{ |
3409 | {0.0, 100.0, 0.5}, {0.0, -100.0, -0.5}}) { |
3410 | torch::Tensor b = torch::arange_out(a, ranges[0], ranges[1], ranges[2]); |
3411 | ForEachDevice([&](const torch::Device& device) { |
3412 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3413 | torch::Tensor lazy_b = |
3414 | torch::arange_out(lazy_a, ranges[0], ranges[1], ranges[2]); |
3415 | AllClose(b, lazy_b); |
3416 | }); |
3417 | } |
3418 | } |
3419 | |
3420 | TEST_F(LazyOpsTest, TestDimARange) { |
3421 | torch::Tensor like = torch::rand( |
3422 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3423 | torch::Tensor a = torch::_dim_arange(like, 1); |
3424 | ForEachDevice([&](const torch::Device& device) { |
3425 | torch::Tensor lazy_like = CopyToDevice(like, device); |
3426 | torch::Tensor lazy_a = torch::_dim_arange(lazy_like, 1); |
3427 | AllClose(a, lazy_a); |
3428 | }); |
3429 | } |
3430 | |
3431 | TEST_F(LazyOpsTest, TestBartlettWindow) { |
3432 | int window_length = 10; |
3433 | for (bool periodic : {false, true}) { |
3434 | ForEachDevice([&](const torch::Device& device) { |
3435 | torch::Tensor output = torch::bartlett_window( |
3436 | window_length, |
3437 | periodic, |
3438 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3439 | |
3440 | torch::Tensor lazy_output = torch::bartlett_window( |
3441 | window_length, |
3442 | periodic, |
3443 | torch::TensorOptions(torch::kFloat).device(device)); |
3444 | AllClose(output, lazy_output, /*rtol=*/1e-5, /*atol=*/1e-7); |
3445 | }); |
3446 | } |
3447 | } |
3448 | |
3449 | TEST_F(LazyOpsTest, TestBlackmanWindow) { |
3450 | int window_length = 10; |
3451 | for (bool periodic : {false, true}) { |
3452 | ForEachDevice([&](const torch::Device& device) { |
3453 | torch::Tensor output = torch::blackman_window( |
3454 | window_length, |
3455 | periodic, |
3456 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3457 | torch::Tensor lazy_output = torch::blackman_window( |
3458 | window_length, |
3459 | periodic, |
3460 | torch::TensorOptions(torch::kFloat).device(device)); |
3461 | AllClose(output, lazy_output, /*rtol=*/1e-5, /*atol=*/1e-7); |
3462 | }); |
3463 | } |
3464 | } |
3465 | |
3466 | TEST_F(LazyOpsTest, TestHammingWindow) { |
3467 | double alpha = 0.54; |
3468 | double beta = 0.46; |
3469 | int window_length = 10; |
3470 | for (bool periodic : {false, true}) { |
3471 | ForEachDevice([&](const torch::Device& device) { |
3472 | torch::Tensor output = torch::hamming_window( |
3473 | window_length, |
3474 | periodic, |
3475 | alpha, |
3476 | beta, |
3477 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3478 | torch::Tensor lazy_output = torch::hamming_window( |
3479 | window_length, |
3480 | periodic, |
3481 | alpha, |
3482 | beta, |
3483 | torch::TensorOptions(torch::kFloat).device(device)); |
3484 | AllClose(output, lazy_output); |
3485 | }); |
3486 | } |
3487 | } |
3488 | |
3489 | TEST_F(LazyOpsTest, TestHannWindow) { |
3490 | int window_length = 10; |
3491 | for (bool periodic : {false, true}) { |
3492 | ForEachDevice([&](const torch::Device& device) { |
3493 | torch::Tensor output = torch::hann_window( |
3494 | window_length, |
3495 | periodic, |
3496 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3497 | torch::Tensor lazy_output = torch::hann_window( |
3498 | window_length, |
3499 | periodic, |
3500 | torch::TensorOptions(torch::kFloat).device(device)); |
3501 | AllClose(output, lazy_output); |
3502 | }); |
3503 | } |
3504 | } |
3505 | |
3506 | TEST_F(LazyOpsTest, TestLogSigmoid) { |
3507 | torch::Tensor a = torch::empty( |
3508 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3509 | a.uniform_(-1.0, 1.0); |
3510 | torch::Tensor b = torch::log_sigmoid(a); |
3511 | ForEachDevice([&](const torch::Device& device) { |
3512 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3513 | torch::Tensor lazy_b = torch::log_sigmoid(lazy_a); |
3514 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
3515 | }); |
3516 | } |
3517 | |
3518 | TEST_F(LazyOpsTest, TestLogSigmoidForward) { |
3519 | torch::Tensor a = torch::empty( |
3520 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3521 | a.uniform_(-1.0, 1.0); |
3522 | auto tuple = torch::log_sigmoid_forward(a); |
3523 | ForEachDevice([&](const torch::Device& device) { |
3524 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3525 | auto lazy_tuple = torch::log_sigmoid_forward(lazy_a); |
3526 | AllClose( |
3527 | std::get<0>(tuple), |
3528 | std::get<0>(lazy_tuple), |
3529 | /*rtol=*/1e-3, |
3530 | /*atol=*/1e-5); |
3531 | AllClose( |
3532 | std::get<1>(tuple), |
3533 | std::get<1>(lazy_tuple), |
3534 | /*rtol=*/1e-3, |
3535 | /*atol=*/1e-5); |
3536 | }); |
3537 | } |
3538 | |
3539 | TEST_F(LazyOpsTest, TestLogsumexp) { |
3540 | torch::Tensor a = torch::rand( |
3541 | {3, 4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3542 | for (auto dims : std::vector<std::vector<int64_t>>{{0, 1}, {-3, -2}}) { |
3543 | for (bool keepdim : {false, true}) { |
3544 | torch::Tensor b = torch::logsumexp(a, dims, keepdim); |
3545 | ForEachDevice([&](const torch::Device& device) { |
3546 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3547 | torch::Tensor lazy_b = torch::logsumexp(lazy_a, dims, keepdim); |
3548 | AllClose(b, lazy_b); |
3549 | }); |
3550 | } |
3551 | } |
3552 | } |
3553 | |
3554 | TEST_F(LazyOpsTest, TestSiLU) { |
3555 | torch::Tensor a = torch::rand( |
3556 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3557 | torch::Tensor b = torch::silu(a); |
3558 | ForEachDevice([&](const torch::Device& device) { |
3559 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3560 | torch::Tensor lazy_b = torch::silu(lazy_a); |
3561 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
3562 | }); |
3563 | ExpectCounterChanged("lazy::silu_out" , GetIgnoredCounters()); |
3564 | } |
3565 | |
3566 | TEST_F(LazyOpsTest, TestSigmoid) { |
3567 | torch::Tensor a = torch::rand( |
3568 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3569 | torch::Tensor b = torch::sigmoid(a); |
3570 | ForEachDevice([&](const torch::Device& device) { |
3571 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3572 | torch::Tensor lazy_b = torch::sigmoid(lazy_a); |
3573 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
3574 | }); |
3575 | } |
3576 | |
3577 | TEST_F(LazyOpsTest, TestMatmul_1x1) { |
3578 | torch::Tensor a = torch::rand( |
3579 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3580 | torch::Tensor b = torch::rand( |
3581 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3582 | torch::Tensor c = torch::matmul(a, b); |
3583 | ForEachDevice([&](const torch::Device& device) { |
3584 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3585 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3586 | torch::Tensor lazy_c = torch::matmul(lazy_a, lazy_b); |
3587 | AllClose(c, lazy_c); |
3588 | }); |
3589 | } |
3590 | |
3591 | TEST_F(LazyOpsTest, TestMatmul_2x1) { |
3592 | torch::Tensor a = torch::rand( |
3593 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3594 | torch::Tensor b = torch::rand( |
3595 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3596 | torch::Tensor c = torch::matmul(a, b); |
3597 | ForEachDevice([&](const torch::Device& device) { |
3598 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3599 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3600 | torch::Tensor lazy_c = torch::matmul(lazy_a, lazy_b); |
3601 | AllClose(c, lazy_c); |
3602 | }); |
3603 | } |
3604 | |
3605 | TEST_F(LazyOpsTest, TestMatmul_1x2) { |
3606 | torch::Tensor a = torch::rand( |
3607 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3608 | torch::Tensor b = torch::rand( |
3609 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3610 | torch::Tensor c = torch::matmul(a, b); |
3611 | ForEachDevice([&](const torch::Device& device) { |
3612 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3613 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3614 | torch::Tensor lazy_c = torch::matmul(lazy_a, lazy_b); |
3615 | AllClose(c, lazy_c); |
3616 | }); |
3617 | } |
3618 | |
3619 | TEST_F(LazyOpsTest, TestMatmul_2x2) { |
3620 | torch::Tensor a = torch::rand( |
3621 | {2, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3622 | torch::Tensor b = torch::rand( |
3623 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3624 | torch::Tensor c = torch::matmul(a, b); |
3625 | ForEachDevice([&](const torch::Device& device) { |
3626 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3627 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3628 | torch::Tensor lazy_c = torch::matmul(lazy_a, lazy_b); |
3629 | AllClose(c, lazy_c, /*rtol=*/1e-3, /*atol=*/1e-4); |
3630 | }); |
3631 | } |
3632 | |
3633 | TEST_F(LazyOpsTest, TestMatmulBcast) { |
3634 | torch::Tensor a = torch::rand( |
3635 | {4, 2, 3, 2, 4}, |
3636 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3637 | torch::Tensor b = torch::rand( |
3638 | {2, 1, 4, 3}, |
3639 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3640 | torch::Tensor c = torch::matmul(a, b); |
3641 | ForEachDevice([&](const torch::Device& device) { |
3642 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3643 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3644 | torch::Tensor lazy_c = torch::matmul(lazy_a, lazy_b); |
3645 | AllClose(c, lazy_c); |
3646 | }); |
3647 | } |
3648 | |
3649 | TEST_F(LazyOpsTest, TestDot) { |
3650 | torch::Tensor a = torch::rand( |
3651 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3652 | torch::Tensor b = torch::rand( |
3653 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3654 | torch::Tensor c = torch::dot(a, b); |
3655 | ForEachDevice([&](const torch::Device& device) { |
3656 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3657 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3658 | torch::Tensor lazy_c = torch::dot(lazy_a, lazy_b); |
3659 | AllClose(c, lazy_c); |
3660 | }); |
3661 | } |
3662 | |
3663 | TEST_F(LazyOpsTest, TestTensorDot) { |
3664 | torch::Tensor a = torch::rand( |
3665 | {6, 4, 8}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3666 | torch::Tensor b = torch::rand( |
3667 | {4, 7, 8}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3668 | std::vector<int64_t> dims_a = {1, 2}; |
3669 | std::vector<int64_t> dims_b = {0, 2}; |
3670 | torch::Tensor c = torch::tensordot(a, b, dims_a, dims_b); |
3671 | ForEachDevice([&](const torch::Device& device) { |
3672 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3673 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3674 | torch::Tensor lazy_c = torch::tensordot(lazy_a, lazy_b, dims_a, dims_b); |
3675 | AllClose(c, lazy_c); |
3676 | }); |
3677 | } |
3678 | |
3679 | TEST_F(LazyOpsTest, TestGer) { |
3680 | torch::Tensor a = torch::rand( |
3681 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3682 | torch::Tensor b = torch::rand( |
3683 | {5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3684 | torch::Tensor c = torch::ger(a, b); |
3685 | ForEachDevice([&](const torch::Device& device) { |
3686 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3687 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3688 | torch::Tensor lazy_c = torch::ger(lazy_a, lazy_b); |
3689 | AllClose(c, lazy_c); |
3690 | }); |
3691 | } |
3692 | |
3693 | TEST_F(LazyOpsTest, TestMv) { |
3694 | torch::Tensor a = torch::rand( |
3695 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3696 | torch::Tensor b = torch::rand( |
3697 | {3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3698 | torch::Tensor c = torch::mv(a, b); |
3699 | ForEachDevice([&](const torch::Device& device) { |
3700 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3701 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3702 | torch::Tensor lazy_c = torch::mv(lazy_a, lazy_b); |
3703 | AllClose(c, lazy_c); |
3704 | }); |
3705 | } |
3706 | |
3707 | TEST_F(LazyOpsTest, TestMvOut) { |
3708 | torch::Tensor a = torch::rand( |
3709 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3710 | torch::Tensor b = torch::rand( |
3711 | {3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3712 | torch::Tensor c = torch::empty( |
3713 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3714 | torch::mv_out(c, a, b); |
3715 | ForEachDevice([&](const torch::Device& device) { |
3716 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3717 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3718 | torch::Tensor lazy_c = torch::empty({4}, lazy_b.options()); |
3719 | torch::mv_out(lazy_c, lazy_a, lazy_b); |
3720 | AllClose(c, lazy_c); |
3721 | }); |
3722 | } |
3723 | |
3724 | TEST_F(LazyOpsTest, TestBatchAddBatchMatMul) { |
3725 | torch::Tensor a = torch::rand( |
3726 | {3, 6, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3727 | torch::Tensor b = torch::rand( |
3728 | {3, 6, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3729 | torch::Tensor c = torch::rand( |
3730 | {3, 4, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3731 | torch::Scalar alpha = 0.5; |
3732 | torch::Scalar beta = 1.5; |
3733 | torch::Tensor d = torch::baddbmm(a, b, c, beta, alpha); |
3734 | ForEachDevice([&](const torch::Device& device) { |
3735 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3736 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3737 | torch::Tensor lazy_c = CopyToDevice(c, device); |
3738 | torch::Tensor lazy_d = torch::baddbmm(lazy_a, lazy_b, lazy_c, beta, alpha); |
3739 | AllClose(d, lazy_d, /*rtol=*/1e-3, /*atol=*/1e-4); |
3740 | }); |
3741 | } |
3742 | |
3743 | TEST_F(LazyOpsTest, TestBatchAddBatchMatMulInPlace) { |
3744 | torch::Tensor a = torch::rand( |
3745 | {3, 6, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3746 | torch::Tensor b = torch::rand( |
3747 | {3, 6, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3748 | torch::Tensor c = torch::rand( |
3749 | {3, 4, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3750 | torch::Scalar alpha = 0.5; |
3751 | torch::Scalar beta = 1.5; |
3752 | ForEachDevice([&](const torch::Device& device) { |
3753 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3754 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3755 | torch::Tensor lazy_c = CopyToDevice(c, device); |
3756 | torch::Tensor d = a.baddbmm_(b, c, beta, alpha); |
3757 | torch::Tensor lazy_d = lazy_a.baddbmm_(lazy_b, lazy_c, beta, alpha); |
3758 | AllClose(d, lazy_d, /*rtol=*/1e-3, /*atol=*/1e-4); |
3759 | AllClose(a, lazy_a, /*rtol=*/1e-3, /*atol=*/1e-4); |
3760 | }); |
3761 | } |
3762 | |
3763 | TEST_F(LazyOpsTest, TestBatchMatMul) { |
3764 | torch::Tensor a = torch::rand( |
3765 | {3, 6, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3766 | torch::Tensor b = torch::rand( |
3767 | {3, 4, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3768 | torch::Tensor c = torch::bmm(a, b); |
3769 | ForEachDevice([&](const torch::Device& device) { |
3770 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3771 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3772 | torch::Tensor lazy_c = torch::bmm(lazy_a, lazy_b); |
3773 | AllClose(c, lazy_c, /*rtol=*/1e-3, /*atol=*/1e-4); |
3774 | }); |
3775 | } |
3776 | |
3777 | TEST_F(LazyOpsTest, TestChainMatMul) { |
3778 | torch::Tensor a = torch::rand( |
3779 | {5, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3780 | torch::Tensor b = torch::rand( |
3781 | {4, 6}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3782 | torch::Tensor c = torch::rand( |
3783 | {6, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3784 | torch::Tensor d = torch::rand( |
3785 | {2, 7}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3786 | torch::Tensor result = torch::chain_matmul({a, b, c, d}); |
3787 | ForEachDevice([&](const torch::Device& device) { |
3788 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3789 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3790 | torch::Tensor lazy_c = CopyToDevice(c, device); |
3791 | torch::Tensor lazy_d = CopyToDevice(d, device); |
3792 | torch::Tensor lazy_result = |
3793 | torch::chain_matmul({lazy_a, lazy_b, lazy_c, lazy_d}); |
3794 | AllClose(result, lazy_result, /*rtol=*/1e-3, /*atol=*/1e-4); |
3795 | }); |
3796 | } |
3797 | |
3798 | TEST_F(LazyOpsTest, TestLinear) { |
3799 | torch::Tensor input = torch::rand( |
3800 | {2, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3801 | torch::Tensor weight = torch::rand( |
3802 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3803 | torch::Tensor bias = torch::rand( |
3804 | {3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3805 | torch::Tensor result = torch::linear(input, weight); |
3806 | torch::Tensor result_with_bias = torch::linear(input, weight, bias); |
3807 | ForEachDevice([&](const torch::Device& device) { |
3808 | torch::Tensor lazy_input = CopyToDevice(input, device); |
3809 | torch::Tensor lazy_weight = CopyToDevice(weight, device); |
3810 | torch::Tensor lazy_bias = CopyToDevice(bias, device); |
3811 | torch::Tensor lazy_result = torch::linear(lazy_input, lazy_weight); |
3812 | torch::Tensor lazy_result_with_bias = |
3813 | torch::linear(lazy_input, lazy_weight, lazy_bias); |
3814 | AllClose(result, lazy_result, /*rtol=*/1e-2, /*atol=*/1e-4); |
3815 | AllClose( |
3816 | result_with_bias, |
3817 | lazy_result_with_bias, |
3818 | /*rtol=*/1e-2, |
3819 | /*atol=*/1e-4); |
3820 | }); |
3821 | } |
3822 | |
3823 | TEST_F(LazyOpsTest, TestPinverse) { |
3824 | torch::Tensor input = torch::rand( |
3825 | {4, 6}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3826 | torch::Tensor result = torch::pinverse(input); |
3827 | ForEachDevice([&](const torch::Device& device) { |
3828 | torch::Tensor lazy_input = CopyToDevice(input, device); |
3829 | torch::Tensor lazy_result = torch::pinverse(lazy_input); |
3830 | AllClose(result, lazy_result, /*rtol=*/1e-4); |
3831 | }); |
3832 | } |
3833 | |
3834 | TEST_F(LazyOpsTest, TestEinsumOuter) { |
3835 | torch::Tensor a = torch::rand( |
3836 | {5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3837 | torch::Tensor b = torch::rand( |
3838 | {5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3839 | std::string equation = "i,j->ij" ; |
3840 | torch::Tensor c = torch::einsum(equation, {a, b}); |
3841 | ForEachDevice([&](const torch::Device& device) { |
3842 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3843 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3844 | torch::Tensor lazy_c = torch::einsum(equation, {lazy_a, lazy_b}); |
3845 | AllClose(c, lazy_c); |
3846 | }); |
3847 | } |
3848 | |
3849 | TEST_F(LazyOpsTest, TestEinsumOuterBackward) { |
3850 | torch::Tensor a = torch::rand( |
3851 | {5}, |
3852 | torch::TensorOptions(torch::kFloat) |
3853 | .device(DefaultDevice()) |
3854 | .requires_grad(true)); |
3855 | torch::Tensor b = torch::rand( |
3856 | {5}, |
3857 | torch::TensorOptions(torch::kFloat) |
3858 | .device(DefaultDevice()) |
3859 | .requires_grad(true)); |
3860 | std::string equation = "i,j->ij" ; |
3861 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
3862 | return torch::einsum(equation, inputs); |
3863 | }; |
3864 | ForEachDevice([&](const torch::Device& device) { |
3865 | TestBackward({a, b}, device, testfn, /*rtol=*/1e-3, /*atol=*/1e-4); |
3866 | }); |
3867 | } |
3868 | |
3869 | TEST_F(LazyOpsTest, TestEinsumBatchMatMul) { |
3870 | torch::Tensor a = torch::rand( |
3871 | {3, 2, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3872 | torch::Tensor b = torch::rand( |
3873 | {3, 5, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3874 | std::string equation = "bij,bjk->bik" ; |
3875 | torch::Tensor c = torch::einsum(equation, {a, b}); |
3876 | ForEachDevice([&](const torch::Device& device) { |
3877 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3878 | torch::Tensor lazy_b = CopyToDevice(b, device); |
3879 | torch::Tensor lazy_c = torch::einsum(equation, {lazy_a, lazy_b}); |
3880 | AllClose(c, lazy_c); |
3881 | }); |
3882 | } |
3883 | |
3884 | TEST_F(LazyOpsTest, TestEinsumPyTorchLowerBilinear) { |
3885 | torch::Tensor a = torch::rand( |
3886 | {3, 5, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3887 | torch::Tensor l = torch::rand( |
3888 | {2, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3889 | torch::Tensor r = torch::rand( |
3890 | {2, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3891 | std::string equation = "bn,anm,bm->ba" ; |
3892 | torch::Tensor c = torch::einsum(equation, {l, a, r}); |
3893 | ForEachDevice([&](const torch::Device& device) { |
3894 | torch::Tensor lazy_l = CopyToDevice(l, device); |
3895 | torch::Tensor lazy_a = CopyToDevice(a, device); |
3896 | torch::Tensor lazy_r = CopyToDevice(r, device); |
3897 | torch::Tensor lazy_c = torch::einsum(equation, {lazy_l, lazy_a, lazy_r}); |
3898 | AllClose(c, lazy_c); |
3899 | }); |
3900 | } |
3901 | |
3902 | TEST_F(LazyOpsTest, TestEinsumPyTorchLowerDiagonal) { |
3903 | torch::Tensor input = torch::rand( |
3904 | {3, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3905 | std::string equation = "ii->i" ; |
3906 | torch::Tensor result = torch::einsum(equation, {input}); |
3907 | ForEachDevice([&](const torch::Device& device) { |
3908 | torch::Tensor lazy_input = CopyToDevice(input, device); |
3909 | torch::Tensor lazy_result = torch::einsum(equation, {lazy_input}); |
3910 | AllClose(result, lazy_result); |
3911 | }); |
3912 | } |
3913 | |
3914 | TEST_F(LazyOpsTest, TestEinsumPyTorchLowerBatchDiagonal) { |
3915 | torch::Tensor input = torch::rand( |
3916 | {4, 3, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3917 | std::string equation = "...ii->...i" ; |
3918 | torch::Tensor result = torch::einsum(equation, {input}); |
3919 | ForEachDevice([&](const torch::Device& device) { |
3920 | torch::Tensor lazy_input = CopyToDevice(input, device); |
3921 | torch::Tensor lazy_result = torch::einsum(equation, {lazy_input}); |
3922 | AllClose(result, lazy_result); |
3923 | }); |
3924 | } |
3925 | |
3926 | TEST_F(LazyOpsTest, TestEinsumPyTorchLowerBatchPermute) { |
3927 | torch::Tensor input = torch::rand( |
3928 | {2, 3, 4, 5}, |
3929 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3930 | std::string equation = "...ij->...ji" ; |
3931 | torch::Tensor result = torch::einsum(equation, {input}); |
3932 | ForEachDevice([&](const torch::Device& device) { |
3933 | torch::Tensor lazy_input = CopyToDevice(input, device); |
3934 | torch::Tensor lazy_result = torch::einsum(equation, {lazy_input}); |
3935 | AllClose(result, lazy_result); |
3936 | }); |
3937 | } |
3938 | |
3939 | TEST_F(LazyOpsTest, TestEinsumPyTorchLowerRepeatedAxis) { |
3940 | torch::Tensor x = torch::rand( |
3941 | {2, 3, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3942 | torch::Tensor y = torch::rand( |
3943 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3944 | std::string equation = "ijj,k->ik" ; |
3945 | torch::Tensor result = torch::einsum(equation, {x, y}); |
3946 | ForEachDevice([&](const torch::Device& device) { |
3947 | torch::Tensor lazy_x = CopyToDevice(x, device); |
3948 | torch::Tensor lazy_y = CopyToDevice(y, device); |
3949 | torch::Tensor lazy_result = torch::einsum(equation, {lazy_x, lazy_y}); |
3950 | AllClose(result, lazy_result); |
3951 | }); |
3952 | } |
3953 | |
3954 | TEST_F(LazyOpsTest, TestBilinear) { |
3955 | int batch_size = 16; |
3956 | int in1_features = 4; |
3957 | int in2_features = 6; |
3958 | int out_features = 8; |
3959 | torch::Tensor input1 = torch::rand( |
3960 | {batch_size, in1_features}, |
3961 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3962 | torch::Tensor input2 = torch::rand( |
3963 | {batch_size, in2_features}, |
3964 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3965 | torch::Tensor weight = torch::rand( |
3966 | {out_features, in1_features, in2_features}, |
3967 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3968 | torch::Tensor bias = torch::rand( |
3969 | {out_features}, |
3970 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3971 | ForEachDevice([&](const torch::Device& device) { |
3972 | torch::Tensor lazy_input1 = CopyToDevice(input1, device); |
3973 | torch::Tensor lazy_input2 = CopyToDevice(input2, device); |
3974 | torch::Tensor lazy_weight = CopyToDevice(weight, device); |
3975 | torch::Tensor lazy_bias = CopyToDevice(bias, device); |
3976 | torch::Tensor result = torch::bilinear(input1, input2, weight, bias); |
3977 | torch::Tensor lazy_result = |
3978 | torch::bilinear(lazy_input1, lazy_input2, lazy_weight, lazy_bias); |
3979 | AllClose(result, lazy_result); |
3980 | }); |
3981 | } |
3982 | |
3983 | TEST_F(LazyOpsTest, TestUpsampleNearest2D) { |
3984 | int batch_size = 2; |
3985 | int h = 5; |
3986 | int w = 5; |
3987 | int uh = 8; |
3988 | int uw = 8; |
3989 | int chans = 2; |
3990 | torch::Tensor input = torch::rand( |
3991 | {batch_size, chans, h, w}, |
3992 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
3993 | ForEachDevice([&](const torch::Device& device) { |
3994 | torch::Tensor lazy_input = CopyToDevice(input, device); |
3995 | torch::Tensor result = torch::upsample_nearest2d(input, {uh, uw}); |
3996 | torch::Tensor lazy_result = torch::upsample_nearest2d(lazy_input, {uh, uw}); |
3997 | AllClose(result, lazy_result); |
3998 | }); |
3999 | } |
4000 | |
4001 | TEST_F(LazyOpsTest, TestUpsampleNearest2DBackward) { |
4002 | int batch_size = 2; |
4003 | int h = 5; |
4004 | int w = 5; |
4005 | int uh = 8; |
4006 | int uw = 8; |
4007 | int chans = 2; |
4008 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
4009 | return torch::upsample_nearest2d(inputs[0], {uh, uw}); |
4010 | }; |
4011 | ForEachDevice([&](const torch::Device& device) { |
4012 | TestBackward( |
4013 | {torch::rand( |
4014 | {batch_size, chans, h, w}, |
4015 | torch::TensorOptions(torch::kFloat) |
4016 | .device(DefaultDevice()) |
4017 | .requires_grad(true))}, |
4018 | device, |
4019 | testfn); |
4020 | }); |
4021 | } |
4022 | |
4023 | TEST_F(LazyOpsTest, TestUpsampleNearest2DWithScale) { |
4024 | int batch_size = 2; |
4025 | int h = 5; |
4026 | int w = 5; |
4027 | int chans = 2; |
4028 | double scale_h = 2.5; |
4029 | double scale_w = 3.4; |
4030 | torch::Tensor input = torch::rand( |
4031 | {batch_size, chans, h, w}, |
4032 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4033 | ForEachDevice([&](const torch::Device& device) { |
4034 | torch::Tensor lazy_input = CopyToDevice(input, device); |
4035 | torch::Tensor result = torch::upsample_nearest2d( |
4036 | input, c10::nullopt, at::ArrayRef<double>{scale_h, scale_w}); |
4037 | torch::Tensor lazy_result = torch::upsample_nearest2d( |
4038 | lazy_input, c10::nullopt, at::ArrayRef<double>{scale_h, scale_w}); |
4039 | AllClose(result, lazy_result); |
4040 | }); |
4041 | } |
4042 | |
4043 | TEST_F(LazyOpsTest, TestUpsampleNearest2DBackwardWithScale) { |
4044 | int batch_size = 2; |
4045 | int h = 5; |
4046 | int w = 5; |
4047 | int chans = 2; |
4048 | double scale_h = 2.5; |
4049 | double scale_w = 3.4; |
4050 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
4051 | return torch::upsample_nearest2d( |
4052 | inputs[0], c10::nullopt, at::ArrayRef<double>{scale_h, scale_w}); |
4053 | }; |
4054 | ForEachDevice([&](const torch::Device& device) { |
4055 | TestBackward( |
4056 | {torch::rand( |
4057 | {batch_size, chans, h, w}, |
4058 | torch::TensorOptions(torch::kFloat) |
4059 | .device(DefaultDevice()) |
4060 | .requires_grad(true))}, |
4061 | device, |
4062 | testfn); |
4063 | }); |
4064 | } |
4065 | |
4066 | TEST_F(LazyOpsTest, TestUpsampleBilinear2D) { |
4067 | int batch_size = 2; |
4068 | int h = 5; |
4069 | int w = 5; |
4070 | int uh = 8; |
4071 | int uw = 8; |
4072 | int chans = 2; |
4073 | for (bool align_corners : {true, false}) { |
4074 | torch::Tensor input = torch::rand( |
4075 | {batch_size, chans, h, w}, |
4076 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4077 | ForEachDevice([&](const torch::Device& device) { |
4078 | torch::Tensor lazy_input = CopyToDevice(input, device); |
4079 | torch::Tensor result = |
4080 | torch::upsample_bilinear2d(input, {uh, uw}, align_corners); |
4081 | torch::Tensor lazy_result = |
4082 | torch::upsample_bilinear2d(lazy_input, {uh, uw}, align_corners); |
4083 | AllClose(result, lazy_result); |
4084 | }); |
4085 | } |
4086 | } |
4087 | |
4088 | TEST_F(LazyOpsTest, TestUpsampleBilinear2DBackward) { |
4089 | int batch_size = 2; |
4090 | int h = 5; |
4091 | int w = 5; |
4092 | int uh = 8; |
4093 | int uw = 8; |
4094 | int chans = 2; |
4095 | for (bool align_corners : {true, false}) { |
4096 | auto testfn = |
4097 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
4098 | return torch::upsample_bilinear2d(inputs[0], {uh, uw}, align_corners); |
4099 | }; |
4100 | ForEachDevice([&](const torch::Device& device) { |
4101 | TestBackward( |
4102 | {torch::rand( |
4103 | {batch_size, chans, h, w}, |
4104 | torch::TensorOptions(torch::kFloat) |
4105 | .device(DefaultDevice()) |
4106 | .requires_grad(true))}, |
4107 | device, |
4108 | testfn); |
4109 | }); |
4110 | } |
4111 | } |
4112 | |
4113 | TEST_F(LazyOpsTest, TestAddCMul) { |
4114 | torch::Tensor a = torch::rand( |
4115 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4116 | torch::Tensor b = torch::rand( |
4117 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4118 | torch::Tensor c = torch::rand( |
4119 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4120 | torch::Tensor d = torch::addcmul(a, b, c, 3.1165); |
4121 | ForEachDevice([&](const torch::Device& device) { |
4122 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4123 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4124 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4125 | torch::Tensor lazy_d = torch::addcmul(lazy_a, lazy_b, lazy_c, 3.1165); |
4126 | AllClose(d, lazy_d); |
4127 | }); |
4128 | } |
4129 | |
4130 | TEST_F(LazyOpsTest, TestAddCDiv) { |
4131 | torch::Tensor a = torch::rand( |
4132 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4133 | torch::Tensor b = torch::rand( |
4134 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4135 | torch::Tensor c = |
4136 | torch::abs(torch::rand( |
4137 | {2, 2}, |
4138 | torch::TensorOptions(torch::kFloat).device(DefaultDevice()))) + |
4139 | 1.0; |
4140 | torch::Tensor d = torch::addcdiv(a, b, c, 3.1165); |
4141 | ForEachDevice([&](const torch::Device& device) { |
4142 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4143 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4144 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4145 | torch::Tensor lazy_d = torch::addcdiv(lazy_a, lazy_b, lazy_c, 3.1165); |
4146 | AllClose(d, lazy_d); |
4147 | }); |
4148 | } |
4149 | |
4150 | TEST_F(LazyOpsTest, TestAddCDivWithBroadcast) { |
4151 | torch::Tensor a = torch::rand( |
4152 | {1, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4153 | torch::Tensor b = torch::rand( |
4154 | {3, 1}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4155 | torch::Tensor c = |
4156 | torch::abs(torch::rand( |
4157 | {1, 3}, |
4158 | torch::TensorOptions(torch::kFloat).device(DefaultDevice()))) + |
4159 | 1.0; |
4160 | torch::Tensor d = torch::addcdiv(a, b, c, 3.1165); |
4161 | ForEachDevice([&](const torch::Device& device) { |
4162 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4163 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4164 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4165 | torch::Tensor lazy_d = torch::addcdiv(lazy_a, lazy_b, lazy_c, 3.1165); |
4166 | AllClose(d, lazy_d); |
4167 | }); |
4168 | } |
4169 | |
4170 | TEST_F(LazyOpsTest, TestSize) { |
4171 | torch::Tensor input = torch::rand( |
4172 | {2, 1, 4, 6}, |
4173 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4174 | int rank = input.dim(); |
4175 | ForEachDevice([&](const torch::Device& device) { |
4176 | torch::Tensor lazy_input = CopyToDevice(input, device); |
4177 | for (int dim = -rank; dim < rank; ++dim) { |
4178 | EXPECT_EQ(torch::size(input, dim), torch::size(lazy_input, dim)); |
4179 | } |
4180 | }); |
4181 | } |
4182 | |
4183 | TEST_F(LazyOpsTest, TestSelect) { |
4184 | std::vector<int64_t> input_sizes = {14, 24, 8}; |
4185 | int rank = input_sizes.size(); |
4186 | for (int dim = -rank; dim < rank; ++dim) { |
4187 | auto testfn = |
4188 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
4189 | return torch::select(inputs[0], dim, 0); |
4190 | }; |
4191 | ForEachDevice([&](const torch::Device& device) { |
4192 | TestBackward( |
4193 | {torch::rand( |
4194 | input_sizes, |
4195 | torch::TensorOptions(torch::kFloat).requires_grad(true))}, |
4196 | device, |
4197 | testfn); |
4198 | }); |
4199 | }; |
4200 | } |
4201 | |
4202 | TEST_F(LazyOpsTest, TestBernoulliScalarProb) { |
4203 | torch::Tensor input = torch::zeros( |
4204 | 1000, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4205 | ForEachDevice([&](const torch::Device& device) { |
4206 | torch::Tensor lazy_input = CopyToDevice(input, device); |
4207 | torch::Tensor lazy_output = torch::bernoulli(lazy_input, 0.1); |
4208 | double frac = lazy_output.sum().item().toDouble() / input.numel(); |
4209 | EXPECT_GT(frac, 0.06); |
4210 | EXPECT_LT(frac, 0.14); |
4211 | }); |
4212 | } |
4213 | |
4214 | TEST_F(LazyOpsTest, TestBernoulliTensorProb) { |
4215 | std::vector<float> prob_values(1000, 0.1); |
4216 | torch::Tensor input = torch::tensor( |
4217 | prob_values, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4218 | ForEachDevice([&](const torch::Device& device) { |
4219 | torch::Tensor lazy_input = CopyToDevice(input, device); |
4220 | torch::Tensor lazy_output = torch::bernoulli(lazy_input); |
4221 | double frac = lazy_output.sum().item().toDouble() / input.numel(); |
4222 | EXPECT_GT(frac, 0.06); |
4223 | EXPECT_LT(frac, 0.14); |
4224 | }); |
4225 | } |
4226 | |
4227 | TEST_F(LazyOpsTest, TestBernoulliScalarProbInPlace) { |
4228 | torch::Tensor input = torch::zeros( |
4229 | 1000, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4230 | ForEachDevice([&](const torch::Device& device) { |
4231 | torch::Tensor lazy_input = CopyToDevice(input, device); |
4232 | lazy_input.bernoulli_(0.1); |
4233 | double frac = lazy_input.sum().item().toDouble() / input.numel(); |
4234 | EXPECT_GT(frac, 0.06); |
4235 | EXPECT_LT(frac, 0.14); |
4236 | }); |
4237 | } |
4238 | |
4239 | TEST_F(LazyOpsTest, TestBernoulliTensorProbInPlace) { |
4240 | torch::Tensor input = torch::zeros( |
4241 | 1000, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4242 | torch::Tensor prob = torch::scalar_tensor( |
4243 | 0.1, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4244 | ForEachDevice([&](const torch::Device& device) { |
4245 | torch::Tensor lazy_input = CopyToDevice(input, device); |
4246 | torch::Tensor lazy_prob = CopyToDevice(prob, device); |
4247 | lazy_input.bernoulli_(lazy_prob); |
4248 | double frac = lazy_input.sum().item().toDouble() / input.numel(); |
4249 | EXPECT_GT(frac, 0.06); |
4250 | EXPECT_LT(frac, 0.14); |
4251 | }); |
4252 | } |
4253 | |
4254 | TEST_F(LazyOpsTest, TestDropout) { |
4255 | torch::Tensor a = torch::rand( |
4256 | {17, 21}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4257 | ForEachDevice([&](const torch::Device& device) { |
4258 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4259 | torch::Tensor lazy_b = torch::dropout(lazy_a, 0.1, /*train=*/true); |
4260 | double prob = |
4261 | static_cast<double>(lazy_b.cpu().ne(0.0f).sum().item().toDouble()) / |
4262 | a.numel(); |
4263 | EXPECT_GT(prob, 0.86); |
4264 | EXPECT_LT(prob, 0.94); |
4265 | }); |
4266 | } |
4267 | |
4268 | TEST_F(LazyOpsTest, TestDropoutInPlace) { |
4269 | torch::Tensor a = torch::rand( |
4270 | {17, 21}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4271 | ForEachDevice([&](const torch::Device& device) { |
4272 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4273 | torch::dropout_(lazy_a, 0.1, /*train=*/true); |
4274 | double prob = |
4275 | static_cast<double>(lazy_a.cpu().ne(0.0f).sum().item().toDouble()) / |
4276 | a.numel(); |
4277 | EXPECT_GT(prob, 0.85); |
4278 | EXPECT_LT(prob, 0.94); |
4279 | }); |
4280 | } |
4281 | |
4282 | TEST_F(LazyOpsTest, TestRandperm) { |
4283 | unsigned n = 5; |
4284 | torch::Tensor shuffle = torch::randperm( |
4285 | n, torch::TensorOptions(torch::kLong).device(torch::kLazy)); |
4286 | torch::Tensor shuffle_cpu = CopyToDevice(shuffle, torch::kCPU); |
4287 | std::vector<int64_t> shuffle_data( |
4288 | shuffle_cpu.data_ptr<int64_t>(), shuffle_cpu.data_ptr<int64_t>() + n); |
4289 | EXPECT_TRUE( |
4290 | shuffle_data.size() == n && torch::lazy::IsPermutation(shuffle_data)); |
4291 | } |
4292 | |
4293 | TEST_F(LazyOpsTest, TestSlice) { |
4294 | torch::Tensor a = torch::rand( |
4295 | {32, 24, 16}, |
4296 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4297 | torch::Tensor b = torch::slice(a, 1, 0, 16, 1); |
4298 | ForEachDevice([&](const torch::Device& device) { |
4299 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4300 | torch::Tensor lazy_b = torch::slice(lazy_a, 1, 0, 16, 1); |
4301 | AllClose(b, lazy_b); |
4302 | }); |
4303 | } |
4304 | |
4305 | TEST_F(LazyOpsTest, TestTake) { |
4306 | torch::Tensor a = torch::rand( |
4307 | {4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4308 | torch::Tensor b = torch::randint( |
4309 | 16, {5}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4310 | torch::Tensor c = torch::take(a, b); |
4311 | ForEachDevice([&](const torch::Device& device) { |
4312 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4313 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4314 | torch::Tensor lazy_c = torch::take(lazy_a, lazy_b); |
4315 | AllClose(c, lazy_c); |
4316 | }); |
4317 | } |
4318 | |
4319 | TEST_F(LazyOpsTest, TestTakeBackward) { |
4320 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
4321 | return torch::take(inputs[0], inputs[1]); |
4322 | }; |
4323 | ForEachDevice([&](const torch::Device& device) { |
4324 | TestBackward( |
4325 | {torch::rand( |
4326 | {4, 4}, |
4327 | torch::TensorOptions(torch::kFloat) |
4328 | .device(DefaultDevice()) |
4329 | .requires_grad(true)), |
4330 | torch::randint( |
4331 | 16, |
4332 | {5}, |
4333 | torch::TensorOptions(torch::kLong).device(DefaultDevice()))}, |
4334 | device, |
4335 | testfn); |
4336 | }); |
4337 | } |
4338 | |
4339 | TEST_F(LazyOpsTest, TestStack) { |
4340 | torch::Tensor a = torch::rand( |
4341 | {2, 4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4342 | torch::Tensor b = torch::rand( |
4343 | {2, 4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4344 | torch::Tensor c = torch::rand( |
4345 | {2, 4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4346 | int rank = a.dim() + 1; |
4347 | for (int dim = -rank; dim < rank; ++dim) { |
4348 | torch::Tensor d = torch::stack({a, b, c}, dim); |
4349 | ForEachDevice([&](const torch::Device& device) { |
4350 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4351 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4352 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4353 | torch::Tensor lazy_d = torch::stack({lazy_a, lazy_b, lazy_c}, dim); |
4354 | AllClose(d, lazy_d); |
4355 | }); |
4356 | } |
4357 | } |
4358 | |
4359 | TEST_F(LazyOpsTest, TestCat) { |
4360 | torch::Tensor a = torch::rand( |
4361 | {2, 1, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4362 | torch::Tensor b = torch::rand( |
4363 | {2, 2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4364 | torch::Tensor c = torch::rand( |
4365 | {2, 3, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4366 | for (int dim : {1, -2}) { |
4367 | torch::Tensor d = torch::cat({a, b, c}, dim); |
4368 | ForEachDevice([&](const torch::Device& device) { |
4369 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4370 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4371 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4372 | torch::Tensor lazy_d = torch::cat({lazy_a, lazy_b, lazy_c}, dim); |
4373 | EXPECT_TRUE(d.sizes() == lazy_d.sizes() && d.dtype() == lazy_d.dtype()); |
4374 | AllClose(d, lazy_d); |
4375 | }); |
4376 | } |
4377 | } |
4378 | |
4379 | TEST_F(LazyOpsTest, TestUnbind) { |
4380 | torch::Tensor input = torch::rand( |
4381 | {4, 3, 7}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4382 | int rank = input.dim(); |
4383 | for (int dim = -rank; dim < rank; ++dim) { |
4384 | std::vector<torch::Tensor> output = torch::unbind(input, dim); |
4385 | ForEachDevice([&](const torch::Device& device) { |
4386 | torch::Tensor lazy_input = CopyToDevice(input, device); |
4387 | std::vector<torch::Tensor> lazy_output = torch::unbind(lazy_input, dim); |
4388 | ASSERT_EQ(output.size(), lazy_output.size()); |
4389 | for (size_t i = 0; i < output.size(); ++i) { |
4390 | AllClose(output[i], lazy_output[i]); |
4391 | } |
4392 | }); |
4393 | } |
4394 | } |
4395 | |
4396 | TEST_F(LazyOpsTest, TestRepeat) { |
4397 | std::vector<std::vector<int64_t>> repeats_list = {{4, 2}, {4, 2, 3}}; |
4398 | std::vector<std::vector<int64_t>> input_size_list = {{3}, {2, 4}}; |
4399 | for (const auto& repeats : repeats_list) { |
4400 | for (const auto& input_size : input_size_list) { |
4401 | torch::Tensor input = torch::rand( |
4402 | input_size, |
4403 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4404 | torch::Tensor output = input.repeat(repeats); |
4405 | ForEachDevice([&](const torch::Device& device) { |
4406 | torch::Tensor lazy_input = CopyToDevice(input, device); |
4407 | torch::Tensor lazy_output = lazy_input.repeat(repeats); |
4408 | AllClose(output, lazy_output); |
4409 | }); |
4410 | } |
4411 | } |
4412 | } |
4413 | |
4414 | TEST_F(LazyOpsTest, TestGather) { |
4415 | torch::Tensor a = torch::rand( |
4416 | {3, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4417 | torch::Tensor b = torch::empty( |
4418 | {3, 3}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4419 | for (int i = 0; i < 3; i++) { |
4420 | for (int j = 0; j < 3; j++) { |
4421 | b[i][j] = (i + j) % 3; |
4422 | } |
4423 | } |
4424 | for (bool sparse_grad : {false, true}) { |
4425 | torch::Tensor c = torch::gather(a, 1, b, sparse_grad); |
4426 | ForEachDevice([&](const torch::Device& device) { |
4427 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4428 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4429 | torch::Tensor lazy_c = torch::gather(lazy_a, 1, lazy_b, sparse_grad); |
4430 | AllClose(c, lazy_c); |
4431 | }); |
4432 | } |
4433 | } |
4434 | |
4435 | TEST_F(LazyOpsTest, TestScatter) { |
4436 | torch::Tensor a = torch::rand( |
4437 | {3, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4438 | torch::Tensor b = torch::rand( |
4439 | {3, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4440 | torch::Tensor c = torch::empty( |
4441 | {3, 5}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4442 | for (int dim = 0; dim < 2; ++dim) { |
4443 | for (int i = 0; i < 3; i++) { |
4444 | for (int j = 0; j < 5; j++) { |
4445 | c[i][j] = (i + j) % c.sizes()[dim]; |
4446 | } |
4447 | } |
4448 | torch::Tensor d = torch::scatter(a, dim, c, b); |
4449 | ForEachDevice([&](const torch::Device& device) { |
4450 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4451 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4452 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4453 | torch::Tensor lazy_d = torch::scatter(lazy_a, dim, lazy_c, lazy_b); |
4454 | AllClose(d, lazy_d); |
4455 | }); |
4456 | } |
4457 | } |
4458 | |
4459 | TEST_F(LazyOpsTest, TestScatterR1) { |
4460 | torch::Tensor a = torch::rand( |
4461 | {5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4462 | torch::Tensor b = torch::rand( |
4463 | {2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4464 | torch::Tensor c = torch::empty( |
4465 | {2}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4466 | c[0] = 1; |
4467 | c[1] = 3; |
4468 | torch::Tensor d = torch::scatter(a, 0, c, b); |
4469 | ForEachDevice([&](const torch::Device& device) { |
4470 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4471 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4472 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4473 | torch::Tensor lazy_d = torch::scatter(lazy_a, 0, lazy_c, lazy_b); |
4474 | AllClose(d, lazy_d); |
4475 | }); |
4476 | } |
4477 | |
4478 | TEST_F(LazyOpsTest, TestScatterR3) { |
4479 | torch::Tensor a = torch::rand( |
4480 | {3, 5, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4481 | torch::Tensor b = torch::rand( |
4482 | {3, 4, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4483 | torch::Tensor c = torch::empty( |
4484 | {3, 4, 2}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4485 | for (int i = 0; i < 3; i++) { |
4486 | for (int j = 0; j < 4; j++) { |
4487 | for (int k = 0; k < 2; k++) { |
4488 | c[i][j][k] = (i + j + k) % 4; |
4489 | } |
4490 | } |
4491 | } |
4492 | torch::Tensor d = torch::scatter(a, 1, c, b); |
4493 | ForEachDevice([&](const torch::Device& device) { |
4494 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4495 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4496 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4497 | torch::Tensor lazy_d = torch::scatter(lazy_a, 1, lazy_c, lazy_b); |
4498 | AllClose(d, lazy_d); |
4499 | }); |
4500 | } |
4501 | |
4502 | TEST_F(LazyOpsTest, TestScatterBiggerSource) { |
4503 | torch::Tensor a = torch::rand( |
4504 | {4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4505 | torch::Tensor b = torch::rand( |
4506 | {8, 8}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4507 | torch::Tensor c = torch::empty( |
4508 | {4, 4}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4509 | for (int i = 0; i < 4; i++) { |
4510 | for (int j = 0; j < 4; j++) { |
4511 | c[i][j] = (i + j) % 4; |
4512 | } |
4513 | } |
4514 | for (int dim = 0; dim < 2; ++dim) { |
4515 | torch::Tensor d = torch::scatter(a, dim, c, b); |
4516 | ForEachDevice([&](const torch::Device& device) { |
4517 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4518 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4519 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4520 | torch::Tensor lazy_d = torch::scatter(lazy_a, dim, lazy_c, lazy_b); |
4521 | AllClose(d, lazy_d); |
4522 | }); |
4523 | } |
4524 | } |
4525 | |
4526 | TEST_F(LazyOpsTest, TestScatterScalar) { |
4527 | torch::Tensor a = torch::rand( |
4528 | {4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4529 | torch::Scalar b = 1.0f; |
4530 | torch::Tensor c = torch::empty( |
4531 | {4, 4}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4532 | for (int i = 0; i < 4; i++) { |
4533 | for (int j = 0; j < 4; j++) { |
4534 | c[i][j] = (i + j) % 4; |
4535 | } |
4536 | } |
4537 | for (int dim = 0; dim < 2; ++dim) { |
4538 | torch::Tensor d = torch::scatter(a, dim, c, b); |
4539 | ForEachDevice([&](const torch::Device& device) { |
4540 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4541 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4542 | torch::Tensor lazy_d = torch::scatter(lazy_a, dim, lazy_c, b); |
4543 | AllClose(d, lazy_d); |
4544 | }); |
4545 | } |
4546 | } |
4547 | |
4548 | TEST_F(LazyOpsTest, TestScatterReduceAdd) { |
4549 | torch::Tensor a = torch::rand( |
4550 | {3, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4551 | torch::Tensor b = torch::rand( |
4552 | {3, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4553 | torch::Tensor c = torch::empty( |
4554 | {3, 5}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4555 | for (int dim = 0; dim < 2; ++dim) { |
4556 | for (int i = 0; i < 3; i++) { |
4557 | for (int j = 0; j < 5; j++) { |
4558 | c[i][j] = (i + j) % c.sizes()[dim]; |
4559 | } |
4560 | } |
4561 | torch::Tensor d = torch::scatter(a, dim, c, b, "add" ); |
4562 | ForEachDevice([&](const torch::Device& device) { |
4563 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4564 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4565 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4566 | torch::Tensor lazy_d = torch::scatter(lazy_a, dim, lazy_c, lazy_b, "add" ); |
4567 | AllClose(d, lazy_d); |
4568 | }); |
4569 | } |
4570 | |
4571 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
4572 | ExpectCounterChanged("lazy::scatter_out" , GetIgnoredCounters()); |
4573 | } |
4574 | |
4575 | TEST_F(LazyOpsTest, TestScatterAdd) { |
4576 | torch::Tensor a = torch::rand( |
4577 | {3, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4578 | torch::Tensor b = torch::rand( |
4579 | {3, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4580 | torch::Tensor c = torch::empty( |
4581 | {3, 5}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4582 | for (int dim = 0; dim < 2; ++dim) { |
4583 | for (int i = 0; i < 3; i++) { |
4584 | for (int j = 0; j < 5; j++) { |
4585 | c[i][j] = (i + j) % c.sizes()[dim]; |
4586 | } |
4587 | } |
4588 | torch::Tensor d = torch::scatter_add(a, dim, c, b); |
4589 | ForEachDevice([&](const torch::Device& device) { |
4590 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4591 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4592 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4593 | torch::Tensor lazy_d = torch::scatter_add(lazy_a, dim, lazy_c, lazy_b); |
4594 | AllClose(d, lazy_d); |
4595 | }); |
4596 | } |
4597 | } |
4598 | |
4599 | TEST_F(LazyOpsTest, TestScatterAddInPlace) { |
4600 | torch::Tensor b = torch::rand( |
4601 | {4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4602 | torch::Tensor c = torch::empty( |
4603 | {4, 4}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4604 | for (int i = 0; i < 4; i++) { |
4605 | for (int j = 0; j < 4; j++) { |
4606 | c[i][j] = (i + j) % 4; |
4607 | } |
4608 | } |
4609 | for (int dim = 0; dim < 2; ++dim) { |
4610 | ForEachDevice([&](const torch::Device& device) { |
4611 | torch::Tensor a = torch::rand( |
4612 | {4, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4613 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4614 | torch::Tensor d = a.scatter_add_(dim, c, b); |
4615 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4616 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4617 | torch::Tensor lazy_d = lazy_a.scatter_add_(dim, lazy_c, lazy_b); |
4618 | AllClose(d, lazy_d); |
4619 | AllClose(a, lazy_a); |
4620 | }); |
4621 | } |
4622 | } |
4623 | |
4624 | TEST_F(LazyOpsTest, TestIndexSelect) { |
4625 | for (torch::ScalarType scalar_type : |
4626 | {torch::kFloat, |
4627 | torch::kByte, |
4628 | torch::kChar, |
4629 | torch::kShort, |
4630 | torch::kInt, |
4631 | torch::kLong}) { |
4632 | torch::Tensor a = isFloatingType(scalar_type) |
4633 | ? torch::rand( |
4634 | {3, 4}, torch::TensorOptions(scalar_type).device(DefaultDevice())) |
4635 | : torch::randint( |
4636 | 100, |
4637 | {3, 4}, |
4638 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
4639 | for (torch::ScalarType index_scalar_type : {torch::kInt, torch::kLong}) { |
4640 | torch::Tensor b = torch::empty( |
4641 | {2}, torch::TensorOptions(index_scalar_type).device(DefaultDevice())); |
4642 | b[0] = 0; |
4643 | b[1] = 2; |
4644 | for (auto offset : {-2, 0}) { |
4645 | torch::Tensor c0 = torch::index_select(a, 0 + offset, b); |
4646 | torch::Tensor c1 = torch::index_select(a, 1 + offset, b); |
4647 | ForEachDevice([&](const torch::Device& device) { |
4648 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4649 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4650 | torch::Tensor lazy_c0 = |
4651 | torch::index_select(lazy_a, 0 + offset, lazy_b); |
4652 | torch::Tensor lazy_c1 = |
4653 | torch::index_select(lazy_a, 1 + offset, lazy_b); |
4654 | AllEqual(c0, lazy_c0); |
4655 | AllEqual(c1, lazy_c1); |
4656 | }); |
4657 | } |
4658 | } |
4659 | } |
4660 | } |
4661 | |
4662 | TEST_F(LazyOpsTest, TestIndexSelectRank0) { |
4663 | for (torch::ScalarType scalar_type : |
4664 | {torch::kFloat, |
4665 | torch::kByte, |
4666 | torch::kChar, |
4667 | torch::kShort, |
4668 | torch::kInt, |
4669 | torch::kLong}) { |
4670 | torch::Tensor a = isFloatingType(scalar_type) |
4671 | ? torch::rand( |
4672 | {3, 4}, torch::TensorOptions(scalar_type).device(DefaultDevice())) |
4673 | : torch::randint( |
4674 | 100, |
4675 | {3, 4}, |
4676 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
4677 | torch::Tensor b = torch::scalar_tensor( |
4678 | 2, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4679 | torch::Tensor c0 = torch::index_select(a, 0, b); |
4680 | torch::Tensor c1 = torch::index_select(a, 1, b); |
4681 | ForEachDevice([&](const torch::Device& device) { |
4682 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4683 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4684 | torch::Tensor lazy_c0 = torch::index_select(lazy_a, 0, lazy_b); |
4685 | torch::Tensor lazy_c1 = torch::index_select(lazy_a, 1, lazy_b); |
4686 | AllEqual(c0, lazy_c0); |
4687 | AllEqual(c1, lazy_c1); |
4688 | }); |
4689 | } |
4690 | } |
4691 | |
4692 | TEST_F(LazyOpsTest, TestInverse) { |
4693 | if (IsCuda()) { |
4694 | // TODO(whc) debug failure on cuda, lazy_b comes back transposed |
4695 | GTEST_SKIP(); |
4696 | } |
4697 | torch::Tensor a = torch::randn( |
4698 | {5, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4699 | torch::Tensor b = torch::inverse(a); |
4700 | ForEachDevice([&](const torch::Device& device) { |
4701 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4702 | torch::Tensor lazy_b = torch::inverse(lazy_a); |
4703 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-4); |
4704 | }); |
4705 | } |
4706 | |
4707 | TEST_F(LazyOpsTest, TestIsnan) { |
4708 | torch::Tensor a = torch::tensor( |
4709 | {1.0, 2.0, std::nan("1" ), 4.0}, |
4710 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4711 | torch::Tensor b = torch::isnan(a); |
4712 | ForEachDevice([&](const torch::Device& device) { |
4713 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4714 | torch::Tensor lazy_b = torch::isnan(lazy_a); |
4715 | AllEqual(b, lazy_b); |
4716 | }); |
4717 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
4718 | ExpectCounterChanged("lazy::isnan" , GetIgnoredCounters()); |
4719 | } |
4720 | |
4721 | TEST_F(LazyOpsTest, TestExpand) { |
4722 | torch::Tensor a = torch::rand( |
4723 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4724 | torch::Tensor b = a.expand({2, 3, 4}, /*implicit=*/false); |
4725 | ForEachDevice([&](const torch::Device& device) { |
4726 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4727 | torch::Tensor lazy_b = lazy_a.expand({2, 3, 4}, /*implicit=*/false); |
4728 | AllClose(b, lazy_b); |
4729 | }); |
4730 | } |
4731 | |
4732 | TEST_F(LazyOpsTest, TestExpandBack) { |
4733 | torch::Tensor a = torch::rand( |
4734 | {3, 1}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4735 | torch::Tensor b = a.expand({3, 4}, /*implicit=*/false); |
4736 | ForEachDevice([&](const torch::Device& device) { |
4737 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4738 | torch::Tensor lazy_b = lazy_a.expand({3, 4}, /*implicit=*/false); |
4739 | AllClose(b, lazy_b); |
4740 | }); |
4741 | } |
4742 | |
4743 | TEST_F(LazyOpsTest, TestExpandAs) { |
4744 | torch::Tensor a = torch::rand( |
4745 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4746 | torch::Tensor b = torch::rand( |
4747 | {2, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4748 | torch::Tensor c = torch::native::expand_as(a, b); |
4749 | ForEachDevice([&](const torch::Device& device) { |
4750 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4751 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4752 | torch::Tensor lazy_c = torch::native::expand_as(lazy_a, lazy_b); |
4753 | AllClose(c, lazy_c); |
4754 | }); |
4755 | } |
4756 | |
4757 | TEST_F(LazyOpsTest, TestEye) { |
4758 | int n = 5; |
4759 | ForEachDevice([&](const torch::Device& device) { |
4760 | torch::Tensor out = torch::eye( |
4761 | n, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4762 | torch::Tensor lazy_out = |
4763 | torch::eye(n, torch::TensorOptions(torch::kFloat).device(device)); |
4764 | AllClose(out, lazy_out); |
4765 | }); |
4766 | } |
4767 | |
4768 | TEST_F(LazyOpsTest, TestEyeWide) { |
4769 | int lines = 3; |
4770 | int cols = 5; |
4771 | ForEachDevice([&](const torch::Device& device) { |
4772 | torch::Tensor out = torch::eye( |
4773 | lines, |
4774 | cols, |
4775 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4776 | torch::Tensor lazy_out = torch::eye( |
4777 | lines, cols, torch::TensorOptions(torch::kFloat).device(device)); |
4778 | AllClose(out, lazy_out); |
4779 | }); |
4780 | } |
4781 | |
4782 | TEST_F(LazyOpsTest, TestEyeNarrow) { |
4783 | int lines = 5; |
4784 | int cols = 3; |
4785 | ForEachDevice([&](const torch::Device& device) { |
4786 | torch::Tensor out = torch::eye( |
4787 | lines, |
4788 | cols, |
4789 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4790 | torch::Tensor lazy_out = torch::eye( |
4791 | lines, cols, torch::TensorOptions(torch::kFloat).device(device)); |
4792 | AllClose(out, lazy_out); |
4793 | }); |
4794 | } |
4795 | |
4796 | TEST_F(LazyOpsTest, TestBroadcastTensors) { |
4797 | torch::Tensor a = torch::rand( |
4798 | {2, 1, 1}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4799 | torch::Tensor b = torch::rand( |
4800 | {2, 1}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4801 | std::vector<torch::Tensor> c = torch::broadcast_tensors({a, b}); |
4802 | ForEachDevice([&](const torch::Device& device) { |
4803 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4804 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4805 | std::vector<torch::Tensor> lazy_c = |
4806 | torch::broadcast_tensors({lazy_a, lazy_b}); |
4807 | ASSERT_EQ(c.size(), lazy_c.size()); |
4808 | for (size_t i = 0; i < c.size(); ++i) { |
4809 | AllClose(c[i], lazy_c[i]); |
4810 | } |
4811 | }); |
4812 | } |
4813 | |
4814 | TEST_F(LazyOpsTest, TestOneIndex) { |
4815 | for (torch::ScalarType scalar_type : |
4816 | {torch::kFloat, |
4817 | torch::kByte, |
4818 | torch::kChar, |
4819 | torch::kShort, |
4820 | torch::kInt, |
4821 | torch::kLong}) { |
4822 | torch::Tensor params = isFloatingType(scalar_type) |
4823 | ? torch::rand( |
4824 | {4, 3, 5, 6, 7}, |
4825 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
4826 | : torch::randint( |
4827 | 100, |
4828 | {4, 3, 5, 6, 7}, |
4829 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
4830 | torch::Tensor indices = torch::randint( |
4831 | -3, |
4832 | 3, |
4833 | {2, 4, 3}, |
4834 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4835 | torch::Tensor result = torch::index(params, {indices}); |
4836 | ForEachDevice([&](const torch::Device& device) { |
4837 | torch::Tensor lazy_params = CopyToDevice(params, device); |
4838 | torch::Tensor lazy_indices = CopyToDevice(indices, device); |
4839 | torch::Tensor lazy_result = torch::index(lazy_params, {lazy_indices}); |
4840 | AllEqual(result, lazy_result); |
4841 | }); |
4842 | } |
4843 | } |
4844 | |
4845 | TEST_F(LazyOpsTest, TestOneIndexTransfer) { |
4846 | for (torch::ScalarType scalar_type : |
4847 | {torch::kFloat, |
4848 | torch::kByte, |
4849 | torch::kChar, |
4850 | torch::kShort, |
4851 | torch::kInt, |
4852 | torch::kLong}) { |
4853 | torch::Tensor params = isFloatingType(scalar_type) |
4854 | ? torch::rand( |
4855 | {4, 3, 5, 6, 7}, |
4856 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
4857 | : torch::randint( |
4858 | 100, |
4859 | {4, 3, 5, 6, 7}, |
4860 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
4861 | torch::Tensor indices = torch::randint( |
4862 | -3, |
4863 | 3, |
4864 | {2, 4, 3}, |
4865 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4866 | torch::Tensor result = torch::index(params, {indices}); |
4867 | ForEachDevice([&](const torch::Device& device) { |
4868 | torch::Tensor lazy_params = CopyToDevice(params, device); |
4869 | torch::Tensor lazy_result = torch::index(lazy_params, {indices.cpu()}); |
4870 | AllEqual(result, lazy_result); |
4871 | }); |
4872 | } |
4873 | } |
4874 | |
4875 | TEST_F(LazyOpsTest, TestNonzero) { |
4876 | torch::Tensor a = torch::zeros( |
4877 | {4, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4878 | a[0][1] = 1.0; |
4879 | a[1][0] = 2.0; |
4880 | a[3][1] = 3.0; |
4881 | torch::Tensor b = torch::nonzero(a); |
4882 | ForEachDevice([&](const torch::Device& device) { |
4883 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4884 | torch::Tensor lazy_b = torch::nonzero(lazy_a); |
4885 | AllClose(b, lazy_b); |
4886 | |
4887 | if (DebugUtil::ExperimentEnabled("nonzero" )) { |
4888 | // If the nonzero support is enabled, we must not see any aten:: calls. |
4889 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
4890 | } |
4891 | ResetCounters(); |
4892 | }); |
4893 | } |
4894 | |
4895 | TEST_F(LazyOpsTest, TestMaskedSelect) { |
4896 | torch::Tensor a = torch::rand( |
4897 | {3, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4898 | torch::Tensor b = torch::randint( |
4899 | 0, 2, {5}, torch::TensorOptions(torch::kBool).device(DefaultDevice())); |
4900 | torch::Tensor c = torch::masked_select(a, b); |
4901 | ForEachDevice([&](const torch::Device& device) { |
4902 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4903 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4904 | torch::Tensor lazy_c = torch::masked_select(lazy_a, lazy_b); |
4905 | AllClose(c, lazy_c); |
4906 | |
4907 | if (DebugUtil::ExperimentEnabled("masked_select" )) { |
4908 | // If the masked_select support is enabled, we must not see any aten:: |
4909 | // calls. |
4910 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
4911 | } |
4912 | ResetCounters(); |
4913 | }); |
4914 | } |
4915 | |
4916 | TEST_F(LazyOpsTest, TestMaskedScatter) { |
4917 | torch::Tensor a = torch::rand( |
4918 | {3, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4919 | torch::Tensor b = torch::randint( |
4920 | 0, 2, {3, 5}, torch::TensorOptions(torch::kBool).device(DefaultDevice())); |
4921 | torch::Tensor c = torch::rand( |
4922 | {15}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
4923 | torch::Tensor d = torch::masked_scatter(a, b, c); |
4924 | ForEachDevice([&](const torch::Device& device) { |
4925 | torch::Tensor lazy_a = CopyToDevice(a, device); |
4926 | torch::Tensor lazy_b = CopyToDevice(b, device); |
4927 | torch::Tensor lazy_c = CopyToDevice(c, device); |
4928 | torch::Tensor lazy_d = torch::masked_scatter(lazy_a, lazy_b, lazy_c); |
4929 | AllClose(d, lazy_d); |
4930 | |
4931 | if (DebugUtil::ExperimentEnabled("masked_scatter" )) { |
4932 | // If the masked_select support is enabled, we must not see any aten:: |
4933 | // calls. |
4934 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
4935 | } |
4936 | ResetCounters(); |
4937 | }); |
4938 | } |
4939 | |
4940 | TEST_F(LazyOpsTest, TestMultiIndexHeadNull) { |
4941 | for (torch::ScalarType scalar_type : |
4942 | {torch::kFloat, |
4943 | torch::kByte, |
4944 | torch::kChar, |
4945 | torch::kShort, |
4946 | torch::kInt, |
4947 | torch::kLong}) { |
4948 | torch::Tensor params = isFloatingType(scalar_type) |
4949 | ? torch::rand( |
4950 | {4, 3, 5, 6, 7}, |
4951 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
4952 | : torch::randint( |
4953 | 100, |
4954 | {4, 3, 5, 6, 7}, |
4955 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
4956 | torch::Tensor indices_null; |
4957 | torch::Tensor indices_0 = torch::randint( |
4958 | -3, |
4959 | 3, |
4960 | {2, 4, 3}, |
4961 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4962 | torch::Tensor indices_1 = torch::randint( |
4963 | -3, |
4964 | 3, |
4965 | {2, 4, 3}, |
4966 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
4967 | torch::Tensor result = |
4968 | torch::index(params, {indices_null, indices_0, indices_1}); |
4969 | ForEachDevice([&](const torch::Device& device) { |
4970 | torch::Tensor lazy_params = CopyToDevice(params, device); |
4971 | torch::Tensor lazy_indices_0 = CopyToDevice(indices_0, device); |
4972 | torch::Tensor lazy_indices_1 = CopyToDevice(indices_1, device); |
4973 | torch::Tensor lazy_result = torch::index( |
4974 | lazy_params, {indices_null, lazy_indices_0, lazy_indices_1}); |
4975 | AllEqual(result, lazy_result); |
4976 | }); |
4977 | } |
4978 | } |
4979 | |
4980 | TEST_F(LazyOpsTest, TestMultiIndexMiddleNull) { |
4981 | for (torch::ScalarType scalar_type : |
4982 | {torch::kFloat, |
4983 | torch::kByte, |
4984 | torch::kChar, |
4985 | torch::kShort, |
4986 | torch::kInt, |
4987 | torch::kLong}) { |
4988 | torch::Tensor params = isFloatingType(scalar_type) |
4989 | ? torch::rand( |
4990 | {4, 3, 5, 6, 7}, |
4991 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
4992 | : torch::randint( |
4993 | 100, |
4994 | {4, 3, 5, 6, 7}, |
4995 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
4996 | torch::Tensor indices_0 = torch::randint( |
4997 | -3, |
4998 | 3, |
4999 | {2, 4, 3}, |
5000 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5001 | torch::Tensor indices_null; |
5002 | torch::Tensor indices_1 = torch::randint( |
5003 | -3, |
5004 | 3, |
5005 | {2, 4, 3}, |
5006 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5007 | torch::Tensor result = |
5008 | torch::index(params, {indices_0, indices_null, indices_1}); |
5009 | ForEachDevice([&](const torch::Device& device) { |
5010 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5011 | torch::Tensor lazy_indices_0 = CopyToDevice(indices_0, device); |
5012 | torch::Tensor lazy_indices_1 = CopyToDevice(indices_1, device); |
5013 | torch::Tensor lazy_result = torch::index( |
5014 | lazy_params, {lazy_indices_0, indices_null, lazy_indices_1}); |
5015 | AllEqual(result, lazy_result); |
5016 | }); |
5017 | } |
5018 | } |
5019 | |
5020 | TEST_F(LazyOpsTest, TestMultiIndexTailNull) { |
5021 | for (torch::ScalarType scalar_type : |
5022 | {torch::kFloat, |
5023 | torch::kByte, |
5024 | torch::kChar, |
5025 | torch::kShort, |
5026 | torch::kInt, |
5027 | torch::kLong}) { |
5028 | torch::Tensor params = isFloatingType(scalar_type) |
5029 | ? torch::rand( |
5030 | {4, 3, 5, 6, 7}, |
5031 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5032 | : torch::randint( |
5033 | 100, |
5034 | {4, 3, 5, 6, 7}, |
5035 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5036 | torch::Tensor indices_0 = torch::randint( |
5037 | -3, |
5038 | 3, |
5039 | {2, 4, 3}, |
5040 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5041 | torch::Tensor indices_null; |
5042 | torch::Tensor indices_1 = torch::randint( |
5043 | -3, |
5044 | 3, |
5045 | {2, 4, 3}, |
5046 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5047 | torch::Tensor result = |
5048 | torch::index(params, {indices_0, indices_1, indices_null}); |
5049 | ForEachDevice([&](const torch::Device& device) { |
5050 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5051 | torch::Tensor lazy_indices_0 = CopyToDevice(indices_0, device); |
5052 | torch::Tensor lazy_indices_1 = CopyToDevice(indices_1, device); |
5053 | torch::Tensor lazy_result = torch::index( |
5054 | lazy_params, {lazy_indices_0, lazy_indices_1, indices_null}); |
5055 | AllEqual(result, lazy_result); |
5056 | }); |
5057 | } |
5058 | } |
5059 | |
5060 | TEST_F(LazyOpsTest, TestMultiIndexMiddleBroadcast) { |
5061 | for (torch::ScalarType scalar_type : |
5062 | {torch::kFloat, |
5063 | torch::kByte, |
5064 | torch::kChar, |
5065 | torch::kShort, |
5066 | torch::kInt, |
5067 | torch::kLong}) { |
5068 | torch::Tensor params = isFloatingType(scalar_type) |
5069 | ? torch::rand( |
5070 | {4, 3, 5, 6, 7}, |
5071 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5072 | : torch::randint( |
5073 | 100, |
5074 | {4, 3, 5, 6, 7}, |
5075 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5076 | torch::Tensor indices_0 = torch::randint( |
5077 | -3, |
5078 | 3, |
5079 | {2, 4, 3}, |
5080 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5081 | torch::Tensor indices_1 = torch::randint( |
5082 | -3, |
5083 | 3, |
5084 | {2, 1, 3}, |
5085 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5086 | torch::Tensor result = torch::index(params, {indices_0, indices_1}); |
5087 | ForEachDevice([&](const torch::Device& device) { |
5088 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5089 | torch::Tensor lazy_indices_0 = CopyToDevice(indices_0, device); |
5090 | torch::Tensor lazy_indices_1 = CopyToDevice(indices_1, device); |
5091 | torch::Tensor lazy_result = |
5092 | torch::index(lazy_params, {lazy_indices_0, lazy_indices_1}); |
5093 | AllEqual(result, lazy_result); |
5094 | }); |
5095 | } |
5096 | } |
5097 | |
5098 | TEST_F(LazyOpsTest, TestMultiIndexTailBroadcast) { |
5099 | for (torch::ScalarType scalar_type : |
5100 | {torch::kFloat, |
5101 | torch::kByte, |
5102 | torch::kChar, |
5103 | torch::kShort, |
5104 | torch::kInt, |
5105 | torch::kLong}) { |
5106 | torch::Tensor params = isFloatingType(scalar_type) |
5107 | ? torch::rand( |
5108 | {4, 3, 5, 6, 7}, |
5109 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5110 | : torch::randint( |
5111 | 100, |
5112 | {4, 3, 5, 6, 7}, |
5113 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5114 | torch::Tensor indices_0 = torch::randint( |
5115 | -3, |
5116 | 3, |
5117 | {2, 1, 3}, |
5118 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5119 | torch::Tensor indices_1 = torch::randint( |
5120 | -3, |
5121 | 3, |
5122 | {2, 1}, |
5123 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5124 | torch::Tensor result = torch::index(params, {indices_0, indices_1}); |
5125 | ForEachDevice([&](const torch::Device& device) { |
5126 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5127 | torch::Tensor lazy_indices_0 = CopyToDevice(indices_0, device); |
5128 | torch::Tensor lazy_indices_1 = CopyToDevice(indices_1, device); |
5129 | torch::Tensor lazy_result = |
5130 | torch::index(lazy_params, {lazy_indices_0, lazy_indices_1}); |
5131 | AllEqual(result, lazy_result); |
5132 | }); |
5133 | } |
5134 | } |
5135 | |
5136 | TEST_F(LazyOpsTest, TestMaskIndex) { |
5137 | for (torch::ScalarType scalar_type : |
5138 | {torch::kFloat, |
5139 | torch::kByte, |
5140 | torch::kChar, |
5141 | torch::kShort, |
5142 | torch::kInt, |
5143 | torch::kLong}) { |
5144 | torch::Tensor params = isFloatingType(scalar_type) |
5145 | ? torch::rand( |
5146 | {2, 2}, torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5147 | : torch::randint( |
5148 | 100, |
5149 | {2, 2}, |
5150 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5151 | torch::Tensor indices = torch::randint( |
5152 | 0, |
5153 | 2, |
5154 | {2, 2}, |
5155 | torch::TensorOptions(torch::kBool).device(DefaultDevice())); |
5156 | torch::Tensor result = torch::index(params, {indices}); |
5157 | ForEachDevice([&](const torch::Device& device) { |
5158 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5159 | torch::Tensor lazy_indices = CopyToDevice(indices, device); |
5160 | torch::Tensor lazy_result = torch::index(lazy_params, {lazy_indices}); |
5161 | AllEqual(result, lazy_result); |
5162 | }); |
5163 | } |
5164 | } |
5165 | |
5166 | TEST_F(LazyOpsTest, TestOneIndexPut) { |
5167 | for (torch::ScalarType scalar_type : |
5168 | {torch::kFloat, |
5169 | torch::kByte, |
5170 | torch::kChar, |
5171 | torch::kShort, |
5172 | torch::kInt, |
5173 | torch::kLong}) { |
5174 | torch::Tensor params = isFloatingType(scalar_type) |
5175 | ? torch::rand( |
5176 | {4, 3, 5, 6, 7}, |
5177 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5178 | : torch::randint( |
5179 | 100, |
5180 | {4, 3, 5, 6, 7}, |
5181 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5182 | torch::Tensor indices = torch::randint( |
5183 | -3, |
5184 | 3, |
5185 | {2, 4, 3}, |
5186 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5187 | torch::Tensor values = isFloatingType(scalar_type) |
5188 | ? torch::rand( |
5189 | {3, 5, 6, 7}, |
5190 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5191 | : torch::randint( |
5192 | 100, |
5193 | {3, 5, 6, 7}, |
5194 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5195 | for (bool accumulate : {false, true}) { |
5196 | if (accumulate && IsCuda()) { |
5197 | GTEST_SKIP(); |
5198 | } |
5199 | torch::Tensor result = |
5200 | torch::index_put(params, {indices}, values, accumulate); |
5201 | ForEachDevice([&](const torch::Device& device) { |
5202 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5203 | torch::Tensor lazy_indices = CopyToDevice(indices, device); |
5204 | torch::Tensor lazy_values = CopyToDevice(values, device); |
5205 | torch::Tensor lazy_result = torch::index_put( |
5206 | lazy_params, {lazy_indices}, lazy_values, accumulate); |
5207 | AllEqual(result, lazy_result); |
5208 | }); |
5209 | } |
5210 | } |
5211 | } |
5212 | |
5213 | TEST_F(LazyOpsTest, TestOneIndexPutInPlace) { |
5214 | torch::Tensor indices = torch::randint( |
5215 | -3, |
5216 | 3, |
5217 | {2, 4, 3}, |
5218 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5219 | for (torch::ScalarType scalar_type : |
5220 | {torch::kFloat, |
5221 | torch::kByte, |
5222 | torch::kChar, |
5223 | torch::kShort, |
5224 | torch::kInt, |
5225 | torch::kLong}) { |
5226 | torch::Tensor values = torch::ones( |
5227 | {3, 5, 6, 7}, |
5228 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5229 | for (bool accumulate : {false, true}) { |
5230 | if (accumulate && IsCuda()) { |
5231 | GTEST_SKIP(); |
5232 | } |
5233 | ForEachDevice([&](const torch::Device& device) { |
5234 | torch::Tensor params = isFloatingType(scalar_type) |
5235 | ? torch::rand( |
5236 | {4, 3, 5, 6, 7}, |
5237 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5238 | : torch::randint( |
5239 | 100, |
5240 | {4, 3, 5, 6, 7}, |
5241 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5242 | torch::Tensor lazy_params = CopyToDevice(params.clone(), device); |
5243 | torch::Tensor result = |
5244 | torch::index_put_(params, {indices}, values, accumulate); |
5245 | torch::Tensor lazy_indices = CopyToDevice(indices, device); |
5246 | torch::Tensor lazy_values = CopyToDevice(values, device); |
5247 | torch::Tensor lazy_result = torch::index_put_( |
5248 | lazy_params, {lazy_indices}, lazy_values, accumulate); |
5249 | AllEqual(result, lazy_result); |
5250 | AllEqual(params, lazy_params); |
5251 | }); |
5252 | } |
5253 | } |
5254 | } |
5255 | |
5256 | TEST_F(LazyOpsTest, TestOneIndexPutTransfer) { |
5257 | torch::Tensor indices = torch::randint( |
5258 | -3, |
5259 | 3, |
5260 | {2, 4, 3}, |
5261 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5262 | for (torch::ScalarType scalar_type : |
5263 | {torch::kFloat, |
5264 | torch::kByte, |
5265 | torch::kChar, |
5266 | torch::kShort, |
5267 | torch::kInt, |
5268 | torch::kLong}) { |
5269 | torch::Tensor params = isFloatingType(scalar_type) |
5270 | ? torch::rand( |
5271 | {4, 3, 5, 6, 7}, |
5272 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5273 | : torch::randint( |
5274 | 100, |
5275 | {4, 3, 5, 6, 7}, |
5276 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5277 | torch::Tensor values = torch::ones( |
5278 | {3, 5, 6, 7}, |
5279 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5280 | for (bool accumulate : {false, true}) { |
5281 | if (accumulate && IsCuda()) { |
5282 | GTEST_SKIP(); |
5283 | } |
5284 | torch::Tensor result = |
5285 | torch::index_put(params, {indices}, values, accumulate); |
5286 | ForEachDevice([&](const torch::Device& device) { |
5287 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5288 | torch::Tensor lazy_values = CopyToDevice(values, device); |
5289 | torch::Tensor lazy_result = |
5290 | torch::index_put(lazy_params, {indices}, lazy_values, accumulate); |
5291 | AllEqual(result, lazy_result); |
5292 | }); |
5293 | } |
5294 | } |
5295 | } |
5296 | |
5297 | TEST_F(LazyOpsTest, TestMultiIndexPut) { |
5298 | torch::Tensor indices_0 = torch::randint( |
5299 | -3, |
5300 | 3, |
5301 | {2, 4, 3}, |
5302 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5303 | torch::Tensor indices_1 = torch::randint( |
5304 | -3, |
5305 | 3, |
5306 | {2, 4, 3}, |
5307 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5308 | for (torch::ScalarType scalar_type : |
5309 | {torch::kFloat, |
5310 | torch::kByte, |
5311 | torch::kChar, |
5312 | torch::kShort, |
5313 | torch::kInt, |
5314 | torch::kLong}) { |
5315 | torch::Tensor params = isFloatingType(scalar_type) |
5316 | ? torch::rand( |
5317 | {4, 3, 5, 6, 7}, |
5318 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5319 | : torch::randint( |
5320 | 100, |
5321 | {4, 3, 5, 6, 7}, |
5322 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5323 | torch::Tensor values = torch::ones( |
5324 | {5, 6, 7}, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5325 | for (bool accumulate : {false, true}) { |
5326 | if (accumulate && IsCuda()) { |
5327 | GTEST_SKIP(); |
5328 | } |
5329 | torch::Tensor result = |
5330 | torch::index_put(params, {indices_0, indices_1}, values, accumulate); |
5331 | ForEachDevice([&](const torch::Device& device) { |
5332 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5333 | torch::Tensor lazy_indices_0 = CopyToDevice(indices_0, device); |
5334 | torch::Tensor lazy_indices_1 = CopyToDevice(indices_1, device); |
5335 | torch::Tensor lazy_values = CopyToDevice(values, device); |
5336 | torch::Tensor lazy_result = torch::index_put( |
5337 | lazy_params, |
5338 | {lazy_indices_0, lazy_indices_1}, |
5339 | lazy_values, |
5340 | accumulate); |
5341 | AllEqual(result, lazy_result); |
5342 | }); |
5343 | } |
5344 | } |
5345 | } |
5346 | |
5347 | TEST_F(LazyOpsTest, TestMultiIndexPutHeadNull) { |
5348 | torch::Tensor indices_0 = torch::randint( |
5349 | -3, |
5350 | 3, |
5351 | {2, 4, 3}, |
5352 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5353 | torch::Tensor indices_null; |
5354 | torch::Tensor indices_1 = torch::randint( |
5355 | -3, |
5356 | 3, |
5357 | {2, 4, 3}, |
5358 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5359 | for (torch::ScalarType scalar_type : |
5360 | {torch::kFloat, |
5361 | torch::kByte, |
5362 | torch::kChar, |
5363 | torch::kShort, |
5364 | torch::kInt, |
5365 | torch::kLong}) { |
5366 | torch::Tensor params = isFloatingType(scalar_type) |
5367 | ? torch::rand( |
5368 | {4, 3, 3, 6, 7}, |
5369 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5370 | : torch::randint( |
5371 | 100, |
5372 | {4, 3, 3, 6, 7}, |
5373 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5374 | torch::Tensor values = torch::ones( |
5375 | {3, 6, 7}, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5376 | for (bool accumulate : {false, true}) { |
5377 | if (accumulate && IsCuda()) { |
5378 | GTEST_SKIP(); |
5379 | } |
5380 | torch::Tensor result = torch::index_put( |
5381 | params, {indices_null, indices_0, indices_1}, values, accumulate); |
5382 | ForEachDevice([&](const torch::Device& device) { |
5383 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5384 | torch::Tensor lazy_indices_0 = CopyToDevice(indices_0, device); |
5385 | torch::Tensor lazy_indices_1 = CopyToDevice(indices_1, device); |
5386 | torch::Tensor lazy_values = CopyToDevice(values, device); |
5387 | torch::Tensor lazy_result = torch::index_put( |
5388 | lazy_params, |
5389 | {indices_null, lazy_indices_0, lazy_indices_1}, |
5390 | lazy_values, |
5391 | accumulate); |
5392 | AllEqual(result, lazy_result); |
5393 | }); |
5394 | } |
5395 | } |
5396 | } |
5397 | |
5398 | TEST_F(LazyOpsTest, TestMultiIndexPutMiddleNull) { |
5399 | torch::Tensor indices_0 = torch::randint( |
5400 | -3, |
5401 | 3, |
5402 | {2, 4, 3}, |
5403 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5404 | torch::Tensor indices_null; |
5405 | torch::Tensor indices_1 = torch::randint( |
5406 | -3, |
5407 | 3, |
5408 | {2, 4, 3}, |
5409 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5410 | for (torch::ScalarType scalar_type : |
5411 | {torch::kFloat, |
5412 | torch::kByte, |
5413 | torch::kChar, |
5414 | torch::kShort, |
5415 | torch::kInt, |
5416 | torch::kLong}) { |
5417 | torch::Tensor params = isFloatingType(scalar_type) |
5418 | ? torch::rand( |
5419 | {4, 3, 3, 6, 7}, |
5420 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5421 | : torch::randint( |
5422 | 100, |
5423 | {4, 3, 3, 6, 7}, |
5424 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5425 | torch::Tensor values = torch::ones( |
5426 | {3, 6, 7}, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5427 | for (bool accumulate : {false, true}) { |
5428 | if (accumulate && IsCuda()) { |
5429 | GTEST_SKIP(); |
5430 | } |
5431 | torch::Tensor result = torch::index_put( |
5432 | params, {indices_0, indices_null, indices_1}, values, accumulate); |
5433 | ForEachDevice([&](const torch::Device& device) { |
5434 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5435 | torch::Tensor lazy_indices_0 = CopyToDevice(indices_0, device); |
5436 | torch::Tensor lazy_indices_1 = CopyToDevice(indices_1, device); |
5437 | torch::Tensor lazy_values = CopyToDevice(values, device); |
5438 | torch::Tensor lazy_result = torch::index_put( |
5439 | lazy_params, |
5440 | {lazy_indices_0, indices_null, lazy_indices_1}, |
5441 | lazy_values, |
5442 | accumulate); |
5443 | AllEqual(result, lazy_result); |
5444 | }); |
5445 | } |
5446 | } |
5447 | } |
5448 | |
5449 | TEST_F(LazyOpsTest, TestMultiIndexPutTailNull) { |
5450 | torch::Tensor indices_0 = torch::randint( |
5451 | -3, |
5452 | 3, |
5453 | {2, 4, 3}, |
5454 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5455 | torch::Tensor indices_1 = torch::randint( |
5456 | -3, |
5457 | 3, |
5458 | {2, 4, 3}, |
5459 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5460 | torch::Tensor indices_null; |
5461 | for (torch::ScalarType scalar_type : |
5462 | {torch::kFloat, |
5463 | torch::kByte, |
5464 | torch::kChar, |
5465 | torch::kShort, |
5466 | torch::kInt, |
5467 | torch::kLong}) { |
5468 | torch::Tensor params = isFloatingType(scalar_type) |
5469 | ? torch::rand( |
5470 | {4, 3, 3, 6, 7}, |
5471 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5472 | : torch::randint( |
5473 | 100, |
5474 | {4, 3, 3, 6, 7}, |
5475 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5476 | torch::Tensor values = torch::ones( |
5477 | {3, 6, 7}, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5478 | for (bool accumulate : {false, true}) { |
5479 | if (accumulate && IsCuda()) { |
5480 | GTEST_SKIP(); |
5481 | } |
5482 | torch::Tensor result = torch::index_put( |
5483 | params, {indices_0, indices_1, indices_null}, values, accumulate); |
5484 | ForEachDevice([&](const torch::Device& device) { |
5485 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5486 | torch::Tensor lazy_indices_0 = CopyToDevice(indices_0, device); |
5487 | torch::Tensor lazy_indices_1 = CopyToDevice(indices_1, device); |
5488 | torch::Tensor lazy_values = CopyToDevice(values, device); |
5489 | torch::Tensor lazy_result = torch::index_put( |
5490 | lazy_params, |
5491 | {lazy_indices_0, lazy_indices_1, indices_null}, |
5492 | lazy_values, |
5493 | accumulate); |
5494 | AllEqual(result, lazy_result); |
5495 | }); |
5496 | } |
5497 | } |
5498 | } |
5499 | |
5500 | TEST_F(LazyOpsTest, TestMultiIndexPutMiddleBroadcast) { |
5501 | torch::Tensor indices_0 = torch::randint( |
5502 | -3, |
5503 | 3, |
5504 | {2, 4, 3}, |
5505 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5506 | torch::Tensor indices_1 = torch::randint( |
5507 | -3, |
5508 | 3, |
5509 | {2, 1, 3}, |
5510 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5511 | for (torch::ScalarType scalar_type : |
5512 | {torch::kFloat, |
5513 | torch::kByte, |
5514 | torch::kChar, |
5515 | torch::kShort, |
5516 | torch::kInt, |
5517 | torch::kLong}) { |
5518 | torch::Tensor params = isFloatingType(scalar_type) |
5519 | ? torch::rand( |
5520 | {4, 3, 5, 6, 7}, |
5521 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5522 | : torch::randint( |
5523 | 100, |
5524 | {4, 3, 5, 6, 7}, |
5525 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5526 | torch::Tensor values = torch::ones( |
5527 | {5, 6, 7}, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5528 | for (bool accumulate : {false, true}) { |
5529 | if (accumulate && IsCuda()) { |
5530 | GTEST_SKIP(); |
5531 | } |
5532 | torch::Tensor result = |
5533 | torch::index_put(params, {indices_0, indices_1}, values, accumulate); |
5534 | ForEachDevice([&](const torch::Device& device) { |
5535 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5536 | torch::Tensor lazy_indices_0 = CopyToDevice(indices_0, device); |
5537 | torch::Tensor lazy_indices_1 = CopyToDevice(indices_1, device); |
5538 | torch::Tensor lazy_values = CopyToDevice(values, device); |
5539 | torch::Tensor lazy_result = torch::index_put( |
5540 | lazy_params, |
5541 | {lazy_indices_0, lazy_indices_1}, |
5542 | lazy_values, |
5543 | accumulate); |
5544 | AllEqual(result, lazy_result); |
5545 | }); |
5546 | } |
5547 | } |
5548 | } |
5549 | |
5550 | TEST_F(LazyOpsTest, TestMultiIndexPutTailBroadcast) { |
5551 | torch::Tensor indices_0 = torch::randint( |
5552 | -3, |
5553 | 3, |
5554 | {2, 1, 3}, |
5555 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5556 | torch::Tensor indices_1 = torch::randint( |
5557 | -3, |
5558 | 3, |
5559 | {2, 1}, |
5560 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5561 | for (torch::ScalarType scalar_type : |
5562 | {torch::kFloat, |
5563 | torch::kByte, |
5564 | torch::kChar, |
5565 | torch::kShort, |
5566 | torch::kInt, |
5567 | torch::kLong}) { |
5568 | torch::Tensor params = isFloatingType(scalar_type) |
5569 | ? torch::rand( |
5570 | {4, 3, 5, 6, 7}, |
5571 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5572 | : torch::randint( |
5573 | 100, |
5574 | {4, 3, 5, 6, 7}, |
5575 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5576 | torch::Tensor values = torch::ones( |
5577 | {5, 6, 7}, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5578 | for (bool accumulate : {false, true}) { |
5579 | if (accumulate && IsCuda()) { |
5580 | GTEST_SKIP(); |
5581 | } |
5582 | torch::Tensor result = |
5583 | torch::index_put(params, {indices_0, indices_1}, values, accumulate); |
5584 | ForEachDevice([&](const torch::Device& device) { |
5585 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5586 | torch::Tensor lazy_indices_0 = CopyToDevice(indices_0, device); |
5587 | torch::Tensor lazy_indices_1 = CopyToDevice(indices_1, device); |
5588 | torch::Tensor lazy_values = CopyToDevice(values, device); |
5589 | torch::Tensor lazy_result = torch::index_put( |
5590 | lazy_params, |
5591 | {lazy_indices_0, lazy_indices_1}, |
5592 | lazy_values, |
5593 | accumulate); |
5594 | AllEqual(result, lazy_result); |
5595 | }); |
5596 | } |
5597 | } |
5598 | } |
5599 | |
5600 | TEST_F(LazyOpsTest, TestMaskIndexPut) { |
5601 | torch::Tensor indices = |
5602 | torch::tensor( |
5603 | {0, 1}, torch::TensorOptions(torch::kByte).device(DefaultDevice())) |
5604 | .to(torch::kBool); |
5605 | for (torch::ScalarType scalar_type : |
5606 | {torch::kFloat, |
5607 | torch::kByte, |
5608 | torch::kChar, |
5609 | torch::kShort, |
5610 | torch::kInt, |
5611 | torch::kLong}) { |
5612 | torch::Tensor params = isFloatingType(scalar_type) |
5613 | ? torch::rand( |
5614 | {2, 2}, torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5615 | : torch::randint( |
5616 | 100, |
5617 | {2, 2}, |
5618 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5619 | torch::Tensor values = torch::ones( |
5620 | {2}, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5621 | for (bool accumulate : {false, true}) { |
5622 | torch::Tensor result = |
5623 | torch::index_put(params, {indices}, values, accumulate); |
5624 | ForEachDevice([&](const torch::Device& device) { |
5625 | torch::Tensor lazy_params = CopyToDevice(params, device); |
5626 | torch::Tensor lazy_indices = CopyToDevice(indices, device); |
5627 | torch::Tensor lazy_values = CopyToDevice(values, device); |
5628 | torch::Tensor lazy_result = torch::index_put( |
5629 | lazy_params, {lazy_indices}, lazy_values, accumulate); |
5630 | AllEqual(result, lazy_result); |
5631 | }); |
5632 | } |
5633 | } |
5634 | } |
5635 | |
5636 | TEST_F(LazyOpsTest, TestIndexPutImpl) { |
5637 | torch::Tensor indices = torch::randint( |
5638 | -3, |
5639 | 3, |
5640 | {2, 4, 3}, |
5641 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5642 | for (torch::ScalarType scalar_type : |
5643 | {torch::kFloat, |
5644 | torch::kByte, |
5645 | torch::kChar, |
5646 | torch::kShort, |
5647 | torch::kInt, |
5648 | torch::kLong}) { |
5649 | torch::Tensor values = torch::ones( |
5650 | {3, 5, 6, 7}, |
5651 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5652 | for (bool accumulate : {false, true}) { |
5653 | if (accumulate && IsCuda()) { |
5654 | GTEST_SKIP(); |
5655 | } |
5656 | ForEachDevice([&](const torch::Device& device) { |
5657 | torch::Tensor params = isFloatingType(scalar_type) |
5658 | ? torch::rand( |
5659 | {4, 3, 5, 6, 7}, |
5660 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5661 | : torch::randint( |
5662 | 100, |
5663 | {4, 3, 5, 6, 7}, |
5664 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5665 | torch::Tensor lazy_params = CopyToDevice(params.clone(), device); |
5666 | torch::Tensor result = torch::_index_put_impl_( |
5667 | params, {indices}, values, accumulate, /*unsafe=*/true); |
5668 | torch::Tensor lazy_indices = CopyToDevice(indices, device); |
5669 | torch::Tensor lazy_values = CopyToDevice(values, device); |
5670 | torch::Tensor lazy_result = torch::_index_put_impl_( |
5671 | lazy_params, |
5672 | {lazy_indices}, |
5673 | lazy_values, |
5674 | accumulate, |
5675 | /*unsafe=*/true); |
5676 | AllEqual(result, lazy_result); |
5677 | AllEqual(params, lazy_params); |
5678 | }); |
5679 | } |
5680 | } |
5681 | } |
5682 | |
5683 | TEST_F(LazyOpsTest, TestIndexFillWithScalar) { |
5684 | torch::Tensor index = torch::tensor( |
5685 | {0, 2}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5686 | torch::Scalar value = 42; |
5687 | for (torch::ScalarType scalar_type : |
5688 | {torch::kFloat, |
5689 | torch::kByte, |
5690 | torch::kChar, |
5691 | torch::kShort, |
5692 | torch::kInt, |
5693 | torch::kLong}) { |
5694 | torch::Tensor base = isFloatingType(scalar_type) |
5695 | ? torch::rand( |
5696 | {3, 4, 5}, |
5697 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5698 | : torch::randint( |
5699 | 100, |
5700 | {3, 4, 5}, |
5701 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5702 | int rank = base.dim(); |
5703 | for (int dim = -rank; dim < rank; ++dim) { |
5704 | torch::Tensor result = torch::index_fill(base, dim, index, value); |
5705 | ForEachDevice([&](const torch::Device& device) { |
5706 | torch::Tensor lazy_base = CopyToDevice(base, device); |
5707 | torch::Tensor lazy_index = CopyToDevice(index, device); |
5708 | torch::Tensor lazy_result = |
5709 | torch::index_fill(lazy_base, dim, lazy_index, value); |
5710 | AllEqual(result, lazy_result); |
5711 | }); |
5712 | } |
5713 | } |
5714 | } |
5715 | |
5716 | TEST_F(LazyOpsTest, TestIndexFillWithScalarInPlace) { |
5717 | torch::Tensor index = torch::tensor( |
5718 | {0, 2}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5719 | torch::Scalar value = 42; |
5720 | int rank = 3; |
5721 | for (torch::ScalarType scalar_type : |
5722 | {torch::kFloat, |
5723 | torch::kByte, |
5724 | torch::kChar, |
5725 | torch::kShort, |
5726 | torch::kInt, |
5727 | torch::kLong}) { |
5728 | for (int dim = -rank; dim < rank; ++dim) { |
5729 | ForEachDevice([&](const torch::Device& device) { |
5730 | torch::Tensor base = isFloatingType(scalar_type) |
5731 | ? torch::rand( |
5732 | {3, 4, 5}, |
5733 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5734 | : torch::randint( |
5735 | 100, |
5736 | {3, 4, 5}, |
5737 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5738 | torch::Tensor lazy_base = CopyToDevice(base.clone(), device); |
5739 | torch::Tensor result = base.index_fill_(dim, index, value); |
5740 | torch::Tensor lazy_index = CopyToDevice(index, device); |
5741 | torch::Tensor lazy_result = |
5742 | lazy_base.index_fill_(dim, lazy_index, value); |
5743 | AllEqual(result, lazy_result); |
5744 | AllEqual(base, lazy_base); |
5745 | }); |
5746 | } |
5747 | } |
5748 | } |
5749 | |
5750 | TEST_F(LazyOpsTest, TestIndexFillWithTensor) { |
5751 | torch::Tensor index = torch::tensor( |
5752 | {0, 2}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5753 | for (torch::ScalarType scalar_type : |
5754 | {torch::kFloat, |
5755 | torch::kByte, |
5756 | torch::kChar, |
5757 | torch::kShort, |
5758 | torch::kInt, |
5759 | torch::kLong}) { |
5760 | torch::Tensor base = isFloatingType(scalar_type) |
5761 | ? torch::rand( |
5762 | {3, 4, 5}, |
5763 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5764 | : torch::randint( |
5765 | 100, |
5766 | {3, 4, 5}, |
5767 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5768 | torch::Tensor value = torch::scalar_tensor( |
5769 | 42, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5770 | int rank = base.dim(); |
5771 | for (int dim = -rank; dim < rank; ++dim) { |
5772 | torch::Tensor result = torch::index_fill(base, dim, index, value); |
5773 | ForEachDevice([&](const torch::Device& device) { |
5774 | torch::Tensor lazy_base = CopyToDevice(base, device); |
5775 | torch::Tensor lazy_index = CopyToDevice(index, device); |
5776 | torch::Tensor lazy_value = CopyToDevice(value, device); |
5777 | torch::Tensor lazy_result = |
5778 | torch::index_fill(lazy_base, dim, lazy_index, lazy_value); |
5779 | AllEqual(result, lazy_result); |
5780 | }); |
5781 | } |
5782 | } |
5783 | } |
5784 | |
5785 | TEST_F(LazyOpsTest, TestIndexFillWithTensorInPlace) { |
5786 | torch::Tensor index = torch::tensor( |
5787 | {0, 2}, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5788 | for (torch::ScalarType scalar_type : |
5789 | {torch::kFloat, |
5790 | torch::kByte, |
5791 | torch::kChar, |
5792 | torch::kShort, |
5793 | torch::kInt, |
5794 | torch::kLong}) { |
5795 | torch::Tensor value = torch::scalar_tensor( |
5796 | 42, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5797 | int rank = 3; |
5798 | for (int dim = -rank; dim < rank; ++dim) { |
5799 | ForEachDevice([&](const torch::Device& device) { |
5800 | torch::Tensor base = isFloatingType(scalar_type) |
5801 | ? torch::rand( |
5802 | {3, 4, 5}, |
5803 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5804 | : torch::randint( |
5805 | 100, |
5806 | {3, 4, 5}, |
5807 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5808 | torch::Tensor lazy_base = CopyToDevice(base.clone(), device); |
5809 | torch::Tensor result = base.index_fill_(dim, index, value); |
5810 | torch::Tensor lazy_index = CopyToDevice(index, device); |
5811 | torch::Tensor lazy_value = CopyToDevice(value, device); |
5812 | torch::Tensor lazy_result = |
5813 | lazy_base.index_fill_(dim, lazy_index, lazy_value); |
5814 | AllEqual(result, lazy_result); |
5815 | AllEqual(base, lazy_base); |
5816 | }); |
5817 | } |
5818 | } |
5819 | } |
5820 | |
5821 | TEST_F(LazyOpsTest, TestIndexFillRank0) { |
5822 | torch::Tensor index = torch::scalar_tensor( |
5823 | 2, torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5824 | for (torch::ScalarType scalar_type : |
5825 | {torch::kFloat, |
5826 | torch::kByte, |
5827 | torch::kChar, |
5828 | torch::kShort, |
5829 | torch::kInt, |
5830 | torch::kLong}) { |
5831 | torch::Tensor base = isFloatingType(scalar_type) |
5832 | ? torch::rand( |
5833 | {3, 4, 5}, |
5834 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5835 | : torch::randint( |
5836 | 100, |
5837 | {3, 4, 5}, |
5838 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5839 | torch::Tensor value = torch::scalar_tensor( |
5840 | 42, torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5841 | int rank = base.dim(); |
5842 | for (int dim = -rank; dim < rank; ++dim) { |
5843 | torch::Tensor result = torch::index_fill(base, dim, index, value); |
5844 | ForEachDevice([&](const torch::Device& device) { |
5845 | torch::Tensor lazy_base = CopyToDevice(base, device); |
5846 | torch::Tensor lazy_index = CopyToDevice(index, device); |
5847 | torch::Tensor lazy_value = CopyToDevice(value, device); |
5848 | torch::Tensor lazy_result = |
5849 | torch::index_fill(lazy_base, dim, lazy_index, lazy_value); |
5850 | AllEqual(result, lazy_result); |
5851 | }); |
5852 | } |
5853 | } |
5854 | } |
5855 | |
5856 | TEST_F(LazyOpsTest, TestIndexAdd) { |
5857 | int index_size = 10; |
5858 | for (torch::ScalarType scalar_type : |
5859 | {torch::kFloat, |
5860 | torch::kByte, |
5861 | torch::kChar, |
5862 | torch::kShort, |
5863 | torch::kInt, |
5864 | torch::kLong}) { |
5865 | torch::Tensor base = isFloatingType(scalar_type) |
5866 | ? torch::rand( |
5867 | {5, 3, 7}, |
5868 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5869 | : torch::randint( |
5870 | 100, |
5871 | {5, 3, 7}, |
5872 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5873 | int rank = base.dim(); |
5874 | for (int dim = -rank; dim < rank; ++dim) { |
5875 | for (torch::ScalarType index_scalar_type : {torch::kInt, torch::kLong}) { |
5876 | torch::Tensor index = torch::randint( |
5877 | 0, |
5878 | base.size(dim), |
5879 | {index_size}, |
5880 | torch::TensorOptions(index_scalar_type).device(DefaultDevice())); |
5881 | std::vector<int64_t> value_sizes( |
5882 | base.sizes().begin(), base.sizes().end()); |
5883 | int canonical_dim = dim < 0 ? dim + rank : dim; |
5884 | value_sizes[canonical_dim] = index_size; |
5885 | torch::Tensor value = isFloatingType(scalar_type) |
5886 | ? torch::rand( |
5887 | value_sizes, |
5888 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5889 | : torch::randint( |
5890 | 100, |
5891 | value_sizes, |
5892 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5893 | torch::Tensor result = torch::index_add(base, dim, index, value); |
5894 | ForEachDevice([&](const torch::Device& device) { |
5895 | torch::Tensor lazy_base = CopyToDevice(base, device); |
5896 | torch::Tensor lazy_index = CopyToDevice(index, device); |
5897 | torch::Tensor lazy_value = CopyToDevice(value, device); |
5898 | torch::Tensor lazy_result = |
5899 | torch::index_add(lazy_base, dim, lazy_index, lazy_value); |
5900 | AllClose(result, lazy_result); |
5901 | }); |
5902 | } |
5903 | } |
5904 | } |
5905 | } |
5906 | |
5907 | TEST_F(LazyOpsTest, TestIndexAddInPlace) { |
5908 | int index_size = 10; |
5909 | int rank = 3; |
5910 | for (torch::ScalarType scalar_type : |
5911 | {torch::kFloat, |
5912 | torch::kByte, |
5913 | torch::kChar, |
5914 | torch::kShort, |
5915 | torch::kInt, |
5916 | torch::kLong}) { |
5917 | for (int dim = -rank; dim < rank; ++dim) { |
5918 | ForEachDevice([&](const torch::Device& device) { |
5919 | torch::Tensor base = isFloatingType(scalar_type) |
5920 | ? torch::rand( |
5921 | {5, 3, 7}, |
5922 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5923 | : torch::randint( |
5924 | 100, |
5925 | {5, 3, 7}, |
5926 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5927 | torch::Tensor index = torch::randint( |
5928 | 0, |
5929 | base.size(dim), |
5930 | {index_size}, |
5931 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5932 | std::vector<int64_t> value_sizes( |
5933 | base.sizes().begin(), base.sizes().end()); |
5934 | int canonical_dim = dim < 0 ? dim + rank : dim; |
5935 | value_sizes[canonical_dim] = index_size; |
5936 | torch::Tensor value = isFloatingType(scalar_type) |
5937 | ? torch::rand( |
5938 | value_sizes, |
5939 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5940 | : torch::randint( |
5941 | 100, |
5942 | value_sizes, |
5943 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5944 | torch::Tensor lazy_base = CopyToDevice(base.clone(), device); |
5945 | torch::Tensor result = base.index_add_(dim, index, value); |
5946 | torch::Tensor lazy_index = CopyToDevice(index, device); |
5947 | torch::Tensor lazy_value = CopyToDevice(value, device); |
5948 | torch::Tensor lazy_result = |
5949 | lazy_base.index_add_(dim, lazy_index, lazy_value); |
5950 | AllClose(result, lazy_result); |
5951 | AllClose(base, lazy_base); |
5952 | }); |
5953 | } |
5954 | } |
5955 | } |
5956 | |
5957 | TEST_F(LazyOpsTest, TestIndexAddRank0) { |
5958 | for (torch::ScalarType scalar_type : |
5959 | {torch::kFloat, |
5960 | torch::kByte, |
5961 | torch::kChar, |
5962 | torch::kShort, |
5963 | torch::kInt, |
5964 | torch::kLong}) { |
5965 | torch::Tensor base = isFloatingType(scalar_type) |
5966 | ? torch::rand( |
5967 | {5, 3, 7}, |
5968 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5969 | : torch::randint( |
5970 | 100, |
5971 | {5, 3, 7}, |
5972 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5973 | int rank = base.dim(); |
5974 | for (int dim = -rank; dim < rank; ++dim) { |
5975 | torch::Tensor index = torch::randint( |
5976 | 0, |
5977 | base.size(dim), |
5978 | at::IntArrayRef{}, |
5979 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
5980 | std::vector<int64_t> value_sizes( |
5981 | base.sizes().begin(), base.sizes().end()); |
5982 | int canonical_dim = dim < 0 ? dim + rank : dim; |
5983 | value_sizes[canonical_dim] = 1; |
5984 | torch::Tensor value = isFloatingType(scalar_type) |
5985 | ? torch::rand( |
5986 | value_sizes, |
5987 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
5988 | : torch::randint( |
5989 | 100, |
5990 | value_sizes, |
5991 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
5992 | torch::Tensor result = torch::index_add(base, dim, index, value); |
5993 | ForEachDevice([&](const torch::Device& device) { |
5994 | torch::Tensor lazy_base = CopyToDevice(base, device); |
5995 | torch::Tensor lazy_index = CopyToDevice(index, device); |
5996 | torch::Tensor lazy_value = CopyToDevice(value, device); |
5997 | torch::Tensor lazy_result = |
5998 | torch::index_add(lazy_base, dim, lazy_index, lazy_value); |
5999 | AllEqual(result, lazy_result); |
6000 | }); |
6001 | } |
6002 | } |
6003 | } |
6004 | |
6005 | TEST_F(LazyOpsTest, TestIndexCopy) { |
6006 | for (torch::ScalarType scalar_type : |
6007 | {torch::kFloat, |
6008 | torch::kByte, |
6009 | torch::kChar, |
6010 | torch::kShort, |
6011 | torch::kInt, |
6012 | torch::kLong}) { |
6013 | torch::Tensor base = isFloatingType(scalar_type) |
6014 | ? torch::rand( |
6015 | {5, 3, 7}, |
6016 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
6017 | : torch::randint( |
6018 | 100, |
6019 | {5, 3, 7}, |
6020 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
6021 | int rank = base.dim(); |
6022 | for (int dim = -rank; dim < rank; ++dim) { |
6023 | torch::Tensor index = torch::randperm( |
6024 | base.size(dim), |
6025 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
6026 | torch::Tensor value = isFloatingType(scalar_type) |
6027 | ? torch::rand( |
6028 | base.sizes(), |
6029 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
6030 | : torch::randint( |
6031 | 100, |
6032 | base.sizes(), |
6033 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
6034 | torch::Tensor result = torch::index_copy(base, dim, index, value); |
6035 | ForEachDevice([&](const torch::Device& device) { |
6036 | torch::Tensor lazy_base = CopyToDevice(base, device); |
6037 | torch::Tensor lazy_index = CopyToDevice(index, device); |
6038 | torch::Tensor lazy_value = CopyToDevice(value, device); |
6039 | torch::Tensor lazy_result = |
6040 | torch::index_copy(lazy_base, dim, lazy_index, lazy_value); |
6041 | AllEqual(result, lazy_result); |
6042 | }); |
6043 | } |
6044 | } |
6045 | } |
6046 | |
6047 | TEST_F(LazyOpsTest, TestIndexCopyInPlace) { |
6048 | if (IsCuda()) { |
6049 | GTEST_SKIP(); |
6050 | } |
6051 | int index_size = 10; |
6052 | int rank = 3; |
6053 | for (torch::ScalarType scalar_type : |
6054 | {torch::kFloat, |
6055 | torch::kByte, |
6056 | torch::kChar, |
6057 | torch::kShort, |
6058 | torch::kInt, |
6059 | torch::kLong}) { |
6060 | for (int dim = -rank; dim < rank; ++dim) { |
6061 | ForEachDevice([&](const torch::Device& device) { |
6062 | torch::Tensor base = isFloatingType(scalar_type) |
6063 | ? torch::rand( |
6064 | {5, 3, 7}, |
6065 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
6066 | : torch::randint( |
6067 | 100, |
6068 | {5, 3, 7}, |
6069 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
6070 | torch::Tensor index = torch::randint( |
6071 | 0, |
6072 | base.size(dim), |
6073 | {index_size}, |
6074 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
6075 | std::vector<int64_t> value_sizes( |
6076 | base.sizes().begin(), base.sizes().end()); |
6077 | int canonical_dim = dim < 0 ? dim + rank : dim; |
6078 | value_sizes[canonical_dim] = index_size; |
6079 | torch::Tensor value = isFloatingType(scalar_type) |
6080 | ? torch::rand( |
6081 | value_sizes, |
6082 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
6083 | : torch::randint( |
6084 | 100, |
6085 | value_sizes, |
6086 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
6087 | torch::Tensor lazy_base = CopyToDevice(base.clone(), device); |
6088 | torch::Tensor result = base.index_copy_(dim, index, value); |
6089 | torch::Tensor lazy_index = CopyToDevice(index, device); |
6090 | torch::Tensor lazy_value = CopyToDevice(value, device); |
6091 | torch::Tensor lazy_result = |
6092 | lazy_base.index_copy_(dim, lazy_index, lazy_value); |
6093 | AllEqual(result, lazy_result); |
6094 | AllEqual(base, lazy_base); |
6095 | }); |
6096 | } |
6097 | } |
6098 | } |
6099 | |
6100 | TEST_F(LazyOpsTest, TestIndexCopyRank0) { |
6101 | for (torch::ScalarType scalar_type : |
6102 | {torch::kFloat, |
6103 | torch::kByte, |
6104 | torch::kChar, |
6105 | torch::kShort, |
6106 | torch::kInt, |
6107 | torch::kLong}) { |
6108 | torch::Tensor base = isFloatingType(scalar_type) |
6109 | ? torch::rand( |
6110 | {5, 3, 7}, |
6111 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
6112 | : torch::randint( |
6113 | 100, |
6114 | {5, 3, 7}, |
6115 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
6116 | int rank = base.dim(); |
6117 | for (int dim = -rank; dim < rank; ++dim) { |
6118 | torch::Tensor index = torch::randint( |
6119 | 0, |
6120 | base.size(dim), |
6121 | at::IntArrayRef{}, |
6122 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
6123 | std::vector<int64_t> value_sizes( |
6124 | base.sizes().begin(), base.sizes().end()); |
6125 | int canonical_dim = dim < 0 ? dim + rank : dim; |
6126 | value_sizes[canonical_dim] = 1; |
6127 | torch::Tensor value = isFloatingType(scalar_type) |
6128 | ? torch::rand( |
6129 | value_sizes, |
6130 | torch::TensorOptions(scalar_type).device(DefaultDevice())) |
6131 | : torch::randint( |
6132 | 100, |
6133 | value_sizes, |
6134 | torch::TensorOptions(scalar_type).device(DefaultDevice())); |
6135 | torch::Tensor result = torch::index_copy(base, dim, index, value); |
6136 | ForEachDevice([&](const torch::Device& device) { |
6137 | torch::Tensor lazy_base = CopyToDevice(base, device); |
6138 | torch::Tensor lazy_index = CopyToDevice(index, device); |
6139 | torch::Tensor lazy_value = CopyToDevice(value, device); |
6140 | torch::Tensor lazy_result = |
6141 | torch::index_copy(lazy_base, dim, lazy_index, lazy_value); |
6142 | AllEqual(result, lazy_result); |
6143 | }); |
6144 | } |
6145 | } |
6146 | } |
6147 | |
6148 | TEST_F(LazyOpsTest, TestRelu) { |
6149 | torch::Tensor input = torch::rand( |
6150 | {2, 1, 4, 6}, |
6151 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6152 | torch::Tensor output = torch::relu(input); |
6153 | ForEachDevice([&](const torch::Device& device) { |
6154 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6155 | torch::Tensor lazy_output = torch::relu(lazy_input); |
6156 | AllClose(output, lazy_output); |
6157 | }); |
6158 | } |
6159 | |
6160 | TEST_F(LazyOpsTest, TestReluInPlace) { |
6161 | torch::Tensor input = torch::rand( |
6162 | {2, 1, 4, 6}, |
6163 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6164 | ForEachDevice([&](const torch::Device& device) { |
6165 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6166 | torch::Tensor output = torch::relu_(input); |
6167 | torch::Tensor lazy_output = torch::relu_(lazy_input); |
6168 | AllClose(output, lazy_output); |
6169 | AllClose(input, lazy_input); |
6170 | }); |
6171 | } |
6172 | |
6173 | TEST_F(LazyOpsTest, TestHardshrink) { |
6174 | torch::Tensor input = torch::randn( |
6175 | {10}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6176 | torch::Tensor output = torch::hardshrink(input); |
6177 | ForEachDevice([&](const torch::Device& device) { |
6178 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6179 | torch::Tensor lazy_output = torch::hardshrink(lazy_input); |
6180 | AllClose(output, lazy_output); |
6181 | }); |
6182 | } |
6183 | |
6184 | TEST_F(LazyOpsTest, TestHardSigmoid) { |
6185 | torch::Tensor input = torch::randn( |
6186 | {10}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6187 | torch::Tensor output = torch::hardsigmoid(input); |
6188 | ForEachDevice([&](const torch::Device& device) { |
6189 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6190 | torch::Tensor lazy_output = torch::hardsigmoid(lazy_input); |
6191 | AllClose(output, lazy_output); |
6192 | }); |
6193 | } |
6194 | |
6195 | TEST_F(LazyOpsTest, TestHardSigmoidInPlace) { |
6196 | ForEachDevice([&](const torch::Device& device) { |
6197 | torch::Tensor input = torch::randn( |
6198 | {10}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6199 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6200 | torch::Tensor output = torch::hardsigmoid_(input); |
6201 | torch::Tensor lazy_output = torch::hardsigmoid_(lazy_input); |
6202 | AllClose(input, lazy_input); |
6203 | AllClose(output, lazy_output); |
6204 | }); |
6205 | } |
6206 | |
6207 | TEST_F(LazyOpsTest, TestHardSigmoidBackward) { |
6208 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
6209 | return torch::hardsigmoid(inputs[0]); |
6210 | }; |
6211 | ForEachDevice([&](const torch::Device& device) { |
6212 | TestBackward( |
6213 | {torch::randn( |
6214 | {10}, |
6215 | torch::TensorOptions(torch::kFloat) |
6216 | .device(DefaultDevice()) |
6217 | .requires_grad(true))}, |
6218 | device, |
6219 | testfn); |
6220 | }); |
6221 | } |
6222 | |
6223 | TEST_F(LazyOpsTest, TestSoftshrink) { |
6224 | torch::Tensor input = torch::randn( |
6225 | {10}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6226 | torch::Tensor output = torch::softshrink(input); |
6227 | ForEachDevice([&](const torch::Device& device) { |
6228 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6229 | torch::Tensor lazy_output = torch::softshrink(lazy_input); |
6230 | AllClose(output, lazy_output); |
6231 | }); |
6232 | } |
6233 | |
6234 | TEST_F(LazyOpsTest, TestHardtanh) { |
6235 | torch::Tensor input = torch::randn( |
6236 | {10}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6237 | torch::Tensor output = torch::hardtanh(input); |
6238 | ForEachDevice([&](const torch::Device& device) { |
6239 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6240 | torch::Tensor lazy_output = torch::hardtanh(lazy_input); |
6241 | AllClose(output, lazy_output); |
6242 | }); |
6243 | } |
6244 | |
6245 | TEST_F(LazyOpsTest, TestHardtanhInPlace) { |
6246 | torch::Tensor input = torch::randn( |
6247 | {10}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6248 | ForEachDevice([&](const torch::Device& device) { |
6249 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6250 | torch::Tensor output = torch::hardtanh_(input); |
6251 | torch::Tensor lazy_output = torch::hardtanh_(lazy_input); |
6252 | AllClose(output, lazy_output); |
6253 | AllClose(input, lazy_input); |
6254 | }); |
6255 | } |
6256 | |
6257 | TEST_F(LazyOpsTest, TestLeakyRelu) { |
6258 | torch::Tensor input = torch::rand( |
6259 | {2, 1, 4, 6}, |
6260 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6261 | double negative_slope = 0.01; |
6262 | torch::Tensor output = torch::leaky_relu(input, negative_slope); |
6263 | ForEachDevice([&](const torch::Device& device) { |
6264 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6265 | torch::Tensor lazy_output = torch::leaky_relu(lazy_input, negative_slope); |
6266 | AllClose(output, lazy_output); |
6267 | }); |
6268 | } |
6269 | |
6270 | TEST_F(LazyOpsTest, TestLeakyReluInPlace) { |
6271 | torch::Tensor input = torch::rand( |
6272 | {2, 1, 4, 6}, |
6273 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6274 | double negative_slope = 0.01; |
6275 | ForEachDevice([&](const torch::Device& device) { |
6276 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6277 | torch::Tensor output = torch::leaky_relu_(input, negative_slope); |
6278 | torch::Tensor lazy_output = torch::leaky_relu_(lazy_input, negative_slope); |
6279 | AllClose(output, lazy_output); |
6280 | AllClose(input, lazy_input); |
6281 | }); |
6282 | } |
6283 | |
6284 | TEST_F(LazyOpsTest, TestExp) { |
6285 | torch::Tensor a = torch::rand( |
6286 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6287 | torch::Tensor b = torch::exp(a); |
6288 | ForEachDevice([&](const torch::Device& device) { |
6289 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6290 | torch::Tensor lazy_b = torch::exp(lazy_a); |
6291 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
6292 | }); |
6293 | } |
6294 | |
6295 | TEST_F(LazyOpsTest, TestExpm1) { |
6296 | torch::Tensor a = torch::rand( |
6297 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6298 | torch::Tensor b = torch::expm1(a); |
6299 | ForEachDevice([&](const torch::Device& device) { |
6300 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6301 | torch::Tensor lazy_b = torch::expm1(lazy_a); |
6302 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
6303 | }); |
6304 | } |
6305 | |
6306 | TEST_F(LazyOpsTest, TestLog) { |
6307 | torch::Tensor a = torch::rand( |
6308 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6309 | torch::Tensor b = torch::log(a); |
6310 | ForEachDevice([&](const torch::Device& device) { |
6311 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6312 | torch::Tensor lazy_b = torch::log(lazy_a); |
6313 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
6314 | }); |
6315 | } |
6316 | |
6317 | TEST_F(LazyOpsTest, TestLog2) { |
6318 | torch::Tensor a = torch::rand( |
6319 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6320 | torch::Tensor b = torch::log2(a); |
6321 | ForEachDevice([&](const torch::Device& device) { |
6322 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6323 | torch::Tensor lazy_b = torch::log2(lazy_a); |
6324 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
6325 | }); |
6326 | } |
6327 | |
6328 | TEST_F(LazyOpsTest, TestLog10) { |
6329 | torch::Tensor a = torch::rand( |
6330 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6331 | torch::Tensor b = torch::log10(a); |
6332 | ForEachDevice([&](const torch::Device& device) { |
6333 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6334 | torch::Tensor lazy_b = torch::log10(lazy_a); |
6335 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
6336 | }); |
6337 | } |
6338 | |
6339 | TEST_F(LazyOpsTest, TestLog1p) { |
6340 | torch::Tensor a = torch::rand( |
6341 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6342 | torch::Tensor b = torch::log1p(a); |
6343 | ForEachDevice([&](const torch::Device& device) { |
6344 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6345 | torch::Tensor lazy_b = torch::log1p(lazy_a); |
6346 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
6347 | }); |
6348 | } |
6349 | |
6350 | TEST_F(LazyOpsTest, TestErf) { |
6351 | torch::Tensor a = torch::randn( |
6352 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6353 | torch::Tensor b = torch::erf(a); |
6354 | ForEachDevice([&](const torch::Device& device) { |
6355 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6356 | torch::Tensor lazy_b = torch::erf(lazy_a); |
6357 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
6358 | }); |
6359 | } |
6360 | |
6361 | TEST_F(LazyOpsTest, TestErfc) { |
6362 | torch::Tensor a = torch::randn( |
6363 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6364 | torch::Tensor b = torch::erfc(a); |
6365 | ForEachDevice([&](const torch::Device& device) { |
6366 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6367 | torch::Tensor lazy_b = torch::erfc(lazy_a); |
6368 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
6369 | }); |
6370 | } |
6371 | |
6372 | TEST_F(LazyOpsTest, TestErfinv) { |
6373 | torch::Tensor a = torch::rand( |
6374 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6375 | torch::Tensor b = torch::erfinv(a); |
6376 | ForEachDevice([&](const torch::Device& device) { |
6377 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6378 | torch::Tensor lazy_b = torch::erfinv(lazy_a); |
6379 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
6380 | }); |
6381 | } |
6382 | |
6383 | TEST_F(LazyOpsTest, TestSqrt) { |
6384 | torch::Tensor a = torch::abs(torch::rand( |
6385 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice()))); |
6386 | torch::Tensor b = torch::sqrt(a); |
6387 | ForEachDevice([&](const torch::Device& device) { |
6388 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6389 | torch::Tensor lazy_b = torch::sqrt(lazy_a); |
6390 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
6391 | }); |
6392 | } |
6393 | |
6394 | TEST_F(LazyOpsTest, TestRsqrt) { |
6395 | torch::Tensor a = torch::abs(torch::rand( |
6396 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice()))); |
6397 | torch::Tensor b = torch::rsqrt(a); |
6398 | ForEachDevice([&](const torch::Device& device) { |
6399 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6400 | torch::Tensor lazy_b = torch::rsqrt(lazy_a); |
6401 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
6402 | }); |
6403 | } |
6404 | |
6405 | TEST_F(LazyOpsTest, TestReciprocal) { |
6406 | torch::Tensor a = torch::randn( |
6407 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6408 | torch::Tensor b = torch::reciprocal(a); |
6409 | ForEachDevice([&](const torch::Device& device) { |
6410 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6411 | torch::Tensor lazy_b = torch::reciprocal(lazy_a); |
6412 | AllClose(b, lazy_b, /*rtol=*/1e-3, /*atol=*/1e-5); |
6413 | }); |
6414 | } |
6415 | |
6416 | TEST_F(LazyOpsTest, TestPowTensorScalar) { |
6417 | torch::Tensor base = torch::rand( |
6418 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6419 | torch::Scalar exponent = 4.09; |
6420 | torch::Tensor result = torch::pow(base, exponent); |
6421 | ForEachDevice([&](const torch::Device& device) { |
6422 | torch::Tensor lazy_base = CopyToDevice(base, device); |
6423 | torch::Tensor lazy_result = torch::pow(lazy_base, exponent); |
6424 | AllClose(result, lazy_result, /*rtol=*/1e-3, /*atol=*/1e-5); |
6425 | }); |
6426 | } |
6427 | |
6428 | TEST_F(LazyOpsTest, TestPowTensorScalarInPlace) { |
6429 | torch::Tensor base = torch::rand( |
6430 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6431 | torch::Scalar exponent = 4.09; |
6432 | ForEachDevice([&](const torch::Device& device) { |
6433 | torch::Tensor lazy_base = CopyToDevice(base.clone(), device); |
6434 | torch::Tensor result = base.pow_(exponent); |
6435 | torch::Tensor lazy_result = lazy_base.pow_(exponent); |
6436 | AllClose(result, lazy_result, /*rtol=*/1e-3, /*atol=*/1e-5); |
6437 | AllClose(base, lazy_base, /*rtol=*/1e-3, /*atol=*/1e-5); |
6438 | }); |
6439 | } |
6440 | |
6441 | TEST_F(LazyOpsTest, TestPowTensorTensor) { |
6442 | torch::Tensor base = torch::abs(torch::rand( |
6443 | {4, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice()))); |
6444 | torch::Tensor exponent = torch::rand( |
6445 | {4, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6446 | torch::Tensor result = torch::pow(base, exponent); |
6447 | ForEachDevice([&](const torch::Device& device) { |
6448 | torch::Tensor lazy_base = CopyToDevice(base, device); |
6449 | torch::Tensor lazy_exponent = CopyToDevice(exponent, device); |
6450 | torch::Tensor lazy_result = torch::pow(lazy_base, lazy_exponent); |
6451 | AllClose(result, lazy_result, /*rtol=*/1e-3, /*atol=*/1e-5); |
6452 | }); |
6453 | } |
6454 | |
6455 | TEST_F(LazyOpsTest, TestPowTensorTensorInPlace) { |
6456 | torch::Tensor base = torch::abs(torch::rand( |
6457 | {4, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice()))); |
6458 | torch::Tensor exponent = torch::rand( |
6459 | {4, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6460 | ForEachDevice([&](const torch::Device& device) { |
6461 | torch::Tensor lazy_base = CopyToDevice(base.clone(), device); |
6462 | torch::Tensor result = base.pow_(exponent); |
6463 | torch::Tensor lazy_exponent = CopyToDevice(exponent, device); |
6464 | torch::Tensor lazy_result = lazy_base.pow_(lazy_exponent); |
6465 | AllClose(result, lazy_result, /*rtol=*/1e-3, /*atol=*/1e-5); |
6466 | AllClose(base, lazy_base, /*rtol=*/1e-3, /*atol=*/1e-5); |
6467 | }); |
6468 | } |
6469 | |
6470 | TEST_F(LazyOpsTest, TestPowTensorTensorBroadcast) { |
6471 | torch::Tensor base = torch::abs(torch::rand( |
6472 | {4, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice()))); |
6473 | torch::Tensor exponent = torch::rand( |
6474 | {4, 1}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6475 | torch::Tensor result = torch::pow(base, exponent); |
6476 | ForEachDevice([&](const torch::Device& device) { |
6477 | torch::Tensor lazy_base = CopyToDevice(base, device); |
6478 | torch::Tensor lazy_exponent = CopyToDevice(exponent, device); |
6479 | torch::Tensor lazy_result = torch::pow(lazy_base, lazy_exponent); |
6480 | AllClose(result, lazy_result, /*rtol=*/1e-3, /*atol=*/1e-5); |
6481 | }); |
6482 | } |
6483 | |
6484 | TEST_F(LazyOpsTest, TestPowScalarTensor) { |
6485 | torch::Scalar base = 3.5; |
6486 | torch::Tensor exponent = torch::rand({4, 2}); |
6487 | torch::Tensor result = torch::pow(base, exponent); |
6488 | ForEachDevice([&](const torch::Device& device) { |
6489 | torch::Tensor lazy_exponent = CopyToDevice(exponent, device); |
6490 | torch::Tensor lazy_result = torch::pow(base, lazy_exponent); |
6491 | AllClose(result, lazy_result, /*rtol=*/1e-3, /*atol=*/1e-5); |
6492 | }); |
6493 | } |
6494 | |
6495 | TEST_F(LazyOpsTest, TestPowIntExponent) { |
6496 | torch::Tensor base = torch::abs(torch::rand( |
6497 | {4, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice()))); |
6498 | torch::Scalar exponent = 3; |
6499 | torch::Tensor result = torch::pow(base, exponent); |
6500 | ForEachDevice([&](const torch::Device& device) { |
6501 | torch::Tensor lazy_base = CopyToDevice(base, device); |
6502 | torch::Tensor lazy_result = torch::pow(lazy_base, exponent); |
6503 | AllClose(result, lazy_result, /*rtol=*/1e-3, /*atol=*/1e-5); |
6504 | }); |
6505 | } |
6506 | |
6507 | TEST_F(LazyOpsTest, TestFmodScalar) { |
6508 | torch::Tensor a = |
6509 | torch::rand( |
6510 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
6511 | 100.0; |
6512 | torch::Scalar divisor = 2.0; |
6513 | torch::Tensor b = torch::fmod(a, divisor); |
6514 | ForEachDevice([&](const torch::Device& device) { |
6515 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6516 | torch::Tensor lazy_b = torch::fmod(lazy_a, divisor); |
6517 | AllClose(b, lazy_b); |
6518 | }); |
6519 | } |
6520 | |
6521 | TEST_F(LazyOpsTest, TestFmodScalarInPlace) { |
6522 | torch::Scalar divisor = 2.0; |
6523 | ForEachDevice([&](const torch::Device& device) { |
6524 | torch::Tensor a = |
6525 | torch::rand( |
6526 | {2, 2}, |
6527 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
6528 | 100.0; |
6529 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6530 | torch::Tensor b = a.fmod_(divisor); |
6531 | torch::Tensor lazy_b = lazy_a.fmod_(divisor); |
6532 | AllClose(b, lazy_b); |
6533 | AllClose(a, lazy_a); |
6534 | }); |
6535 | } |
6536 | |
6537 | TEST_F(LazyOpsTest, TestFmodTensor) { |
6538 | torch::Tensor a = |
6539 | torch::rand( |
6540 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
6541 | 100.0; |
6542 | torch::Tensor b = |
6543 | torch::rand( |
6544 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
6545 | 10.0; |
6546 | torch::Tensor c = torch::fmod(a, b); |
6547 | ForEachDevice([&](const torch::Device& device) { |
6548 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6549 | torch::Tensor lazy_b = CopyToDevice(b, device); |
6550 | torch::Tensor lazy_c = torch::fmod(lazy_a, lazy_b); |
6551 | AllClose(c, lazy_c); |
6552 | }); |
6553 | } |
6554 | |
6555 | TEST_F(LazyOpsTest, TestFmodTensorInPlace) { |
6556 | torch::Tensor b = |
6557 | torch::rand( |
6558 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
6559 | 10.0; |
6560 | ForEachDevice([&](const torch::Device& device) { |
6561 | torch::Tensor a = |
6562 | torch::rand( |
6563 | {2, 2}, |
6564 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
6565 | 100.0; |
6566 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6567 | torch::Tensor c = a.fmod_(b); |
6568 | torch::Tensor lazy_b = CopyToDevice(b, device); |
6569 | torch::Tensor lazy_c = lazy_a.fmod_(lazy_b); |
6570 | AllClose(c, lazy_c); |
6571 | AllClose(a, lazy_a); |
6572 | }); |
6573 | } |
6574 | |
6575 | TEST_F(LazyOpsTest, TestRemainderScalar) { |
6576 | torch::Tensor a = |
6577 | torch::randn( |
6578 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
6579 | 100.0; |
6580 | torch::Scalar divisor = -2.0; |
6581 | torch::Tensor b = torch::remainder(a, divisor); |
6582 | ForEachDevice([&](const torch::Device& device) { |
6583 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6584 | torch::Tensor lazy_b = torch::remainder(lazy_a, divisor); |
6585 | AllClose(b, lazy_b); |
6586 | }); |
6587 | } |
6588 | |
6589 | TEST_F(LazyOpsTest, TestRemainderScalarInPlace) { |
6590 | torch::Scalar divisor = -2.0; |
6591 | ForEachDevice([&](const torch::Device& device) { |
6592 | torch::Tensor a = |
6593 | torch::randn( |
6594 | {2, 2}, |
6595 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
6596 | 100.0; |
6597 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6598 | torch::Tensor b = a.remainder_(divisor); |
6599 | torch::Tensor lazy_b = lazy_a.remainder_(divisor); |
6600 | AllClose(b, lazy_b); |
6601 | AllClose(a, lazy_a); |
6602 | }); |
6603 | } |
6604 | |
6605 | TEST_F(LazyOpsTest, TestRemainderTensor) { |
6606 | torch::Tensor a = |
6607 | torch::randn( |
6608 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
6609 | 100.0; |
6610 | torch::Tensor b = |
6611 | torch::randn( |
6612 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
6613 | 10.0; |
6614 | torch::Tensor c = torch::remainder(a, b); |
6615 | ForEachDevice([&](const torch::Device& device) { |
6616 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6617 | torch::Tensor lazy_b = CopyToDevice(b, device); |
6618 | torch::Tensor lazy_c = torch::remainder(lazy_a, lazy_b); |
6619 | AllClose(c, lazy_c, /*rtol=*/1e-4, /*atol=*/1e-6); |
6620 | }); |
6621 | } |
6622 | |
6623 | TEST_F(LazyOpsTest, TestRemainderTensorInPlace) { |
6624 | torch::Tensor b = |
6625 | torch::randn( |
6626 | {2, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
6627 | 10.0; |
6628 | ForEachDevice([&](const torch::Device& device) { |
6629 | torch::Tensor a = |
6630 | torch::randn( |
6631 | {2, 2}, |
6632 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())) * |
6633 | 100.0; |
6634 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6635 | torch::Tensor c = a.remainder_(b); |
6636 | torch::Tensor lazy_b = CopyToDevice(b, device); |
6637 | torch::Tensor lazy_c = lazy_a.remainder_(lazy_b); |
6638 | AllClose(c, lazy_c, /*rtol=*/1e-4, /*atol=*/1e-6); |
6639 | AllClose(a, lazy_a, /*rtol=*/1e-4, /*atol=*/1e-6); |
6640 | }); |
6641 | } |
6642 | |
6643 | TEST_F(LazyOpsTest, TestWhere) { |
6644 | torch::Tensor a = torch::rand( |
6645 | {3, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6646 | torch::Tensor b = torch::rand( |
6647 | {3, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6648 | torch::Tensor c = torch::empty( |
6649 | {3, 3}, torch::TensorOptions(torch::kByte).device(DefaultDevice())); |
6650 | for (int i = 0; i < 3; ++i) { |
6651 | for (int j = 0; j < 3; ++j) { |
6652 | c[i][j] = i == j; |
6653 | } |
6654 | } |
6655 | torch::Tensor d = torch::where(c, a, b); |
6656 | ForEachDevice([&](const torch::Device& device) { |
6657 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6658 | torch::Tensor lazy_b = CopyToDevice(b, device); |
6659 | torch::Tensor lazy_c = CopyToDevice(c, device); |
6660 | torch::Tensor lazy_d = torch::where(lazy_c, lazy_a, lazy_b); |
6661 | AllClose(d, lazy_d); |
6662 | }); |
6663 | } |
6664 | |
6665 | TEST_F(LazyOpsTest, TestWhereBroadcast) { |
6666 | torch::Tensor a = torch::rand( |
6667 | {3, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6668 | torch::Tensor b = torch::zeros( |
6669 | {}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6670 | torch::Tensor c = torch::empty( |
6671 | {3, 3}, torch::TensorOptions(torch::kByte).device(DefaultDevice())); |
6672 | for (int i = 0; i < 3; ++i) { |
6673 | for (int j = 0; j < 3; ++j) { |
6674 | c[i][j] = i == j; |
6675 | } |
6676 | } |
6677 | torch::Tensor d = torch::where(c, a, b); |
6678 | ForEachDevice([&](const torch::Device& device) { |
6679 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6680 | torch::Tensor lazy_b = CopyToDevice(b, device); |
6681 | torch::Tensor lazy_c = CopyToDevice(c, device); |
6682 | torch::Tensor lazy_d = torch::where(lazy_c, lazy_a, lazy_b); |
6683 | AllClose(d, lazy_d); |
6684 | }); |
6685 | } |
6686 | |
6687 | TEST_F(LazyOpsTest, TestThreshold) { |
6688 | torch::Tensor input = torch::rand( |
6689 | {2, 1, 4, 6}, |
6690 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6691 | float threshold = 0.4; |
6692 | float value = 20; |
6693 | torch::Tensor output = torch::threshold(input, threshold, value); |
6694 | ForEachDevice([&](const torch::Device& device) { |
6695 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6696 | torch::Tensor lazy_output = torch::threshold(lazy_input, threshold, value); |
6697 | AllClose(output, lazy_output); |
6698 | }); |
6699 | } |
6700 | |
6701 | TEST_F(LazyOpsTest, TestThresholdBackward) { |
6702 | float threshold = 0.4; |
6703 | float value = 20; |
6704 | |
6705 | auto testFunction = |
6706 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
6707 | return torch::threshold(inputs[0], threshold, value); |
6708 | }; |
6709 | |
6710 | ForEachDevice([&](const torch::Device& device) { |
6711 | TestBackward( |
6712 | {torch::rand( |
6713 | {2, 1, 4, 6}, |
6714 | torch::TensorOptions(torch::kFloat) |
6715 | .device(DefaultDevice()) |
6716 | .requires_grad(true))}, |
6717 | device, |
6718 | testFunction); |
6719 | }); |
6720 | } |
6721 | |
6722 | TEST_F(LazyOpsTest, TestThresholdInPlace) { |
6723 | torch::Tensor input = torch::rand( |
6724 | {2, 1, 4, 6}, |
6725 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6726 | torch::Tensor output = input.clone(); |
6727 | float threshold = 0.4; |
6728 | float value = 20; |
6729 | torch::threshold_(output, threshold, value); |
6730 | ForEachDevice([&](const torch::Device& device) { |
6731 | torch::Tensor lazy_output = CopyToDevice(input, device); |
6732 | torch::threshold_(lazy_output, threshold, value); |
6733 | AllClose(output, lazy_output); |
6734 | }); |
6735 | } |
6736 | |
6737 | TEST_F(LazyOpsTest, TestElu) { |
6738 | torch::Tensor input = torch::rand( |
6739 | {2, 1, 4, 6}, |
6740 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6741 | torch::Scalar alpha = 0.5; |
6742 | torch::Scalar scale = 2.5; |
6743 | torch::Scalar input_scale = 1.5; |
6744 | torch::Tensor output = torch::elu(input, alpha, scale, input_scale); |
6745 | ForEachDevice([&](const torch::Device& device) { |
6746 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6747 | torch::Tensor lazy_output = |
6748 | torch::elu(lazy_input, alpha, scale, input_scale); |
6749 | AllClose(output, lazy_output); |
6750 | }); |
6751 | } |
6752 | |
6753 | TEST_F(LazyOpsTest, TestEluInPlace) { |
6754 | torch::Tensor input = torch::rand( |
6755 | {2, 1, 4, 6}, |
6756 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6757 | torch::Scalar alpha = 0.5; |
6758 | torch::Scalar scale = 2.5; |
6759 | torch::Scalar input_scale = 1.5; |
6760 | ForEachDevice([&](const torch::Device& device) { |
6761 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6762 | torch::Tensor output = torch::elu_(input, alpha, scale, input_scale); |
6763 | torch::Tensor lazy_output = |
6764 | torch::elu_(lazy_input, alpha, scale, input_scale); |
6765 | AllClose(output, lazy_output); |
6766 | AllClose(input, lazy_input); |
6767 | }); |
6768 | } |
6769 | |
6770 | TEST_F(LazyOpsTest, TestSelu) { |
6771 | torch::Tensor input = torch::rand( |
6772 | {2, 1, 4, 6}, |
6773 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6774 | torch::Tensor output = torch::selu(input); |
6775 | ForEachDevice([&](const torch::Device& device) { |
6776 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6777 | torch::Tensor lazy_output = torch::selu(lazy_input); |
6778 | AllClose(output, lazy_output); |
6779 | }); |
6780 | } |
6781 | |
6782 | TEST_F(LazyOpsTest, TestSeluInPlace) { |
6783 | torch::Tensor input = torch::rand( |
6784 | {2, 1, 4, 6}, |
6785 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6786 | ForEachDevice([&](const torch::Device& device) { |
6787 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6788 | torch::Tensor output = torch::selu_(input); |
6789 | torch::Tensor lazy_output = torch::selu_(lazy_input); |
6790 | AllClose(output, lazy_output); |
6791 | AllClose(input, lazy_input); |
6792 | }); |
6793 | } |
6794 | |
6795 | TEST_F(LazyOpsTest, TestCelu) { |
6796 | torch::Tensor input = torch::rand( |
6797 | {2, 1, 4, 6}, |
6798 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6799 | torch::Scalar alpha = 2.5; |
6800 | torch::Tensor output = torch::celu(input, alpha); |
6801 | ForEachDevice([&](const torch::Device& device) { |
6802 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6803 | torch::Tensor lazy_output = torch::celu(lazy_input, alpha); |
6804 | AllClose(output, lazy_output); |
6805 | }); |
6806 | } |
6807 | |
6808 | TEST_F(LazyOpsTest, TestCeluInPlace) { |
6809 | torch::Tensor input = torch::rand( |
6810 | {2, 1, 4, 6}, |
6811 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6812 | torch::Scalar alpha = 2.5; |
6813 | ForEachDevice([&](const torch::Device& device) { |
6814 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6815 | torch::Tensor output = torch::celu_(input, alpha); |
6816 | torch::Tensor lazy_output = torch::celu_(lazy_input, alpha); |
6817 | AllClose(output, lazy_output); |
6818 | AllClose(input, lazy_input); |
6819 | }); |
6820 | } |
6821 | |
6822 | TEST_F(LazyOpsTest, TestGelu) { |
6823 | torch::Tensor input = torch::rand( |
6824 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6825 | torch::Tensor output = torch::gelu(input); |
6826 | ForEachDevice([&](const torch::Device& device) { |
6827 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6828 | torch::Tensor lazy_output = torch::gelu(lazy_input); |
6829 | AllClose(output, lazy_output); |
6830 | }); |
6831 | } |
6832 | |
6833 | TEST_F(LazyOpsTest, TestAddMatMul) { |
6834 | int in_channels = 32; |
6835 | int out_channels = 320; |
6836 | int labels = 50; |
6837 | torch::Tensor input = torch::rand( |
6838 | {in_channels, out_channels}, |
6839 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6840 | torch::Tensor weight = torch::rand( |
6841 | {out_channels, labels}, |
6842 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6843 | torch::Tensor bias = torch::rand( |
6844 | {labels}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6845 | // Test beta != 1. through the CPU interop. |
6846 | for (double beta : {1., 2.}) { |
6847 | torch::Tensor output = torch::addmm(bias, input, weight, /*beta=*/beta); |
6848 | ForEachDevice([&](const torch::Device& device) { |
6849 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6850 | torch::Tensor lazy_weight = CopyToDevice(weight, device); |
6851 | torch::Tensor lazy_bias = CopyToDevice(bias, device); |
6852 | torch::Tensor lazy_output = |
6853 | torch::addmm(lazy_bias, lazy_input, lazy_weight, /*beta=*/beta); |
6854 | AllClose(output, lazy_output); |
6855 | }); |
6856 | } |
6857 | } |
6858 | |
6859 | TEST_F(LazyOpsTest, TestEmbedding) { |
6860 | torch::Tensor a = torch::rand( |
6861 | {32, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6862 | torch::Tensor i = torch::randint( |
6863 | 0, |
6864 | 31, |
6865 | {3, 4}, |
6866 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
6867 | torch::Tensor b = torch::embedding( |
6868 | a, |
6869 | i, |
6870 | /*padding_idx=*/0, |
6871 | /*scale_grad_by_freq=*/false, |
6872 | /*sparse=*/false); |
6873 | ForEachDevice([&](const torch::Device& device) { |
6874 | torch::Tensor lazy_a = CopyToDevice(a, device); |
6875 | torch::Tensor lazy_i = CopyToDevice(i, device); |
6876 | torch::Tensor lazy_b = torch::embedding( |
6877 | lazy_a, |
6878 | lazy_i, |
6879 | /*padding_idx=*/0, |
6880 | /*scale_grad_by_freq=*/false, |
6881 | /*sparse=*/false); |
6882 | AllClose(b, lazy_b); |
6883 | }); |
6884 | } |
6885 | |
6886 | TEST_F(LazyOpsTest, TestOneHot) { |
6887 | int num_classes = 5; |
6888 | torch::Tensor input = torch::randint( |
6889 | 0, |
6890 | num_classes, |
6891 | {10}, |
6892 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
6893 | torch::Tensor output = torch::one_hot(input, num_classes); |
6894 | ForEachDevice([&](const torch::Device& device) { |
6895 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6896 | torch::Tensor lazy_output = torch::one_hot(lazy_input, num_classes); |
6897 | AllEqual(output, lazy_output); |
6898 | }); |
6899 | } |
6900 | |
6901 | TEST_F(LazyOpsTest, TestTranspose) { |
6902 | torch::Tensor input = torch::rand( |
6903 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6904 | torch::Tensor output = torch::t(input); |
6905 | ForEachDevice([&](const torch::Device& device) { |
6906 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6907 | torch::Tensor lazy_output = torch::t(lazy_input); |
6908 | AllClose(output, lazy_output); |
6909 | }); |
6910 | } |
6911 | |
6912 | TEST_F(LazyOpsTest, TestTransposeInPlace) { |
6913 | torch::Tensor input = torch::rand( |
6914 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6915 | ForEachDevice([&](const torch::Device& device) { |
6916 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6917 | torch::Tensor output = input.t_(); |
6918 | torch::Tensor lazy_output = lazy_input.t_(); |
6919 | EXPECT_EQ(lazy_output.sizes(), output.sizes()); |
6920 | AllClose(output, lazy_output); |
6921 | AllClose(input, lazy_input); |
6922 | }); |
6923 | } |
6924 | |
6925 | TEST_F(LazyOpsTest, TestReshape) { |
6926 | torch::Tensor input = torch::rand( |
6927 | {32, 20, 4, 4}, |
6928 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6929 | torch::Tensor output = torch::reshape(input, {-1, 320}); |
6930 | ForEachDevice([&](const torch::Device& device) { |
6931 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6932 | torch::Tensor lazy_output = torch::reshape(lazy_input, {-1, 320}); |
6933 | AllClose(output, lazy_output); |
6934 | }); |
6935 | } |
6936 | |
6937 | TEST_F(LazyOpsTest, TestResize) { |
6938 | // Testing a resize_() with target size bigger than original size is not |
6939 | // possible, as we fill with zeros, while pytorch fills with random garbage. |
6940 | torch::Tensor input = torch::rand( |
6941 | {2, 2, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6942 | torch::Tensor saved_input = input.clone(); |
6943 | input.resize_({3, 3}); |
6944 | ForEachDevice([&](const torch::Device& device) { |
6945 | torch::Tensor lazy_input = CopyToDevice(saved_input, device); |
6946 | lazy_input.resize_({3, 3}); |
6947 | AllClose(input, lazy_input); |
6948 | }); |
6949 | } |
6950 | |
6951 | TEST_F(LazyOpsTest, TestViewResize) { |
6952 | torch::Tensor input = torch::zeros( |
6953 | {8, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6954 | torch::Tensor saved_input = input.clone(); |
6955 | torch::Tensor output = input.view({4, 4}); |
6956 | output.resize_({3, 3}); |
6957 | ForEachDevice([&](const torch::Device& device) { |
6958 | torch::Tensor lazy_input = CopyToDevice(saved_input, device); |
6959 | torch::Tensor lazy_output = lazy_input.view({4, 4}); |
6960 | lazy_output.resize_({3, 3}); |
6961 | AllClose(input, lazy_input); |
6962 | AllClose(output, lazy_output); |
6963 | }); |
6964 | } |
6965 | |
6966 | TEST_F(LazyOpsTest, TestView) { |
6967 | torch::Tensor input = torch::rand( |
6968 | {32, 20, 4, 4}, |
6969 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6970 | torch::Tensor output = input.view({-1, 320}); |
6971 | ForEachDevice([&](const torch::Device& device) { |
6972 | torch::Tensor lazy_input = CopyToDevice(input, device); |
6973 | torch::Tensor lazy_output = lazy_input.view({-1, 320}); |
6974 | AllClose(output, lazy_output); |
6975 | }); |
6976 | } |
6977 | |
6978 | TEST_F(LazyOpsTest, TestViewMod) { |
6979 | torch::Tensor input = torch::zeros( |
6980 | {32, 20, 4, 4}, |
6981 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6982 | torch::Tensor one = torch::tensor( |
6983 | 1.0, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6984 | torch::Tensor output = input.view({-1, 320}); |
6985 | output.add_(one, 1.0); |
6986 | input.add_(one, 1.0); |
6987 | ForEachDevice([&](const torch::Device& device) { |
6988 | torch::Tensor xinput = torch::zeros( |
6989 | {32, 20, 4, 4}, |
6990 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
6991 | torch::Tensor lazy_input = CopyToDevice(xinput, device); |
6992 | torch::Tensor lazy_one = CopyToDevice(one, device); |
6993 | torch::Tensor lazy_output = lazy_input.view({-1, 320}); |
6994 | lazy_output.add_(lazy_one, 1.0); |
6995 | lazy_input.add_(lazy_one, 1.0); |
6996 | AllClose(output, lazy_output); |
6997 | AllClose(input, lazy_input); |
6998 | }); |
6999 | } |
7000 | |
7001 | TEST_F(LazyOpsTest, TestViewModComplex) { |
7002 | torch::Tensor input = torch::zeros( |
7003 | {32, 20, 4, 4}, |
7004 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7005 | torch::Tensor one = torch::tensor( |
7006 | 1.0, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7007 | torch::Tensor output1 = input.view({-1, 320}); |
7008 | output1.add_(one, 1.0); |
7009 | torch::Tensor output2 = input.view({-1, 160}); |
7010 | output2.add_(one, 1.0); |
7011 | ForEachDevice([&](const torch::Device& device) { |
7012 | torch::Tensor xinput = torch::zeros( |
7013 | {32, 20, 4, 4}, |
7014 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7015 | torch::Tensor lazy_input = CopyToDevice(xinput, device); |
7016 | torch::Tensor lazy_one = CopyToDevice(one, device); |
7017 | torch::Tensor lazy_output1 = lazy_input.view({-1, 320}); |
7018 | lazy_output1.add_(lazy_one, 1.0); |
7019 | torch::Tensor lazy_output2 = lazy_input.view({-1, 160}); |
7020 | lazy_output2.add_(lazy_one, 1.0); |
7021 | AllClose(output1, lazy_output1); |
7022 | AllClose(output2, lazy_output2); |
7023 | }); |
7024 | } |
7025 | |
7026 | TEST_F(LazyOpsTest, TestViewOfViewMod) { |
7027 | torch::Tensor input = torch::zeros( |
7028 | {32, 20, 4, 4}, |
7029 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7030 | torch::Tensor one = torch::tensor( |
7031 | 1.0, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7032 | torch::Tensor output1 = input.view({-1, 320}); |
7033 | output1.add_(one, 1.0); |
7034 | torch::Tensor output2 = output1.view({-1, 160}); |
7035 | output2.add_(one, 1.0); |
7036 | ForEachDevice([&](const torch::Device& device) { |
7037 | torch::Tensor xinput = torch::zeros( |
7038 | {32, 20, 4, 4}, |
7039 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7040 | torch::Tensor lazy_input = CopyToDevice(xinput, device); |
7041 | torch::Tensor lazy_one = CopyToDevice(one, device); |
7042 | torch::Tensor lazy_output1 = lazy_input.view({-1, 320}); |
7043 | lazy_output1.add_(lazy_one, 1.0); |
7044 | torch::Tensor lazy_output2 = lazy_output1.view({-1, 160}); |
7045 | lazy_output2.add_(lazy_one, 1.0); |
7046 | AllClose(output1, lazy_output1); |
7047 | AllClose(output2, lazy_output2); |
7048 | }); |
7049 | } |
7050 | |
7051 | TEST_F(LazyOpsTest, TestViewSqueezeAddInPlace) { |
7052 | torch::Tensor input = torch::zeros( |
7053 | {2, 3, 1}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7054 | std::vector<int64_t> view_size = {2, 3, 1, 1}; |
7055 | int squeeze_dim = 2; |
7056 | torch::Tensor one = torch::tensor( |
7057 | 1.0, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7058 | ForEachDevice([&](const torch::Device& device) { |
7059 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7060 | torch::Tensor output = input.view(view_size); |
7061 | output.squeeze_(squeeze_dim); |
7062 | output.add_(one, 1.0); |
7063 | torch::Tensor lazy_one = CopyToDevice(one, device); |
7064 | torch::Tensor lazy_output = lazy_input.view(view_size); |
7065 | lazy_output.squeeze_(squeeze_dim); |
7066 | lazy_output.add_(lazy_one, 1.0); |
7067 | AllClose(output, lazy_output); |
7068 | AllClose(input, lazy_input); |
7069 | }); |
7070 | } |
7071 | |
7072 | TEST_F(LazyOpsTest, TestUnsafeView) { |
7073 | torch::Tensor input = torch::rand( |
7074 | {32, 20, 4, 4}, |
7075 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7076 | torch::Tensor output = torch::_unsafe_view(input, {-1, 320}); |
7077 | ForEachDevice([&](const torch::Device& device) { |
7078 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7079 | torch::Tensor lazy_output = torch::_unsafe_view(lazy_input, {-1, 320}); |
7080 | AllClose(output, lazy_output); |
7081 | }); |
7082 | } |
7083 | |
7084 | TEST_F(LazyOpsTest, TestNarrow) { |
7085 | torch::Tensor a = torch::rand( |
7086 | {8, 10, 4, 4}, |
7087 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7088 | for (int64_t dim : {1, -3}) { |
7089 | for (int64_t start : {2, -8}) { |
7090 | torch::Tensor b = a.narrow(dim, start, 6); |
7091 | ForEachDevice([&](const torch::Device& device) { |
7092 | torch::Tensor lazy_a = CopyToDevice(a, device); |
7093 | torch::Tensor lazy_b = lazy_a.narrow(dim, start, 6); |
7094 | AllClose(b, lazy_b); |
7095 | }); |
7096 | } |
7097 | } |
7098 | } |
7099 | |
7100 | TEST_F(LazyOpsTest, TestNarrowUpdate) { |
7101 | for (int64_t dim : {1, -2}) { |
7102 | for (int64_t start : {2, -6}) { |
7103 | torch::Tensor a = torch::rand( |
7104 | {3, 8, 3}, |
7105 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7106 | torch::Tensor a_copy = a.clone(); |
7107 | torch::Tensor b = torch::rand( |
7108 | {3, 4, 3}, |
7109 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7110 | torch::Tensor c = a.narrow(dim, start, 4); |
7111 | c.add_(b, 1.0); |
7112 | ForEachDevice([&](const torch::Device& device) { |
7113 | torch::Tensor lazy_a = CopyToDevice(a_copy, device); |
7114 | torch::Tensor lazy_b = CopyToDevice(b, device); |
7115 | torch::Tensor lazy_c = lazy_a.narrow(dim, start, 4); |
7116 | lazy_c.add_(lazy_b, 1.0); |
7117 | AllClose(c, lazy_c); |
7118 | }); |
7119 | } |
7120 | } |
7121 | } |
7122 | |
7123 | TEST_F(LazyOpsTest, TestNarrowUpdateBaseCheck) { |
7124 | for (int64_t dim : {0, -2}) { |
7125 | for (int64_t start : {2, -6}) { |
7126 | torch::Tensor a = torch::zeros( |
7127 | {8, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7128 | torch::Tensor a_copy = a.clone(); |
7129 | torch::Tensor b = torch::ones( |
7130 | {4, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7131 | torch::Tensor c = a.narrow(dim, start, 4); |
7132 | c.add_(b, 1.0); |
7133 | ForEachDevice([&](const torch::Device& device) { |
7134 | torch::Tensor lazy_a = CopyToDevice(a_copy, device); |
7135 | torch::Tensor lazy_b = CopyToDevice(b, device); |
7136 | torch::Tensor lazy_c = lazy_a.narrow(dim, start, 4); |
7137 | lazy_c.add_(lazy_b, 1.0); |
7138 | AllClose(a, lazy_a); |
7139 | }); |
7140 | } |
7141 | } |
7142 | } |
7143 | |
7144 | TEST_F(LazyOpsTest, TestNarrowUpdateTwoSlices) { |
7145 | for (int64_t dim : {0, -2}) { |
7146 | for (int64_t start0 : {2, -6}) { |
7147 | for (int64_t start1 : {6, -2}) { |
7148 | torch::Tensor a = torch::zeros( |
7149 | {8, 3}, |
7150 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7151 | torch::Tensor a_copy = a.clone(); |
7152 | torch::Tensor b = torch::ones( |
7153 | {2, 3}, |
7154 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7155 | torch::Tensor c = b + 1; |
7156 | torch::Tensor d = a.narrow(dim, start0, 2); |
7157 | torch::Tensor e = a.narrow(dim, start1, 2); |
7158 | d.add_(b, 1.0); |
7159 | e.add_(c, 1.0); |
7160 | ForEachDevice([&](const torch::Device& device) { |
7161 | torch::Tensor lazy_a = CopyToDevice(a_copy, device); |
7162 | torch::Tensor lazy_b = CopyToDevice(b, device); |
7163 | torch::Tensor lazy_c = CopyToDevice(c, device); |
7164 | torch::Tensor lazy_d = lazy_a.narrow(dim, start0, 2); |
7165 | torch::Tensor lazy_e = lazy_a.narrow(dim, start1, 2); |
7166 | lazy_d.add_(lazy_b, 1.0); |
7167 | lazy_e.add_(lazy_c, 1.0); |
7168 | AllClose(d, lazy_d); |
7169 | AllClose(e, lazy_e); |
7170 | AllClose(a, lazy_a); |
7171 | }); |
7172 | } |
7173 | } |
7174 | } |
7175 | } |
7176 | |
7177 | TEST_F(LazyOpsTest, TestNarrowUpdateView) { |
7178 | for (int64_t dim : {0, -3}) { |
7179 | for (int64_t start : {2, -6}) { |
7180 | torch::Tensor a = torch::rand( |
7181 | {8, 2, 3}, |
7182 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7183 | torch::Tensor a_copy = a.clone(); |
7184 | torch::Tensor b = torch::rand( |
7185 | {4, 6}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7186 | torch::Tensor c = a.narrow(dim, start, 4); |
7187 | torch::Tensor d = c.view({4, 6}); |
7188 | d.add_(b, 1.0); |
7189 | ForEachDevice([&](const torch::Device& device) { |
7190 | torch::Tensor lazy_a = CopyToDevice(a_copy, device); |
7191 | torch::Tensor lazy_b = CopyToDevice(b, device); |
7192 | torch::Tensor lazy_c = lazy_a.narrow(dim, start, 4); |
7193 | torch::Tensor lazy_d = lazy_c.view({4, 6}); |
7194 | lazy_d.add_(lazy_b, 1.0); |
7195 | AllClose(d, lazy_d); |
7196 | }); |
7197 | } |
7198 | } |
7199 | } |
7200 | |
7201 | TEST_F(LazyOpsTest, TestNarrowInNarrowUpdate) { |
7202 | for (int64_t dim : {1, -2}) { |
7203 | for (int64_t start0 : {1, -7}) { |
7204 | for (int64_t start1 : {1, -5}) { |
7205 | torch::Tensor a = torch::rand( |
7206 | {3, 8, 3}, |
7207 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7208 | torch::Tensor a_copy = a.clone(); |
7209 | torch::Tensor b = torch::rand( |
7210 | {3, 2, 3}, |
7211 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7212 | torch::Tensor c = a.narrow(dim, start0, 6); |
7213 | torch::Tensor d = c.narrow(dim, start1, 2); |
7214 | d.add_(b, 1.0); |
7215 | ForEachDevice([&](const torch::Device& device) { |
7216 | torch::Tensor lazy_a = CopyToDevice(a_copy, device); |
7217 | torch::Tensor lazy_b = CopyToDevice(b, device); |
7218 | torch::Tensor lazy_c = lazy_a.narrow(dim, start0, 6); |
7219 | torch::Tensor lazy_d = lazy_c.narrow(dim, start1, 2); |
7220 | lazy_d.add_(lazy_b, 1.0); |
7221 | AllClose(a, lazy_a); |
7222 | }); |
7223 | } |
7224 | } |
7225 | } |
7226 | } |
7227 | |
7228 | TEST_F(LazyOpsTest, TestNarrowCopy) { |
7229 | for (int64_t dim : {1, -3}) { |
7230 | for (int64_t start : {2, -8}) { |
7231 | ForEachDevice([&](const torch::Device& device) { |
7232 | torch::Tensor input = torch::rand( |
7233 | {8, 10, 4, 4}, |
7234 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7235 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7236 | torch::Tensor result = input.narrow_copy(dim, start, 6); |
7237 | input.add_(1); |
7238 | torch::Tensor lazy_result = lazy_input.narrow_copy(dim, start, 6); |
7239 | lazy_input.add_(1); |
7240 | AllClose(result, lazy_result); |
7241 | }); |
7242 | } |
7243 | } |
7244 | } |
7245 | |
7246 | TEST_F(LazyOpsTest, TestViewAs) { |
7247 | torch::Tensor input = torch::rand( |
7248 | {32, 20, 4, 4}, |
7249 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7250 | torch::Tensor empty = torch::empty({32, 320}); |
7251 | torch::Tensor output = input.view_as(empty); |
7252 | ForEachDevice([&](const torch::Device& device) { |
7253 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7254 | torch::Tensor lazy_empty = CopyToDevice(empty, device); |
7255 | torch::Tensor lazy_output = lazy_input.view_as(lazy_empty); |
7256 | AllClose(output, lazy_output); |
7257 | }); |
7258 | } |
7259 | |
7260 | TEST_F(LazyOpsTest, TestLogSoftmax) { |
7261 | torch::Tensor input = torch::rand( |
7262 | {5, 3, 4, 2}, |
7263 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7264 | ForEachDevice([&](const torch::Device& device) { |
7265 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7266 | int rank = input.dim(); |
7267 | for (int dim = -rank; dim < rank; ++dim) { |
7268 | torch::Tensor output = torch::log_softmax(input, dim); |
7269 | torch::Tensor lazy_output = torch::log_softmax(lazy_input, dim); |
7270 | AllClose(output, lazy_output, /*rtol=*/1e-3); |
7271 | } |
7272 | }); |
7273 | } |
7274 | |
7275 | TEST_F(LazyOpsTest, TestLogSoftmaxCast) { |
7276 | torch::Tensor input = torch::rand( |
7277 | {5, 3, 4, 2}, |
7278 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7279 | ForEachDevice([&](const torch::Device& device) { |
7280 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7281 | int rank = input.dim(); |
7282 | for (int dim = -rank; dim < rank; ++dim) { |
7283 | torch::Tensor output = torch::log_softmax(input, dim, torch::kDouble); |
7284 | torch::Tensor lazy_output = |
7285 | torch::log_softmax(lazy_input, dim, torch::kDouble); |
7286 | AllClose(output, lazy_output, /*rtol=*/1e-3); |
7287 | } |
7288 | }); |
7289 | } |
7290 | |
7291 | TEST_F(LazyOpsTest, TestLogSoftmaxWrapper) { |
7292 | torch::Tensor input = torch::rand( |
7293 | {10, 2, 6, 4}, |
7294 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7295 | ForEachDevice([&](const torch::Device& device) { |
7296 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7297 | int rank = input.dim(); |
7298 | for (int dim = -rank; dim < rank; ++dim) { |
7299 | torch::Tensor output = |
7300 | torch::_log_softmax(input, dim, /*half_to_float=*/false); |
7301 | torch::Tensor lazy_output = |
7302 | torch::_log_softmax(lazy_input, dim, /*half_to_float=*/false); |
7303 | AllClose(output, lazy_output, /*rtol=*/1e-3); |
7304 | } |
7305 | }); |
7306 | } |
7307 | |
7308 | TEST_F(LazyOpsTest, TestSoftmax) { |
7309 | torch::Tensor input = torch::rand( |
7310 | {10, 2, 6, 4}, |
7311 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7312 | ForEachDevice([&](const torch::Device& device) { |
7313 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7314 | int rank = input.dim(); |
7315 | for (int dim = -rank; dim < rank; ++dim) { |
7316 | torch::Tensor output = torch::softmax(input, dim); |
7317 | torch::Tensor lazy_output = torch::softmax(lazy_input, dim); |
7318 | AllClose(output, lazy_output, /*rtol=*/1e-3); |
7319 | } |
7320 | }); |
7321 | } |
7322 | |
7323 | TEST_F(LazyOpsTest, TestSoftmaxCast) { |
7324 | torch::Tensor input = torch::rand( |
7325 | {10, 2, 6, 4}, |
7326 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7327 | ForEachDevice([&](const torch::Device& device) { |
7328 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7329 | int rank = input.dim(); |
7330 | for (int dim = -rank; dim < rank; ++dim) { |
7331 | torch::Tensor output = torch::softmax(input, dim, torch::kDouble); |
7332 | torch::Tensor lazy_output = |
7333 | torch::softmax(lazy_input, dim, torch::kDouble); |
7334 | AllClose(output, lazy_output, /*rtol=*/1e-3); |
7335 | } |
7336 | }); |
7337 | } |
7338 | |
7339 | TEST_F(LazyOpsTest, TestSoftmaxWrapper) { |
7340 | torch::Tensor input = torch::rand( |
7341 | {10, 2, 6, 4}, |
7342 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7343 | ForEachDevice([&](const torch::Device& device) { |
7344 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7345 | int rank = input.dim(); |
7346 | for (int dim = -rank; dim < rank; ++dim) { |
7347 | torch::Tensor output = |
7348 | torch::_softmax(input, dim, /*half_to_float=*/false); |
7349 | torch::Tensor lazy_output = |
7350 | torch::_softmax(lazy_input, dim, /*half_to_float=*/false); |
7351 | AllClose(output, lazy_output, /*rtol=*/1e-3); |
7352 | } |
7353 | }); |
7354 | } |
7355 | |
7356 | TEST_F(LazyOpsTest, TestSoftplus) { |
7357 | torch::Tensor input = torch::rand( |
7358 | {2, 1, 4, 6}, |
7359 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7360 | torch::Tensor output = torch::softplus(input); |
7361 | ForEachDevice([&](const torch::Device& device) { |
7362 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7363 | torch::Tensor lazy_output = torch::softplus(lazy_input); |
7364 | AllClose(output, lazy_output, /*rtol=*/1e-4); |
7365 | }); |
7366 | } |
7367 | |
7368 | TEST_F(LazyOpsTest, TestMaxPool1D) { |
7369 | torch::Tensor input = torch::rand( |
7370 | {1, 16, 56}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7371 | int kernel_size = 3; |
7372 | for (int stride = 1; stride <= 2; ++stride) { |
7373 | for (int padding = 0; padding <= 1; ++padding) { |
7374 | // Test ceil_mode=true through the CPU interop. |
7375 | for (bool ceil_mode : {false, true}) { |
7376 | // Test dilation through the CPU interop. |
7377 | for (int dilation = 1; dilation <= 2; ++dilation) { |
7378 | torch::Tensor output = torch::max_pool1d( |
7379 | input, |
7380 | /*kernel_size=*/{kernel_size}, |
7381 | /*stride=*/{stride}, |
7382 | /*padding=*/{padding}, |
7383 | /*dilation=*/{dilation}, |
7384 | /*ceil_mode=*/ceil_mode); |
7385 | ForEachDevice([&](const torch::Device& device) { |
7386 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7387 | torch::Tensor lazy_output = torch::max_pool1d( |
7388 | lazy_input, |
7389 | /*kernel_size=*/{kernel_size}, |
7390 | /*stride=*/{stride}, |
7391 | /*padding=*/{padding}, |
7392 | /*dilation=*/{dilation}, |
7393 | /*ceil_mode=*/ceil_mode); |
7394 | AllClose(output, lazy_output); |
7395 | }); |
7396 | } |
7397 | } |
7398 | } |
7399 | } |
7400 | } |
7401 | |
7402 | TEST_F(LazyOpsTest, TestMaxPool2D) { |
7403 | torch::Tensor input = torch::rand( |
7404 | {1, 4, 14, 14}, |
7405 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7406 | int kernel_size = 3; |
7407 | for (int stride = 1; stride <= 2; ++stride) { |
7408 | for (int padding = 0; padding <= 1; ++padding) { |
7409 | // Test ceil_mode=true through the CPU interop. |
7410 | for (bool ceil_mode : {false, true}) { |
7411 | // Test dilation through the CPU interop. |
7412 | for (int dilation = 1; dilation <= 2; ++dilation) { |
7413 | torch::Tensor output = torch::max_pool2d( |
7414 | input, |
7415 | /*kernel_size=*/{kernel_size, kernel_size}, |
7416 | /*stride=*/{stride, stride}, |
7417 | /*padding=*/{padding, padding}, |
7418 | /*dilation=*/{dilation, dilation}, |
7419 | /*ceil_mode=*/ceil_mode); |
7420 | ForEachDevice([&](const torch::Device& device) { |
7421 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7422 | torch::Tensor lazy_output = torch::max_pool2d( |
7423 | lazy_input, |
7424 | /*kernel_size=*/{kernel_size, kernel_size}, |
7425 | /*stride=*/{stride, stride}, |
7426 | /*padding=*/{padding, padding}, |
7427 | /*dilation=*/{dilation, dilation}, |
7428 | /*ceil_mode=*/ceil_mode); |
7429 | AllClose(output, lazy_output); |
7430 | }); |
7431 | } |
7432 | } |
7433 | } |
7434 | } |
7435 | } |
7436 | |
7437 | TEST_F(LazyOpsTest, TestMaxPool2DWithIndices) { |
7438 | torch::Tensor input = torch::rand( |
7439 | {1, 4, 14, 14}, |
7440 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7441 | int kernel_size = 3; |
7442 | for (int stride = 1; stride <= 2; ++stride) { |
7443 | for (int padding = 0; padding <= 1; ++padding) { |
7444 | // Test ceil_mode=true through the CPU interop. |
7445 | for (bool ceil_mode : {false, true}) { |
7446 | // Test dilation through the CPU interop. |
7447 | for (int dilation = 1; dilation <= 2; ++dilation) { |
7448 | auto outputs = torch::max_pool2d_with_indices( |
7449 | input, |
7450 | /*kernel_size=*/{kernel_size, kernel_size}, |
7451 | /*stride=*/{stride, stride}, |
7452 | /*padding=*/{padding, padding}, |
7453 | /*dilation=*/{dilation, dilation}, |
7454 | /*ceil_mode=*/ceil_mode); |
7455 | ForEachDevice([&](const torch::Device& device) { |
7456 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7457 | auto lazy_outputs = torch::max_pool2d_with_indices( |
7458 | lazy_input, |
7459 | /*kernel_size=*/{kernel_size, kernel_size}, |
7460 | /*stride=*/{stride, stride}, |
7461 | /*padding=*/{padding, padding}, |
7462 | /*dilation=*/{dilation, dilation}, |
7463 | /*ceil_mode=*/ceil_mode); |
7464 | AllClose(std::get<0>(outputs), std::get<0>(lazy_outputs)); |
7465 | AllClose(std::get<1>(outputs), std::get<1>(lazy_outputs)); |
7466 | }); |
7467 | } |
7468 | } |
7469 | } |
7470 | } |
7471 | } |
7472 | |
7473 | TEST_F(LazyOpsTest, TestMaxPool2DNonSquare) { |
7474 | torch::Tensor input = torch::rand( |
7475 | {1, 4, 14, 14}, |
7476 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7477 | int kernel_size = 4; |
7478 | for (int stride = 1; stride <= 2; ++stride) { |
7479 | for (int padding = 0; padding <= 1; ++padding) { |
7480 | // Test ceil_mode=true through the CPU interop. |
7481 | for (bool ceil_mode : {false, true}) { |
7482 | // Test dilation through the CPU interop. |
7483 | for (int dilation = 1; dilation <= 2; ++dilation) { |
7484 | torch::Tensor output = torch::max_pool2d( |
7485 | input, |
7486 | /*kernel_size=*/{kernel_size, kernel_size + 1}, |
7487 | /*stride=*/{stride, stride + 1}, |
7488 | /*padding=*/{padding, padding + 1}, |
7489 | /*dilation=*/{dilation, dilation}, |
7490 | /*ceil_mode=*/ceil_mode); |
7491 | ForEachDevice([&](const torch::Device& device) { |
7492 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7493 | torch::Tensor lazy_output = torch::max_pool2d( |
7494 | lazy_input, |
7495 | /*kernel_size=*/{kernel_size, kernel_size + 1}, |
7496 | /*stride=*/{stride, stride + 1}, |
7497 | /*padding=*/{padding, padding + 1}, |
7498 | /*dilation=*/{dilation, dilation}, |
7499 | /*ceil_mode=*/ceil_mode); |
7500 | AllClose(output, lazy_output); |
7501 | }); |
7502 | } |
7503 | } |
7504 | } |
7505 | } |
7506 | } |
7507 | |
7508 | TEST_F(LazyOpsTest, TestMaxPool3D) { |
7509 | torch::Tensor input = torch::rand( |
7510 | {1, 1, 8, 8, 8}, |
7511 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7512 | int kernel_size = 3; |
7513 | for (int stride = 1; stride <= 2; ++stride) { |
7514 | for (int padding = 0; padding <= 1; ++padding) { |
7515 | // Test ceil_mode=true through the CPU interop. |
7516 | for (bool ceil_mode : {false, true}) { |
7517 | // Test dilation through the CPU interop. |
7518 | for (int dilation = 1; dilation <= 2; ++dilation) { |
7519 | torch::Tensor output = torch::max_pool3d( |
7520 | input, |
7521 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7522 | /*stride=*/{stride, stride, stride}, |
7523 | /*padding=*/{padding, padding, padding}, |
7524 | /*dilation=*/{dilation, dilation, dilation}, |
7525 | /*ceil_mode=*/ceil_mode); |
7526 | ForEachDevice([&](const torch::Device& device) { |
7527 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7528 | torch::Tensor lazy_output = torch::max_pool3d( |
7529 | lazy_input, |
7530 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7531 | /*stride=*/{stride, stride, stride}, |
7532 | /*padding=*/{padding, padding, padding}, |
7533 | /*dilation=*/{dilation, dilation, dilation}, |
7534 | /*ceil_mode=*/ceil_mode); |
7535 | AllClose(output, lazy_output); |
7536 | }); |
7537 | } |
7538 | } |
7539 | } |
7540 | } |
7541 | } |
7542 | |
7543 | TEST_F(LazyOpsTest, TestMaxPool3DWithIndices) { |
7544 | torch::Tensor input = torch::rand( |
7545 | {1, 1, 8, 8, 8}, |
7546 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7547 | int kernel_size = 3; |
7548 | for (int stride = 1; stride <= 2; ++stride) { |
7549 | for (int padding = 0; padding <= 1; ++padding) { |
7550 | // Test ceil_mode=true through the CPU interop. |
7551 | for (bool ceil_mode : {false, true}) { |
7552 | // Test dilation through the CPU interop. |
7553 | for (int dilation = 1; dilation <= 2; ++dilation) { |
7554 | auto outputs = torch::max_pool3d_with_indices( |
7555 | input, |
7556 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7557 | /*stride=*/{stride, stride, stride}, |
7558 | /*padding=*/{padding, padding, padding}, |
7559 | /*dilation=*/{dilation, dilation, dilation}, |
7560 | /*ceil_mode=*/ceil_mode); |
7561 | ForEachDevice([&](const torch::Device& device) { |
7562 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7563 | auto lazy_outputs = torch::max_pool3d_with_indices( |
7564 | lazy_input, |
7565 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7566 | /*stride=*/{stride, stride, stride}, |
7567 | /*padding=*/{padding, padding, padding}, |
7568 | /*dilation=*/{dilation, dilation, dilation}, |
7569 | /*ceil_mode=*/ceil_mode); |
7570 | |
7571 | AllClose(std::get<0>(outputs), std::get<0>(lazy_outputs)); |
7572 | AllClose(std::get<1>(outputs), std::get<1>(lazy_outputs)); |
7573 | }); |
7574 | } |
7575 | } |
7576 | } |
7577 | } |
7578 | } |
7579 | |
7580 | TEST_F(LazyOpsTest, TestMaxPool3DIncompleteAttributes) { |
7581 | torch::Tensor input = torch::rand( |
7582 | {1, 1, 8, 8, 8}, |
7583 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7584 | int kernel_size = 3; |
7585 | for (int stride = 1; stride <= 2; ++stride) { |
7586 | for (int padding = 0; padding <= 1; ++padding) { |
7587 | // Test ceil_mode=true through the CPU interop. |
7588 | for (bool ceil_mode : {false, true}) { |
7589 | // Test dilation through the CPU interop. |
7590 | for (int dilation = 1; dilation <= 2; ++dilation) { |
7591 | torch::Tensor output = torch::max_pool3d( |
7592 | input, |
7593 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7594 | /*stride=*/{}, |
7595 | /*padding=*/{padding}, |
7596 | /*dilation=*/{dilation, dilation, dilation}, |
7597 | /*ceil_mode=*/ceil_mode); |
7598 | ForEachDevice([&](const torch::Device& device) { |
7599 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7600 | torch::Tensor lazy_output = torch::max_pool3d( |
7601 | lazy_input, |
7602 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7603 | /*stride=*/{}, |
7604 | /*padding=*/{padding}, |
7605 | /*dilation=*/{dilation, dilation, dilation}, |
7606 | /*ceil_mode=*/ceil_mode); |
7607 | AllClose(output, lazy_output); |
7608 | }); |
7609 | } |
7610 | } |
7611 | } |
7612 | } |
7613 | } |
7614 | |
7615 | TEST_F(LazyOpsTest, TestMaxPool3DNonSquare) { |
7616 | torch::Tensor input = torch::rand( |
7617 | {1, 1, 8, 8, 8}, |
7618 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7619 | int kernel_size = 4; |
7620 | for (int stride = 1; stride <= 2; ++stride) { |
7621 | for (int padding = 0; padding <= 1; ++padding) { |
7622 | // Test ceil_mode=true through the CPU interop. |
7623 | for (bool ceil_mode : {false, true}) { |
7624 | // Test dilation through the CPU interop. |
7625 | for (int dilation = 1; dilation <= 2; ++dilation) { |
7626 | torch::Tensor output = torch::max_pool3d( |
7627 | input, |
7628 | /*kernel_size=*/{kernel_size, kernel_size + 1, kernel_size}, |
7629 | /*stride=*/{stride, stride + 1, stride}, |
7630 | /*padding=*/{padding, padding + 1, padding}, |
7631 | /*dilation=*/{dilation, dilation, dilation}, |
7632 | /*ceil_mode=*/ceil_mode); |
7633 | ForEachDevice([&](const torch::Device& device) { |
7634 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7635 | torch::Tensor lazy_output = torch::max_pool3d( |
7636 | lazy_input, |
7637 | /*kernel_size=*/{kernel_size, kernel_size + 1, kernel_size}, |
7638 | /*stride=*/{stride, stride + 1, stride}, |
7639 | /*padding=*/{padding, padding + 1, padding}, |
7640 | /*dilation=*/{dilation, dilation, dilation}, |
7641 | /*ceil_mode=*/ceil_mode); |
7642 | AllClose(output, lazy_output); |
7643 | }); |
7644 | } |
7645 | } |
7646 | } |
7647 | } |
7648 | } |
7649 | |
7650 | TEST_F(LazyOpsTest, TestMaxPool2DNoBatch) { |
7651 | torch::Tensor input = torch::rand( |
7652 | {4, 14, 14}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7653 | int kernel_size = 3; |
7654 | for (int stride = 1; stride <= 2; ++stride) { |
7655 | for (int padding = 0; padding <= 1; ++padding) { |
7656 | // Test ceil_mode=true through the CPU interop. |
7657 | for (bool ceil_mode : {false, true}) { |
7658 | // Test dilation through the CPU interop. |
7659 | for (int dilation = 1; dilation <= 2; ++dilation) { |
7660 | torch::Tensor output = torch::max_pool2d( |
7661 | input, |
7662 | /*kernel_size=*/{kernel_size, kernel_size}, |
7663 | /*stride=*/{stride, stride}, |
7664 | /*padding=*/{padding, padding}, |
7665 | /*dilation=*/{dilation, dilation}, |
7666 | /*ceil_mode=*/ceil_mode); |
7667 | ForEachDevice([&](const torch::Device& device) { |
7668 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7669 | torch::Tensor lazy_output = torch::max_pool2d( |
7670 | lazy_input, |
7671 | /*kernel_size=*/{kernel_size, kernel_size}, |
7672 | /*stride=*/{stride, stride}, |
7673 | /*padding=*/{padding, padding}, |
7674 | /*dilation=*/{dilation, dilation}, |
7675 | /*ceil_mode=*/ceil_mode); |
7676 | AllClose(output, lazy_output); |
7677 | }); |
7678 | } |
7679 | } |
7680 | } |
7681 | } |
7682 | } |
7683 | |
7684 | TEST_F(LazyOpsTest, TestMaxPool3DNoBatch) { |
7685 | torch::Tensor input = torch::rand( |
7686 | {1, 8, 8, 8}, |
7687 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7688 | int kernel_size = 3; |
7689 | for (int stride = 1; stride <= 2; ++stride) { |
7690 | for (int padding = 0; padding <= 1; ++padding) { |
7691 | // Test ceil_mode=true through the CPU interop. |
7692 | for (bool ceil_mode : {false, true}) { |
7693 | // Test dilation through the CPU interop. |
7694 | for (int dilation = 1; dilation <= 2; ++dilation) { |
7695 | torch::Tensor output = torch::max_pool3d( |
7696 | input, |
7697 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7698 | /*stride=*/{stride, stride, stride}, |
7699 | /*padding=*/{padding, padding, padding}, |
7700 | /*dilation=*/{dilation, dilation, dilation}, |
7701 | /*ceil_mode=*/ceil_mode); |
7702 | ForEachDevice([&](const torch::Device& device) { |
7703 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7704 | torch::Tensor lazy_output = torch::max_pool3d( |
7705 | lazy_input, |
7706 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7707 | /*stride=*/{stride, stride, stride}, |
7708 | /*padding=*/{padding, padding, padding}, |
7709 | /*dilation=*/{dilation, dilation, dilation}, |
7710 | /*ceil_mode=*/ceil_mode); |
7711 | AllClose(output, lazy_output); |
7712 | }); |
7713 | } |
7714 | } |
7715 | } |
7716 | } |
7717 | } |
7718 | |
7719 | TEST_F(LazyOpsTest, TestAvgPool1D) { |
7720 | torch::Tensor input = torch::rand( |
7721 | {4, 1, 28}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7722 | int kernel_size = 2; |
7723 | for (int stride = 1; stride <= 2; ++stride) { |
7724 | for (int padding = 0; padding <= 1; ++padding) { |
7725 | for (bool count_include_pad : {true, false}) { |
7726 | // Test ceil_mode=true through the CPU interop. |
7727 | for (bool ceil_mode : {false, true}) { |
7728 | torch::Tensor output = torch::avg_pool1d( |
7729 | input, |
7730 | /*kernel_size=*/{kernel_size}, |
7731 | /*stride=*/{stride}, |
7732 | /*padding=*/{padding}, |
7733 | /*ceil_mode=*/ceil_mode, |
7734 | /*count_include_pad=*/count_include_pad); |
7735 | ForEachDevice([&](const torch::Device& device) { |
7736 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7737 | torch::Tensor lazy_output = torch::avg_pool1d( |
7738 | lazy_input, |
7739 | /*kernel_size=*/{kernel_size}, |
7740 | /*stride=*/{stride}, |
7741 | /*padding=*/{padding}, |
7742 | /*ceil_mode=*/ceil_mode, |
7743 | /*count_include_pad=*/count_include_pad); |
7744 | AllClose(output, lazy_output); |
7745 | }); |
7746 | } |
7747 | } |
7748 | } |
7749 | } |
7750 | } |
7751 | |
7752 | TEST_F(LazyOpsTest, TestAvgPool2D) { |
7753 | torch::Tensor input = torch::rand( |
7754 | {2, 1, 14, 14}, |
7755 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7756 | int kernel_size = 2; |
7757 | for (int stride = 1; stride <= 2; ++stride) { |
7758 | for (int padding = 0; padding <= 1; ++padding) { |
7759 | for (bool count_include_pad : {true, false}) { |
7760 | // Test ceil_mode=true through the CPU interop. |
7761 | for (bool ceil_mode : {false, true}) { |
7762 | torch::Tensor output = torch::avg_pool2d( |
7763 | input, |
7764 | /*kernel_size=*/{kernel_size, kernel_size}, |
7765 | /*stride=*/{stride, stride}, |
7766 | /*padding=*/{padding, padding}, |
7767 | /*ceil_mode=*/ceil_mode, |
7768 | /*count_include_pad=*/count_include_pad); |
7769 | ForEachDevice([&](const torch::Device& device) { |
7770 | // torch::Tensor lazy_input = CopyToDevice(input, device); |
7771 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7772 | torch::Tensor lazy_output = torch::avg_pool2d( |
7773 | lazy_input, |
7774 | /*kernel_size=*/{kernel_size, kernel_size}, |
7775 | /*stride=*/{stride, stride}, |
7776 | /*padding=*/{padding, padding}, |
7777 | /*ceil_mode=*/ceil_mode, |
7778 | /*count_include_pad=*/count_include_pad); |
7779 | AllClose(output, lazy_output.to(torch::kCPU)); |
7780 | }); |
7781 | } |
7782 | } |
7783 | } |
7784 | } |
7785 | } |
7786 | |
7787 | TEST_F(LazyOpsTest, TestAvgPool2DNonSquare) { |
7788 | torch::Tensor input = torch::rand( |
7789 | {2, 1, 14, 14}, |
7790 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7791 | int kernel_size = 4; |
7792 | for (int stride = 1; stride <= 2; ++stride) { |
7793 | for (int padding = 0; padding <= 1; ++padding) { |
7794 | for (bool count_include_pad : {true, false}) { |
7795 | // Test ceil_mode=true through the CPU interop. |
7796 | for (bool ceil_mode : {false, true}) { |
7797 | torch::Tensor output = torch::avg_pool2d( |
7798 | input, |
7799 | /*kernel_size=*/{kernel_size, kernel_size + 1}, |
7800 | /*stride=*/{stride, stride + 1}, |
7801 | /*padding=*/{padding, padding + 1}, |
7802 | /*ceil_mode=*/ceil_mode, |
7803 | /*count_include_pad=*/count_include_pad); |
7804 | ForEachDevice([&](const torch::Device& device) { |
7805 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7806 | torch::Tensor lazy_output = torch::avg_pool2d( |
7807 | lazy_input, |
7808 | /*kernel_size=*/{kernel_size, kernel_size + 1}, |
7809 | /*stride=*/{stride, stride + 1}, |
7810 | /*padding=*/{padding, padding + 1}, |
7811 | /*ceil_mode=*/ceil_mode, |
7812 | /*count_include_pad=*/count_include_pad); |
7813 | AllClose(output, lazy_output); |
7814 | }); |
7815 | } |
7816 | } |
7817 | } |
7818 | } |
7819 | } |
7820 | |
7821 | TEST_F(LazyOpsTest, TestAvgPool3D) { |
7822 | torch::Tensor input = torch::rand( |
7823 | {1, 1, 7, 7, 7}, |
7824 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7825 | int kernel_size = 2; |
7826 | for (int stride = 1; stride <= 2; ++stride) { |
7827 | for (int padding = 0; padding <= 1; ++padding) { |
7828 | for (bool count_include_pad : {true, false}) { |
7829 | // Test ceil_mode=true through the CPU interop. |
7830 | for (bool ceil_mode : {false, true}) { |
7831 | torch::Tensor output = torch::avg_pool3d( |
7832 | input, |
7833 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7834 | /*stride=*/{stride, stride, stride}, |
7835 | /*padding=*/{padding, padding, padding}, |
7836 | /*ceil_mode=*/ceil_mode, |
7837 | /*count_include_pad=*/count_include_pad); |
7838 | ForEachDevice([&](const torch::Device& device) { |
7839 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7840 | torch::Tensor lazy_output = torch::avg_pool3d( |
7841 | lazy_input, |
7842 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7843 | /*stride=*/{stride, stride, stride}, |
7844 | /*padding=*/{padding, padding, padding}, |
7845 | /*ceil_mode=*/ceil_mode, |
7846 | /*count_include_pad=*/count_include_pad); |
7847 | AllClose(output, lazy_output); |
7848 | }); |
7849 | } |
7850 | } |
7851 | } |
7852 | } |
7853 | } |
7854 | |
7855 | TEST_F(LazyOpsTest, TestAvgPool3DIncompleteAttributes) { |
7856 | torch::Tensor input = torch::rand( |
7857 | {1, 1, 7, 7, 7}, |
7858 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7859 | int kernel_size = 2; |
7860 | for (int stride = 1; stride <= 2; ++stride) { |
7861 | for (int padding = 0; padding <= 1; ++padding) { |
7862 | for (bool count_include_pad : {true, false}) { |
7863 | // Test ceil_mode=true through the CPU interop. |
7864 | for (bool ceil_mode : {false, true}) { |
7865 | torch::Tensor output = torch::avg_pool3d( |
7866 | input, |
7867 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7868 | /*stride=*/{}, |
7869 | /*padding=*/{padding, padding, padding}, |
7870 | /*ceil_mode=*/ceil_mode, |
7871 | /*count_include_pad=*/count_include_pad); |
7872 | ForEachDevice([&](const torch::Device& device) { |
7873 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7874 | torch::Tensor lazy_output = torch::avg_pool3d( |
7875 | lazy_input, |
7876 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7877 | /*stride=*/{}, |
7878 | /*padding=*/{padding, padding, padding}, |
7879 | /*ceil_mode=*/ceil_mode, |
7880 | /*count_include_pad=*/count_include_pad); |
7881 | AllClose(output, lazy_output); |
7882 | }); |
7883 | } |
7884 | } |
7885 | } |
7886 | } |
7887 | } |
7888 | |
7889 | TEST_F(LazyOpsTest, TestAvgPool3DNonSquare) { |
7890 | torch::Tensor input = torch::rand( |
7891 | {1, 1, 7, 7, 7}, |
7892 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7893 | int kernel_size = 4; |
7894 | for (int stride = 1; stride <= 2; ++stride) { |
7895 | for (int padding = 0; padding <= 1; ++padding) { |
7896 | for (bool count_include_pad : {true, false}) { |
7897 | // Test ceil_mode=true through the CPU interop. |
7898 | for (bool ceil_mode : {false, true}) { |
7899 | torch::Tensor output = torch::avg_pool3d( |
7900 | input, |
7901 | /*kernel_size=*/{kernel_size, kernel_size + 1, kernel_size}, |
7902 | /*stride=*/{stride, stride + 1, stride}, |
7903 | /*padding=*/{padding, padding + 1, padding}, |
7904 | /*ceil_mode=*/ceil_mode, |
7905 | /*count_include_pad=*/count_include_pad); |
7906 | ForEachDevice([&](const torch::Device& device) { |
7907 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7908 | torch::Tensor lazy_output = torch::avg_pool3d( |
7909 | lazy_input, |
7910 | /*kernel_size=*/{kernel_size, kernel_size + 1, kernel_size}, |
7911 | /*stride=*/{stride, stride + 1, stride}, |
7912 | /*padding=*/{padding, padding + 1, padding}, |
7913 | /*ceil_mode=*/ceil_mode, |
7914 | /*count_include_pad=*/count_include_pad); |
7915 | AllClose(output, lazy_output); |
7916 | }); |
7917 | } |
7918 | } |
7919 | } |
7920 | } |
7921 | } |
7922 | |
7923 | TEST_F(LazyOpsTest, TestAvgPool2DNoBatch) { |
7924 | torch::Tensor input = torch::rand( |
7925 | {1, 7, 7}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7926 | int kernel_size = 2; |
7927 | for (int stride = 1; stride <= 2; ++stride) { |
7928 | for (int padding = 0; padding <= 1; ++padding) { |
7929 | for (bool count_include_pad : {true, false}) { |
7930 | // Test ceil_mode=true through the CPU interop. |
7931 | for (bool ceil_mode : {false, true}) { |
7932 | torch::Tensor output = torch::avg_pool2d( |
7933 | input, |
7934 | /*kernel_size=*/{kernel_size, kernel_size}, |
7935 | /*stride=*/{stride, stride}, |
7936 | /*padding=*/{padding, padding}, |
7937 | /*ceil_mode=*/ceil_mode, |
7938 | /*count_include_pad=*/count_include_pad); |
7939 | ForEachDevice([&](const torch::Device& device) { |
7940 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7941 | torch::Tensor lazy_output = torch::avg_pool2d( |
7942 | lazy_input, |
7943 | /*kernel_size=*/{kernel_size, kernel_size}, |
7944 | /*stride=*/{stride, stride}, |
7945 | /*padding=*/{padding, padding}, |
7946 | /*ceil_mode=*/ceil_mode, |
7947 | /*count_include_pad=*/count_include_pad); |
7948 | AllClose(output, lazy_output); |
7949 | }); |
7950 | } |
7951 | } |
7952 | } |
7953 | } |
7954 | } |
7955 | |
7956 | TEST_F(LazyOpsTest, TestAvgPool3DNoBatch) { |
7957 | torch::Tensor input = torch::rand( |
7958 | {1, 7, 7, 7}, |
7959 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7960 | int kernel_size = 2; |
7961 | for (int stride = 1; stride <= 2; ++stride) { |
7962 | for (int padding = 0; padding <= 1; ++padding) { |
7963 | for (bool count_include_pad : {true, false}) { |
7964 | // Test ceil_mode=true through the CPU interop. |
7965 | for (bool ceil_mode : {false, true}) { |
7966 | torch::Tensor output = torch::avg_pool3d( |
7967 | input, |
7968 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7969 | /*stride=*/{stride, stride, stride}, |
7970 | /*padding=*/{padding, padding, padding}, |
7971 | /*ceil_mode=*/ceil_mode, |
7972 | /*count_include_pad=*/count_include_pad); |
7973 | ForEachDevice([&](const torch::Device& device) { |
7974 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7975 | torch::Tensor lazy_output = torch::avg_pool3d( |
7976 | lazy_input, |
7977 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
7978 | /*stride=*/{stride, stride, stride}, |
7979 | /*padding=*/{padding, padding, padding}, |
7980 | /*ceil_mode=*/ceil_mode, |
7981 | /*count_include_pad=*/count_include_pad); |
7982 | AllClose(output, lazy_output); |
7983 | }); |
7984 | } |
7985 | } |
7986 | } |
7987 | } |
7988 | } |
7989 | |
7990 | TEST_F(LazyOpsTest, TestAdaptiveAvgPool2D) { |
7991 | torch::Tensor input = torch::rand( |
7992 | {4, 1, 28, 28}, |
7993 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
7994 | for (int64_t output_size : {7, 4}) { |
7995 | torch::Tensor output = |
7996 | torch::adaptive_avg_pool2d(input, {output_size, output_size}); |
7997 | ForEachDevice([&](const torch::Device& device) { |
7998 | torch::Tensor lazy_input = CopyToDevice(input, device); |
7999 | torch::Tensor lazy_output = |
8000 | torch::adaptive_avg_pool2d(lazy_input, {output_size, output_size}); |
8001 | AllClose(output, lazy_output); |
8002 | }); |
8003 | } |
8004 | } |
8005 | |
8006 | TEST_F(LazyOpsTest, TestAdaptiveAvgPool3D) { |
8007 | torch::Tensor input = torch::rand( |
8008 | {9, 4, 56, 28, 28}, |
8009 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8010 | for (int64_t output_size : {7, 4}) { |
8011 | torch::Tensor output = torch::adaptive_avg_pool3d( |
8012 | input, {output_size, output_size, output_size}); |
8013 | ForEachDevice([&](const torch::Device& device) { |
8014 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8015 | torch::Tensor lazy_output = torch::adaptive_avg_pool3d( |
8016 | lazy_input, {output_size, output_size, output_size}); |
8017 | AllClose(output, lazy_output); |
8018 | }); |
8019 | } |
8020 | } |
8021 | |
8022 | TEST_F(LazyOpsTest, TestAdaptiveAvgPool3DNoBatch) { |
8023 | torch::Tensor input = torch::rand( |
8024 | {3, 56, 28, 28}, |
8025 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8026 | for (int64_t output_size : {7, 4}) { |
8027 | torch::Tensor output = torch::adaptive_avg_pool3d( |
8028 | input, {output_size, output_size, output_size}); |
8029 | ForEachDevice([&](const torch::Device& device) { |
8030 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8031 | torch::Tensor lazy_output = torch::adaptive_avg_pool3d( |
8032 | lazy_input, {output_size, output_size, output_size}); |
8033 | AllClose(output, lazy_output); |
8034 | }); |
8035 | } |
8036 | } |
8037 | |
8038 | TEST_F(LazyOpsTest, TestAdaptiveAvgPool2DNoBatch) { |
8039 | torch::Tensor input = torch::rand( |
8040 | {1, 56, 56}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8041 | for (int64_t output_size : {7, 8}) { |
8042 | torch::Tensor output = |
8043 | torch::adaptive_avg_pool2d(input, {output_size, output_size}); |
8044 | ForEachDevice([&](const torch::Device& device) { |
8045 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8046 | torch::Tensor lazy_output = |
8047 | torch::adaptive_avg_pool2d(lazy_input, {output_size, output_size}); |
8048 | AllClose(output, lazy_output); |
8049 | }); |
8050 | } |
8051 | } |
8052 | |
8053 | TEST_F(LazyOpsTest, TestMaxUnpool2D) { |
8054 | int kernel_size = 2; |
8055 | torch::Tensor input = torch::rand( |
8056 | {2, 2, 8, 8}, |
8057 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8058 | for (int stride = 1; stride <= 2; ++stride) { |
8059 | for (int padding = 0; padding <= 1; ++padding) { |
8060 | // Test ceil_mode=true through the CPU interop. |
8061 | for (bool ceil_mode : {false, true}) { |
8062 | // Test dilation through the CPU interop. |
8063 | for (int dilation = 1; dilation <= 2; ++dilation) { |
8064 | torch::Tensor output; |
8065 | torch::Tensor indices; |
8066 | std::tie(output, indices) = torch::max_pool2d_with_indices( |
8067 | input, |
8068 | /*kernel_size=*/{kernel_size, kernel_size}, |
8069 | /*stride=*/{stride, stride}, |
8070 | /*padding=*/{padding, padding}, |
8071 | /*dilation=*/{dilation, dilation}, |
8072 | /*ceil_mode=*/ceil_mode); |
8073 | |
8074 | std::vector<int64_t> output_size({input.size(2), input.size(3)}); |
8075 | at::Tensor utensor = |
8076 | torch::max_unpool2d(output, indices, output_size); |
8077 | |
8078 | ForEachDevice([&](const torch::Device& device) { |
8079 | torch::Tensor lazy_output = CopyToDevice(output, device); |
8080 | torch::Tensor lazy_indices = CopyToDevice(indices, device); |
8081 | at::Tensor lazy_utensor = |
8082 | torch::max_unpool2d(lazy_output, lazy_indices, output_size); |
8083 | AllClose(utensor, lazy_utensor); |
8084 | }); |
8085 | } |
8086 | } |
8087 | } |
8088 | } |
8089 | } |
8090 | |
8091 | TEST_F(LazyOpsTest, TestMaxUnpool3D) { |
8092 | int kernel_size = 2; |
8093 | torch::Tensor input = torch::rand( |
8094 | {1, 1, 4, 4, 4}, |
8095 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8096 | for (int stride = 1; stride <= 2; ++stride) { |
8097 | for (int padding = 0; padding <= 1; ++padding) { |
8098 | // Test ceil_mode=true through the CPU interop. |
8099 | for (bool ceil_mode : {false, true}) { |
8100 | // Test dilation through the CPU interop. |
8101 | for (int dilation = 1; dilation <= 2; ++dilation) { |
8102 | torch::Tensor output; |
8103 | torch::Tensor indices; |
8104 | std::tie(output, indices) = torch::max_pool3d_with_indices( |
8105 | input, |
8106 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
8107 | /*stride=*/{stride, stride, stride}, |
8108 | /*padding=*/{padding, padding, padding}, |
8109 | /*dilation=*/{dilation, dilation, dilation}, |
8110 | /*ceil_mode=*/ceil_mode); |
8111 | |
8112 | std::vector<int64_t> output_size( |
8113 | {input.size(2), input.size(3), input.size(4)}); |
8114 | at::Tensor utensor = torch::max_unpool3d( |
8115 | output, |
8116 | indices, |
8117 | output_size, |
8118 | /*stride=*/{stride, stride, stride}, |
8119 | /*padding=*/{padding, padding, padding}); |
8120 | |
8121 | ForEachDevice([&](const torch::Device& device) { |
8122 | torch::Tensor lazy_output = CopyToDevice(output, device); |
8123 | torch::Tensor lazy_indices = CopyToDevice(indices, device); |
8124 | at::Tensor lazy_utensor = torch::max_unpool3d( |
8125 | lazy_output, |
8126 | lazy_indices, |
8127 | output_size, |
8128 | /*stride=*/{stride, stride, stride}, |
8129 | /*padding=*/{padding, padding, padding}); |
8130 | AllClose(utensor, lazy_utensor); |
8131 | }); |
8132 | } |
8133 | } |
8134 | } |
8135 | } |
8136 | } |
8137 | |
8138 | TEST_F(LazyOpsTest, TestNllLoss) { |
8139 | // TODO(whc) debug divide-by-zero failure under ASAN |
8140 | GTEST_SKIP(); |
8141 | |
8142 | int batch = 6; |
8143 | int classes = 2; |
8144 | // TODO(asuhan): Fix the torch::kDouble case. |
8145 | for (auto dtype : {torch::kFloat}) { |
8146 | for (int ignore_index : {-1, 0, 1, 5}) { |
8147 | for (bool def_weight : {false, true}) { |
8148 | torch::Tensor input = torch::rand( |
8149 | {batch, classes}, |
8150 | torch::TensorOptions(dtype).device(DefaultDevice())); |
8151 | torch::Tensor target = torch::randint( |
8152 | std::min(ignore_index, 0), |
8153 | classes, |
8154 | {batch}, |
8155 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
8156 | torch::Tensor weight; |
8157 | if (def_weight) { |
8158 | weight = torch::rand( |
8159 | {classes}, torch::TensorOptions(dtype).device(DefaultDevice())); |
8160 | } |
8161 | for (torch::Reduction::Reduction reduction : |
8162 | {torch::Reduction::Mean, |
8163 | torch::Reduction::Sum, |
8164 | torch::Reduction::None}) { |
8165 | torch::Tensor output = torch::nll_loss( |
8166 | /*self=*/input, |
8167 | /*target=*/target, |
8168 | /*weight=*/weight, |
8169 | /*reduction=*/reduction, |
8170 | /*ignore_index=*/ignore_index); |
8171 | |
8172 | ForEachDevice([&](const torch::Device& device) { |
8173 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8174 | torch::Tensor lazy_target = CopyToDevice(target, device); |
8175 | torch::Tensor lazy_weight = |
8176 | def_weight ? CopyToDevice(weight, device) : torch::Tensor(); |
8177 | torch::Tensor lazy_output = torch::nll_loss( |
8178 | /*self=*/lazy_input, |
8179 | /*target=*/lazy_target, |
8180 | /*weight=*/lazy_weight, |
8181 | /*reduction=*/reduction, |
8182 | /*ignore_index=*/ignore_index); |
8183 | AllClose(output, lazy_output); |
8184 | }); |
8185 | } |
8186 | } |
8187 | } |
8188 | } |
8189 | } |
8190 | |
8191 | TEST_F(LazyOpsTest, TestNllLoss2d) { |
8192 | int batch = 6; |
8193 | int classes = 2; |
8194 | int height = 3; |
8195 | int width = 3; |
8196 | // TODO(asuhan): Fix the torch::kDouble case. |
8197 | for (auto dtype : {torch::kFloat}) { |
8198 | for (int ignore_index : {-1, 0, 1, 5}) { |
8199 | for (bool def_weight : {false, true}) { |
8200 | torch::Tensor input = torch::rand( |
8201 | {batch, classes, height, width}, |
8202 | torch::TensorOptions(dtype).device(DefaultDevice())); |
8203 | torch::Tensor target = torch::randint( |
8204 | std::min(ignore_index, 0), |
8205 | classes, |
8206 | {batch, height, width}, |
8207 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
8208 | torch::Tensor weight; |
8209 | if (def_weight) { |
8210 | weight = torch::rand( |
8211 | {classes}, torch::TensorOptions(dtype).device(DefaultDevice())); |
8212 | } |
8213 | for (torch::Reduction::Reduction reduction : |
8214 | {torch::Reduction::Mean, |
8215 | torch::Reduction::Sum, |
8216 | torch::Reduction::None}) { |
8217 | torch::Tensor output = torch::nll_loss2d( |
8218 | /*self=*/input, |
8219 | /*target=*/target, |
8220 | /*weight=*/weight, |
8221 | /*reduction=*/reduction, |
8222 | /*ignore_index=*/ignore_index); |
8223 | |
8224 | ForEachDevice([&](const torch::Device& device) { |
8225 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8226 | torch::Tensor lazy_target = CopyToDevice(target, device); |
8227 | torch::Tensor lazy_weight = |
8228 | def_weight ? CopyToDevice(weight, device) : torch::Tensor(); |
8229 | torch::Tensor lazy_output = torch::nll_loss2d( |
8230 | /*self=*/lazy_input, |
8231 | /*target=*/lazy_target, |
8232 | /*weight=*/lazy_weight, |
8233 | /*reduction=*/reduction, |
8234 | /*ignore_index=*/ignore_index); |
8235 | AllClose(output, lazy_output); |
8236 | }); |
8237 | } |
8238 | } |
8239 | } |
8240 | } |
8241 | } |
8242 | |
8243 | TEST_F(LazyOpsTest, TestSmoothL1Loss) { |
8244 | torch::Tensor input = torch::randn( |
8245 | {2, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8246 | torch::Tensor target = torch::randn( |
8247 | {2, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8248 | for (torch::Reduction::Reduction reduction : |
8249 | {torch::Reduction::None, |
8250 | torch::Reduction::Mean, |
8251 | torch::Reduction::Sum}) { |
8252 | for (double beta : {0.25, 1.}) { |
8253 | torch::Tensor output = |
8254 | torch::smooth_l1_loss(input, target, reduction, beta); |
8255 | ForEachDevice([&](const torch::Device& device) { |
8256 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8257 | torch::Tensor lazy_target = CopyToDevice(target, device); |
8258 | torch::Tensor lazy_output = |
8259 | torch::smooth_l1_loss(lazy_input, lazy_target, reduction, beta); |
8260 | AllClose(output, lazy_output); |
8261 | }); |
8262 | } |
8263 | } |
8264 | } |
8265 | |
8266 | TEST_F(LazyOpsTest, TestL1Loss) { |
8267 | torch::Tensor input = torch::randn( |
8268 | {2, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8269 | torch::Tensor target = torch::randn( |
8270 | {2, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8271 | for (torch::Reduction::Reduction reduction : |
8272 | {torch::Reduction::None, |
8273 | torch::Reduction::Mean, |
8274 | torch::Reduction::Sum}) { |
8275 | torch::Tensor output = torch::l1_loss(input, target, reduction); |
8276 | ForEachDevice([&](const torch::Device& device) { |
8277 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8278 | torch::Tensor lazy_target = CopyToDevice(target, device); |
8279 | torch::Tensor lazy_output = |
8280 | torch::l1_loss(lazy_input, lazy_target, reduction); |
8281 | AllClose(output, lazy_output); |
8282 | }); |
8283 | } |
8284 | } |
8285 | |
8286 | TEST_F(LazyOpsTest, TestL1LossBackward) { |
8287 | for (torch::Reduction::Reduction reduction : |
8288 | {torch::Reduction::None, |
8289 | torch::Reduction::Mean, |
8290 | torch::Reduction::Sum}) { |
8291 | auto testfn = |
8292 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
8293 | return torch::l1_loss(inputs[0], inputs[1], reduction); |
8294 | }; |
8295 | ForEachDevice([&](const torch::Device& device) { |
8296 | TestBackward( |
8297 | {torch::rand( |
8298 | {2, 4}, |
8299 | torch::TensorOptions(torch::kFloat) |
8300 | .device(DefaultDevice()) |
8301 | .requires_grad(true)), |
8302 | torch::rand( |
8303 | {2, 4}, |
8304 | torch::TensorOptions(torch::kFloat).device(DefaultDevice()))}, |
8305 | device, |
8306 | testfn); |
8307 | }); |
8308 | } |
8309 | } |
8310 | |
8311 | TEST_F(LazyOpsTest, TestMseLoss) { |
8312 | torch::Tensor input = torch::randn( |
8313 | {2, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8314 | torch::Tensor target = torch::randn( |
8315 | {2, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8316 | for (torch::Reduction::Reduction reduction : |
8317 | {torch::Reduction::None, |
8318 | torch::Reduction::Mean, |
8319 | torch::Reduction::Sum}) { |
8320 | torch::Tensor output = torch::mse_loss(input, target, reduction); |
8321 | ForEachDevice([&](const torch::Device& device) { |
8322 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8323 | torch::Tensor lazy_target = CopyToDevice(target, device); |
8324 | torch::Tensor lazy_output = |
8325 | torch::mse_loss(lazy_input, lazy_target, reduction); |
8326 | AllClose(output, lazy_output); |
8327 | }); |
8328 | } |
8329 | } |
8330 | |
8331 | TEST_F(LazyOpsTest, TestMseLossBackward) { |
8332 | for (torch::Reduction::Reduction reduction : |
8333 | {torch::Reduction::None, |
8334 | torch::Reduction::Mean, |
8335 | torch::Reduction::Sum}) { |
8336 | auto testfn = |
8337 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
8338 | return torch::mse_loss(inputs[0], inputs[1], reduction); |
8339 | }; |
8340 | ForEachDevice([&](const torch::Device& device) { |
8341 | TestBackward( |
8342 | {torch::rand( |
8343 | {2, 4}, |
8344 | torch::TensorOptions(torch::kFloat) |
8345 | .device(DefaultDevice()) |
8346 | .requires_grad(true)), |
8347 | torch::rand( |
8348 | {2, 4}, |
8349 | torch::TensorOptions(torch::kFloat).device(DefaultDevice()))}, |
8350 | device, |
8351 | testfn); |
8352 | }); |
8353 | } |
8354 | } |
8355 | |
8356 | TEST_F(LazyOpsTest, TestBatchNorm1D) { |
8357 | int num_features = 3; |
8358 | torch::Tensor input = torch::rand( |
8359 | {2, num_features, 4}, |
8360 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8361 | torch::Tensor weight = torch::rand( |
8362 | {num_features}, |
8363 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8364 | torch::Tensor bias = torch::rand( |
8365 | {num_features}, |
8366 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8367 | torch::Tensor running_mean = torch::zeros( |
8368 | {num_features}, |
8369 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8370 | torch::Tensor running_var = torch::ones( |
8371 | {num_features}, |
8372 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8373 | double momentum = 0.1; |
8374 | double eps = 0.5; |
8375 | torch::Tensor undef; |
8376 | for (bool training : {true, false}) { |
8377 | for (bool undef_weight_bias : {false, true}) { |
8378 | torch::Tensor output = torch::batch_norm( |
8379 | /*input=*/input, |
8380 | /*weight=*/undef_weight_bias ? undef : weight, |
8381 | /*bias=*/undef_weight_bias ? undef : bias, |
8382 | /*running_mean=*/running_mean, |
8383 | /*running_var=*/running_var, |
8384 | /*training=*/training, |
8385 | /*momentum=*/momentum, |
8386 | /*eps=*/eps, |
8387 | /*cudnn_enabled=*/false); |
8388 | ForEachDevice([&](const torch::Device& device) { |
8389 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8390 | torch::Tensor lazy_weight = |
8391 | undef_weight_bias ? undef : CopyToDevice(weight, device); |
8392 | torch::Tensor lazy_bias = |
8393 | undef_weight_bias ? undef : CopyToDevice(bias, device); |
8394 | torch::Tensor lazy_running_mean = CopyToDevice(running_mean, device); |
8395 | torch::Tensor lazy_running_var = CopyToDevice(running_var, device); |
8396 | torch::Tensor lazy_output = torch::batch_norm( |
8397 | /*input=*/lazy_input, |
8398 | /*weight=*/lazy_weight, |
8399 | /*bias=*/lazy_bias, |
8400 | /*running_mean=*/lazy_running_mean, |
8401 | /*running_var=*/lazy_running_var, |
8402 | /*training=*/training, |
8403 | /*momentum=*/momentum, |
8404 | /*eps=*/eps, |
8405 | /*cudnn_enabled=*/false); |
8406 | AllClose(output, lazy_output, /*rtol=*/1e-3, /*atol=*/1e-5); |
8407 | }); |
8408 | } |
8409 | } |
8410 | } |
8411 | |
8412 | TEST_F(LazyOpsTest, TestBatchNorm2D) { |
8413 | int num_features = 3; |
8414 | torch::Tensor input = torch::rand( |
8415 | {2, num_features, 4, 4}, |
8416 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8417 | torch::Tensor weight = torch::rand( |
8418 | {num_features}, |
8419 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8420 | torch::Tensor bias = torch::rand( |
8421 | {num_features}, |
8422 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8423 | torch::Tensor running_mean = torch::zeros( |
8424 | {num_features}, |
8425 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8426 | torch::Tensor running_var = torch::ones( |
8427 | {num_features}, |
8428 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8429 | double momentum = 0.1; |
8430 | double eps = 0.5; |
8431 | torch::Tensor undef; |
8432 | for (bool training : {true, false}) { |
8433 | for (bool undef_weight_bias : {false, true}) { |
8434 | torch::Tensor output = torch::batch_norm( |
8435 | /*input=*/input, |
8436 | /*weight=*/undef_weight_bias ? undef : weight, |
8437 | /*bias=*/undef_weight_bias ? undef : bias, |
8438 | /*running_mean=*/running_mean, |
8439 | /*running_var=*/running_var, |
8440 | /*training=*/training, |
8441 | /*momentum=*/momentum, |
8442 | /*eps=*/eps, |
8443 | /*cudnn_enabled=*/false); |
8444 | ForEachDevice([&](const torch::Device& device) { |
8445 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8446 | torch::Tensor lazy_weight = |
8447 | undef_weight_bias ? undef : CopyToDevice(weight, device); |
8448 | torch::Tensor lazy_bias = |
8449 | undef_weight_bias ? undef : CopyToDevice(bias, device); |
8450 | torch::Tensor lazy_running_mean = CopyToDevice(running_mean, device); |
8451 | torch::Tensor lazy_running_var = CopyToDevice(running_var, device); |
8452 | torch::Tensor lazy_output = torch::batch_norm( |
8453 | /*input=*/lazy_input, |
8454 | /*weight=*/lazy_weight, |
8455 | /*bias=*/lazy_bias, |
8456 | /*running_mean=*/lazy_running_mean, |
8457 | /*running_var=*/lazy_running_var, |
8458 | /*training=*/training, |
8459 | /*momentum=*/momentum, |
8460 | /*eps=*/eps, |
8461 | /*cudnn_enabled=*/false); |
8462 | AllClose(output, lazy_output, /*rtol=*/1e-3, /*atol=*/1e-5); |
8463 | }); |
8464 | } |
8465 | } |
8466 | } |
8467 | |
8468 | TEST_F(LazyOpsTest, TestDim) { |
8469 | torch::Tensor input = torch::rand( |
8470 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8471 | ForEachDevice([&](const torch::Device& device) { |
8472 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8473 | EXPECT_EQ(input.dim(), lazy_input.dim()); |
8474 | }); |
8475 | } |
8476 | |
8477 | TEST_F(LazyOpsTest, TestContiguous) { |
8478 | torch::Tensor input = torch::rand( |
8479 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8480 | torch::Tensor output = torch::native::contiguous(input); |
8481 | ForEachDevice([&](const torch::Device& device) { |
8482 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8483 | torch::Tensor lazy_output = torch::native::contiguous(lazy_input); |
8484 | AllClose(output, lazy_output); |
8485 | }); |
8486 | } |
8487 | |
8488 | TEST_F(LazyOpsTest, TestSqueezeAll) { |
8489 | torch::Tensor input = torch::rand( |
8490 | {2, 1, 3, 1}, |
8491 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8492 | torch::Tensor output = torch::squeeze(input); |
8493 | ForEachDevice([&](const torch::Device& device) { |
8494 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8495 | torch::Tensor lazy_output = torch::squeeze(lazy_input); |
8496 | AllClose(output, lazy_output); |
8497 | }); |
8498 | } |
8499 | |
8500 | TEST_F(LazyOpsTest, TestSqueezeAllInPlace) { |
8501 | ForEachDevice([&](const torch::Device& device) { |
8502 | torch::Tensor input = torch::rand( |
8503 | {2, 1, 3, 1}, |
8504 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8505 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8506 | torch::Tensor output = input.squeeze_(); |
8507 | torch::Tensor lazy_output = lazy_input.squeeze_(); |
8508 | AllClose(output, lazy_output); |
8509 | AllClose(input, lazy_input); |
8510 | ASSERT_EQ(input.dim(), lazy_input.dim()); |
8511 | for (int64_t dim_idx = 0; dim_idx < input.dim(); ++dim_idx) { |
8512 | ASSERT_EQ(input.size(dim_idx), lazy_input.size(dim_idx)); |
8513 | } |
8514 | }); |
8515 | } |
8516 | |
8517 | TEST_F(LazyOpsTest, TestSqueezeOne) { |
8518 | torch::Tensor input = torch::rand( |
8519 | {2, 1, 3, 1}, |
8520 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8521 | int rank = input.dim(); |
8522 | for (int dim = -rank; dim < rank; ++dim) { |
8523 | torch::Tensor output = torch::squeeze(input, dim); |
8524 | ForEachDevice([&](const torch::Device& device) { |
8525 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8526 | torch::Tensor lazy_output = torch::squeeze(lazy_input, dim); |
8527 | AllClose(output, lazy_output); |
8528 | }); |
8529 | } |
8530 | } |
8531 | |
8532 | TEST_F(LazyOpsTest, TestSqueezeOneInPlace) { |
8533 | int rank = 4; |
8534 | for (int dim = -rank; dim < rank; ++dim) { |
8535 | ForEachDevice([&](const torch::Device& device) { |
8536 | torch::Tensor input = torch::rand( |
8537 | {2, 1, 3, 1}, |
8538 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8539 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8540 | torch::Tensor output = input.squeeze_(dim); |
8541 | torch::Tensor lazy_output = lazy_input.squeeze_(dim); |
8542 | AllClose(output, lazy_output); |
8543 | AllClose(input, lazy_input); |
8544 | ASSERT_EQ(input.dim(), lazy_input.dim()); |
8545 | for (int64_t dim_idx = 0; dim_idx < input.dim(); ++dim_idx) { |
8546 | ASSERT_EQ(input.size(dim_idx), lazy_input.size(dim_idx)); |
8547 | } |
8548 | }); |
8549 | } |
8550 | } |
8551 | |
8552 | TEST_F(LazyOpsTest, TestUnsqueeze) { |
8553 | torch::Tensor input = torch::rand( |
8554 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8555 | int rank = input.dim() + 1; |
8556 | for (int dim = -rank; dim < rank; ++dim) { |
8557 | torch::Tensor output = torch::unsqueeze(input, dim); |
8558 | ForEachDevice([&](const torch::Device& device) { |
8559 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8560 | torch::Tensor lazy_output = torch::unsqueeze(lazy_input, dim); |
8561 | AllClose(output, lazy_output); |
8562 | }); |
8563 | } |
8564 | } |
8565 | |
8566 | TEST_F(LazyOpsTest, TestUnsqueezeInPlace) { |
8567 | torch::Tensor input = torch::rand( |
8568 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8569 | int rank = input.dim() + 1; |
8570 | for (int dim = -rank; dim < rank; ++dim) { |
8571 | ForEachDevice([&](const torch::Device& device) { |
8572 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8573 | torch::Tensor output = input.unsqueeze_(dim); |
8574 | torch::Tensor lazy_output = lazy_input.unsqueeze_(dim); |
8575 | AllClose(output, lazy_output); |
8576 | AllClose(input, lazy_input); |
8577 | ASSERT_EQ(input.dim(), lazy_input.dim()); |
8578 | for (int64_t dim_idx = 0; dim_idx < input.dim(); ++dim_idx) { |
8579 | ASSERT_EQ(input.size(dim_idx), lazy_input.size(dim_idx)); |
8580 | } |
8581 | }); |
8582 | } |
8583 | } |
8584 | |
8585 | TEST_F(LazyOpsTest, TestMaskedFill) { |
8586 | torch::Tensor input = torch::rand( |
8587 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8588 | torch::Tensor mask = torch::randint( |
8589 | 0, 2, {2, 3}, torch::TensorOptions(torch::kBool).device(DefaultDevice())); |
8590 | torch::Scalar value(42); |
8591 | torch::Tensor result = torch::masked_fill(input, mask, value); |
8592 | ForEachDevice([&](const torch::Device& device) { |
8593 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8594 | torch::Tensor lazy_mask = CopyToDevice(mask, device); |
8595 | torch::Tensor lazy_result = |
8596 | torch::masked_fill(lazy_input, lazy_mask, value); |
8597 | AllClose(result, lazy_result); |
8598 | }); |
8599 | } |
8600 | |
8601 | TEST_F(LazyOpsTest, TestMaskedFillInPlace) { |
8602 | torch::Scalar value(42); |
8603 | torch::Tensor mask = torch::randint( |
8604 | 0, 2, {2, 3}, torch::TensorOptions(torch::kBool).device(DefaultDevice())); |
8605 | ForEachDevice([&](const torch::Device& device) { |
8606 | torch::Tensor input = torch::rand( |
8607 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8608 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8609 | torch::Tensor lazy_mask = CopyToDevice(mask, device); |
8610 | torch::Tensor result = input.masked_fill_(mask, value); |
8611 | torch::Tensor lazy_result = lazy_input.masked_fill_(lazy_mask, value); |
8612 | AllClose(result, lazy_result); |
8613 | AllClose(input, lazy_input); |
8614 | }); |
8615 | } |
8616 | |
8617 | TEST_F(LazyOpsTest, TestMaskedFillBroadcast) { |
8618 | torch::Tensor input = torch::rand( |
8619 | {2, 5, 4, 3}, |
8620 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8621 | torch::Tensor mask = torch::randint( |
8622 | 0, 2, {4, 1}, torch::TensorOptions(torch::kBool).device(DefaultDevice())); |
8623 | torch::Scalar value(42); |
8624 | torch::Tensor result = torch::masked_fill(input, mask, value); |
8625 | ForEachDevice([&](const torch::Device& device) { |
8626 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8627 | torch::Tensor lazy_mask = CopyToDevice(mask, device); |
8628 | torch::Tensor lazy_result = |
8629 | torch::masked_fill(lazy_input, lazy_mask, value); |
8630 | AllClose(result, lazy_result); |
8631 | }); |
8632 | } |
8633 | |
8634 | TEST_F(LazyOpsTest, TestFill) { |
8635 | torch::Scalar value(42); |
8636 | ForEachDevice([&](const torch::Device& device) { |
8637 | torch::Tensor input = torch::empty( |
8638 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8639 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8640 | torch::Tensor result = torch::fill_(input, value); |
8641 | torch::Tensor lazy_result = torch::fill_(lazy_input, value); |
8642 | AllClose(result, lazy_result); |
8643 | AllClose(input, lazy_input); |
8644 | }); |
8645 | } |
8646 | |
8647 | TEST_F(LazyOpsTest, TestFillWithRank0) { |
8648 | torch::Tensor value = torch::scalar_tensor(42); |
8649 | ForEachDevice([&](const torch::Device& device) { |
8650 | torch::Tensor input = torch::empty( |
8651 | {2, 3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8652 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8653 | torch::Tensor result = torch::fill_(input, value); |
8654 | torch::Tensor lazy_value = CopyToDevice(value, device); |
8655 | torch::Tensor lazy_result = torch::fill_(lazy_input, value); |
8656 | AllClose(result, lazy_result); |
8657 | AllClose(input, lazy_input); |
8658 | }); |
8659 | } |
8660 | |
8661 | TEST_F(LazyOpsTest, TestPermute) { |
8662 | torch::Tensor input = torch::rand( |
8663 | {2, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8664 | std::vector<std::vector<int64_t>> dims_permutations = { |
8665 | {0, 1, 2}, {0, 2, 1}, {1, 0, 2}, {1, 2, 0}, {2, 0, 1}, {2, 1, 0}}; |
8666 | int rank = input.dim(); |
8667 | for (std::vector<int64_t> dims_permutation : dims_permutations) { |
8668 | for (bool negative_dims : {false, true}) { |
8669 | if (negative_dims) { |
8670 | std::for_each( |
8671 | dims_permutation.begin(), |
8672 | dims_permutation.end(), |
8673 | [rank](int64_t& dim) { dim -= rank; }); |
8674 | } |
8675 | torch::Tensor output = input.permute(dims_permutation); |
8676 | ForEachDevice([&](const torch::Device& device) { |
8677 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8678 | torch::Tensor lazy_output = lazy_input.permute(dims_permutation); |
8679 | AllClose(output, lazy_output); |
8680 | }); |
8681 | } |
8682 | } |
8683 | } |
8684 | |
8685 | TEST_F(LazyOpsTest, TestPermuteMod) { |
8686 | std::vector<std::vector<int64_t>> dims_permutations = { |
8687 | {0, 1, 2}, {0, 2, 1}, {1, 0, 2}, {1, 2, 0}, {2, 0, 1}, {2, 1, 0}}; |
8688 | std::vector<int64_t> input_sizes = {2, 3, 4}; |
8689 | int rank = input_sizes.size(); |
8690 | for (std::vector<int64_t> dims_permutation : dims_permutations) { |
8691 | for (bool negative_dims : {false, true}) { |
8692 | if (negative_dims) { |
8693 | std::for_each( |
8694 | dims_permutation.begin(), |
8695 | dims_permutation.end(), |
8696 | [rank](int64_t& dim) { dim -= rank; }); |
8697 | } |
8698 | torch::Tensor input = torch::zeros( |
8699 | input_sizes, |
8700 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8701 | torch::Tensor one = torch::tensor( |
8702 | 1.0, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8703 | torch::Tensor output = input.permute(dims_permutation); |
8704 | output.add_(one, 1.0); |
8705 | input.add_(one, 1.0); |
8706 | ForEachDevice([&](const torch::Device& device) { |
8707 | torch::Tensor xinput = torch::zeros( |
8708 | input_sizes, |
8709 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8710 | torch::Tensor lazy_input = CopyToDevice(xinput, device); |
8711 | torch::Tensor lazy_one = CopyToDevice(one, device); |
8712 | torch::Tensor lazy_output = lazy_input.permute(dims_permutation); |
8713 | lazy_output.add_(lazy_one, 1.0); |
8714 | lazy_input.add_(lazy_one, 1.0); |
8715 | AllClose(output, lazy_output); |
8716 | AllClose(input, lazy_input); |
8717 | }); |
8718 | } |
8719 | } |
8720 | } |
8721 | |
8722 | TEST_F(LazyOpsTest, TestFlip) { |
8723 | torch::Tensor input = torch::rand( |
8724 | {2, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8725 | std::vector<std::vector<int64_t>> dim_powerset = { |
8726 | {0}, {1}, {2}, {0, 1}, {1, 2}, {2, 0}, {0, 1, 2}}; |
8727 | for (std::vector<int64_t> flip_dims : dim_powerset) { |
8728 | for (bool negative_dims : {false, true}) { |
8729 | if (negative_dims) { |
8730 | std::for_each( |
8731 | flip_dims.begin(), flip_dims.end(), [](int64_t& dim) { dim -= 3; }); |
8732 | } |
8733 | torch::Tensor output = torch::flip(input, flip_dims); |
8734 | ForEachDevice([&](const torch::Device& device) { |
8735 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8736 | torch::Tensor lazy_output = torch::flip(lazy_input, flip_dims); |
8737 | AllClose(output, lazy_output); |
8738 | }); |
8739 | } |
8740 | } |
8741 | } |
8742 | |
8743 | TEST_F(LazyOpsTest, TestPixelShuffle) { |
8744 | torch::Tensor input = torch::rand( |
8745 | {5, 18, 4, 4}, |
8746 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8747 | int upscale_factor = 3; |
8748 | ForEachDevice([&](const torch::Device& device) { |
8749 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8750 | torch::Tensor output = torch::pixel_shuffle(input, upscale_factor); |
8751 | torch::Tensor lazy_output = |
8752 | torch::pixel_shuffle(lazy_input, upscale_factor); |
8753 | AllClose(output, lazy_output); |
8754 | }); |
8755 | } |
8756 | |
8757 | TEST_F(LazyOpsTest, TestSumToSize) { |
8758 | torch::Tensor input = torch::rand( |
8759 | {4, 6, 3, 7}, |
8760 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8761 | std::vector<int64_t> out_size = {4, 1, 1, 7}; |
8762 | ForEachDevice([&](const torch::Device& device) { |
8763 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8764 | torch::Tensor output = input.sum_to_size(out_size); |
8765 | torch::Tensor lazy_output = lazy_input.sum_to_size(out_size); |
8766 | AllClose(output, lazy_output); |
8767 | }); |
8768 | } |
8769 | |
8770 | TEST_F(LazyOpsTest, TestTransposeDims) { |
8771 | torch::Tensor input = torch::rand( |
8772 | {2, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8773 | int dim0 = 0; |
8774 | int dim1 = 2; |
8775 | torch::Tensor output = torch::transpose(input, dim0, dim1); |
8776 | ForEachDevice([&](const torch::Device& device) { |
8777 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8778 | torch::Tensor lazy_output = torch::transpose(lazy_input, dim0, dim1); |
8779 | AllClose(output, lazy_output); |
8780 | }); |
8781 | } |
8782 | |
8783 | TEST_F(LazyOpsTest, TestTransposeDimsMod) { |
8784 | std::vector<int64_t> input_sizes = {2, 3, 4}; |
8785 | int dim0 = 0; |
8786 | int dim1 = 2; |
8787 | torch::Tensor input = torch::zeros( |
8788 | input_sizes, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8789 | torch::Tensor one = torch::tensor( |
8790 | 1.0, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8791 | torch::Tensor output = torch::transpose(input, dim0, dim1); |
8792 | output.add_(one, 1.0); |
8793 | input.add_(one, 1.0); |
8794 | ForEachDevice([&](const torch::Device& device) { |
8795 | torch::Tensor xinput = torch::zeros( |
8796 | input_sizes, |
8797 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8798 | torch::Tensor lazy_input = CopyToDevice(xinput, device); |
8799 | torch::Tensor lazy_one = CopyToDevice(one, device); |
8800 | torch::Tensor lazy_output = torch::transpose(lazy_input, dim0, dim1); |
8801 | lazy_output.add_(lazy_one, 1.0); |
8802 | lazy_input.add_(lazy_one, 1.0); |
8803 | AllClose(output, lazy_output); |
8804 | AllClose(input, lazy_input); |
8805 | }); |
8806 | } |
8807 | |
8808 | TEST_F(LazyOpsTest, TestTransposeDimsInPlace) { |
8809 | torch::Tensor input = torch::rand( |
8810 | {2, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8811 | int dim0 = 0; |
8812 | int dim1 = 2; |
8813 | ForEachDevice([&](const torch::Device& device) { |
8814 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8815 | torch::Tensor output = input.transpose_(dim0, dim1); |
8816 | torch::Tensor lazy_output = lazy_input.transpose_(dim0, dim1); |
8817 | AllClose(output, lazy_output); |
8818 | AllClose(input, lazy_input); |
8819 | }); |
8820 | } |
8821 | |
8822 | TEST_F(LazyOpsTest, TestSplit) { |
8823 | torch::Tensor input = torch::rand( |
8824 | {7, 8, 9}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8825 | int rank = input.dim(); |
8826 | for (int split_size : {2, 3}) { |
8827 | for (int dim = -rank; dim < rank; ++dim) { |
8828 | std::vector<torch::Tensor> outputs = torch::split(input, split_size, dim); |
8829 | ForEachDevice([&](const torch::Device& device) { |
8830 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8831 | std::vector<torch::Tensor> lazy_outputs = |
8832 | torch::split(lazy_input, split_size, dim); |
8833 | ASSERT_EQ(outputs.size(), lazy_outputs.size()); |
8834 | for (size_t i = 0; i < outputs.size(); ++i) { |
8835 | AllClose(outputs[i], lazy_outputs[i]); |
8836 | } |
8837 | }); |
8838 | } |
8839 | } |
8840 | } |
8841 | |
8842 | TEST_F(LazyOpsTest, TestSplitEmpty) { |
8843 | torch::Tensor input = torch::rand( |
8844 | {0}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8845 | int split_size = 0; |
8846 | int dim = 0; |
8847 | std::vector<torch::Tensor> outputs = torch::split(input, split_size, dim); |
8848 | ForEachDevice([&](const torch::Device& device) { |
8849 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8850 | std::vector<torch::Tensor> lazy_outputs = |
8851 | torch::split(lazy_input, split_size, dim); |
8852 | ASSERT_EQ(outputs.size(), lazy_outputs.size()); |
8853 | for (size_t i = 0; i < outputs.size(); ++i) { |
8854 | AllClose(outputs[i], lazy_outputs[i]); |
8855 | } |
8856 | }); |
8857 | } |
8858 | |
8859 | TEST_F(LazyOpsTest, TestSplitWithSizes) { |
8860 | torch::Tensor input = torch::rand( |
8861 | {15, 15, 15}, |
8862 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8863 | int rank = input.dim(); |
8864 | for (int dim = -rank; dim < rank; ++dim) { |
8865 | std::vector<torch::Tensor> outputs = |
8866 | torch::split_with_sizes(input, {4, 5, 6}, dim); |
8867 | ForEachDevice([&](const torch::Device& device) { |
8868 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8869 | std::vector<torch::Tensor> lazy_outputs = |
8870 | torch::split_with_sizes(lazy_input, {4, 5, 6}, dim); |
8871 | ASSERT_EQ(outputs.size(), lazy_outputs.size()); |
8872 | for (size_t i = 0; i < outputs.size(); ++i) { |
8873 | AllClose(outputs[i], lazy_outputs[i]); |
8874 | } |
8875 | }); |
8876 | } |
8877 | } |
8878 | |
8879 | TEST_F(LazyOpsTest, TestCrossImplicitDim) { |
8880 | std::vector<std::vector<int64_t>> dim_sizes = { |
8881 | {4, 5, 3}, {4, 3, 5}, {3, 4, 5}}; |
8882 | for (auto dim_size : dim_sizes) { |
8883 | torch::Tensor input = torch::rand( |
8884 | dim_size, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8885 | torch::Tensor other = torch::rand( |
8886 | dim_size, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8887 | torch::Tensor result = torch::cross(input, other); |
8888 | ForEachDevice([&](const torch::Device& device) { |
8889 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8890 | torch::Tensor lazy_other = CopyToDevice(other, device); |
8891 | torch::Tensor lazy_result = torch::cross(lazy_input, lazy_other); |
8892 | AllClose(result, lazy_result); |
8893 | }); |
8894 | } |
8895 | } |
8896 | |
8897 | TEST_F(LazyOpsTest, TestCrossExplicitDim) { |
8898 | std::vector<int64_t> dim_size = {3, 3}; |
8899 | torch::Tensor input = torch::rand( |
8900 | dim_size, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8901 | torch::Tensor other = torch::rand( |
8902 | dim_size, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8903 | int rank = dim_size.size(); |
8904 | for (int dim = -rank; dim < rank; ++dim) { |
8905 | torch::Tensor result = torch::cross(input, other, dim); |
8906 | ForEachDevice([&](const torch::Device& device) { |
8907 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8908 | torch::Tensor lazy_other = CopyToDevice(other, device); |
8909 | torch::Tensor lazy_result = torch::cross(lazy_input, lazy_other, dim); |
8910 | AllClose(result, lazy_result); |
8911 | }); |
8912 | } |
8913 | } |
8914 | |
8915 | TEST_F(LazyOpsTest, TestCrossZeroDim) { |
8916 | torch::Tensor input = torch::rand( |
8917 | {0, 1, 3, 0}, |
8918 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8919 | torch::Tensor result = torch::cross(input, input); |
8920 | ForEachDevice([&](const torch::Device& device) { |
8921 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8922 | torch::Tensor lazy_result = torch::cross(lazy_input, lazy_input); |
8923 | AllClose(result, lazy_result); |
8924 | }); |
8925 | } |
8926 | |
8927 | TEST_F(LazyOpsTest, TestTriu) { |
8928 | int size = 5; |
8929 | torch::Tensor input = torch::rand( |
8930 | {size, size}, |
8931 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8932 | // Test all diagonals and out of bounds (must be no-op). |
8933 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
8934 | torch::Tensor output = torch::triu(input, diagonal); |
8935 | ForEachDevice([&](const torch::Device& device) { |
8936 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8937 | torch::Tensor lazy_output = torch::triu(lazy_input, diagonal); |
8938 | AllClose(output, lazy_output); |
8939 | }); |
8940 | } |
8941 | } |
8942 | |
8943 | TEST_F(LazyOpsTest, TestTriuNonSquare) { |
8944 | int size = 5; |
8945 | torch::Tensor input = torch::rand( |
8946 | {size, size + 1}, |
8947 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8948 | // Test all diagonals and out of bounds (must be no-op). |
8949 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
8950 | torch::Tensor output = torch::triu(input, diagonal); |
8951 | ForEachDevice([&](const torch::Device& device) { |
8952 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8953 | torch::Tensor lazy_output = torch::triu(lazy_input, diagonal); |
8954 | AllClose(output, lazy_output); |
8955 | }); |
8956 | } |
8957 | } |
8958 | |
8959 | TEST_F(LazyOpsTest, TestTriuBatch) { |
8960 | int size = 5; |
8961 | int batch_size = 3; |
8962 | torch::Tensor input = torch::rand( |
8963 | {batch_size, size, size}, |
8964 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8965 | // Test all diagonals and out of bounds (must be no-op). |
8966 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
8967 | torch::Tensor output = torch::triu(input, diagonal); |
8968 | ForEachDevice([&](const torch::Device& device) { |
8969 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8970 | torch::Tensor lazy_output = torch::triu(lazy_input, diagonal); |
8971 | AllClose(output, lazy_output); |
8972 | }); |
8973 | } |
8974 | } |
8975 | |
8976 | TEST_F(LazyOpsTest, TestTril) { |
8977 | int size = 5; |
8978 | torch::Tensor input = torch::rand( |
8979 | {size, size}, |
8980 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8981 | // Test all diagonals and out of bounds (must be no-op). |
8982 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
8983 | torch::Tensor output = torch::tril(input, diagonal); |
8984 | ForEachDevice([&](const torch::Device& device) { |
8985 | torch::Tensor lazy_input = CopyToDevice(input, device); |
8986 | torch::Tensor lazy_output = torch::tril(lazy_input, diagonal); |
8987 | AllClose(output, lazy_output); |
8988 | }); |
8989 | } |
8990 | } |
8991 | |
8992 | TEST_F(LazyOpsTest, TestTrilNonSquare) { |
8993 | int size = 5; |
8994 | torch::Tensor input = torch::rand( |
8995 | {size, size + 1}, |
8996 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
8997 | // Test all diagonals and out of bounds (must be no-op). |
8998 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
8999 | torch::Tensor output = torch::tril(input, diagonal); |
9000 | ForEachDevice([&](const torch::Device& device) { |
9001 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9002 | torch::Tensor lazy_output = torch::tril(lazy_input, diagonal); |
9003 | AllClose(output, lazy_output); |
9004 | }); |
9005 | } |
9006 | } |
9007 | |
9008 | TEST_F(LazyOpsTest, TestTrilBatch) { |
9009 | int size = 5; |
9010 | int batch_size = 3; |
9011 | torch::Tensor input = torch::rand( |
9012 | {batch_size, size, size}, |
9013 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9014 | // Test all diagonals and out of bounds (must be no-op). |
9015 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
9016 | torch::Tensor output = torch::tril(input, diagonal); |
9017 | ForEachDevice([&](const torch::Device& device) { |
9018 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9019 | torch::Tensor lazy_output = torch::tril(lazy_input, diagonal); |
9020 | AllClose(output, lazy_output); |
9021 | }); |
9022 | } |
9023 | } |
9024 | |
9025 | TEST_F(LazyOpsTest, TestTriuInPlace) { |
9026 | int size = 5; |
9027 | // Test all diagonals and out of bounds (must be no-op). |
9028 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
9029 | ForEachDevice([&](const torch::Device& device) { |
9030 | torch::Tensor input = torch::rand( |
9031 | {size, size}, |
9032 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9033 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9034 | torch::Tensor output = input.triu_(diagonal); |
9035 | torch::Tensor lazy_output = lazy_input.triu_(diagonal); |
9036 | AllClose(output, lazy_output); |
9037 | AllClose(input, lazy_input); |
9038 | }); |
9039 | } |
9040 | } |
9041 | |
9042 | TEST_F(LazyOpsTest, TestTrilInPlace) { |
9043 | int size = 5; |
9044 | // Test all diagonals and out of bounds (must be no-op). |
9045 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
9046 | ForEachDevice([&](const torch::Device& device) { |
9047 | torch::Tensor input = torch::rand( |
9048 | {size, size}, |
9049 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9050 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9051 | torch::Tensor output = input.tril_(diagonal); |
9052 | torch::Tensor lazy_output = lazy_input.tril_(diagonal); |
9053 | AllClose(output, lazy_output); |
9054 | AllClose(input, lazy_input); |
9055 | }); |
9056 | } |
9057 | } |
9058 | |
9059 | TEST_F(LazyOpsTest, TestTrace) { |
9060 | int n = 5; |
9061 | torch::Tensor input = torch::rand( |
9062 | {n, n}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9063 | torch::Tensor output = torch::trace(input); |
9064 | ForEachDevice([&](const torch::Device& device) { |
9065 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9066 | torch::Tensor lazy_output = torch::trace(lazy_input); |
9067 | AllClose(output, lazy_output); |
9068 | }); |
9069 | } |
9070 | |
9071 | TEST_F(LazyOpsTest, TestTraceWide) { |
9072 | int lines = 3; |
9073 | int cols = 5; |
9074 | torch::Tensor input = torch::rand( |
9075 | {lines, cols}, |
9076 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9077 | torch::Tensor output = torch::trace(input); |
9078 | ForEachDevice([&](const torch::Device& device) { |
9079 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9080 | torch::Tensor lazy_output = torch::trace(lazy_input); |
9081 | AllClose(output, lazy_output); |
9082 | }); |
9083 | } |
9084 | |
9085 | TEST_F(LazyOpsTest, TestTraceNarrow) { |
9086 | int lines = 5; |
9087 | int cols = 3; |
9088 | torch::Tensor input = torch::rand( |
9089 | {lines, cols}, |
9090 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9091 | torch::Tensor output = torch::trace(input); |
9092 | ForEachDevice([&](const torch::Device& device) { |
9093 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9094 | torch::Tensor lazy_output = torch::trace(lazy_input); |
9095 | AllClose(output, lazy_output); |
9096 | }); |
9097 | } |
9098 | |
9099 | TEST_F(LazyOpsTest, TestDiagRank1) { |
9100 | int size = 7; |
9101 | torch::Tensor input = torch::rand( |
9102 | {size}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9103 | // Test all diagonals and out of bounds (must be no-op). |
9104 | for (int diagonal = -2 * size; diagonal <= 2 * size; ++diagonal) { |
9105 | torch::Tensor output = torch::diag(input, diagonal); |
9106 | ForEachDevice([&](const torch::Device& device) { |
9107 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9108 | torch::Tensor lazy_output = torch::diag(lazy_input, diagonal); |
9109 | AllClose(output, lazy_output); |
9110 | }); |
9111 | } |
9112 | } |
9113 | |
9114 | TEST_F(LazyOpsTest, TestDiagRank2) { |
9115 | int size = 7; |
9116 | torch::Tensor input = torch::rand( |
9117 | {size, size}, |
9118 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9119 | // Test all diagonals and out of bounds (must be no-op). |
9120 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
9121 | torch::Tensor output = torch::diag(input, diagonal); |
9122 | ForEachDevice([&](const torch::Device& device) { |
9123 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9124 | torch::Tensor lazy_output = torch::diag(lazy_input, diagonal); |
9125 | AllClose(output, lazy_output); |
9126 | }); |
9127 | } |
9128 | } |
9129 | |
9130 | TEST_F(LazyOpsTest, TestDiagFlat) { |
9131 | torch::Tensor input = torch::rand( |
9132 | {4, 3, 6, 7}, |
9133 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9134 | for (int diagonal = -10; diagonal < 10; ++diagonal) { |
9135 | torch::Tensor output = torch::diagflat(input, diagonal); |
9136 | ForEachDevice([&](const torch::Device& device) { |
9137 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9138 | torch::Tensor lazy_output = torch::diagflat(lazy_input, diagonal); |
9139 | AllClose(output, lazy_output); |
9140 | }); |
9141 | } |
9142 | } |
9143 | |
9144 | TEST_F(LazyOpsTest, TestDiagonal) { |
9145 | int size = 5; |
9146 | torch::Tensor input = torch::rand( |
9147 | {size, size}, |
9148 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9149 | // Test all diagonals and out of bounds (must be no-op). |
9150 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
9151 | torch::Tensor output = torch::diagonal(input, diagonal); |
9152 | ForEachDevice([&](const torch::Device& device) { |
9153 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9154 | torch::Tensor lazy_output = torch::diagonal(lazy_input, diagonal); |
9155 | AllClose(output, lazy_output); |
9156 | }); |
9157 | } |
9158 | } |
9159 | |
9160 | TEST_F(LazyOpsTest, TestDiagonalUpdate) { |
9161 | int size = 5; |
9162 | // Test all diagonals and out of bounds (must be no-op). |
9163 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
9164 | auto input = torch::rand( |
9165 | {size, size}, |
9166 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9167 | auto input_clone = input.clone(); |
9168 | auto output = torch::diagonal(input, diagonal); |
9169 | output.add_(1); |
9170 | |
9171 | ForEachDevice([&](const torch::Device& device) { |
9172 | torch::Tensor lazy_input = CopyToDevice(input_clone, device); |
9173 | torch::Tensor lazy_output = torch::diagonal(lazy_input, diagonal); |
9174 | lazy_output.add_(1); |
9175 | |
9176 | AllClose(output, lazy_output); |
9177 | AllClose(input, lazy_input); |
9178 | }); |
9179 | } |
9180 | } |
9181 | |
9182 | TEST_F(LazyOpsTest, TestDiagonalNonSquare) { |
9183 | int size = 5; |
9184 | torch::Tensor input = torch::rand( |
9185 | {size, size + 1}, |
9186 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9187 | // Test all diagonals and out of bounds (must be no-op). |
9188 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
9189 | torch::Tensor output = torch::diagonal(input, diagonal); |
9190 | ForEachDevice([&](const torch::Device& device) { |
9191 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9192 | torch::Tensor lazy_output = torch::diagonal(lazy_input, diagonal); |
9193 | AllClose(output, lazy_output); |
9194 | }); |
9195 | } |
9196 | } |
9197 | |
9198 | TEST_F(LazyOpsTest, TestDiagonalBatch) { |
9199 | int size = 5; |
9200 | int batch_size = 3; |
9201 | int dim1 = 1; |
9202 | int dim2 = 2; |
9203 | torch::Tensor input = torch::rand( |
9204 | {batch_size, size, size}, |
9205 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9206 | // Test all diagonals and out of bounds (must be no-op). |
9207 | for (int diagonal = -size; diagonal <= size; ++diagonal) { |
9208 | torch::Tensor output = |
9209 | torch::diagonal(input, diagonal, /*dim1=*/dim1, /*dim1=*/dim2); |
9210 | ForEachDevice([&](const torch::Device& device) { |
9211 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9212 | torch::Tensor lazy_output = |
9213 | torch::diagonal(lazy_input, diagonal, /*dim1=*/dim1, /*dim1=*/dim2); |
9214 | AllClose(output, lazy_output); |
9215 | }); |
9216 | } |
9217 | } |
9218 | |
9219 | TEST_F(LazyOpsTest, TestFlatten) { |
9220 | torch::Tensor input = torch::rand({4, 7, 5, 3}); |
9221 | int rank = input.dim(); |
9222 | for (int pos_start_dim = 0; pos_start_dim < rank; ++pos_start_dim) { |
9223 | for (int pos_end_dim = pos_start_dim; pos_end_dim < rank; ++pos_end_dim) { |
9224 | for (bool negative_start_dim : {false, true}) { |
9225 | for (bool negative_end_dim : {false, true}) { |
9226 | int start_dim = |
9227 | negative_start_dim ? pos_start_dim - rank : pos_start_dim; |
9228 | int end_dim = negative_end_dim ? pos_end_dim - rank : pos_end_dim; |
9229 | torch::Tensor output = torch::flatten(input, start_dim, end_dim); |
9230 | ForEachDevice([&](const torch::Device& device) { |
9231 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9232 | torch::Tensor lazy_output = |
9233 | torch::flatten(lazy_input, start_dim, end_dim); |
9234 | AllClose(output, lazy_output); |
9235 | }); |
9236 | } |
9237 | } |
9238 | } |
9239 | } |
9240 | } |
9241 | |
9242 | TEST_F(LazyOpsTest, TestLogicalAnd) { |
9243 | for (torch::ScalarType scalar_type1 : |
9244 | {torch::kFloat, |
9245 | torch::kByte, |
9246 | torch::kChar, |
9247 | torch::kShort, |
9248 | torch::kInt, |
9249 | torch::kLong}) { |
9250 | torch::Tensor lhs = isFloatingType(scalar_type1) |
9251 | ? torch::rand({3, 4}, torch::TensorOptions(scalar_type1)) |
9252 | : torch::randint(0, 100, {3, 4}, torch::TensorOptions(scalar_type1)); |
9253 | for (torch::ScalarType scalar_type2 : |
9254 | {torch::kFloat, |
9255 | torch::kByte, |
9256 | torch::kChar, |
9257 | torch::kShort, |
9258 | torch::kInt, |
9259 | torch::kLong}) { |
9260 | torch::Tensor rhs = isFloatingType(scalar_type2) |
9261 | ? torch::rand({3, 4}, torch::TensorOptions(scalar_type2)) |
9262 | : torch::randint(1, 100, {3, 4}, torch::TensorOptions(scalar_type2)); |
9263 | torch::Tensor result = torch::logical_and(lhs, rhs); |
9264 | ForEachDevice([&](const torch::Device& device) { |
9265 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9266 | torch::Tensor lazy_rhs = CopyToDevice(rhs, device); |
9267 | torch::Tensor lazy_result = torch::logical_and(lazy_lhs, lazy_rhs); |
9268 | AllEqual(result, lazy_result); |
9269 | }); |
9270 | } |
9271 | } |
9272 | |
9273 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
9274 | ExpectCounterChanged("xla::logical_and_out" , GetIgnoredCounters()); |
9275 | } |
9276 | |
9277 | TEST_F(LazyOpsTest, TestBitwiseAnd) { |
9278 | torch::Tensor lhs = torch::randint( |
9279 | 0, |
9280 | std::numeric_limits<int32_t>::max(), |
9281 | {4, 2}, |
9282 | torch::TensorOptions(torch::kInt)); |
9283 | torch::Tensor rhs = torch::randint( |
9284 | 0, |
9285 | std::numeric_limits<int32_t>::max(), |
9286 | {4, 2}, |
9287 | torch::TensorOptions(torch::kInt)); |
9288 | torch::Tensor result = lhs.__and__(rhs); |
9289 | ForEachDevice([&](const torch::Device& device) { |
9290 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9291 | torch::Tensor lazy_rhs = CopyToDevice(rhs, device); |
9292 | torch::Tensor lazy_result = lazy_lhs.__and__(lazy_rhs); |
9293 | AllEqual(result, lazy_result); |
9294 | }); |
9295 | } |
9296 | |
9297 | TEST_F(LazyOpsTest, TestBitwiseAndInPlace) { |
9298 | torch::Tensor lhs = torch::randint( |
9299 | 0, |
9300 | std::numeric_limits<int32_t>::max(), |
9301 | {4, 2}, |
9302 | torch::TensorOptions(torch::kInt)); |
9303 | torch::Tensor rhs = torch::randint( |
9304 | 0, |
9305 | std::numeric_limits<int32_t>::max(), |
9306 | {4, 2}, |
9307 | torch::TensorOptions(torch::kInt)); |
9308 | ForEachDevice([&](const torch::Device& device) { |
9309 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9310 | torch::Tensor result = lhs.__iand__(rhs); |
9311 | torch::Tensor lazy_rhs = CopyToDevice(rhs, device); |
9312 | torch::Tensor lazy_result = lazy_lhs.__iand__(lazy_rhs); |
9313 | AllEqual(result, lazy_result); |
9314 | AllEqual(lhs, lazy_lhs); |
9315 | }); |
9316 | } |
9317 | |
9318 | TEST_F(LazyOpsTest, TestBitwiseAndScalar) { |
9319 | torch::Tensor lhs = torch::randint( |
9320 | 0, |
9321 | std::numeric_limits<int32_t>::max(), |
9322 | {4, 2}, |
9323 | torch::TensorOptions(torch::kInt)); |
9324 | torch::Scalar rhs(123456789); |
9325 | torch::Tensor result = lhs.__and__(rhs); |
9326 | ForEachDevice([&](const torch::Device& device) { |
9327 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9328 | torch::Tensor lazy_result = lazy_lhs.__and__(rhs); |
9329 | AllEqual(result, lazy_result); |
9330 | }); |
9331 | } |
9332 | |
9333 | TEST_F(LazyOpsTest, TestBitwiseAndScalarInPlace) { |
9334 | torch::Tensor lhs = torch::randint( |
9335 | 0, |
9336 | std::numeric_limits<int32_t>::max(), |
9337 | {4, 2}, |
9338 | torch::TensorOptions(torch::kInt)); |
9339 | torch::Scalar rhs(123456789); |
9340 | ForEachDevice([&](const torch::Device& device) { |
9341 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9342 | torch::Tensor result = lhs.__iand__(rhs); |
9343 | torch::Tensor lazy_result = lazy_lhs.__iand__(rhs); |
9344 | AllEqual(result, lazy_result); |
9345 | AllEqual(lhs, lazy_lhs); |
9346 | }); |
9347 | } |
9348 | |
9349 | TEST_F(LazyOpsTest, TestBitwiseAndPromotion) { |
9350 | torch::Tensor input = torch::rand( |
9351 | {4, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9352 | torch::Tensor view = input.reshape(-1); |
9353 | torch::Tensor result = torch::__and__(view.gt(0), view.ne(0)); |
9354 | ForEachDevice([&](const torch::Device& device) { |
9355 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9356 | torch::Tensor lazy_view = lazy_input.reshape(-1); |
9357 | torch::Tensor lazy_result = |
9358 | torch::__and__(lazy_view.gt(0), lazy_view.ne(0)); |
9359 | AllEqual(result, lazy_result); |
9360 | }); |
9361 | } |
9362 | |
9363 | TEST_F(LazyOpsTest, TestBitwiseOr) { |
9364 | torch::Tensor lhs = torch::randint( |
9365 | 0, |
9366 | std::numeric_limits<int32_t>::max(), |
9367 | {4, 2}, |
9368 | torch::TensorOptions(torch::kInt)); |
9369 | torch::Tensor rhs = torch::randint( |
9370 | 0, |
9371 | std::numeric_limits<int32_t>::max(), |
9372 | {4, 2}, |
9373 | torch::TensorOptions(torch::kInt)); |
9374 | torch::Tensor result = lhs.__or__(rhs); |
9375 | ForEachDevice([&](const torch::Device& device) { |
9376 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9377 | torch::Tensor lazy_rhs = CopyToDevice(rhs, device); |
9378 | torch::Tensor lazy_result = lazy_lhs.__or__(lazy_rhs); |
9379 | AllEqual(result, lazy_result); |
9380 | }); |
9381 | } |
9382 | |
9383 | TEST_F(LazyOpsTest, TestBitwiseOrInPlace) { |
9384 | torch::Tensor lhs = torch::randint( |
9385 | 0, |
9386 | std::numeric_limits<int32_t>::max(), |
9387 | {4, 2}, |
9388 | torch::TensorOptions(torch::kInt)); |
9389 | torch::Tensor rhs = torch::randint( |
9390 | 0, |
9391 | std::numeric_limits<int32_t>::max(), |
9392 | {4, 2}, |
9393 | torch::TensorOptions(torch::kInt)); |
9394 | ForEachDevice([&](const torch::Device& device) { |
9395 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9396 | torch::Tensor result = lhs.__ior__(rhs); |
9397 | torch::Tensor lazy_rhs = CopyToDevice(rhs, device); |
9398 | torch::Tensor lazy_result = lazy_lhs.__ior__(lazy_rhs); |
9399 | AllEqual(result, lazy_result); |
9400 | AllEqual(lhs, lazy_lhs); |
9401 | }); |
9402 | } |
9403 | |
9404 | TEST_F(LazyOpsTest, TestBitwiseOrScalar) { |
9405 | torch::Tensor lhs = torch::randint( |
9406 | 0, |
9407 | std::numeric_limits<int32_t>::max(), |
9408 | {4, 2}, |
9409 | torch::TensorOptions(torch::kInt)); |
9410 | torch::Scalar rhs(123456789); |
9411 | torch::Tensor result = lhs.__or__(rhs); |
9412 | ForEachDevice([&](const torch::Device& device) { |
9413 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9414 | torch::Tensor lazy_result = lazy_lhs.__or__(rhs); |
9415 | AllEqual(result, lazy_result); |
9416 | }); |
9417 | } |
9418 | |
9419 | TEST_F(LazyOpsTest, TestBitwiseOrScalarInPlace) { |
9420 | torch::Tensor lhs = torch::randint( |
9421 | 0, |
9422 | std::numeric_limits<int32_t>::max(), |
9423 | {4, 2}, |
9424 | torch::TensorOptions(torch::kInt)); |
9425 | torch::Scalar rhs(123456789); |
9426 | ForEachDevice([&](const torch::Device& device) { |
9427 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9428 | torch::Tensor result = lhs.__ior__(rhs); |
9429 | torch::Tensor lazy_result = lazy_lhs.__ior__(rhs); |
9430 | AllEqual(result, lazy_result); |
9431 | AllEqual(lhs, lazy_lhs); |
9432 | }); |
9433 | } |
9434 | |
9435 | TEST_F(LazyOpsTest, TestBitwiseXor) { |
9436 | torch::Tensor lhs = torch::randint( |
9437 | 0, |
9438 | std::numeric_limits<int32_t>::max(), |
9439 | {4, 2}, |
9440 | torch::TensorOptions(torch::kInt)); |
9441 | torch::Tensor rhs = torch::randint( |
9442 | 0, |
9443 | std::numeric_limits<int32_t>::max(), |
9444 | {4, 2}, |
9445 | torch::TensorOptions(torch::kInt)); |
9446 | torch::Tensor result = lhs.__xor__(rhs); |
9447 | ForEachDevice([&](const torch::Device& device) { |
9448 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9449 | torch::Tensor lazy_rhs = CopyToDevice(rhs, device); |
9450 | torch::Tensor lazy_result = lazy_lhs.__xor__(lazy_rhs); |
9451 | AllEqual(result, lazy_result); |
9452 | }); |
9453 | } |
9454 | |
9455 | TEST_F(LazyOpsTest, TestBitwiseXorInPlace) { |
9456 | torch::Tensor lhs = torch::randint( |
9457 | 0, |
9458 | std::numeric_limits<int32_t>::max(), |
9459 | {4, 2}, |
9460 | torch::TensorOptions(torch::kInt)); |
9461 | torch::Tensor rhs = torch::randint( |
9462 | 0, |
9463 | std::numeric_limits<int32_t>::max(), |
9464 | {4, 2}, |
9465 | torch::TensorOptions(torch::kInt)); |
9466 | ForEachDevice([&](const torch::Device& device) { |
9467 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9468 | torch::Tensor result = lhs.__ixor__(rhs); |
9469 | torch::Tensor lazy_rhs = CopyToDevice(rhs, device); |
9470 | torch::Tensor lazy_result = lazy_lhs.__ixor__(lazy_rhs); |
9471 | AllEqual(result, lazy_result); |
9472 | AllEqual(lhs, lazy_lhs); |
9473 | }); |
9474 | } |
9475 | |
9476 | TEST_F(LazyOpsTest, TestBitwiseXorScalar) { |
9477 | torch::Tensor lhs = torch::randint( |
9478 | 0, |
9479 | std::numeric_limits<int32_t>::max(), |
9480 | {4, 2}, |
9481 | torch::TensorOptions(torch::kInt)); |
9482 | torch::Scalar rhs(123456789); |
9483 | torch::Tensor result = lhs.__xor__(rhs); |
9484 | ForEachDevice([&](const torch::Device& device) { |
9485 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9486 | torch::Tensor lazy_result = lazy_lhs.__xor__(rhs); |
9487 | AllEqual(result, lazy_result); |
9488 | }); |
9489 | } |
9490 | |
9491 | TEST_F(LazyOpsTest, TestBitwiseXorScalarInPlace) { |
9492 | torch::Tensor lhs = torch::randint( |
9493 | 0, |
9494 | std::numeric_limits<int32_t>::max(), |
9495 | {4, 2}, |
9496 | torch::TensorOptions(torch::kInt)); |
9497 | torch::Scalar rhs(123456789); |
9498 | ForEachDevice([&](const torch::Device& device) { |
9499 | torch::Tensor lazy_lhs = CopyToDevice(lhs, device); |
9500 | torch::Tensor result = lhs.__ixor__(rhs); |
9501 | torch::Tensor lazy_result = lazy_lhs.__ixor__(rhs); |
9502 | AllEqual(result, lazy_result); |
9503 | AllEqual(lhs, lazy_lhs); |
9504 | }); |
9505 | } |
9506 | |
9507 | TEST_F(LazyOpsTest, TestLshift) { |
9508 | torch::Tensor input = torch::ones( |
9509 | {4, 2}, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
9510 | torch::Tensor shift_amount = torch::randint( |
9511 | 16, |
9512 | input.sizes(), |
9513 | torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
9514 | torch::Tensor result = torch::__lshift__(input, shift_amount); |
9515 | ForEachDevice([&](const torch::Device& device) { |
9516 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9517 | torch::Tensor lazy_shift_amount = CopyToDevice(shift_amount, device); |
9518 | torch::Tensor lazy_result = |
9519 | torch::__lshift__(lazy_input, lazy_shift_amount); |
9520 | AllClose(result, lazy_result); |
9521 | }); |
9522 | } |
9523 | |
9524 | TEST_F(LazyOpsTest, TestLshiftInPlace) { |
9525 | torch::Tensor input = torch::ones( |
9526 | {4, 2}, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
9527 | ForEachDevice([&](const torch::Device& device) { |
9528 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9529 | torch::Tensor shift_amount = torch::randint( |
9530 | 16, |
9531 | input.sizes(), |
9532 | torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
9533 | torch::Tensor result = input.__ilshift__(shift_amount); |
9534 | torch::Tensor lazy_shift_amount = CopyToDevice(shift_amount, device); |
9535 | torch::Tensor lazy_result = lazy_input.__ilshift__(lazy_shift_amount); |
9536 | AllClose(result, lazy_result); |
9537 | AllClose(input, lazy_input); |
9538 | }); |
9539 | } |
9540 | |
9541 | TEST_F(LazyOpsTest, TestLshiftScalar) { |
9542 | torch::Tensor input = torch::ones( |
9543 | {4, 2}, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
9544 | torch::Scalar shift_amount = 3; |
9545 | torch::Tensor result = torch::__lshift__(input, shift_amount); |
9546 | ForEachDevice([&](const torch::Device& device) { |
9547 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9548 | torch::Tensor lazy_result = torch::__lshift__(lazy_input, shift_amount); |
9549 | AllClose(result, lazy_result); |
9550 | }); |
9551 | } |
9552 | |
9553 | TEST_F(LazyOpsTest, TestLshiftScalarInPlace) { |
9554 | torch::Tensor input = torch::ones( |
9555 | {4, 2}, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
9556 | torch::Scalar shift_amount = 3; |
9557 | ForEachDevice([&](const torch::Device& device) { |
9558 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9559 | torch::Tensor result = input.__ilshift__(shift_amount); |
9560 | torch::Tensor lazy_result = lazy_input.__ilshift__(shift_amount); |
9561 | AllClose(result, lazy_result); |
9562 | AllClose(input, lazy_input); |
9563 | }); |
9564 | } |
9565 | |
9566 | TEST_F(LazyOpsTest, TestRshift) { |
9567 | torch::Tensor input = torch::ones( |
9568 | {4, 2}, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
9569 | torch::Tensor shift_amount = torch::randint( |
9570 | 16, |
9571 | input.sizes(), |
9572 | torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
9573 | torch::Tensor result = torch::__rshift__(input, shift_amount); |
9574 | ForEachDevice([&](const torch::Device& device) { |
9575 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9576 | torch::Tensor lazy_shift_amount = CopyToDevice(shift_amount, device); |
9577 | torch::Tensor lazy_result = |
9578 | torch::__rshift__(lazy_input, lazy_shift_amount); |
9579 | AllClose(result, lazy_result); |
9580 | }); |
9581 | } |
9582 | |
9583 | TEST_F(LazyOpsTest, TestRshiftInPlace) { |
9584 | torch::Tensor input = torch::ones( |
9585 | {4, 2}, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
9586 | ForEachDevice([&](const torch::Device& device) { |
9587 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9588 | torch::Tensor shift_amount = torch::randint( |
9589 | 16, |
9590 | input.sizes(), |
9591 | torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
9592 | torch::Tensor result = input.__irshift__(shift_amount); |
9593 | torch::Tensor lazy_shift_amount = CopyToDevice(shift_amount, device); |
9594 | torch::Tensor lazy_result = lazy_input.__irshift__(lazy_shift_amount); |
9595 | AllClose(result, lazy_result); |
9596 | AllClose(input, lazy_input); |
9597 | }); |
9598 | } |
9599 | |
9600 | TEST_F(LazyOpsTest, TestRshiftScalar) { |
9601 | torch::Tensor input = torch::ones( |
9602 | {4, 2}, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
9603 | torch::Scalar shift_amount = 3; |
9604 | torch::Tensor result = torch::__rshift__(input, shift_amount); |
9605 | ForEachDevice([&](const torch::Device& device) { |
9606 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9607 | torch::Tensor lazy_result = torch::__rshift__(lazy_input, shift_amount); |
9608 | AllClose(result, lazy_result); |
9609 | }); |
9610 | } |
9611 | |
9612 | TEST_F(LazyOpsTest, TestRshiftScalarInPlace) { |
9613 | torch::Tensor input = torch::ones( |
9614 | {4, 2}, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
9615 | torch::Scalar shift_amount = 3; |
9616 | ForEachDevice([&](const torch::Device& device) { |
9617 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9618 | torch::Tensor result = input.__irshift__(shift_amount); |
9619 | torch::Tensor lazy_result = lazy_input.__irshift__(shift_amount); |
9620 | AllClose(result, lazy_result); |
9621 | AllClose(input, lazy_input); |
9622 | }); |
9623 | } |
9624 | |
9625 | TEST_F(LazyOpsTest, TestMeshgrid) { |
9626 | torch::Tensor a = torch::rand( |
9627 | {3}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9628 | torch::Tensor b = torch::rand( |
9629 | {2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9630 | torch::Tensor c = torch::rand( |
9631 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9632 | auto d = torch::meshgrid({a, b, c}); |
9633 | ForEachDevice([&](const torch::Device& device) { |
9634 | torch::Tensor lazy_a = CopyToDevice(a, device); |
9635 | torch::Tensor lazy_b = CopyToDevice(b, device); |
9636 | torch::Tensor lazy_c = CopyToDevice(c, device); |
9637 | auto lazy_d = torch::meshgrid({lazy_a, lazy_b, lazy_c}); |
9638 | EXPECT_EQ(d.size(), lazy_d.size()); |
9639 | for (size_t i = 0; i < d.size(); ++i) { |
9640 | AllClose(d[i], lazy_d[i]); |
9641 | } |
9642 | }); |
9643 | } |
9644 | |
9645 | TEST_F(LazyOpsTest, TestConstantPad) { |
9646 | torch::Tensor input = torch::rand( |
9647 | {4, 2, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9648 | std::vector<int64_t> pad{1, 2, 3, 4, 5, 6}; |
9649 | float pad_value = 5; |
9650 | torch::Tensor output = torch::constant_pad_nd(input, pad, pad_value); |
9651 | ForEachDevice([&](const torch::Device& device) { |
9652 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9653 | torch::Tensor lazy_output = |
9654 | torch::constant_pad_nd(lazy_input, pad, pad_value); |
9655 | AllClose(output, lazy_output); |
9656 | }); |
9657 | } |
9658 | |
9659 | TEST_F(LazyOpsTest, TestConstantPadIncomplete) { |
9660 | torch::Tensor input = torch::rand( |
9661 | {4, 2, 5}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9662 | std::vector<int64_t> pad{1, 2}; |
9663 | float pad_value = 5; |
9664 | torch::Tensor output = torch::constant_pad_nd(input, pad, pad_value); |
9665 | ForEachDevice([&](const torch::Device& device) { |
9666 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9667 | torch::Tensor lazy_output = |
9668 | torch::constant_pad_nd(lazy_input, pad, pad_value); |
9669 | AllClose(output, lazy_output); |
9670 | }); |
9671 | } |
9672 | |
9673 | TEST_F(LazyOpsTest, TestReflectionPad2dRank3) { |
9674 | torch::Tensor input = torch::rand( |
9675 | {2, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9676 | std::vector<int64_t> pad{2, 2, 2, 2}; |
9677 | torch::Tensor output = torch::reflection_pad2d(input, pad); |
9678 | ForEachDevice([&](const torch::Device& device) { |
9679 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9680 | torch::Tensor lazy_output = torch::reflection_pad2d(lazy_input, pad); |
9681 | AllClose(output, lazy_output); |
9682 | }); |
9683 | } |
9684 | |
9685 | TEST_F(LazyOpsTest, TestReflectionPad2dRank4) { |
9686 | torch::Tensor input = torch::rand( |
9687 | {2, 2, 3, 4}, |
9688 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9689 | std::vector<int64_t> pad{2, 2, 2, 2}; |
9690 | torch::Tensor output = torch::reflection_pad2d(input, pad); |
9691 | ForEachDevice([&](const torch::Device& device) { |
9692 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9693 | torch::Tensor lazy_output = torch::reflection_pad2d(lazy_input, pad); |
9694 | AllClose(output, lazy_output); |
9695 | }); |
9696 | } |
9697 | |
9698 | TEST_F(LazyOpsTest, TestReflectionPad2dBackward) { |
9699 | std::vector<int64_t> pad{2, 3, 1, 2}; |
9700 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
9701 | return torch::reflection_pad2d(inputs[0], pad); |
9702 | }; |
9703 | ForEachDevice([&](const torch::Device& device) { |
9704 | TestBackward( |
9705 | {torch::rand( |
9706 | {1, 2, 4, 4}, |
9707 | torch::TensorOptions(torch::kFloat) |
9708 | .device(DefaultDevice()) |
9709 | .requires_grad(true))}, |
9710 | device, |
9711 | testfn); |
9712 | }); |
9713 | } |
9714 | |
9715 | TEST_F(LazyOpsTest, TestReplicationPad1d) { |
9716 | torch::Tensor input = torch::rand( |
9717 | {1, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9718 | std::vector<int64_t> pad{1, 2}; |
9719 | torch::Tensor output = torch::replication_pad1d(input, pad); |
9720 | ForEachDevice([&](const torch::Device& device) { |
9721 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9722 | torch::Tensor lazy_output = torch::replication_pad1d(lazy_input, pad); |
9723 | AllClose(output, lazy_output); |
9724 | }); |
9725 | } |
9726 | |
9727 | TEST_F(LazyOpsTest, TestReplicationPad1dZeroPad) { |
9728 | torch::Tensor input = torch::rand( |
9729 | {1, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9730 | std::vector<int64_t> pad{1, 0}; |
9731 | torch::Tensor output = torch::replication_pad1d(input, pad); |
9732 | ForEachDevice([&](const torch::Device& device) { |
9733 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9734 | torch::Tensor lazy_output = torch::replication_pad1d(lazy_input, pad); |
9735 | AllClose(output, lazy_output); |
9736 | }); |
9737 | } |
9738 | |
9739 | TEST_F(LazyOpsTest, TestReplicationPad1dBackward) { |
9740 | std::vector<int64_t> pad{2, 3}; |
9741 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
9742 | return torch::replication_pad1d(inputs[0], pad); |
9743 | }; |
9744 | ForEachDevice([&](const torch::Device& device) { |
9745 | TestBackward( |
9746 | {torch::rand( |
9747 | {2, 4}, |
9748 | torch::TensorOptions(torch::kFloat) |
9749 | .device(DefaultDevice()) |
9750 | .requires_grad(true))}, |
9751 | device, |
9752 | testfn); |
9753 | }); |
9754 | } |
9755 | |
9756 | TEST_F(LazyOpsTest, TestReplicationPad2d) { |
9757 | torch::Tensor input = torch::rand( |
9758 | {1, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9759 | std::vector<int64_t> pad{1, 2, 2, 1}; |
9760 | torch::Tensor output = torch::replication_pad2d(input, pad); |
9761 | ForEachDevice([&](const torch::Device& device) { |
9762 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9763 | torch::Tensor lazy_output = torch::replication_pad2d(lazy_input, pad); |
9764 | AllClose(output, lazy_output); |
9765 | }); |
9766 | } |
9767 | |
9768 | TEST_F(LazyOpsTest, TestReplicationPad2dZeroPad) { |
9769 | torch::Tensor input = torch::rand( |
9770 | {1, 3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9771 | std::vector<int64_t> pad{1, 0, 0, 1}; |
9772 | torch::Tensor output = torch::replication_pad2d(input, pad); |
9773 | ForEachDevice([&](const torch::Device& device) { |
9774 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9775 | torch::Tensor lazy_output = torch::replication_pad2d(lazy_input, pad); |
9776 | AllClose(output, lazy_output); |
9777 | }); |
9778 | } |
9779 | |
9780 | TEST_F(LazyOpsTest, TestReplicationPad2dBackward) { |
9781 | std::vector<int64_t> pad{2, 3, 1, 1}; |
9782 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
9783 | return torch::replication_pad2d(inputs[0], pad); |
9784 | }; |
9785 | ForEachDevice([&](const torch::Device& device) { |
9786 | TestBackward( |
9787 | {torch::rand( |
9788 | {2, 3, 4}, |
9789 | torch::TensorOptions(torch::kFloat) |
9790 | .device(DefaultDevice()) |
9791 | .requires_grad(true))}, |
9792 | device, |
9793 | testfn); |
9794 | }); |
9795 | } |
9796 | |
9797 | TEST_F(LazyOpsTest, TestAsStrided) { |
9798 | torch::Tensor input = torch::rand( |
9799 | {128, 320}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9800 | std::vector<int64_t> size = {128, 20, 4, 4}; |
9801 | std::vector<int64_t> stride = {320, 16, 4, 1}; |
9802 | torch::Tensor output = |
9803 | torch::as_strided(input, /*size=*/size, /*stride=*/stride); |
9804 | ForEachDevice([&](const torch::Device& device) { |
9805 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9806 | torch::Tensor lazy_output = |
9807 | torch::as_strided(lazy_input, /*size=*/size, /*stride=*/stride); |
9808 | AllClose(output, lazy_output); |
9809 | }); |
9810 | } |
9811 | |
9812 | TEST_F(LazyOpsTest, TestAsStridedInPlace) { |
9813 | torch::Tensor input = torch::rand( |
9814 | {128, 320}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9815 | std::vector<int64_t> size = {128, 20, 4, 4}; |
9816 | std::vector<int64_t> stride = {320, 16, 4, 1}; |
9817 | ForEachDevice([&](const torch::Device& device) { |
9818 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9819 | torch::Tensor output = |
9820 | torch::as_strided_(input, /*size=*/size, /*stride=*/stride); |
9821 | torch::Tensor lazy_output = |
9822 | torch::as_strided_(lazy_input, /*size=*/size, /*stride=*/stride); |
9823 | AllClose(output, lazy_output); |
9824 | AllClose(input, lazy_input); |
9825 | }); |
9826 | } |
9827 | |
9828 | TEST_F(LazyOpsTest, TestAsStridedWithOffset) { |
9829 | torch::Tensor input = torch::rand( |
9830 | {4, 8, 2}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9831 | std::vector<int64_t> size = {4, 4, 2}; |
9832 | std::vector<int64_t> stride = {8, 2, 1}; |
9833 | int64_t storage_offset = 4; |
9834 | torch::Tensor output = torch::as_strided( |
9835 | input, |
9836 | /*size=*/size, |
9837 | /*stride=*/stride, |
9838 | /*storage_offset=*/storage_offset); |
9839 | ForEachDevice([&](const torch::Device& device) { |
9840 | torch::Tensor lazy_input = CopyToDevice(input, device); |
9841 | torch::Tensor lazy_output = torch::as_strided( |
9842 | lazy_input, |
9843 | /*size=*/size, |
9844 | /*stride=*/stride, |
9845 | /*storage_offset=*/storage_offset); |
9846 | AllClose(output, lazy_output); |
9847 | }); |
9848 | } |
9849 | |
9850 | TEST_F(LazyOpsTest, TestAsStridedWithInplaceCopy) { |
9851 | torch::Tensor grad = torch::ones( |
9852 | {4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
9853 | std::vector<int64_t> size = {4}; |
9854 | std::vector<int64_t> stride = {1}; |
9855 | torch::Tensor output = torch::zeros({4}, grad.options()); |
9856 | output.as_strided(size, stride).copy_(grad); |
9857 | ForEachDevice([&](const torch::Device& device) { |
9858 | torch::Tensor lazy_grad = CopyToDevice(grad, device); |
9859 | torch::Tensor lazy_output = torch::zeros({4}, lazy_grad.options()); |
9860 | lazy_output.as_strided(size, stride).copy_(lazy_grad); |
9861 | AllClose(output, lazy_output); |
9862 | }); |
9863 | } |
9864 | |
9865 | TEST_F(LazyOpsTest, TestEmptyStrided) { |
9866 | std::vector<int64_t> size = {4, 4, 2}; |
9867 | std::vector<int64_t> stride = {8, 2, 1}; |
9868 | torch::Tensor output = torch::empty_strided(/*size=*/size, /*stride=*/stride); |
9869 | ForEachDevice([&](const torch::Device& device) { |
9870 | torch::Tensor lazy_output = |
9871 | torch::empty_strided(/*size=*/size, /*stride=*/stride); |
9872 | EXPECT_EQ(output.sizes(), lazy_output.sizes()); |
9873 | EXPECT_EQ(output.strides(), lazy_output.strides()); |
9874 | }); |
9875 | } |
9876 | |
9877 | TEST_F(LazyOpsTest, TestAvgPool2DBackward) { |
9878 | int kernel_size = 2; |
9879 | for (int stride = 1; stride <= 2; ++stride) { |
9880 | for (int padding = 0; padding <= 1; ++padding) { |
9881 | for (bool count_include_pad : {true, false}) { |
9882 | // Test ceil_mode=true through the CPU interop. |
9883 | for (bool ceil_mode : {false, true}) { |
9884 | auto testfn = |
9885 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
9886 | return torch::avg_pool2d( |
9887 | inputs[0], |
9888 | /*kernel_size=*/{kernel_size, kernel_size}, |
9889 | /*stride=*/{stride, stride}, |
9890 | /*padding=*/{padding, padding}, |
9891 | /*ceil_mode=*/ceil_mode, |
9892 | /*count_include_pad=*/count_include_pad); |
9893 | }; |
9894 | |
9895 | ForEachDevice([&](const torch::Device& device) { |
9896 | TestBackward( |
9897 | {torch::rand( |
9898 | {1, 1, 7, 7}, |
9899 | torch::TensorOptions(torch::kFloat) |
9900 | .device(DefaultDevice()) |
9901 | .requires_grad(true))}, |
9902 | device, |
9903 | testfn); |
9904 | }); |
9905 | } |
9906 | } |
9907 | } |
9908 | } |
9909 | } |
9910 | |
9911 | TEST_F(LazyOpsTest, TestAvgPool3DBackward) { |
9912 | int kernel_size = 2; |
9913 | for (int stride = 1; stride <= 2; ++stride) { |
9914 | for (int padding = 0; padding <= 1; ++padding) { |
9915 | for (bool count_include_pad : {true, false}) { |
9916 | // Test ceil_mode=true through the CPU interop. |
9917 | for (bool ceil_mode : {false, true}) { |
9918 | auto testfn = |
9919 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
9920 | return torch::avg_pool3d( |
9921 | inputs[0], |
9922 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
9923 | /*stride=*/{stride, stride, stride}, |
9924 | /*padding=*/{padding, padding, padding}, |
9925 | /*ceil_mode=*/ceil_mode, |
9926 | /*count_include_pad=*/count_include_pad); |
9927 | }; |
9928 | |
9929 | ForEachDevice([&](const torch::Device& device) { |
9930 | TestBackward( |
9931 | {torch::rand( |
9932 | {1, 1, 7, 7, 7}, |
9933 | torch::TensorOptions(torch::kFloat) |
9934 | .device(DefaultDevice()) |
9935 | .requires_grad(true))}, |
9936 | device, |
9937 | testfn); |
9938 | }); |
9939 | } |
9940 | } |
9941 | } |
9942 | } |
9943 | } |
9944 | |
9945 | TEST_F(LazyOpsTest, TestAvgPool2DNoBatchBackward) { |
9946 | int kernel_size = 2; |
9947 | for (int stride = 1; stride <= 2; ++stride) { |
9948 | for (int padding = 0; padding <= 1; ++padding) { |
9949 | for (bool count_include_pad : {true, false}) { |
9950 | // Test ceil_mode=true through the CPU interop. |
9951 | for (bool ceil_mode : {false, true}) { |
9952 | auto testfn = |
9953 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
9954 | return torch::avg_pool2d( |
9955 | inputs[0], |
9956 | /*kernel_size=*/{kernel_size, kernel_size}, |
9957 | /*stride=*/{stride, stride}, |
9958 | /*padding=*/{padding, padding}, |
9959 | /*ceil_mode=*/ceil_mode, |
9960 | /*count_include_pad=*/count_include_pad); |
9961 | }; |
9962 | |
9963 | ForEachDevice([&](const torch::Device& device) { |
9964 | TestBackward( |
9965 | {torch::rand( |
9966 | {1, 7, 7}, |
9967 | torch::TensorOptions(torch::kFloat) |
9968 | .device(DefaultDevice()) |
9969 | .requires_grad(true))}, |
9970 | device, |
9971 | testfn); |
9972 | }); |
9973 | } |
9974 | } |
9975 | } |
9976 | } |
9977 | } |
9978 | |
9979 | TEST_F(LazyOpsTest, TestAvgPool3DNoBatchBackward) { |
9980 | int kernel_size = 2; |
9981 | for (int stride = 1; stride <= 2; ++stride) { |
9982 | for (int padding = 0; padding <= 1; ++padding) { |
9983 | for (bool count_include_pad : {true, false}) { |
9984 | // Test ceil_mode=true through the CPU interop. |
9985 | for (bool ceil_mode : {false, true}) { |
9986 | auto testfn = |
9987 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
9988 | return torch::avg_pool3d( |
9989 | inputs[0], |
9990 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
9991 | /*stride=*/{stride, stride, stride}, |
9992 | /*padding=*/{padding, padding, padding}, |
9993 | /*ceil_mode=*/ceil_mode, |
9994 | /*count_include_pad=*/count_include_pad); |
9995 | }; |
9996 | |
9997 | ForEachDevice([&](const torch::Device& device) { |
9998 | TestBackward( |
9999 | {torch::rand( |
10000 | {1, 7, 7, 7}, |
10001 | torch::TensorOptions(torch::kFloat) |
10002 | .device(DefaultDevice()) |
10003 | .requires_grad(true))}, |
10004 | device, |
10005 | testfn); |
10006 | }); |
10007 | } |
10008 | } |
10009 | } |
10010 | } |
10011 | } |
10012 | |
10013 | TEST_F(LazyOpsTest, TestAdaptiveAvgPool3DNoBatchBackward) { |
10014 | if (IsCuda()) { |
10015 | GTEST_SKIP(); |
10016 | } |
10017 | for (int64_t output_size : {7, 4}) { |
10018 | auto testfn = |
10019 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10020 | return torch::adaptive_avg_pool3d( |
10021 | inputs[0], {output_size, output_size, output_size}); |
10022 | }; |
10023 | ForEachDevice([&](const torch::Device& device) { |
10024 | TestBackward( |
10025 | {torch::rand( |
10026 | {1, 56, 28, 28}, |
10027 | torch::TensorOptions(torch::kFloat) |
10028 | .device(DefaultDevice()) |
10029 | .requires_grad(true))}, |
10030 | device, |
10031 | testfn); |
10032 | }); |
10033 | } |
10034 | } |
10035 | |
10036 | TEST_F(LazyOpsTest, TestAdaptiveAvgPool3DBackward) { |
10037 | if (IsCuda()) { |
10038 | GTEST_SKIP(); |
10039 | } |
10040 | for (int64_t output_size : {7, 4}) { |
10041 | auto testfn = |
10042 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10043 | return torch::adaptive_avg_pool3d( |
10044 | inputs[0], {output_size, output_size, output_size}); |
10045 | }; |
10046 | ForEachDevice([&](const torch::Device& device) { |
10047 | TestBackward( |
10048 | {torch::rand( |
10049 | {4, 1, 56, 28, 28}, |
10050 | torch::TensorOptions(torch::kFloat) |
10051 | .device(DefaultDevice()) |
10052 | .requires_grad(true))}, |
10053 | device, |
10054 | testfn); |
10055 | }); |
10056 | } |
10057 | } |
10058 | |
10059 | TEST_F(LazyOpsTest, TestAdaptiveAvgPool2DBackward) { |
10060 | for (int64_t output_size : {7, 8}) { |
10061 | auto testfn = |
10062 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10063 | return torch::adaptive_avg_pool2d(inputs[0], {output_size, output_size}); |
10064 | }; |
10065 | ForEachDevice([&](const torch::Device& device) { |
10066 | TestBackward( |
10067 | {torch::rand( |
10068 | {4, 1, 56, 56}, |
10069 | torch::TensorOptions(torch::kFloat) |
10070 | .device(DefaultDevice()) |
10071 | .requires_grad(true))}, |
10072 | device, |
10073 | testfn); |
10074 | }); |
10075 | } |
10076 | } |
10077 | |
10078 | TEST_F(LazyOpsTest, TestAdaptiveAvgPool2DNoBatchBackward) { |
10079 | for (int64_t output_size : {7, 8}) { |
10080 | auto testfn = |
10081 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10082 | return torch::adaptive_avg_pool2d(inputs[0], {output_size, output_size}); |
10083 | }; |
10084 | ForEachDevice([&](const torch::Device& device) { |
10085 | TestBackward( |
10086 | {torch::rand( |
10087 | {1, 56, 56}, |
10088 | torch::TensorOptions(torch::kFloat).requires_grad(true))}, |
10089 | device, |
10090 | testfn); |
10091 | }); |
10092 | } |
10093 | } |
10094 | |
10095 | TEST_F(LazyOpsTest, TestConv2D) { |
10096 | int in_channels = 4; |
10097 | int out_channels = 4; |
10098 | int kernel_size = 3; |
10099 | for (int stride = 1; stride <= 3; ++stride) { |
10100 | for (int padding = 0; padding <= 2; ++padding) { |
10101 | for (bool with_bias : {true, false}) { |
10102 | for (int dilation = 1; dilation <= 3; ++dilation) { |
10103 | for (int groups : |
10104 | {1, 2, 4}) { // covers normal, grouped, depthwise conv. |
10105 | ForEachDevice([&](const torch::Device& device) { |
10106 | torch::Tensor input = torch::rand( |
10107 | {1, in_channels, 7, 7}, |
10108 | torch::TensorOptions(torch::kDouble).device(DefaultDevice())); |
10109 | torch::Tensor weight = torch::rand( |
10110 | {out_channels, |
10111 | in_channels / groups, |
10112 | kernel_size, |
10113 | kernel_size}, |
10114 | torch::TensorOptions(torch::kDouble).device(DefaultDevice())); |
10115 | torch::Tensor bias = with_bias |
10116 | ? torch::rand( |
10117 | {out_channels}, |
10118 | torch::TensorOptions(torch::kDouble) |
10119 | .device(DefaultDevice())) |
10120 | : torch::Tensor(); |
10121 | |
10122 | torch::Tensor lazy_input = CopyToDevice(input, device); |
10123 | torch::Tensor lazy_weight = CopyToDevice(weight, device); |
10124 | torch::Tensor lazy_bias = |
10125 | with_bias ? CopyToDevice(bias, device) : torch::Tensor(); |
10126 | |
10127 | torch::Tensor output = torch::conv2d( |
10128 | input, |
10129 | weight, |
10130 | bias, |
10131 | /*stride=*/{stride, stride}, |
10132 | /*padding=*/{padding, padding}, |
10133 | /*dilation=*/{dilation, dilation}, |
10134 | groups); |
10135 | torch::Tensor lazy_output = torch::conv2d( |
10136 | lazy_input, |
10137 | lazy_weight, |
10138 | lazy_bias, |
10139 | /*stride=*/{stride, stride}, |
10140 | /*padding=*/{padding, padding}, |
10141 | /*dilation=*/{dilation, dilation}, |
10142 | groups); |
10143 | AllClose(output, lazy_output); |
10144 | }); |
10145 | } |
10146 | } |
10147 | } |
10148 | } |
10149 | } |
10150 | } |
10151 | |
10152 | TEST_F(LazyOpsTest, TestConv2DBackward) { |
10153 | int in_channels = 4; |
10154 | int out_channels = 4; |
10155 | int kernel_size = 3; |
10156 | for (int stride = 1; stride <= 3; ++stride) { |
10157 | for (int padding = 0; padding <= 2; ++padding) { |
10158 | for (bool with_bias : {true, false}) { |
10159 | for (int dilation = 1; dilation <= 3; ++dilation) { |
10160 | for (int groups : |
10161 | {1, 2, 4}) { // covers normal, grouped, depthwise conv. |
10162 | auto testfn = |
10163 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10164 | return torch::conv2d( |
10165 | inputs[0], |
10166 | inputs[1], |
10167 | inputs[2], |
10168 | /*stride=*/{stride, stride}, |
10169 | /*padding=*/{padding, padding}, |
10170 | /*dilation=*/{dilation, dilation}, |
10171 | groups); |
10172 | }; |
10173 | |
10174 | ForEachDevice([&](const torch::Device& device) { |
10175 | torch::Tensor bias = with_bias |
10176 | ? torch::rand( |
10177 | {out_channels}, |
10178 | torch::TensorOptions(torch::kDouble) |
10179 | .device(DefaultDevice())) |
10180 | : torch::Tensor(); |
10181 | TestBackward( |
10182 | {torch::rand( |
10183 | {1, in_channels, 7, 7}, |
10184 | torch::TensorOptions(torch::kDouble) |
10185 | .device(DefaultDevice()) |
10186 | .requires_grad(true)), |
10187 | torch::rand( |
10188 | {out_channels, |
10189 | in_channels / groups, |
10190 | kernel_size, |
10191 | kernel_size}, |
10192 | torch::TensorOptions(torch::kDouble) |
10193 | .device(DefaultDevice()) |
10194 | .requires_grad(true)), |
10195 | bias}, |
10196 | device, |
10197 | testfn); |
10198 | }); |
10199 | } |
10200 | }; |
10201 | } |
10202 | } |
10203 | } |
10204 | } |
10205 | |
10206 | TEST_F(LazyOpsTest, TestTransposedConv2DBackward) { |
10207 | int in_channels = 4; |
10208 | int out_channels = 4; |
10209 | int kernel_size = 3; |
10210 | for (int stride = 1; stride <= 2; ++stride) { |
10211 | for (int padding = 0; padding <= 1; ++padding) { |
10212 | for (int dilation = 1; dilation <= 2; ++dilation) { |
10213 | for (int output_padding = 0; |
10214 | output_padding < std::max(stride, dilation); |
10215 | ++output_padding) { |
10216 | for (bool with_bias : {true, false}) { |
10217 | for (int groups : |
10218 | {1, 2, 4}) { // covers normal, grouped, depthwise conv. |
10219 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) |
10220 | -> torch::Tensor { |
10221 | return torch::conv_transpose2d( |
10222 | inputs[0], |
10223 | inputs[1], |
10224 | inputs[2], |
10225 | /*stride=*/{stride, stride + 1}, |
10226 | /*padding=*/{padding, padding + 1}, |
10227 | /*output_padding=*/output_padding, |
10228 | /*groups=*/groups, |
10229 | /*dilation=*/{dilation, dilation + 1}); |
10230 | }; |
10231 | ForEachDevice([&](const torch::Device& device) { |
10232 | torch::Tensor input = torch::rand( |
10233 | {4, out_channels, 7, 7}, |
10234 | torch::TensorOptions(torch::kFloat) |
10235 | .device(DefaultDevice()) |
10236 | .requires_grad(true)); |
10237 | torch::Tensor weight = torch::rand( |
10238 | {out_channels, |
10239 | in_channels / groups, |
10240 | kernel_size, |
10241 | kernel_size}, |
10242 | torch::TensorOptions(torch::kFloat) |
10243 | .device(DefaultDevice()) |
10244 | .requires_grad(true)); |
10245 | torch::Tensor bias = with_bias |
10246 | ? torch::rand( |
10247 | {in_channels}, |
10248 | torch::TensorOptions(torch::kFloat) |
10249 | .device(DefaultDevice()) |
10250 | .requires_grad(true)) |
10251 | : torch::Tensor(); |
10252 | TestBackward( |
10253 | {input, weight, bias}, |
10254 | device, |
10255 | testfn, |
10256 | /*rtol=*/1e-5, |
10257 | /*atol=*/1e-5); |
10258 | }); |
10259 | } |
10260 | }; |
10261 | } |
10262 | } |
10263 | } |
10264 | } |
10265 | } |
10266 | |
10267 | TEST_F(LazyOpsTest, TestConv3DBackward) { |
10268 | int in_channels = 4; |
10269 | int out_channels = 4; |
10270 | int kernel_size = 3; |
10271 | for (int stride = 1; stride <= 3; ++stride) { |
10272 | for (int padding = 1; padding <= 2; ++padding) { |
10273 | for (bool with_bias : {true, false}) { |
10274 | for (int dilation = 1; dilation <= 2; ++dilation) { |
10275 | for (int groups : |
10276 | {1, 2, 4}) { // covers normal, grouped, depthwise conv. |
10277 | auto testfn = |
10278 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10279 | return torch::conv3d( |
10280 | inputs[0], |
10281 | inputs[1], |
10282 | inputs[2], |
10283 | /*stride=*/{stride, stride, stride}, |
10284 | /*padding=*/{padding, padding, padding}, |
10285 | /*dilation=*/{dilation, dilation, dilation}, |
10286 | groups); |
10287 | }; |
10288 | |
10289 | ForEachDevice([&](const torch::Device& device) { |
10290 | torch::Tensor bias = with_bias |
10291 | ? torch::rand( |
10292 | {out_channels}, |
10293 | torch::TensorOptions(torch::kDouble) |
10294 | .device(DefaultDevice())) |
10295 | : torch::Tensor(); |
10296 | TestBackward( |
10297 | {torch::rand( |
10298 | {4, in_channels, 7, 7, 7}, |
10299 | torch::TensorOptions(torch::kDouble) |
10300 | .device(DefaultDevice()) |
10301 | .requires_grad(true)), |
10302 | torch::rand( |
10303 | {out_channels, |
10304 | in_channels / groups, |
10305 | kernel_size, |
10306 | kernel_size, |
10307 | kernel_size}, |
10308 | torch::TensorOptions(torch::kDouble) |
10309 | .device(DefaultDevice()) |
10310 | .requires_grad(true)), |
10311 | bias}, |
10312 | device, |
10313 | testfn); |
10314 | }); |
10315 | } |
10316 | }; |
10317 | } |
10318 | } |
10319 | } |
10320 | } |
10321 | |
10322 | TEST_F(LazyOpsTest, TestTransposedConv3DBackward) { |
10323 | int in_channels = 4; |
10324 | int out_channels = 4; |
10325 | int kernel_size = 3; |
10326 | for (int stride = 1; stride <= 2; ++stride) { |
10327 | for (int padding = 0; padding <= 1; ++padding) { |
10328 | for (int dilation = 1; dilation <= 2; ++dilation) { |
10329 | for (int output_padding = 0; |
10330 | output_padding < std::max(stride, dilation); |
10331 | ++output_padding) { |
10332 | for (bool with_bias : {true, false}) { |
10333 | for (int groups : |
10334 | {1, 2, 4}) { // covers normal, grouped, depthwise conv. |
10335 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) |
10336 | -> torch::Tensor { |
10337 | return torch::conv_transpose3d( |
10338 | inputs[0], |
10339 | inputs[1], |
10340 | inputs[2], |
10341 | /*stride=*/{stride, stride + 1, stride}, |
10342 | /*padding=*/{padding, padding + 1, stride}, |
10343 | /*output_padding=*/output_padding, |
10344 | /*groups=*/groups, |
10345 | /*dilation=*/{dilation, dilation + 1, dilation}); |
10346 | }; |
10347 | ForEachDevice([&](const torch::Device& device) { |
10348 | torch::Tensor input = torch::rand( |
10349 | {4, out_channels, 7, 7, 7}, |
10350 | torch::TensorOptions(torch::kDouble) |
10351 | .device(DefaultDevice()) |
10352 | .requires_grad(true)); |
10353 | torch::Tensor weight = torch::rand( |
10354 | {out_channels, |
10355 | in_channels / groups, |
10356 | kernel_size, |
10357 | kernel_size, |
10358 | kernel_size}, |
10359 | torch::TensorOptions(torch::kDouble) |
10360 | .device(DefaultDevice()) |
10361 | .requires_grad(true)); |
10362 | torch::Tensor bias = with_bias |
10363 | ? torch::rand( |
10364 | {in_channels}, |
10365 | torch::TensorOptions(torch::kDouble) |
10366 | .device(DefaultDevice()) |
10367 | .requires_grad(true)) |
10368 | : torch::Tensor(); |
10369 | TestBackward({input, weight, bias}, device, testfn); |
10370 | }); |
10371 | } |
10372 | }; |
10373 | } |
10374 | } |
10375 | } |
10376 | } |
10377 | } |
10378 | |
10379 | TEST_F(LazyOpsTest, TestMaxPool2DBackward) { |
10380 | int kernel_size = 3; |
10381 | for (int stride = 1; stride <= 2; ++stride) { |
10382 | for (int padding = 0; padding <= 1; ++padding) { |
10383 | // Test ceil_mode=true through the CPU interop. |
10384 | for (bool ceil_mode : {false, true}) { |
10385 | auto testfn = |
10386 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10387 | return torch::max_pool2d( |
10388 | inputs[0], |
10389 | /*kernel_size=*/{kernel_size, kernel_size}, |
10390 | /*stride=*/{stride, stride}, |
10391 | /*padding=*/{padding, padding}, |
10392 | /*dilation=*/{1, 1}, |
10393 | /*ceil_mode=*/ceil_mode); |
10394 | }; |
10395 | |
10396 | ForEachDevice([&](const torch::Device& device) { |
10397 | TestBackward( |
10398 | {torch::rand( |
10399 | {1, 2, 8, 8}, |
10400 | torch::TensorOptions(torch::kFloat) |
10401 | .device(DefaultDevice()) |
10402 | .requires_grad(true))}, |
10403 | device, |
10404 | testfn); |
10405 | }); |
10406 | } |
10407 | } |
10408 | } |
10409 | } |
10410 | |
10411 | TEST_F(LazyOpsTest, TestMaxPool3DBackward) { |
10412 | int kernel_size = 3; |
10413 | for (int stride = 1; stride <= 2; ++stride) { |
10414 | for (int padding = 0; padding <= 1; ++padding) { |
10415 | // Test ceil_mode=true through the CPU interop. |
10416 | for (bool ceil_mode : {false, true}) { |
10417 | auto testfn = |
10418 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10419 | return torch::max_pool3d( |
10420 | inputs[0], |
10421 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
10422 | /*stride=*/{stride, stride, stride}, |
10423 | /*padding=*/{padding, padding, padding}, |
10424 | /*dilation=*/{1, 1, 1}, |
10425 | /*ceil_mode=*/ceil_mode); |
10426 | }; |
10427 | |
10428 | ForEachDevice([&](const torch::Device& device) { |
10429 | TestBackward( |
10430 | {torch::rand( |
10431 | {1, 2, 4, 4, 4}, |
10432 | torch::TensorOptions(torch::kFloat) |
10433 | .device(DefaultDevice()) |
10434 | .requires_grad(true))}, |
10435 | device, |
10436 | testfn); |
10437 | }); |
10438 | } |
10439 | } |
10440 | } |
10441 | } |
10442 | |
10443 | TEST_F(LazyOpsTest, TestMaxPool2DNoBatchBackward) { |
10444 | int kernel_size = 3; |
10445 | for (int stride = 1; stride <= 2; ++stride) { |
10446 | for (int padding = 0; padding <= 1; ++padding) { |
10447 | // Test ceil_mode=true through the CPU interop. |
10448 | for (bool ceil_mode : {false, true}) { |
10449 | auto testfn = |
10450 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10451 | return torch::max_pool2d( |
10452 | inputs[0], |
10453 | /*kernel_size=*/{kernel_size, kernel_size}, |
10454 | /*stride=*/{stride, stride}, |
10455 | /*padding=*/{padding, padding}, |
10456 | /*dilation=*/{1, 1}, |
10457 | /*ceil_mode=*/ceil_mode); |
10458 | }; |
10459 | |
10460 | ForEachDevice([&](const torch::Device& device) { |
10461 | TestBackward( |
10462 | {torch::rand( |
10463 | {2, 8, 8}, |
10464 | torch::TensorOptions(torch::kFloat) |
10465 | .device(DefaultDevice()) |
10466 | .requires_grad(true))}, |
10467 | device, |
10468 | testfn); |
10469 | }); |
10470 | } |
10471 | } |
10472 | } |
10473 | } |
10474 | |
10475 | TEST_F(LazyOpsTest, TestMaxPool3DNoBatchBackward) { |
10476 | int kernel_size = 3; |
10477 | for (int stride = 1; stride <= 2; ++stride) { |
10478 | for (int padding = 0; padding <= 1; ++padding) { |
10479 | // Test ceil_mode=true through the CPU interop. |
10480 | for (bool ceil_mode : {false, true}) { |
10481 | auto testfn = |
10482 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10483 | return torch::max_pool3d( |
10484 | inputs[0], |
10485 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
10486 | /*stride=*/{stride, stride, stride}, |
10487 | /*padding=*/{padding, padding, padding}, |
10488 | /*dilation=*/{1, 1, 1}, |
10489 | /*ceil_mode=*/ceil_mode); |
10490 | }; |
10491 | |
10492 | ForEachDevice([&](const torch::Device& device) { |
10493 | TestBackward( |
10494 | {torch::rand( |
10495 | {2, 4, 4, 4}, |
10496 | torch::TensorOptions(torch::kFloat) |
10497 | .device(DefaultDevice()) |
10498 | .requires_grad(true))}, |
10499 | device, |
10500 | testfn); |
10501 | }); |
10502 | } |
10503 | } |
10504 | } |
10505 | } |
10506 | |
10507 | TEST_F(LazyOpsTest, TestMaxUnpool2DBackward) { |
10508 | int kernel_size = 2; |
10509 | torch::Tensor input = torch::rand( |
10510 | {2, 2, 8, 8}, |
10511 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
10512 | for (int stride = 1; stride <= 2; ++stride) { |
10513 | for (int padding = 0; padding <= 1; ++padding) { |
10514 | // Test ceil_mode=true through the CPU interop. |
10515 | for (bool ceil_mode : {false, true}) { |
10516 | for (int dilation = 1; dilation <= 2; ++dilation) { |
10517 | torch::Tensor output; |
10518 | torch::Tensor indices; |
10519 | std::tie(output, indices) = torch::max_pool2d_with_indices( |
10520 | input, |
10521 | /*kernel_size=*/{kernel_size, kernel_size}, |
10522 | /*stride=*/{stride, stride}, |
10523 | /*padding=*/{padding, padding}, |
10524 | /*dilation=*/{dilation, dilation}, |
10525 | /*ceil_mode=*/ceil_mode); |
10526 | |
10527 | std::vector<int64_t> output_size({input.size(2), input.size(3)}); |
10528 | auto testfn = |
10529 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10530 | return torch::max_unpool2d(inputs[0], inputs[1], output_size); |
10531 | }; |
10532 | |
10533 | ForEachDevice([&](const torch::Device& device) { |
10534 | TestBackward( |
10535 | {output.requires_grad_(true), indices}, device, testfn); |
10536 | }); |
10537 | } |
10538 | } |
10539 | } |
10540 | } |
10541 | } |
10542 | |
10543 | TEST_F(LazyOpsTest, TestMaxUnpool3DBackward) { |
10544 | int kernel_size = 2; |
10545 | torch::Tensor input = torch::rand( |
10546 | {1, 1, 4, 4, 4}, |
10547 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
10548 | for (int stride = 1; stride <= 2; ++stride) { |
10549 | for (int padding = 0; padding <= 1; ++padding) { |
10550 | // Test ceil_mode=true through the CPU interop. |
10551 | for (bool ceil_mode : {false, true}) { |
10552 | for (int dilation = 1; dilation <= 2; ++dilation) { |
10553 | torch::Tensor output; |
10554 | torch::Tensor indices; |
10555 | std::tie(output, indices) = torch::max_pool3d_with_indices( |
10556 | input, |
10557 | /*kernel_size=*/{kernel_size, kernel_size, kernel_size}, |
10558 | /*stride=*/{stride, stride, stride}, |
10559 | /*padding=*/{padding, padding, padding}, |
10560 | /*dilation=*/{dilation, dilation, dilation}, |
10561 | /*ceil_mode=*/ceil_mode); |
10562 | |
10563 | std::vector<int64_t> output_size( |
10564 | {input.size(2), input.size(3), input.size(4)}); |
10565 | auto testfn = |
10566 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10567 | return torch::max_unpool3d( |
10568 | inputs[0], |
10569 | inputs[1], |
10570 | output_size, |
10571 | /*stride=*/{stride, stride, stride}, |
10572 | /*padding=*/{padding, padding, padding}); |
10573 | }; |
10574 | |
10575 | ForEachDevice([&](const torch::Device& device) { |
10576 | TestBackward( |
10577 | {output.requires_grad_(true), indices}, device, testfn); |
10578 | }); |
10579 | } |
10580 | } |
10581 | } |
10582 | } |
10583 | } |
10584 | |
10585 | TEST_F(LazyOpsTest, TestTanhBackward) { |
10586 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10587 | return torch::tanh(inputs[0]); |
10588 | }; |
10589 | ForEachDevice([&](const torch::Device& device) { |
10590 | TestBackward( |
10591 | {torch::rand( |
10592 | {2, 2}, |
10593 | torch::TensorOptions(torch::kFloat) |
10594 | .device(DefaultDevice()) |
10595 | .requires_grad(true))}, |
10596 | device, |
10597 | testfn, |
10598 | /*rtol=*/1e-3, |
10599 | /*atol=*/1e-5); |
10600 | }); |
10601 | } |
10602 | |
10603 | TEST_F(LazyOpsTest, TestSigmoidBackward) { |
10604 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10605 | return torch::sigmoid(inputs[0]); |
10606 | }; |
10607 | ForEachDevice([&](const torch::Device& device) { |
10608 | TestBackward( |
10609 | {torch::rand( |
10610 | {2, 2}, |
10611 | torch::TensorOptions(torch::kFloat) |
10612 | .device(DefaultDevice()) |
10613 | .requires_grad(true))}, |
10614 | device, |
10615 | testfn); |
10616 | }); |
10617 | } |
10618 | |
10619 | TEST_F(LazyOpsTest, TestLogSigmoidBackward) { |
10620 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10621 | return torch::log_sigmoid(inputs[0]); |
10622 | }; |
10623 | ForEachDevice([&](const torch::Device& device) { |
10624 | TestBackward( |
10625 | {torch::rand( |
10626 | {2, 2}, |
10627 | torch::TensorOptions(torch::kFloat) |
10628 | .device(DefaultDevice()) |
10629 | .requires_grad(true))}, |
10630 | device, |
10631 | testfn, |
10632 | /*rtol=*/1e-3, |
10633 | /*atol=*/1e-5); |
10634 | }); |
10635 | } |
10636 | |
10637 | TEST_F(LazyOpsTest, TestLogSoftmaxBackward) { |
10638 | for (int dim = -4; dim < 4; ++dim) { |
10639 | auto testfn = |
10640 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10641 | return torch::log_softmax(inputs[0], dim); |
10642 | }; |
10643 | |
10644 | ForEachDevice([&](const torch::Device& device) { |
10645 | TestBackward( |
10646 | {torch::rand( |
10647 | {5, 3, 4, 2}, |
10648 | torch::TensorOptions(torch::kFloat) |
10649 | .device(DefaultDevice()) |
10650 | .requires_grad(true))}, |
10651 | device, |
10652 | testfn, |
10653 | /*rtol=*/1e-3, |
10654 | /*atol=*/1e-4); |
10655 | }); |
10656 | } |
10657 | } |
10658 | |
10659 | TEST_F(LazyOpsTest, TestSoftmaxBackward) { |
10660 | for (int dim = -4; dim < 4; ++dim) { |
10661 | auto testfn = |
10662 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10663 | return torch::softmax(inputs[0], dim); |
10664 | }; |
10665 | |
10666 | ForEachDevice([&](const torch::Device& device) { |
10667 | TestBackward( |
10668 | {torch::rand( |
10669 | {5, 3, 4, 2}, |
10670 | torch::TensorOptions(torch::kFloat) |
10671 | .device(DefaultDevice()) |
10672 | .requires_grad(true))}, |
10673 | device, |
10674 | testfn, |
10675 | /*rtol=*/1e-3, |
10676 | /*atol=*/1e-4); |
10677 | }); |
10678 | } |
10679 | } |
10680 | |
10681 | TEST_F(LazyOpsTest, TestSoftplusBackward) { |
10682 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10683 | return torch::softplus(inputs[0]); |
10684 | }; |
10685 | ForEachDevice([&](const torch::Device& device) { |
10686 | TestBackward( |
10687 | {torch::rand( |
10688 | {2, 1, 4, 6}, |
10689 | torch::TensorOptions(torch::kFloat) |
10690 | .device(DefaultDevice()) |
10691 | .requires_grad(true))}, |
10692 | device, |
10693 | testfn, |
10694 | /*rtol=*/1e-4); |
10695 | }); |
10696 | } |
10697 | |
10698 | TEST_F(LazyOpsTest, TestReluBackward) { |
10699 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10700 | return torch::relu(inputs[0]); |
10701 | }; |
10702 | ForEachDevice([&](const torch::Device& device) { |
10703 | TestBackward( |
10704 | {torch::rand( |
10705 | {2, 1, 4, 6}, |
10706 | torch::TensorOptions(torch::kFloat) |
10707 | .device(DefaultDevice()) |
10708 | .requires_grad(true))}, |
10709 | device, |
10710 | testfn); |
10711 | }); |
10712 | } |
10713 | |
10714 | TEST_F(LazyOpsTest, TestRreluBackward) { |
10715 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10716 | return torch::rrelu(inputs[0]); |
10717 | }; |
10718 | ForEachDevice([&](const torch::Device& device) { |
10719 | TestBackward( |
10720 | {torch::rand( |
10721 | {2, 1, 4, 6}, |
10722 | torch::TensorOptions(torch::kFloat) |
10723 | .device(DefaultDevice()) |
10724 | .requires_grad(true))}, |
10725 | device, |
10726 | testfn); |
10727 | }); |
10728 | } |
10729 | |
10730 | TEST_F(LazyOpsTest, TestHardshrinkBackward) { |
10731 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10732 | return torch::hardshrink(inputs[0]); |
10733 | }; |
10734 | ForEachDevice([&](const torch::Device& device) { |
10735 | TestBackward( |
10736 | {torch::randn( |
10737 | {100}, |
10738 | torch::TensorOptions(torch::kFloat) |
10739 | .device(DefaultDevice()) |
10740 | .requires_grad(true))}, |
10741 | device, |
10742 | testfn); |
10743 | }); |
10744 | } |
10745 | |
10746 | TEST_F(LazyOpsTest, TestSoftshrinkBackward) { |
10747 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10748 | return torch::softshrink(inputs[0]); |
10749 | }; |
10750 | ForEachDevice([&](const torch::Device& device) { |
10751 | TestBackward( |
10752 | {torch::randn( |
10753 | {100}, |
10754 | torch::TensorOptions(torch::kFloat) |
10755 | .device(DefaultDevice()) |
10756 | .requires_grad(true))}, |
10757 | device, |
10758 | testfn); |
10759 | }); |
10760 | } |
10761 | |
10762 | TEST_F(LazyOpsTest, TestHardtanhBackward) { |
10763 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10764 | return torch::hardtanh(inputs[0]); |
10765 | }; |
10766 | ForEachDevice([&](const torch::Device& device) { |
10767 | TestBackward( |
10768 | {torch::randn( |
10769 | {100}, |
10770 | torch::TensorOptions(torch::kFloat) |
10771 | .device(DefaultDevice()) |
10772 | .requires_grad(true))}, |
10773 | device, |
10774 | testfn); |
10775 | }); |
10776 | } |
10777 | |
10778 | TEST_F(LazyOpsTest, TestEluBackward) { |
10779 | torch::Scalar alpha = 0.5; |
10780 | torch::Scalar scale = 2.5; |
10781 | torch::Scalar input_scale = 1.5; |
10782 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10783 | return torch::elu(inputs[0], alpha, scale, input_scale); |
10784 | }; |
10785 | ForEachDevice([&](const torch::Device& device) { |
10786 | TestBackward( |
10787 | {torch::rand( |
10788 | {2, 1, 4, 6}, |
10789 | torch::TensorOptions(torch::kFloat) |
10790 | .device(DefaultDevice()) |
10791 | .requires_grad(true))}, |
10792 | device, |
10793 | testfn); |
10794 | }); |
10795 | } |
10796 | |
10797 | TEST_F(LazyOpsTest, TestGeluBackward) { |
10798 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10799 | return torch::gelu(inputs[0]); |
10800 | }; |
10801 | ForEachDevice([&](const torch::Device& device) { |
10802 | TestBackward( |
10803 | {torch::rand( |
10804 | {2, 3}, |
10805 | torch::TensorOptions(torch::kFloat) |
10806 | .device(DefaultDevice()) |
10807 | .requires_grad(true))}, |
10808 | device, |
10809 | testfn); |
10810 | }); |
10811 | ExpectCounterChanged("lazy::gelu_backward" , GetIgnoredCounters()); |
10812 | } |
10813 | |
10814 | TEST_F(LazyOpsTest, TestLeakyReluBackward) { |
10815 | double negative_slope = 0.01; |
10816 | auto testfn = [=](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10817 | return torch::leaky_relu(inputs[0], negative_slope); |
10818 | }; |
10819 | ForEachDevice([&](const torch::Device& device) { |
10820 | TestBackward( |
10821 | {torch::rand( |
10822 | {2, 1, 4, 6}, |
10823 | torch::TensorOptions(torch::kFloat) |
10824 | .device(DefaultDevice()) |
10825 | .requires_grad(true))}, |
10826 | device, |
10827 | testfn); |
10828 | }); |
10829 | } |
10830 | |
10831 | TEST_F(LazyOpsTest, TestTransposeBackward) { |
10832 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10833 | return torch::t(inputs[0]); |
10834 | }; |
10835 | ForEachDevice([&](const torch::Device& device) { |
10836 | TestBackward( |
10837 | {torch::rand( |
10838 | {2, 3}, |
10839 | torch::TensorOptions(torch::kFloat) |
10840 | .device(DefaultDevice()) |
10841 | .requires_grad(true))}, |
10842 | device, |
10843 | testfn); |
10844 | }); |
10845 | } |
10846 | |
10847 | TEST_F(LazyOpsTest, TestAddMatMulBackward) { |
10848 | int in_channels = 32; |
10849 | int out_channels = 320; |
10850 | int labels = 50; |
10851 | // Test beta != 1. through the CPU interop. |
10852 | for (double beta : {1., 2.}) { |
10853 | auto testfn = |
10854 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10855 | return torch::addmm(inputs[0], inputs[1], inputs[2], /*beta=*/beta); |
10856 | }; |
10857 | ForEachDevice([&](const torch::Device& device) { |
10858 | TestBackward( |
10859 | {torch::rand( |
10860 | {labels}, |
10861 | torch::TensorOptions(torch::kFloat) |
10862 | .device(DefaultDevice()) |
10863 | .requires_grad(true)), |
10864 | torch::rand( |
10865 | {in_channels, out_channels}, |
10866 | torch::TensorOptions(torch::kFloat) |
10867 | .device(DefaultDevice()) |
10868 | .requires_grad(true)), |
10869 | torch::rand( |
10870 | {out_channels, labels}, |
10871 | torch::TensorOptions(torch::kFloat) |
10872 | .device(DefaultDevice()) |
10873 | .requires_grad(true))}, |
10874 | device, |
10875 | testfn); |
10876 | }); |
10877 | } |
10878 | } |
10879 | |
10880 | TEST_F(LazyOpsTest, TestBinaryCrossEntropyBackward) { |
10881 | int batch = 6; |
10882 | int classes = 2; |
10883 | // TODO(asuhan): Fix the torch::kDouble case. |
10884 | for (auto dtype : {torch::kFloat}) { |
10885 | for (bool def_weight : {false, true}) { |
10886 | torch::Tensor input = torch::rand( |
10887 | {batch, classes}, torch::TensorOptions(dtype).requires_grad(true)); |
10888 | torch::Tensor target = |
10889 | torch::rand({batch, classes}, torch::TensorOptions(dtype)); |
10890 | torch::Tensor weight; |
10891 | if (def_weight) { |
10892 | weight = torch::rand({batch, classes}, torch::TensorOptions(dtype)); |
10893 | } |
10894 | for (torch::Reduction::Reduction reduction : |
10895 | {torch::Reduction::Mean, |
10896 | torch::Reduction::Sum, |
10897 | torch::Reduction::None}) { |
10898 | auto testfn = |
10899 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10900 | return torch::binary_cross_entropy( |
10901 | /*self=*/inputs[0], |
10902 | /*target=*/inputs[1], |
10903 | /*weight=*/inputs[2], |
10904 | /*reduction=*/reduction); |
10905 | }; |
10906 | ForEachDevice([&](const torch::Device& device) { |
10907 | TestBackward( |
10908 | {input, target, weight}, |
10909 | device, |
10910 | testfn, |
10911 | /*rtol=*/1e-4, |
10912 | /*atol=*/1e-7); |
10913 | }); |
10914 | } |
10915 | } |
10916 | } |
10917 | } |
10918 | |
10919 | TEST_F(LazyOpsTest, TestNllLossBackward) { |
10920 | // TODO(whc) debug divide-by-zero failure under ASAN |
10921 | GTEST_SKIP(); |
10922 | |
10923 | int batch = 6; |
10924 | int classes = 2; |
10925 | // TODO(asuhan): Fix the torch::kDouble case. |
10926 | for (auto dtype : {torch::kFloat}) { |
10927 | for (int ignore_index : {-1, 0, 1, 5}) { |
10928 | for (bool def_weight : {false, true}) { |
10929 | torch::Tensor input = torch::rand( |
10930 | {batch, classes}, |
10931 | torch::TensorOptions(dtype) |
10932 | .device(DefaultDevice()) |
10933 | .requires_grad(true)); |
10934 | torch::Tensor target = torch::randint( |
10935 | std::min(ignore_index, 0), |
10936 | classes, |
10937 | {batch}, |
10938 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
10939 | torch::Tensor weight; |
10940 | if (def_weight) { |
10941 | weight = torch::rand( |
10942 | {classes}, torch::TensorOptions(dtype).device(DefaultDevice())); |
10943 | } |
10944 | for (torch::Reduction::Reduction reduction : |
10945 | {torch::Reduction::Mean, |
10946 | torch::Reduction::Sum, |
10947 | torch::Reduction::None}) { |
10948 | auto testfn = |
10949 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
10950 | return torch::nll_loss( |
10951 | /*self=*/inputs[0], |
10952 | /*target=*/inputs[1], |
10953 | /*weight=*/inputs[2], |
10954 | /*reduction=*/reduction, |
10955 | /*ignore_index=*/ignore_index); |
10956 | }; |
10957 | ForEachDevice([&](const torch::Device& device) { |
10958 | TestBackward( |
10959 | {input, target, weight}, |
10960 | device, |
10961 | testfn, |
10962 | /*rtol=*/1e-5, |
10963 | /*atol=*/1e-8); |
10964 | }); |
10965 | } |
10966 | } |
10967 | } |
10968 | } |
10969 | } |
10970 | |
10971 | TEST_F(LazyOpsTest, TestNllLoss2dBackward) { |
10972 | int batch = 6; |
10973 | int classes = 2; |
10974 | int height = 3; |
10975 | int width = 3; |
10976 | // TODO(asuhan): Fix the torch::kDouble case. |
10977 | for (auto dtype : {torch::kFloat}) { |
10978 | for (int ignore_index : {-1, 0, 1, 5}) { |
10979 | for (bool def_weight : {false, true}) { |
10980 | torch::Tensor input = torch::rand( |
10981 | {batch, classes, height, width}, |
10982 | torch::TensorOptions(dtype) |
10983 | .device(DefaultDevice()) |
10984 | .requires_grad(true)); |
10985 | torch::Tensor target = torch::randint( |
10986 | std::min(ignore_index, 0), |
10987 | classes, |
10988 | {batch, height, width}, |
10989 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
10990 | torch::Tensor weight; |
10991 | if (def_weight) { |
10992 | weight = torch::rand( |
10993 | {classes}, torch::TensorOptions(dtype).device(DefaultDevice())); |
10994 | } |
10995 | for (torch::Reduction::Reduction reduction : |
10996 | {torch::Reduction::Mean, |
10997 | torch::Reduction::Sum, |
10998 | torch::Reduction::None}) { |
10999 | auto testfn = |
11000 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
11001 | return torch::nll_loss2d( |
11002 | /*self=*/inputs[0], |
11003 | /*target=*/inputs[1], |
11004 | /*weight=*/inputs[2], |
11005 | /*reduction=*/reduction, |
11006 | /*ignore_index=*/ignore_index); |
11007 | }; |
11008 | ForEachDevice([&](const torch::Device& device) { |
11009 | TestBackward( |
11010 | {input, target, weight}, |
11011 | device, |
11012 | testfn, |
11013 | /*rtol=*/1e-5, |
11014 | /*atol=*/1e-8); |
11015 | }); |
11016 | } |
11017 | } |
11018 | } |
11019 | } |
11020 | } |
11021 | |
11022 | TEST_F(LazyOpsTest, TestSmoothL1LossBackward) { |
11023 | torch::Tensor input = torch::randn( |
11024 | {2, 4}, |
11025 | torch::TensorOptions(torch::kFloat) |
11026 | .device(DefaultDevice()) |
11027 | .requires_grad(true)); |
11028 | torch::Tensor target = torch::randn( |
11029 | {2, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11030 | for (torch::Reduction::Reduction reduction : |
11031 | {torch::Reduction::None, |
11032 | torch::Reduction::Mean, |
11033 | torch::Reduction::Sum}) { |
11034 | for (double beta : {0.25, 1.}) { |
11035 | auto testfn = |
11036 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
11037 | return torch::smooth_l1_loss( |
11038 | /*input=*/inputs[0], |
11039 | /*target=*/inputs[1], |
11040 | /*reduction=*/reduction, |
11041 | /*beta=*/beta); |
11042 | }; |
11043 | ForEachDevice([&](const torch::Device& device) { |
11044 | TestBackward( |
11045 | {input, target}, |
11046 | device, |
11047 | testfn, |
11048 | /*rtol=*/1e-5, |
11049 | /*atol=*/1e-8); |
11050 | }); |
11051 | } |
11052 | } |
11053 | } |
11054 | |
11055 | TEST_F(LazyOpsTest, TestViewBackward) { |
11056 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
11057 | return inputs[0].view({-1, 320}); |
11058 | }; |
11059 | ForEachDevice([&](const torch::Device& device) { |
11060 | TestBackward( |
11061 | {torch::rand( |
11062 | {32, 20, 4, 4}, |
11063 | torch::TensorOptions(torch::kFloat) |
11064 | .device(DefaultDevice()) |
11065 | .requires_grad(true))}, |
11066 | device, |
11067 | testfn); |
11068 | }); |
11069 | } |
11070 | |
11071 | TEST_F(LazyOpsTest, TestBatchNorm2DBackward) { |
11072 | double momentum = 0.1; |
11073 | double eps = 0.5; |
11074 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
11075 | return torch::batch_norm( |
11076 | /*input=*/inputs[0], |
11077 | /*weight=*/inputs[1], |
11078 | /*bias=*/inputs[2], |
11079 | /*running_mean=*/inputs[3], |
11080 | /*running_var=*/inputs[4], |
11081 | /*training=*/true, |
11082 | /*momentum=*/momentum, |
11083 | /*eps=*/eps, |
11084 | /*cudnn_enabled=*/false); |
11085 | }; |
11086 | int num_features = 3; |
11087 | torch::Tensor undef; |
11088 | for (bool undef_weight_bias : {false, true}) { |
11089 | ForEachDevice([&](const torch::Device& device) { |
11090 | torch::Tensor input = torch::rand( |
11091 | {2, num_features, 4, 4}, |
11092 | torch::TensorOptions(torch::kFloat) |
11093 | .device(DefaultDevice()) |
11094 | .requires_grad(true)); |
11095 | torch::Tensor weight = undef_weight_bias |
11096 | ? undef |
11097 | : torch::rand( |
11098 | {num_features}, |
11099 | torch::TensorOptions(torch::kFloat) |
11100 | .device(DefaultDevice()) |
11101 | .requires_grad(true)); |
11102 | torch::Tensor bias = undef_weight_bias |
11103 | ? undef |
11104 | : torch::rand( |
11105 | {num_features}, |
11106 | torch::TensorOptions(torch::kFloat) |
11107 | .device(DefaultDevice()) |
11108 | .requires_grad(true)); |
11109 | torch::Tensor running_mean = torch::zeros( |
11110 | {num_features}, |
11111 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11112 | torch::Tensor running_var = torch::ones( |
11113 | {num_features}, |
11114 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11115 | TestBackward( |
11116 | {input, weight, bias, running_mean, running_var}, |
11117 | device, |
11118 | testfn, |
11119 | /*rtol=*/1e-3, |
11120 | /*atol=*/1e-4); |
11121 | }); |
11122 | } |
11123 | } |
11124 | |
11125 | TEST_F(LazyOpsTest, TestBatchNorm3DBackward) { |
11126 | double momentum = 0.1; |
11127 | double eps = 0.5; |
11128 | auto testfn = [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
11129 | return torch::batch_norm( |
11130 | /*input=*/inputs[0], |
11131 | /*weight=*/inputs[1], |
11132 | /*bias=*/inputs[2], |
11133 | /*running_mean=*/inputs[3], |
11134 | /*running_var=*/inputs[4], |
11135 | /*training=*/true, |
11136 | /*momentum=*/momentum, |
11137 | /*eps=*/eps, |
11138 | /*cudnn_enabled=*/false); |
11139 | }; |
11140 | int num_features = 3; |
11141 | torch::Tensor undef; |
11142 | for (bool undef_weight_bias : {false, true}) { |
11143 | ForEachDevice([&](const torch::Device& device) { |
11144 | torch::Tensor input = torch::rand( |
11145 | {2, num_features, 4, 4, 2}, |
11146 | torch::TensorOptions(torch::kFloat) |
11147 | .device(DefaultDevice()) |
11148 | .requires_grad(true)); |
11149 | torch::Tensor weight = undef_weight_bias |
11150 | ? undef |
11151 | : torch::rand( |
11152 | {num_features}, |
11153 | torch::TensorOptions(torch::kFloat) |
11154 | .device(DefaultDevice()) |
11155 | .requires_grad(true)); |
11156 | torch::Tensor bias = undef_weight_bias |
11157 | ? undef |
11158 | : torch::rand( |
11159 | {num_features}, |
11160 | torch::TensorOptions(torch::kFloat) |
11161 | .device(DefaultDevice()) |
11162 | .requires_grad(true)); |
11163 | torch::Tensor running_mean = torch::zeros( |
11164 | {num_features}, |
11165 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11166 | torch::Tensor running_var = torch::ones( |
11167 | {num_features}, |
11168 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11169 | TestBackward( |
11170 | {input, weight, bias, running_mean, running_var}, |
11171 | device, |
11172 | testfn, |
11173 | /*rtol=*/1e-3, |
11174 | /*atol=*/1e-3); |
11175 | }); |
11176 | } |
11177 | } |
11178 | |
11179 | TEST_F(LazyOpsTest, TestBCEWithLogitsBackward) { |
11180 | int batch = 10; |
11181 | int classes = 5; |
11182 | torch::Tensor undef; |
11183 | for (torch::Reduction::Reduction reduction : |
11184 | {torch::Reduction::None, |
11185 | torch::Reduction::Mean, |
11186 | torch::Reduction::Sum}) { |
11187 | auto testfn = |
11188 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
11189 | return torch::binary_cross_entropy_with_logits( |
11190 | /*input=*/inputs[0], |
11191 | /*target=*/inputs[1], |
11192 | /*weight=*/inputs[2], |
11193 | /*pos_weight=*/inputs[3], |
11194 | /*reduction=*/reduction); |
11195 | }; |
11196 | for (bool undef_weight : {false, true}) { |
11197 | for (bool undef_pos_weight : {false, true}) { |
11198 | torch::Tensor input = torch::rand( |
11199 | {batch, classes}, |
11200 | torch::TensorOptions(torch::kFloat) |
11201 | .device(DefaultDevice()) |
11202 | .requires_grad(true)); |
11203 | torch::Tensor target = torch::rand( |
11204 | {batch, classes}, |
11205 | torch::TensorOptions(torch::kFloat) |
11206 | .device(DefaultDevice()) |
11207 | .requires_grad(true)); |
11208 | torch::Tensor weight = undef_weight |
11209 | ? undef |
11210 | : torch::rand( |
11211 | {classes}, |
11212 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11213 | torch::Tensor pos_weight = undef_pos_weight |
11214 | ? undef |
11215 | : torch::rand( |
11216 | {classes}, |
11217 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11218 | ForEachDevice([&](const torch::Device& device) { |
11219 | TestBackward( |
11220 | {input, target, weight, pos_weight}, |
11221 | device, |
11222 | testfn, |
11223 | /*rtol=*/1e-3, |
11224 | /*atol=*/1e-5); |
11225 | }); |
11226 | } |
11227 | } |
11228 | } |
11229 | } |
11230 | |
11231 | TEST_F(LazyOpsTest, TestKlDivBackward) { |
11232 | torch::Tensor input = torch::rand( |
11233 | {4, 3}, |
11234 | torch::TensorOptions(torch::kFloat) |
11235 | .device(DefaultDevice()) |
11236 | .requires_grad(true)); |
11237 | torch::Tensor target = torch::rand( |
11238 | {4, 3}, |
11239 | torch::TensorOptions(torch::kFloat) |
11240 | .device(DefaultDevice()) |
11241 | .requires_grad(true)); |
11242 | for (torch::Reduction::Reduction reduction : |
11243 | {torch::Reduction::Mean, |
11244 | torch::Reduction::Sum, |
11245 | torch::Reduction::None}) { |
11246 | auto testfn = |
11247 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
11248 | return torch::kl_div(/*self=*/inputs[0], /*target=*/inputs[1], reduction); |
11249 | }; |
11250 | ForEachDevice([&](const torch::Device& device) { |
11251 | TestBackward( |
11252 | {input, target}, |
11253 | device, |
11254 | testfn, |
11255 | /*rtol=*/1e-4, |
11256 | /*atol=*/1e-5); |
11257 | }); |
11258 | } |
11259 | } |
11260 | |
11261 | TEST_F(LazyOpsTest, TestEmbeddingBackward) { |
11262 | int num_weights = 32; |
11263 | for (int padding_idx = -1; padding_idx < num_weights; ++padding_idx) { |
11264 | for (bool scale_grad_by_freq : {false, true}) { |
11265 | auto testfn = |
11266 | [&](const std::vector<torch::Tensor>& inputs) -> torch::Tensor { |
11267 | return torch::embedding( |
11268 | inputs[0], |
11269 | inputs[1], |
11270 | /*padding_idx=*/padding_idx, |
11271 | /*scale_grad_by_freq=*/scale_grad_by_freq, |
11272 | /*sparse=*/false); |
11273 | }; |
11274 | ForEachDevice([&](const torch::Device& device) { |
11275 | torch::Tensor weight = torch::rand( |
11276 | {num_weights, 7}, |
11277 | torch::TensorOptions(torch::kFloat) |
11278 | .device(DefaultDevice()) |
11279 | .requires_grad(true)); |
11280 | torch::Tensor indices = torch::randint( |
11281 | num_weights, |
11282 | {3, 9, 4}, |
11283 | torch::TensorOptions(torch::kLong).device(DefaultDevice())); |
11284 | TestBackward( |
11285 | {weight, indices}, |
11286 | device, |
11287 | testfn, |
11288 | /*rtol=*/1e-5, |
11289 | /*atol=*/1e-8); |
11290 | }); |
11291 | } |
11292 | } |
11293 | } |
11294 | |
11295 | TEST_F(LazyOpsTest, TestAmpForeachNonFiniteCheckAndUnscale) { |
11296 | if (IsCuda()) { |
11297 | // TODO(whc) debug failure on cuda |
11298 | GTEST_SKIP(); |
11299 | } |
11300 | |
11301 | torch::Tensor grads0 = torch::tensor( |
11302 | {1, 2, 3, 4}, |
11303 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11304 | torch::Tensor grads1 = torch::tensor( |
11305 | {1.0, 2.0, std::nan("1" ), 4.0}, |
11306 | torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11307 | torch::Tensor inv_scale = torch::scalar_tensor( |
11308 | 0.2, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11309 | torch::Tensor found_inf = torch::scalar_tensor( |
11310 | 0, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11311 | torch::Tensor grads_output0 = grads0 * inv_scale; |
11312 | torch::Tensor found_inf_output0 = torch::scalar_tensor( |
11313 | 0, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11314 | torch::Tensor found_inf_output1 = torch::scalar_tensor( |
11315 | 1, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11316 | ForEachDevice([&](const torch::Device& device) { |
11317 | if (grads0.device() == at::kCPU) { |
11318 | GTEST_SKIP(); |
11319 | } |
11320 | torch::Tensor lazy_grads0 = CopyToDevice(grads0, device); |
11321 | torch::Tensor lazy_inv_scale = CopyToDevice(inv_scale, device); |
11322 | torch::Tensor lazy_found_inf = CopyToDevice(found_inf, device); |
11323 | torch::_amp_foreach_non_finite_check_and_unscale_( |
11324 | lazy_grads0, lazy_found_inf, lazy_inv_scale); |
11325 | AllClose(grads_output0, lazy_grads0, /*rtol=*/1e-2, /*atol=*/1e-4); |
11326 | AllEqual(found_inf_output0, lazy_found_inf); |
11327 | |
11328 | torch::Tensor lazy_grads1 = CopyToDevice(grads1, device); |
11329 | torch::_amp_foreach_non_finite_check_and_unscale_( |
11330 | lazy_grads1, lazy_found_inf, lazy_inv_scale); |
11331 | AllEqual(found_inf_output1, lazy_found_inf); |
11332 | }); |
11333 | } |
11334 | |
11335 | TEST_F(LazyOpsTest, TestAmpUpdateScale) { |
11336 | torch::Tensor growth_tracker = torch::scalar_tensor( |
11337 | 0, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
11338 | torch::Tensor current_scale = torch::scalar_tensor( |
11339 | 4, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11340 | torch::Tensor found_inf = torch::scalar_tensor( |
11341 | 1, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11342 | torch::Tensor not_found_inf = torch::scalar_tensor( |
11343 | 0, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11344 | float scale_growth_factor = 2.0; |
11345 | float scale_backoff_factor = 0.5; |
11346 | int growth_interval = 3; |
11347 | |
11348 | torch::Tensor growth_tracker_result0 = torch::scalar_tensor( |
11349 | 1, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
11350 | torch::Tensor current_scale_result0 = torch::scalar_tensor( |
11351 | 4, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11352 | torch::Tensor growth_tracker_result1 = torch::scalar_tensor( |
11353 | 2, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
11354 | torch::Tensor current_scale_result1 = torch::scalar_tensor( |
11355 | 4, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11356 | torch::Tensor growth_tracker_result2 = torch::scalar_tensor( |
11357 | 0, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
11358 | torch::Tensor current_scale_result2 = torch::scalar_tensor( |
11359 | 8, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11360 | torch::Tensor growth_tracker_result3 = torch::scalar_tensor( |
11361 | 0, torch::TensorOptions(torch::kInt32).device(DefaultDevice())); |
11362 | torch::Tensor current_scale_result3 = torch::scalar_tensor( |
11363 | 4, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11364 | |
11365 | ForEachDevice([&](const torch::Device& device) { |
11366 | if (growth_tracker.device() == at::kCPU) { |
11367 | GTEST_SKIP(); |
11368 | } |
11369 | torch::Tensor lazy_growth_tracker = CopyToDevice(growth_tracker, device); |
11370 | torch::Tensor lazy_current_scale = CopyToDevice(current_scale, device); |
11371 | torch::Tensor lazy_found_inf = CopyToDevice(found_inf, device); |
11372 | torch::Tensor lazy_not_found_inf = CopyToDevice(not_found_inf, device); |
11373 | |
11374 | torch::_amp_update_scale_( |
11375 | lazy_current_scale, |
11376 | lazy_growth_tracker, |
11377 | lazy_not_found_inf, |
11378 | scale_growth_factor, |
11379 | scale_backoff_factor, |
11380 | growth_interval); |
11381 | AllClose( |
11382 | current_scale_result0, |
11383 | lazy_current_scale, |
11384 | /*rtol=*/1e-2, |
11385 | /*atol=*/1e-4); |
11386 | AllEqual(growth_tracker_result0, lazy_growth_tracker); |
11387 | |
11388 | torch::_amp_update_scale_( |
11389 | lazy_current_scale, |
11390 | lazy_growth_tracker, |
11391 | lazy_not_found_inf, |
11392 | scale_growth_factor, |
11393 | scale_backoff_factor, |
11394 | growth_interval); |
11395 | AllClose( |
11396 | current_scale_result1, |
11397 | lazy_current_scale, |
11398 | /*rtol=*/1e-2, |
11399 | /*atol=*/1e-4); |
11400 | AllEqual(growth_tracker_result1, lazy_growth_tracker); |
11401 | |
11402 | // torch::_amp_update_scale_ returns the reference of current_scale |
11403 | lazy_current_scale = torch::_amp_update_scale_( |
11404 | lazy_current_scale, |
11405 | lazy_growth_tracker, |
11406 | lazy_not_found_inf, |
11407 | scale_growth_factor, |
11408 | scale_backoff_factor, |
11409 | growth_interval); |
11410 | AllClose( |
11411 | current_scale_result2, |
11412 | lazy_current_scale, |
11413 | /*rtol=*/1e-2, |
11414 | /*atol=*/1e-4); |
11415 | AllEqual(growth_tracker_result2, lazy_growth_tracker); |
11416 | |
11417 | lazy_current_scale = torch::_amp_update_scale_( |
11418 | lazy_current_scale, |
11419 | lazy_growth_tracker, |
11420 | lazy_found_inf, |
11421 | scale_growth_factor, |
11422 | scale_backoff_factor, |
11423 | growth_interval); |
11424 | AllClose( |
11425 | current_scale_result3, |
11426 | lazy_current_scale, |
11427 | /*rtol=*/1e-2, |
11428 | /*atol=*/1e-4); |
11429 | AllEqual(growth_tracker_result3, lazy_growth_tracker); |
11430 | }); |
11431 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
11432 | ExpectCounterChanged("lazy::_amp_update_scale_" , GetIgnoredCounters()); |
11433 | } |
11434 | |
11435 | TEST_F(LazyOpsTest, TestEarlySyncLiveTensors) { |
11436 | torch::Tensor scalar_tensor = torch::scalar_tensor( |
11437 | 1., torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11438 | torch::Scalar scalar1 = scalar_tensor.item(); |
11439 | ForEachDevice([&](const torch::Device& device) { |
11440 | torch::Tensor lazy_scalar_tensor = CopyToDevice(scalar_tensor, device); |
11441 | torch::Scalar scalar2 = lazy_scalar_tensor.item(); |
11442 | ASSERT_EQ(scalar1.to<float>(), scalar2.to<float>()); |
11443 | }); |
11444 | if (DebugUtil::ExperimentEnabled("early_sync" )) { |
11445 | ExpectCounterChanged("EarlySyncLiveTensorsCount" , GetIgnoredCounters()); |
11446 | } else { |
11447 | ExpectCounterNotChanged("EarlySyncLiveTensorsCount" , GetIgnoredCounters()); |
11448 | } |
11449 | ExpectCounterChanged("aten::_local_scalar_dense" , GetIgnoredCounters()); |
11450 | } |
11451 | |
11452 | TEST_F(LazyOpsTest, TestLerp) { |
11453 | torch::Tensor start = torch::rand( |
11454 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11455 | torch::Tensor end = torch::rand( |
11456 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11457 | torch::Tensor weight = torch::rand( |
11458 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11459 | torch::Tensor res = torch::lerp(start, end, weight); |
11460 | ForEachDevice([&](const torch::Device& device) { |
11461 | torch::Tensor lazy_start = CopyToDevice(start, device); |
11462 | torch::Tensor lazy_end = CopyToDevice(end, device); |
11463 | torch::Tensor lazy_weight = CopyToDevice(weight, device); |
11464 | torch::Tensor lazy_res = torch::lerp(lazy_start, lazy_end, lazy_weight); |
11465 | AllClose(res, lazy_res); |
11466 | }); |
11467 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
11468 | ExpectCounterChanged("lazy::lerp" , GetIgnoredCounters()); |
11469 | } |
11470 | |
11471 | TEST_F(LazyOpsTest, TestLerpScalar) { |
11472 | torch::Tensor start = torch::rand( |
11473 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11474 | torch::Tensor end = torch::rand( |
11475 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11476 | torch::Scalar weight = torch::Scalar(3.0); |
11477 | torch::Tensor res = torch::lerp(start, end, weight); |
11478 | ForEachDevice([&](const torch::Device& device) { |
11479 | torch::Tensor lazy_start = CopyToDevice(start, device); |
11480 | torch::Tensor lazy_end = CopyToDevice(end, device); |
11481 | torch::Tensor lazy_res = torch::lerp(lazy_start, lazy_end, weight); |
11482 | AllClose(res, lazy_res); |
11483 | }); |
11484 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
11485 | ExpectCounterChanged("lazy::lerp" , GetIgnoredCounters()); |
11486 | } |
11487 | |
11488 | TEST_F(LazyOpsTest, TestLerpInplace) { |
11489 | torch::Tensor input = torch::rand( |
11490 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11491 | torch::Tensor end = torch::rand( |
11492 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11493 | torch::Tensor weight = torch::rand( |
11494 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11495 | torch::Tensor input_copy = input.clone(); |
11496 | input.lerp_(end, weight); |
11497 | ForEachDevice([&](const torch::Device& device) { |
11498 | torch::Tensor lazy_input = CopyToDevice(input_copy, device); |
11499 | torch::Tensor lazy_end = CopyToDevice(end, device); |
11500 | torch::Tensor lazy_weight = CopyToDevice(weight, device); |
11501 | lazy_input.lerp_(lazy_end, lazy_weight); |
11502 | AllClose(lazy_input, input); |
11503 | }); |
11504 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
11505 | ExpectCounterChanged("lazy::lerp" , GetIgnoredCounters()); |
11506 | } |
11507 | |
11508 | TEST_F(LazyOpsTest, TestLerpScalarInplace) { |
11509 | torch::Tensor input = torch::rand( |
11510 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11511 | torch::Tensor end = torch::rand( |
11512 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11513 | torch::Scalar weight = torch::Scalar(3.0); |
11514 | torch::Tensor input_copy = input.clone(); |
11515 | input.lerp_(end, weight); |
11516 | ForEachDevice([&](const torch::Device& device) { |
11517 | torch::Tensor lazy_input = CopyToDevice(input_copy, device); |
11518 | torch::Tensor lazy_end = CopyToDevice(end, device); |
11519 | lazy_input.lerp_(lazy_end, weight); |
11520 | AllClose(lazy_input, input); |
11521 | }); |
11522 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
11523 | ExpectCounterChanged("lazy::lerp" , GetIgnoredCounters()); |
11524 | } |
11525 | |
11526 | TEST_F(LazyOpsTest, TestLerpOut) { |
11527 | torch::Tensor start = torch::rand( |
11528 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11529 | torch::Tensor end = torch::rand( |
11530 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11531 | torch::Tensor weight = torch::rand( |
11532 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11533 | torch::Tensor res = torch::empty( |
11534 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11535 | ; |
11536 | torch::lerp_out(res, start, end, weight); |
11537 | ForEachDevice([&](const torch::Device& device) { |
11538 | torch::Tensor lazy_start = CopyToDevice(start, device); |
11539 | torch::Tensor lazy_end = CopyToDevice(end, device); |
11540 | torch::Tensor lazy_weight = CopyToDevice(weight, device); |
11541 | torch::Tensor lazy_res = torch::empty({3, 4}, lazy_start.options()); |
11542 | torch::lerp_out(lazy_res, lazy_start, lazy_end, lazy_weight); |
11543 | AllClose(res, lazy_res); |
11544 | }); |
11545 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
11546 | ExpectCounterChanged("lazy::lerp" , GetIgnoredCounters()); |
11547 | } |
11548 | |
11549 | TEST_F(LazyOpsTest, TestLerpScalarOut) { |
11550 | torch::Tensor start = torch::rand( |
11551 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11552 | torch::Tensor end = torch::rand( |
11553 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11554 | torch::Scalar weight = torch::Scalar(3.0); |
11555 | torch::Tensor res = torch::empty( |
11556 | {3, 4}, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11557 | torch::lerp_out(res, start, end, weight); |
11558 | ForEachDevice([&](const torch::Device& device) { |
11559 | torch::Tensor lazy_start = CopyToDevice(start, device); |
11560 | torch::Tensor lazy_end = CopyToDevice(end, device); |
11561 | torch::Tensor lazy_res = torch::empty({3, 4}, lazy_start.options()); |
11562 | torch::lerp_out(lazy_res, lazy_start, lazy_end, weight); |
11563 | AllClose(res, lazy_res); |
11564 | }); |
11565 | ExpectCounterNotChanged("aten::.*" , GetIgnoredCounters()); |
11566 | ExpectCounterChanged("lazy::lerp" , GetIgnoredCounters()); |
11567 | } |
11568 | |
11569 | TEST_F(LazyOpsTest, IsAliasOf) { |
11570 | auto a = torch::empty( |
11571 | 4, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11572 | auto b = torch::empty( |
11573 | 4, torch::TensorOptions(torch::kFloat).device(DefaultDevice())); |
11574 | |
11575 | ForEachDevice([&](const torch::Device& device) { |
11576 | auto lazy_a = CopyToDevice(a, device); |
11577 | auto lazy_b = CopyToDevice(b, device); |
11578 | EXPECT_EQ(!a.is_alias_of(b), !lazy_a.is_alias_of(lazy_b)); |
11579 | |
11580 | auto c = a.view({2, 2}); |
11581 | auto lazy_c = lazy_a.view({2, 2}); |
11582 | EXPECT_EQ(a.is_alias_of(c), lazy_a.is_alias_of(lazy_c)); |
11583 | |
11584 | auto d = c.view({1, 4}); |
11585 | auto lazy_d = lazy_c.view({1, 4}); |
11586 | EXPECT_EQ(d.is_alias_of(c), lazy_d.is_alias_of(lazy_c)); |
11587 | EXPECT_EQ(d.is_alias_of(a), lazy_d.is_alias_of(lazy_a)); |
11588 | }); |
11589 | } |
11590 | |
11591 | #endif // FBCODE_CAFFE2 |
11592 | |
11593 | } // namespace lazy |
11594 | } // namespace torch |
11595 | |