1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684
|
# Owner(s): ["module: tests"]
import torch
import numpy as np
from itertools import product, combinations, permutations, chain
from functools import partial
import random
import warnings
from torch._six import nan
from torch.testing import make_tensor
from torch.testing._internal.common_utils import (
TestCase, run_tests, skipIfTorchDynamo, torch_to_numpy_dtype_dict)
from torch.testing._internal.common_device_type import (
instantiate_device_type_tests, onlyCPU, onlyCUDA, dtypes, onlyNativeDeviceTypes,
dtypesIfCUDA, largeTensorTest)
from torch.testing._internal.common_dtype import all_types_and_complex_and, all_types, all_types_and
# TODO: replace with make_tensor
def _generate_input(shape, dtype, device, with_extremal):
if shape == ():
x = torch.tensor((), dtype=dtype, device=device)
else:
if dtype.is_floating_point or dtype.is_complex:
# work around torch.randn not being implemented for bfloat16
if dtype == torch.bfloat16:
x = torch.randn(*shape, device=device) * random.randint(30, 100)
x = x.to(torch.bfloat16)
else:
x = torch.randn(*shape, dtype=dtype, device=device) * random.randint(30, 100)
x[torch.randn(*shape) > 0.5] = 0
if with_extremal and dtype.is_floating_point:
# Use extremal values
x[torch.randn(*shape) > 0.5] = float('nan')
x[torch.randn(*shape) > 0.5] = float('inf')
x[torch.randn(*shape) > 0.5] = float('-inf')
elif with_extremal and dtype.is_complex:
x[torch.randn(*shape) > 0.5] = complex('nan')
x[torch.randn(*shape) > 0.5] = complex('inf')
x[torch.randn(*shape) > 0.5] = complex('-inf')
elif dtype == torch.bool:
x = torch.zeros(shape, dtype=dtype, device=device)
x[torch.randn(*shape) > 0.5] = True
else:
x = torch.randint(15, 100, shape, dtype=dtype, device=device)
return x
class TestShapeOps(TestCase):
# TODO: update to work on CUDA, too
@onlyCPU
def test_unbind(self, device):
x = torch.rand(2, 3, 4, 5)
for dim in range(4):
res = torch.unbind(x, dim)
res2 = x.unbind(dim)
self.assertEqual(x.size(dim), len(res))
self.assertEqual(x.size(dim), len(res2))
for i in range(dim):
self.assertEqual(x.select(dim, i), res[i])
self.assertEqual(x.select(dim, i), res2[i])
# TODO: update to work on CUDA, too?
@skipIfTorchDynamo("TorchDynamo fails with an unknown error")
@onlyCPU
def test_tolist(self, device):
list0D = []
tensor0D = torch.tensor(list0D)
self.assertEqual(tensor0D.tolist(), list0D)
table1D = [1., 2., 3.]
tensor1D = torch.tensor(table1D)
storage = torch.Storage(table1D)
self.assertEqual(tensor1D.tolist(), table1D)
self.assertEqual(storage.tolist(), table1D)
self.assertEqual(tensor1D.tolist(), table1D)
self.assertEqual(storage.tolist(), table1D)
table2D = [[1, 2], [3, 4]]
tensor2D = torch.tensor(table2D)
self.assertEqual(tensor2D.tolist(), table2D)
tensor3D = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
tensorNonContig = tensor3D.select(1, 1)
self.assertFalse(tensorNonContig.is_contiguous())
self.assertEqual(tensorNonContig.tolist(), [[3, 4], [7, 8]])
@dtypes(torch.int64, torch.float, torch.complex128)
def test_movedim_invalid(self, device, dtype):
shape = self._rand_shape(4, min_size=5, max_size=10)
x = _generate_input(shape, dtype, device, False)
for fn in [torch.movedim, torch.moveaxis]:
# Invalid `source` and `destination` dimension
with self.assertRaisesRegex(IndexError, "Dimension out of range"):
fn(x, 5, 0)
with self.assertRaisesRegex(IndexError, "Dimension out of range"):
fn(x, 0, 5)
# Mismatch in size of `source` and `destination`
with self.assertRaisesRegex(RuntimeError, "movedim: Invalid source or destination dims:"):
fn(x, (1, 0), (0, ))
with self.assertRaisesRegex(RuntimeError, "movedim: repeated dim in `source`"):
fn(x, (0, 0), (0, 1))
with self.assertRaisesRegex(RuntimeError, "movedim: repeated dim in `source`"):
fn(x, (0, 1, 0), (0, 1, 2))
with self.assertRaisesRegex(RuntimeError, "movedim: repeated dim in `destination`"):
fn(x, (0, 1), (1, 1))
with self.assertRaisesRegex(RuntimeError, "movedim: repeated dim in `destination`"):
fn(x, (0, 1, 2), (1, 0, 1))
@dtypes(torch.int64, torch.float, torch.complex128)
def test_movedim(self, device, dtype):
for fn in [torch.moveaxis, torch.movedim]:
for nd in range(5):
shape = self._rand_shape(nd, min_size=5, max_size=10)
x = _generate_input(shape, dtype, device, with_extremal=False)
for random_negative in [True, False]:
for src_dim, dst_dim in permutations(range(nd), r=2):
random_prob = random.random()
if random_negative and random_prob > 0.66:
src_dim = src_dim - nd
elif random_negative and random_prob > 0.33:
dst_dim = dst_dim - nd
elif random_negative:
src_dim = src_dim - nd
dst_dim = dst_dim - nd
# Integer `source` and `destination`
torch_fn = partial(fn, source=src_dim, destination=dst_dim)
np_fn = partial(np.moveaxis, source=src_dim, destination=dst_dim)
self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None)
if nd == 0:
continue
def make_index_negative(sequence, idx):
sequence = list(sequence)
sequence[random_idx] = sequence[random_idx] - nd
return tuple(src_sequence)
for src_sequence in permutations(range(nd), r=random.randint(1, nd)):
# Sequence `source` and `destination`
dst_sequence = tuple(random.sample(range(nd), len(src_sequence)))
# Randomly change a dim to a negative dim representation of itself.
random_prob = random.random()
if random_negative and random_prob > 0.66:
random_idx = random.randint(0, len(src_sequence) - 1)
src_sequence = make_index_negative(src_sequence, random_idx)
elif random_negative and random_prob > 0.33:
random_idx = random.randint(0, len(src_sequence) - 1)
dst_sequence = make_index_negative(dst_sequence, random_idx)
elif random_negative:
random_idx = random.randint(0, len(src_sequence) - 1)
dst_sequence = make_index_negative(dst_sequence, random_idx)
random_idx = random.randint(0, len(src_sequence) - 1)
src_sequence = make_index_negative(src_sequence, random_idx)
torch_fn = partial(fn, source=src_sequence, destination=dst_sequence)
np_fn = partial(np.moveaxis, source=src_sequence, destination=dst_sequence)
self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None)
# Move dim to same position
x = torch.randn(2, 3, 5, 7, 11)
torch_fn = partial(fn, source=(0, 1), destination=(0, 1))
np_fn = partial(np.moveaxis, source=(0, 1), destination=(0, 1))
self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None)
torch_fn = partial(fn, source=1, destination=1)
np_fn = partial(np.moveaxis, source=1, destination=1)
self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None)
# Empty Sequence
torch_fn = partial(fn, source=(), destination=())
np_fn = partial(np.moveaxis, source=(), destination=())
self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None)
@dtypes(torch.float, torch.bool)
def test_diag(self, device, dtype):
if dtype is torch.bool:
x = torch.rand(100, 100, device=device) >= 0.5
else:
x = torch.rand(100, 100, dtype=dtype, device=device)
res1 = torch.diag(x)
res2 = torch.tensor((), dtype=dtype, device=device)
torch.diag(x, out=res2)
self.assertEqual(res1, res2)
def test_diagonal(self, device):
x = torch.randn((100, 100), device=device)
result = torch.diagonal(x)
expected = torch.diag(x)
self.assertEqual(result, expected)
x = torch.randn((100, 100), device=device)
result = torch.diagonal(x, 17)
expected = torch.diag(x, 17)
self.assertEqual(result, expected)
@onlyCPU
@dtypes(torch.float)
def test_diagonal_multidim(self, device, dtype):
x = torch.randn(10, 11, 12, 13, dtype=dtype, device=device)
xn = x.numpy()
for args in [(2, 2, 3),
(2,),
(-2, 1, 2),
(0, -2, -1)]:
result = torch.diagonal(x, *args)
expected = xn.diagonal(*args)
self.assertEqual(expected.shape, result.shape)
self.assertEqual(expected, result)
# test non-continguous
xp = x.permute(1, 2, 3, 0)
result = torch.diagonal(xp, 0, -2, -1)
expected = xp.numpy().diagonal(0, -2, -1)
self.assertEqual(expected.shape, result.shape)
self.assertEqual(expected, result)
@onlyNativeDeviceTypes
@dtypes(*all_types())
@dtypesIfCUDA(*all_types_and(torch.half))
def test_trace(self, device, dtype):
def test(shape):
tensor = make_tensor(shape, dtype=dtype, device=device, low=-9, high=9)
expected_dtype = tensor.sum().dtype
expected_dtype = torch_to_numpy_dtype_dict[expected_dtype]
result = np.trace(tensor.cpu().numpy(), dtype=expected_dtype)
expected = torch.tensor(result, device=device)
self.assertEqual(tensor.trace(), expected)
shapes = (
[10, 1],
[1, 10],
[100, 100],
[20, 100],
[100, 20],
)
for shape in shapes:
test(shape)
def generate_clamp_baseline(self, device, dtype, *, min_vals, max_vals, with_nans):
"""
Creates a random tensor for a given device and dtype, and computes the expected clamped
values given the min_vals and/or max_vals.
If with_nans is provided, then some values are randomly set to nan.
"""
X = torch.rand(100, device=device).mul(50).add(-25) # uniform in [-25, 25]
X = X.to(dtype)
if with_nans:
mask = torch.randint(0, 2, X.shape, dtype=torch.bool, device=device)
X[mask] = nan
if isinstance(min_vals, torch.Tensor):
min_vals = min_vals.cpu().numpy()
if isinstance(max_vals, torch.Tensor):
max_vals = max_vals.cpu().numpy()
# Use NumPy implementation as reference
X_clamped = torch.tensor(np.clip(X.cpu().numpy(), a_min=min_vals, a_max=max_vals), device=device)
return X, X_clamped
# Tests clamp and its alias, clip
@dtypes(torch.int64, torch.float32)
def test_clamp(self, device, dtype):
op_list = (torch.clamp, torch.Tensor.clamp, torch.Tensor.clamp_,
torch.clip, torch.Tensor.clip, torch.Tensor.clip_)
# min/max argument product
args = product((-10, None), (10, None))
for op in op_list:
for min_val, max_val in args:
if min_val is None and max_val is None:
continue
X, Y_expected = self.generate_clamp_baseline(device, dtype,
min_vals=min_val,
max_vals=max_val,
with_nans=False)
# Test op
X1 = X.clone() # So that the in-place ops do not change X
Y_actual = op(X1, min_val, max_val)
self.assertEqual(Y_expected, Y_actual)
# Test op-out behavior (out does not exist for method versions)
if op in (torch.clamp, torch.clip):
Y_out = torch.empty_like(X)
op(X, min=min_val, max=max_val, out=Y_out)
self.assertEqual(Y_expected, Y_out)
def test_clamp_propagates_nans(self, device):
op_list = (torch.clamp, torch.Tensor.clamp, torch.Tensor.clamp_,
torch.clip, torch.Tensor.clip, torch.Tensor.clip_)
# min/max argument product
args = product((-10, None), (10, None))
for op in op_list:
for min_val, max_val in args:
if min_val is None and max_val is None:
continue
X, Y_expected = self.generate_clamp_baseline(device, torch.float,
min_vals=min_val,
max_vals=max_val,
with_nans=True)
Y_expected = torch.isnan(Y_expected)
# Test op
X1 = X.clone() # So that the in-place ops do not change X
Y_actual = op(X1, min_val, max_val)
self.assertEqual(Y_expected, torch.isnan(Y_actual))
# Test op-out behavior (out does not exist for method versions)
if op in (torch.clamp, torch.clip):
Y_out = torch.empty_like(X)
op(X, min_val, max_val, out=Y_out)
self.assertEqual(Y_expected, torch.isnan(Y_out))
def test_clamp_raises_arg_errors(self, device):
X = torch.randn(100, dtype=torch.float, device=device)
error_msg = 'At least one of \'min\' or \'max\' must not be None'
with self.assertRaisesRegex(RuntimeError, error_msg):
X.clamp()
with self.assertRaisesRegex(RuntimeError, error_msg):
X.clamp_()
with self.assertRaisesRegex(RuntimeError, error_msg):
torch.clamp(X)
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
def test_flip(self, device, dtype):
make_from_data = partial(torch.tensor, device=device, dtype=dtype)
make_from_size = partial(make_tensor, device=device, dtype=dtype)
def test_flip_impl(input_t, dims, output_t):
def all_t():
yield input_t, output_t
if dtype is torch.float:
# We generate quantized versions as well
for qdtype in (torch.quint8, torch.qint8, torch.qint32):
qinput_t = torch.quantize_per_tensor(input_t, 0.1, 5, qdtype)
qoutput_t = torch.quantize_per_tensor(output_t, 0.1, 5, qdtype)
yield qinput_t, qoutput_t
for in_t, out_t in all_t():
self.assertEqual(in_t.flip(dims), out_t)
n = in_t.ndim
if not isinstance(dims, tuple):
# Wrap dim
self.assertEqual(in_t.flip(-n + dims), out_t)
else:
# Permute dimensions
for p_dims in permutations(dims):
self.assertEqual(in_t.flip(p_dims), out_t)
if len(p_dims) > 0:
# Wrap 1st dim
self.assertEqual(in_t.flip((-n + p_dims[0],) + p_dims[1:]), out_t)
def gen_data():
# Basic tests
data = make_from_data([1, 2, 3, 4, 5, 6, 7, 8]).view(2, 2, 2)
nonctg = make_from_size((2, 2, 2), noncontiguous=True).copy_(data)
dims_result = ((0, make_from_data([5, 6, 7, 8, 1, 2, 3, 4]).view(2, 2, 2)),
(1, make_from_data([3, 4, 1, 2, 7, 8, 5, 6]).view(2, 2, 2)),
(2, make_from_data([2, 1, 4, 3, 6, 5, 8, 7]).view(2, 2, 2)),
((0, 1), make_from_data([7, 8, 5, 6, 3, 4, 1, 2]).view(2, 2, 2)),
((0, 1, 2), make_from_data([8, 7, 6, 5, 4, 3, 2, 1]).view(2, 2, 2)))
for in_tensor, (dims, out_tensor) in product((data, nonctg), dims_result):
yield in_tensor, dims, out_tensor
# Expanded
in_t = make_from_data([1, 2, 3]).view(3, 1).expand(3, 2)
dims = 0
out_t = make_from_data([3, 3, 2, 2, 1, 1]).view(3, 2)
yield in_t, dims, out_t
# Noop on expanded dimension
yield in_t, 1, in_t
# Transposed
in_t = make_from_data([1, 2, 3, 4, 5, 6, 7, 8]).view(2, 2, 2).transpose(0, 1)
dims = (0, 1, 2)
out_t = make_from_data([8, 7, 4, 3, 6, 5, 2, 1]).view(2, 2, 2)
yield in_t, dims, out_t
# Rectangular case
in_t = make_from_data([1, 2, 3, 4, 5, 6]).view(2, 3)
dims = 0
out_t = make_from_data([[4, 5, 6], [1, 2, 3]])
yield in_t, dims, out_t
dims = 1
out_t = make_from_data([[3, 2, 1], [6, 5, 4]])
yield in_t, dims, out_t
# Noops (edge cases)
# Size 0
in_t = make_from_data(())
yield in_t, 0, in_t
yield in_t, (), in_t
# dims = ()
in_t = make_from_size((3, 2, 1))
yield in_t, (), in_t
# Zero elements, non-zero size
in_t = make_from_size((3, 0, 2))
for i in range(in_t.ndim):
yield in_t, i, in_t
# Size 1
in_t = make_from_size(())
yield in_t, 0, in_t
in_t = make_from_size((1,))
yield in_t, 0, in_t
for in_tensor, dims, out_tensor in gen_data():
test_flip_impl(in_tensor, dims, out_tensor)
# test for shape
size = [2, 3, 4]
data = make_from_size(size)
possible_dims = range(len(size))
test_dims = chain(combinations(possible_dims, 1), combinations(possible_dims, 2))
for dims in test_dims:
self.assertEqual(size, list(data.flip(dims).size()))
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
def test_flip_errors(self, device, dtype):
make_arg = partial(make_tensor, dtype=dtype, device=device)
data = make_arg((2, 2, 2))
# not allow flip on the same dim more than once
self.assertRaises(RuntimeError, lambda: data.flip(0, 1, 1))
# not allow empty list as input
self.assertRaises(TypeError, lambda: data.flip())
# not allow dim > max dim
self.assertRaises(IndexError, lambda: data.flip(0, 1, 2, 3))
self.assertRaises(IndexError, lambda: data.flip(3))
def _rand_shape(self, dim, min_size, max_size):
return tuple(torch.randint(min_size, max_size + 1, (dim,)))
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
def test_flip_numpy(self, device, dtype):
make_arg = partial(make_tensor, dtype=dtype, device=device)
for ndim in [3, 4]:
shape = self._rand_shape(ndim, 5, 10)
data = make_arg(shape)
# Axis to sample for given shape.
for i in range(1, ndim + 1):
# Check all combinations of `i` axis.
for flip_dim in combinations(range(ndim), i):
torch_fn = partial(torch.flip, dims=flip_dim)
np_fn = partial(np.flip, axis=flip_dim)
self.compare_with_numpy(torch_fn, np_fn, data)
@onlyCUDA # CPU is too slow
@largeTensorTest('17GB') # 4 tensors of 4GB (in, out) x (torch, numpy) + 1GB
@largeTensorTest("81GB", "cpu") # even for CUDA test, sufficient system memory is required
def test_flip_large_tensor(self, device):
t_in = torch.empty(2**32 + 1, dtype=torch.uint8).random_()
torch_fn = partial(torch.flip, dims=(0,))
np_fn = partial(np.flip, axis=0)
self.compare_with_numpy(torch_fn, np_fn, t_in)
del t_in
def _test_fliplr_flipud(self, torch_fn, np_fn, min_dim, max_dim, device, dtype):
for dim in range(min_dim, max_dim + 1):
shape = self._rand_shape(dim, 5, 10)
# Randomly scale the input
if dtype.is_floating_point or dtype.is_complex:
data = torch.randn(*shape, device=device, dtype=dtype)
else:
data = torch.randint(0, 10, shape, device=device, dtype=dtype)
self.compare_with_numpy(torch_fn, np_fn, data)
@dtypes(torch.int64, torch.double, torch.cdouble)
def test_fliplr(self, device, dtype):
self._test_fliplr_flipud(torch.fliplr, np.fliplr, 2, 4, device, dtype)
@dtypes(torch.int64, torch.double, torch.cdouble)
def test_fliplr_invalid(self, device, dtype):
x = torch.randn(42).to(dtype)
with self.assertRaisesRegex(RuntimeError, "Input must be >= 2-d."):
torch.fliplr(x)
with self.assertRaisesRegex(RuntimeError, "Input must be >= 2-d."):
torch.fliplr(torch.tensor(42, device=device, dtype=dtype))
@dtypes(torch.int64, torch.double, torch.cdouble)
def test_flipud(self, device, dtype):
self._test_fliplr_flipud(torch.flipud, np.flipud, 1, 4, device, dtype)
@dtypes(torch.int64, torch.double, torch.cdouble)
def test_flipud_invalid(self, device, dtype):
with self.assertRaisesRegex(RuntimeError, "Input must be >= 1-d."):
torch.flipud(torch.tensor(42, device=device, dtype=dtype))
def test_rot90(self, device):
data = torch.arange(1, 5, device=device).view(2, 2)
self.assertEqual(torch.tensor([1, 2, 3, 4]).view(2, 2), data.rot90(0, [0, 1]))
self.assertEqual(torch.tensor([2, 4, 1, 3]).view(2, 2), data.rot90(1, [0, 1]))
self.assertEqual(torch.tensor([4, 3, 2, 1]).view(2, 2), data.rot90(2, [0, 1]))
self.assertEqual(torch.tensor([3, 1, 4, 2]).view(2, 2), data.rot90(3, [0, 1]))
# test for default args k=1, dims=[0, 1]
self.assertEqual(data.rot90(), data.rot90(1, [0, 1]))
# test for reversed order of dims
self.assertEqual(data.rot90(3, [0, 1]), data.rot90(1, [1, 0]))
# test for modulo of k
self.assertEqual(data.rot90(5, [0, 1]), data.rot90(1, [0, 1]))
self.assertEqual(data.rot90(3, [0, 1]), data.rot90(-1, [0, 1]))
self.assertEqual(data.rot90(-5, [0, 1]), data.rot90(-1, [0, 1]))
# test for dims out-of-range error
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0, -3]))
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0, 2]))
# test tensor with more than 2D
data = torch.arange(1, 9, device=device).view(2, 2, 2)
self.assertEqual(torch.tensor([2, 4, 1, 3, 6, 8, 5, 7]).view(2, 2, 2), data.rot90(1, [1, 2]))
self.assertEqual(data.rot90(1, [1, -1]), data.rot90(1, [1, 2]))
# test for errors
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0, 3]))
self.assertRaises(RuntimeError, lambda: data.rot90(1, [1, 1]))
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0, 1, 2]))
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0]))
@skipIfTorchDynamo("TorchDynamo fails with an unknown error")
@dtypes(torch.cfloat, torch.cdouble)
def test_complex_rot90(self, device, dtype):
shape = self._rand_shape(random.randint(2, 4), 5, 10)
for rot_times in range(4):
data = torch.randn(*shape, device=device, dtype=dtype)
torch_fn = partial(torch.rot90, k=rot_times, dims=[0, 1])
np_fn = partial(np.rot90, k=rot_times, axes=[0, 1])
self.compare_with_numpy(torch_fn, np_fn, data)
# TODO: update once warning flag is available to always trigger ONCE warnings
# Ensures nonzero does not throw a warning, even when the as_tuple argument
# is not provided
def test_nonzero_no_warning(self, device):
t = torch.randn((2, 2), device=device)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
torch.nonzero(t)
t.nonzero()
self.assertEqual(len(w), 0)
@dtypes(*all_types_and(torch.half, torch.bool, torch.bfloat16))
def test_nonzero(self, device, dtype):
shapes = [
torch.Size((12,)),
torch.Size((12, 1)),
torch.Size((1, 12)),
torch.Size((6, 2)),
torch.Size((3, 2, 2)),
torch.Size((5, 5, 5)),
]
def gen_nontrivial_input(shape, dtype, device):
if dtype != torch.bfloat16:
return torch.randint(2, shape, device=device, dtype=dtype)
else:
# windows does not work for bfloat16 randing
return torch.randint(2, shape, device=device, dtype=torch.float).to(dtype)
for shape in shapes:
tensor = gen_nontrivial_input(shape, dtype, device)
dst1 = torch.nonzero(tensor, as_tuple=False)
dst2 = tensor.nonzero(as_tuple=False)
dst3 = torch.empty([], dtype=torch.long, device=device)
torch.nonzero(tensor, out=dst3)
if self.device_type != 'xla':
# xla does not raise runtime error
self.assertRaisesRegex(
RuntimeError,
"scalar type Long",
lambda: torch.nonzero(tensor, out=torch.empty([], dtype=torch.float, device=device))
)
if self.device_type == 'cuda':
self.assertRaisesRegex(
RuntimeError,
"on the same device",
lambda: torch.nonzero(tensor, out=torch.empty([], dtype=torch.long))
)
np_array = tensor.cpu().numpy() if dtype != torch.bfloat16 else tensor.float().cpu().numpy()
np_result = torch.from_numpy(np.stack(np_array.nonzero())).t()
self.assertEqual(dst1.cpu(), np_result, atol=0, rtol=0)
self.assertEqual(dst2.cpu(), np_result, atol=0, rtol=0)
self.assertEqual(dst3.cpu(), np_result, atol=0, rtol=0)
tup1 = torch.nonzero(tensor, as_tuple=True)
tup2 = tensor.nonzero(as_tuple=True)
tup1 = torch.stack(tup1).t().cpu()
tup2 = torch.stack(tup2).t().cpu()
self.assertEqual(tup1, np_result, atol=0, rtol=0)
self.assertEqual(tup2, np_result, atol=0, rtol=0)
def test_nonzero_astuple_out(self, device):
t = torch.randn((3, 3, 3), device=device)
out = torch.empty_like(t, dtype=torch.long)
with self.assertRaises(RuntimeError):
torch.nonzero(t, as_tuple=True, out=out)
self.assertEqual(torch.nonzero(t, as_tuple=False, out=out), torch.nonzero(t, out=out))
# Verifies that JIT script cannot handle the as_tuple kwarg
# See Issue https://github.com/pytorch/pytorch/issues/45499.
def _foo(t):
tuple_result = torch.nonzero(t, as_tuple=True)
nontuple_result = torch.nonzero(t, as_tuple=False)
out = torch.empty_like(nontuple_result)
torch.nonzero(t, as_tuple=False, out=out)
return tuple_result, nontuple_result, out
with self.assertRaises(RuntimeError):
scripted_foo = torch.jit.script(_foo)
# Verifies that JIT tracing works fine
traced_foo = torch.jit.trace(_foo, t)
traced_tuple, traced_nontuple, traced_out = traced_foo(t)
expected_tuple = torch.nonzero(t, as_tuple=True)
expected_nontuple = torch.nonzero(t)
self.assertEqual(traced_tuple, expected_tuple)
self.assertEqual(traced_nontuple, expected_nontuple)
self.assertEqual(traced_out, expected_nontuple)
@onlyNativeDeviceTypes
def test_nonzero_discontiguous(self, device):
shape = (4, 4)
tensor = torch.randint(2, shape, device=device)
tensor_nc = torch.empty(shape[0], shape[1] * 2, device=device)[:, ::2].copy_(tensor)
dst1 = tensor.nonzero(as_tuple=False)
dst2 = tensor_nc.nonzero(as_tuple=False)
self.assertEqual(dst1, dst2, atol=0, rtol=0)
dst3 = torch.empty_like(dst1)
data_ptr = dst3.data_ptr()
# expect dst3 storage to be reused
torch.nonzero(tensor, out=dst3)
self.assertEqual(data_ptr, dst3.data_ptr())
self.assertEqual(dst1, dst3, atol=0, rtol=0)
# discontiguous out
dst4 = torch.empty(dst1.size(0), dst1.size(1) * 2, dtype=torch.long, device=device)[:, ::2]
data_ptr = dst4.data_ptr()
strides = dst4.stride()
torch.nonzero(tensor, out=dst4)
self.assertEqual(data_ptr, dst4.data_ptr())
self.assertEqual(dst1, dst4, atol=0, rtol=0)
self.assertEqual(strides, dst4.stride())
def test_nonzero_non_diff(self, device):
x = torch.randn(10, requires_grad=True)
nz = x.nonzero()
self.assertFalse(nz.requires_grad)
instantiate_device_type_tests(TestShapeOps, globals())
if __name__ == '__main__':
run_tests()
|