# Owner(s): ["module: tests"] import operator import random import unittest import warnings from functools import reduce import numpy as np import torch from torch import tensor from torch.testing import make_tensor from torch.testing._internal.common_device_type import ( dtypes, dtypesIfCPU, dtypesIfCUDA, instantiate_device_type_tests, onlyCUDA, onlyNativeDeviceTypes, skipXLA, ) from torch.testing._internal.common_utils import ( DeterministicGuard, run_tests, serialTest, skipIfTorchDynamo, TEST_CUDA, TestCase, xfailIfTorchDynamo, ) class TestIndexing(TestCase): def test_index(self, device): def consec(size, start=1): sequence = torch.ones(torch.tensor(size).prod(0)).cumsum(0) sequence.add_(start - 1) return sequence.view(*size) reference = consec((3, 3, 3)).to(device) # empty tensor indexing self.assertEqual( reference[torch.LongTensor().to(device)], reference.new(0, 3, 3) ) self.assertEqual(reference[0], consec((3, 3)), atol=0, rtol=0) self.assertEqual(reference[1], consec((3, 3), 10), atol=0, rtol=0) self.assertEqual(reference[2], consec((3, 3), 19), atol=0, rtol=0) self.assertEqual(reference[0, 1], consec((3,), 4), atol=0, rtol=0) self.assertEqual(reference[0:2], consec((2, 3, 3)), atol=0, rtol=0) self.assertEqual(reference[2, 2, 2], 27, atol=0, rtol=0) self.assertEqual(reference[:], consec((3, 3, 3)), atol=0, rtol=0) # indexing with Ellipsis self.assertEqual( reference[..., 2], torch.tensor([[3.0, 6.0, 9.0], [12.0, 15.0, 18.0], [21.0, 24.0, 27.0]]), atol=0, rtol=0, ) self.assertEqual( reference[0, ..., 2], torch.tensor([3.0, 6.0, 9.0]), atol=0, rtol=0 ) self.assertEqual(reference[..., 2], reference[:, :, 2], atol=0, rtol=0) self.assertEqual(reference[0, ..., 2], reference[0, :, 2], atol=0, rtol=0) self.assertEqual(reference[0, 2, ...], reference[0, 2], atol=0, rtol=0) self.assertEqual(reference[..., 2, 2, 2], 27, atol=0, rtol=0) self.assertEqual(reference[2, ..., 2, 2], 27, atol=0, rtol=0) self.assertEqual(reference[2, 2, ..., 2], 27, atol=0, rtol=0) self.assertEqual(reference[2, 2, 2, ...], 27, atol=0, rtol=0) self.assertEqual(reference[...], reference, atol=0, rtol=0) reference_5d = consec((3, 3, 3, 3, 3)).to(device) self.assertEqual( reference_5d[..., 1, 0], reference_5d[:, :, :, 1, 0], atol=0, rtol=0 ) self.assertEqual( reference_5d[2, ..., 1, 0], reference_5d[2, :, :, 1, 0], atol=0, rtol=0 ) self.assertEqual( reference_5d[2, 1, 0, ..., 1], reference_5d[2, 1, 0, :, 1], atol=0, rtol=0 ) self.assertEqual(reference_5d[...], reference_5d, atol=0, rtol=0) # LongTensor indexing reference = consec((5, 5, 5)).to(device) idx = torch.LongTensor([2, 4]).to(device) self.assertEqual(reference[idx], torch.stack([reference[2], reference[4]])) # TODO: enable one indexing is implemented like in numpy # self.assertEqual(reference[2, idx], torch.stack([reference[2, 2], reference[2, 4]])) # self.assertEqual(reference[3, idx, 1], torch.stack([reference[3, 2], reference[3, 4]])[:, 1]) # None indexing self.assertEqual(reference[2, None], reference[2].unsqueeze(0)) self.assertEqual( reference[2, None, None], reference[2].unsqueeze(0).unsqueeze(0) ) self.assertEqual(reference[2:4, None], reference[2:4].unsqueeze(1)) self.assertEqual( reference[None, 2, None, None], reference.unsqueeze(0)[:, 2].unsqueeze(0).unsqueeze(0), ) self.assertEqual( reference[None, 2:5, None, None], reference.unsqueeze(0)[:, 2:5].unsqueeze(2).unsqueeze(2), ) # indexing 0-length slice self.assertEqual(torch.empty(0, 5, 5), reference[slice(0)]) self.assertEqual(torch.empty(0, 5), reference[slice(0), 2]) self.assertEqual(torch.empty(0, 5), reference[2, slice(0)]) self.assertEqual(torch.tensor([]), reference[2, 1:1, 2]) # indexing with step reference = consec((10, 10, 10)).to(device) self.assertEqual(reference[1:5:2], torch.stack([reference[1], reference[3]], 0)) self.assertEqual( reference[1:6:2], torch.stack([reference[1], reference[3], reference[5]], 0) ) self.assertEqual(reference[1:9:4], torch.stack([reference[1], reference[5]], 0)) self.assertEqual( reference[2:4, 1:5:2], torch.stack([reference[2:4, 1], reference[2:4, 3]], 1), ) self.assertEqual( reference[3, 1:6:2], torch.stack([reference[3, 1], reference[3, 3], reference[3, 5]], 0), ) self.assertEqual( reference[None, 2, 1:9:4], torch.stack([reference[2, 1], reference[2, 5]], 0).unsqueeze(0), ) self.assertEqual( reference[:, 2, 1:6:2], torch.stack( [reference[:, 2, 1], reference[:, 2, 3], reference[:, 2, 5]], 1 ), ) lst = [list(range(i, i + 10)) for i in range(0, 100, 10)] tensor = torch.DoubleTensor(lst).to(device) for _i in range(100): idx1_start = random.randrange(10) idx1_end = idx1_start + random.randrange(1, 10 - idx1_start + 1) idx1_step = random.randrange(1, 8) idx1 = slice(idx1_start, idx1_end, idx1_step) if random.randrange(2) == 0: idx2_start = random.randrange(10) idx2_end = idx2_start + random.randrange(1, 10 - idx2_start + 1) idx2_step = random.randrange(1, 8) idx2 = slice(idx2_start, idx2_end, idx2_step) lst_indexed = [l[idx2] for l in lst[idx1]] tensor_indexed = tensor[idx1, idx2] else: lst_indexed = lst[idx1] tensor_indexed = tensor[idx1] self.assertEqual(torch.DoubleTensor(lst_indexed), tensor_indexed) self.assertRaises(ValueError, lambda: reference[1:9:0]) self.assertRaises(ValueError, lambda: reference[1:9:-1]) self.assertRaises(IndexError, lambda: reference[1, 1, 1, 1]) self.assertRaises(IndexError, lambda: reference[1, 1, 1, 1:1]) self.assertRaises(IndexError, lambda: reference[3, 3, 3, 3, 3, 3, 3, 3]) self.assertRaises(IndexError, lambda: reference[0.0]) self.assertRaises(TypeError, lambda: reference[0.0:2.0]) self.assertRaises(IndexError, lambda: reference[0.0, 0.0:2.0]) self.assertRaises(IndexError, lambda: reference[0.0, :, 0.0:2.0]) self.assertRaises(IndexError, lambda: reference[0.0, ..., 0.0:2.0]) self.assertRaises(IndexError, lambda: reference[0.0, :, 0.0]) def delitem(): del reference[0] self.assertRaises(TypeError, delitem) @onlyNativeDeviceTypes @dtypes(torch.half, torch.double) def test_advancedindex(self, device, dtype): # Tests for Integer Array Indexing, Part I - Purely integer array # indexing def consec(size, start=1): # Creates the sequence in float since CPU half doesn't support the # needed operations. Converts to dtype before returning. numel = reduce(operator.mul, size, 1) sequence = torch.ones(numel, dtype=torch.float, device=device).cumsum(0) sequence.add_(start - 1) return sequence.view(*size).to(dtype=dtype) # pick a random valid indexer type def ri(indices): choice = random.randint(0, 2) if choice == 0: return torch.LongTensor(indices).to(device) elif choice == 1: return list(indices) else: return tuple(indices) def validate_indexing(x): self.assertEqual(x[[0]], consec((1,))) self.assertEqual(x[ri([0]),], consec((1,))) self.assertEqual(x[ri([3]),], consec((1,), 4)) self.assertEqual(x[[2, 3, 4]], consec((3,), 3)) self.assertEqual(x[ri([2, 3, 4]),], consec((3,), 3)) self.assertEqual( x[ri([0, 2, 4]),], torch.tensor([1, 3, 5], dtype=dtype, device=device) ) def validate_setting(x): x[[0]] = -2 self.assertEqual(x[[0]], torch.tensor([-2], dtype=dtype, device=device)) x[[0]] = -1 self.assertEqual( x[ri([0]),], torch.tensor([-1], dtype=dtype, device=device) ) x[[2, 3, 4]] = 4 self.assertEqual( x[[2, 3, 4]], torch.tensor([4, 4, 4], dtype=dtype, device=device) ) x[ri([2, 3, 4]),] = 3 self.assertEqual( x[ri([2, 3, 4]),], torch.tensor([3, 3, 3], dtype=dtype, device=device) ) x[ri([0, 2, 4]),] = torch.tensor([5, 4, 3], dtype=dtype, device=device) self.assertEqual( x[ri([0, 2, 4]),], torch.tensor([5, 4, 3], dtype=dtype, device=device) ) # Only validates indexing and setting for halfs if dtype == torch.half: reference = consec((10,)) validate_indexing(reference) validate_setting(reference) return # Case 1: Purely Integer Array Indexing reference = consec((10,)) validate_indexing(reference) # setting values validate_setting(reference) # Tensor with stride != 1 # strided is [1, 3, 5, 7] reference = consec((10,)) strided = torch.tensor((), dtype=dtype, device=device) strided.set_( reference.storage(), storage_offset=0, size=torch.Size([4]), stride=[2] ) self.assertEqual(strided[[0]], torch.tensor([1], dtype=dtype, device=device)) self.assertEqual( strided[ri([0]),], torch.tensor([1], dtype=dtype, device=device) ) self.assertEqual( strided[ri([3]),], torch.tensor([7], dtype=dtype, device=device) ) self.assertEqual( strided[[1, 2]], torch.tensor([3, 5], dtype=dtype, device=device) ) self.assertEqual( strided[ri([1, 2]),], torch.tensor([3, 5], dtype=dtype, device=device) ) self.assertEqual( strided[ri([[2, 1], [0, 3]]),], torch.tensor([[5, 3], [1, 7]], dtype=dtype, device=device), ) # stride is [4, 8] strided = torch.tensor((), dtype=dtype, device=device) strided.set_( reference.storage(), storage_offset=4, size=torch.Size([2]), stride=[4] ) self.assertEqual(strided[[0]], torch.tensor([5], dtype=dtype, device=device)) self.assertEqual( strided[ri([0]),], torch.tensor([5], dtype=dtype, device=device) ) self.assertEqual( strided[ri([1]),], torch.tensor([9], dtype=dtype, device=device) ) self.assertEqual( strided[[0, 1]], torch.tensor([5, 9], dtype=dtype, device=device) ) self.assertEqual( strided[ri([0, 1]),], torch.tensor([5, 9], dtype=dtype, device=device) ) self.assertEqual( strided[ri([[0, 1], [1, 0]]),], torch.tensor([[5, 9], [9, 5]], dtype=dtype, device=device), ) # reference is 1 2 # 3 4 # 5 6 reference = consec((3, 2)) self.assertEqual( reference[ri([0, 1, 2]), ri([0])], torch.tensor([1, 3, 5], dtype=dtype, device=device), ) self.assertEqual( reference[ri([0, 1, 2]), ri([1])], torch.tensor([2, 4, 6], dtype=dtype, device=device), ) self.assertEqual(reference[ri([0]), ri([0])], consec((1,))) self.assertEqual(reference[ri([2]), ri([1])], consec((1,), 6)) self.assertEqual( reference[[ri([0, 0]), ri([0, 1])]], torch.tensor([1, 2], dtype=dtype, device=device), ) self.assertEqual( reference[[ri([0, 1, 1, 0, 2]), ri([1])]], torch.tensor([2, 4, 4, 2, 6], dtype=dtype, device=device), ) self.assertEqual( reference[[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])]], torch.tensor([1, 2, 3, 3], dtype=dtype, device=device), ) rows = ri([[0, 0], [1, 2]]) columns = ([0],) self.assertEqual( reference[rows, columns], torch.tensor([[1, 1], [3, 5]], dtype=dtype, device=device), ) rows = ri([[0, 0], [1, 2]]) columns = ri([1, 0]) self.assertEqual( reference[rows, columns], torch.tensor([[2, 1], [4, 5]], dtype=dtype, device=device), ) rows = ri([[0, 0], [1, 2]]) columns = ri([[0, 1], [1, 0]]) self.assertEqual( reference[rows, columns], torch.tensor([[1, 2], [4, 5]], dtype=dtype, device=device), ) # setting values reference[ri([0]), ri([1])] = -1 self.assertEqual( reference[ri([0]), ri([1])], torch.tensor([-1], dtype=dtype, device=device) ) reference[ri([0, 1, 2]), ri([0])] = torch.tensor( [-1, 2, -4], dtype=dtype, device=device ) self.assertEqual( reference[ri([0, 1, 2]), ri([0])], torch.tensor([-1, 2, -4], dtype=dtype, device=device), ) reference[rows, columns] = torch.tensor( [[4, 6], [2, 3]], dtype=dtype, device=device ) self.assertEqual( reference[rows, columns], torch.tensor([[4, 6], [2, 3]], dtype=dtype, device=device), ) # Verify still works with Transposed (i.e. non-contiguous) Tensors reference = torch.tensor( [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], dtype=dtype, device=device ).t_() # Transposed: [[0, 4, 8], # [1, 5, 9], # [2, 6, 10], # [3, 7, 11]] self.assertEqual( reference[ri([0, 1, 2]), ri([0])], torch.tensor([0, 1, 2], dtype=dtype, device=device), ) self.assertEqual( reference[ri([0, 1, 2]), ri([1])], torch.tensor([4, 5, 6], dtype=dtype, device=device), ) self.assertEqual( reference[ri([0]), ri([0])], torch.tensor([0], dtype=dtype, device=device) ) self.assertEqual( reference[ri([2]), ri([1])], torch.tensor([6], dtype=dtype, device=device) ) self.assertEqual( reference[[ri([0, 0]), ri([0, 1])]], torch.tensor([0, 4], dtype=dtype, device=device), ) self.assertEqual( reference[[ri([0, 1, 1, 0, 3]), ri([1])]], torch.tensor([4, 5, 5, 4, 7], dtype=dtype, device=device), ) self.assertEqual( reference[[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])]], torch.tensor([0, 4, 1, 1], dtype=dtype, device=device), ) rows = ri([[0, 0], [1, 2]]) columns = ([0],) self.assertEqual( reference[rows, columns], torch.tensor([[0, 0], [1, 2]], dtype=dtype, device=device), ) rows = ri([[0, 0], [1, 2]]) columns = ri([1, 0]) self.assertEqual( reference[rows, columns], torch.tensor([[4, 0], [5, 2]], dtype=dtype, device=device), ) rows = ri([[0, 0], [1, 3]]) columns = ri([[0, 1], [1, 2]]) self.assertEqual( reference[rows, columns], torch.tensor([[0, 4], [5, 11]], dtype=dtype, device=device), ) # setting values reference[ri([0]), ri([1])] = -1 self.assertEqual( reference[ri([0]), ri([1])], torch.tensor([-1], dtype=dtype, device=device) ) reference[ri([0, 1, 2]), ri([0])] = torch.tensor( [-1, 2, -4], dtype=dtype, device=device ) self.assertEqual( reference[ri([0, 1, 2]), ri([0])], torch.tensor([-1, 2, -4], dtype=dtype, device=device), ) reference[rows, columns] = torch.tensor( [[4, 6], [2, 3]], dtype=dtype, device=device ) self.assertEqual( reference[rows, columns], torch.tensor([[4, 6], [2, 3]], dtype=dtype, device=device), ) # stride != 1 # strided is [[1 3 5 7], # [9 11 13 15]] reference = torch.arange(0.0, 24, dtype=dtype, device=device).view(3, 8) strided = torch.tensor((), dtype=dtype, device=device) strided.set_(reference.storage(), 1, size=torch.Size([2, 4]), stride=[8, 2]) self.assertEqual( strided[ri([0, 1]), ri([0])], torch.tensor([1, 9], dtype=dtype, device=device), ) self.assertEqual( strided[ri([0, 1]), ri([1])], torch.tensor([3, 11], dtype=dtype, device=device), ) self.assertEqual( strided[ri([0]), ri([0])], torch.tensor([1], dtype=dtype, device=device) ) self.assertEqual( strided[ri([1]), ri([3])], torch.tensor([15], dtype=dtype, device=device) ) self.assertEqual( strided[[ri([0, 0]), ri([0, 3])]], torch.tensor([1, 7], dtype=dtype, device=device), ) self.assertEqual( strided[[ri([1]), ri([0, 1, 1, 0, 3])]], torch.tensor([9, 11, 11, 9, 15], dtype=dtype, device=device), ) self.assertEqual( strided[[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])]], torch.tensor([1, 3, 9, 9], dtype=dtype, device=device), ) rows = ri([[0, 0], [1, 1]]) columns = ([0],) self.assertEqual( strided[rows, columns], torch.tensor([[1, 1], [9, 9]], dtype=dtype, device=device), ) rows = ri([[0, 1], [1, 0]]) columns = ri([1, 2]) self.assertEqual( strided[rows, columns], torch.tensor([[3, 13], [11, 5]], dtype=dtype, device=device), ) rows = ri([[0, 0], [1, 1]]) columns = ri([[0, 1], [1, 2]]) self.assertEqual( strided[rows, columns], torch.tensor([[1, 3], [11, 13]], dtype=dtype, device=device), ) # setting values # strided is [[10, 11], # [17, 18]] reference = torch.arange(0.0, 24, dtype=dtype, device=device).view(3, 8) strided = torch.tensor((), dtype=dtype, device=device) strided.set_(reference.storage(), 10, size=torch.Size([2, 2]), stride=[7, 1]) self.assertEqual( strided[ri([0]), ri([1])], torch.tensor([11], dtype=dtype, device=device) ) strided[ri([0]), ri([1])] = -1 self.assertEqual( strided[ri([0]), ri([1])], torch.tensor([-1], dtype=dtype, device=device) ) reference = torch.arange(0.0, 24, dtype=dtype, device=device).view(3, 8) strided = torch.tensor((), dtype=dtype, device=device) strided.set_(reference.storage(), 10, size=torch.Size([2, 2]), stride=[7, 1]) self.assertEqual( strided[ri([0, 1]), ri([1, 0])], torch.tensor([11, 17], dtype=dtype, device=device), ) strided[ri([0, 1]), ri([1, 0])] = torch.tensor( [-1, 2], dtype=dtype, device=device ) self.assertEqual( strided[ri([0, 1]), ri([1, 0])], torch.tensor([-1, 2], dtype=dtype, device=device), ) reference = torch.arange(0.0, 24, dtype=dtype, device=device).view(3, 8) strided = torch.tensor((), dtype=dtype, device=device) strided.set_(reference.storage(), 10, size=torch.Size([2, 2]), stride=[7, 1]) rows = ri([[0], [1]]) columns = ri([[0, 1], [0, 1]]) self.assertEqual( strided[rows, columns], torch.tensor([[10, 11], [17, 18]], dtype=dtype, device=device), ) strided[rows, columns] = torch.tensor( [[4, 6], [2, 3]], dtype=dtype, device=device ) self.assertEqual( strided[rows, columns], torch.tensor([[4, 6], [2, 3]], dtype=dtype, device=device), ) # Tests using less than the number of dims, and ellipsis # reference is 1 2 # 3 4 # 5 6 reference = consec((3, 2)) self.assertEqual( reference[ri([0, 2]),], torch.tensor([[1, 2], [5, 6]], dtype=dtype, device=device), ) self.assertEqual( reference[ri([1]), ...], torch.tensor([[3, 4]], dtype=dtype, device=device) ) self.assertEqual( reference[..., ri([1])], torch.tensor([[2], [4], [6]], dtype=dtype, device=device), ) # verify too many indices fails with self.assertRaises(IndexError): reference[ri([1]), ri([0, 2]), ri([3])] # test invalid index fails reference = torch.empty(10, dtype=dtype, device=device) # can't test cuda because it is a device assert if not reference.is_cuda: for err_idx in (10, -11): with self.assertRaisesRegex(IndexError, r"out of"): reference[err_idx] with self.assertRaisesRegex(IndexError, r"out of"): reference[torch.LongTensor([err_idx]).to(device)] with self.assertRaisesRegex(IndexError, r"out of"): reference[[err_idx]] def tensor_indices_to_np(tensor, indices): # convert the Torch Tensor to a numpy array tensor = tensor.to(device="cpu") npt = tensor.numpy() # convert indices idxs = tuple( i.tolist() if isinstance(i, torch.LongTensor) else i for i in indices ) return npt, idxs def get_numpy(tensor, indices): npt, idxs = tensor_indices_to_np(tensor, indices) # index and return as a Torch Tensor return torch.tensor(npt[idxs], dtype=dtype, device=device) def set_numpy(tensor, indices, value): if not isinstance(value, int): if self.device_type != "cpu": value = value.cpu() value = value.numpy() npt, idxs = tensor_indices_to_np(tensor, indices) npt[idxs] = value return npt def assert_get_eq(tensor, indexer): self.assertEqual(tensor[indexer], get_numpy(tensor, indexer)) def assert_set_eq(tensor, indexer, val): pyt = tensor.clone() numt = tensor.clone() pyt[indexer] = val numt = torch.tensor( set_numpy(numt, indexer, val), dtype=dtype, device=device ) self.assertEqual(pyt, numt) def assert_backward_eq(tensor, indexer): cpu = tensor.float().clone().detach().requires_grad_(True) outcpu = cpu[indexer] gOcpu = torch.rand_like(outcpu) outcpu.backward(gOcpu) dev = cpu.to(device).detach().requires_grad_(True) outdev = dev[indexer] outdev.backward(gOcpu.to(device)) self.assertEqual(cpu.grad, dev.grad) def get_set_tensor(indexed, indexer): set_size = indexed[indexer].size() set_count = indexed[indexer].numel() set_tensor = torch.randperm(set_count).view(set_size).double().to(device) return set_tensor # Tensor is 0 1 2 3 4 # 5 6 7 8 9 # 10 11 12 13 14 # 15 16 17 18 19 reference = torch.arange(0.0, 20, dtype=dtype, device=device).view(4, 5) indices_to_test = [ # grab the second, fourth columns [slice(None), [1, 3]], # first, third rows, [[0, 2], slice(None)], # weird shape [slice(None), [[0, 1], [2, 3]]], # negatives [[-1], [0]], [[0, 2], [-1]], [slice(None), [-1]], ] # only test dupes on gets get_indices_to_test = indices_to_test + [[slice(None), [0, 1, 1, 2, 2]]] for indexer in get_indices_to_test: assert_get_eq(reference, indexer) if self.device_type != "cpu": assert_backward_eq(reference, indexer) for indexer in indices_to_test: assert_set_eq(reference, indexer, 44) assert_set_eq(reference, indexer, get_set_tensor(reference, indexer)) reference = torch.arange(0.0, 160, dtype=dtype, device=device).view(4, 8, 5) indices_to_test = [ [slice(None), slice(None), [0, 3, 4]], [slice(None), [2, 4, 5, 7], slice(None)], [[2, 3], slice(None), slice(None)], [slice(None), [0, 2, 3], [1, 3, 4]], [slice(None), [0], [1, 2, 4]], [slice(None), [0, 1, 3], [4]], [slice(None), [[0, 1], [1, 0]], [[2, 3]]], [slice(None), [[0, 1], [2, 3]], [[0]]], [slice(None), [[5, 6]], [[0, 3], [4, 4]]], [[0, 2, 3], [1, 3, 4], slice(None)], [[0], [1, 2, 4], slice(None)], [[0, 1, 3], [4], slice(None)], [[[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None)], [[[0, 1], [1, 0]], [[2, 3]], slice(None)], [[[0, 1], [2, 3]], [[0]], slice(None)], [[[2, 1]], [[0, 3], [4, 4]], slice(None)], [[[2]], [[0, 3], [4, 1]], slice(None)], # non-contiguous indexing subspace [[0, 2, 3], slice(None), [1, 3, 4]], # [...] # less dim, ellipsis [[0, 2]], [[0, 2], slice(None)], [[0, 2], Ellipsis], [[0, 2], slice(None), Ellipsis], [[0, 2], Ellipsis, slice(None)], [[0, 2], [1, 3]], [[0, 2], [1, 3], Ellipsis], [Ellipsis, [1, 3], [2, 3]], [Ellipsis, [2, 3, 4]], [Ellipsis, slice(None), [2, 3, 4]], [slice(None), Ellipsis, [2, 3, 4]], # ellipsis counts for nothing [Ellipsis, slice(None), slice(None), [0, 3, 4]], [slice(None), Ellipsis, slice(None), [0, 3, 4]], [slice(None), slice(None), Ellipsis, [0, 3, 4]], [slice(None), slice(None), [0, 3, 4], Ellipsis], [Ellipsis, [[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None)], [[[0, 1], [1, 0]], [[2, 1], [3, 5]], Ellipsis, slice(None)], [[[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None), Ellipsis], ] for indexer in indices_to_test: assert_get_eq(reference, indexer) assert_set_eq(reference, indexer, 212) assert_set_eq(reference, indexer, get_set_tensor(reference, indexer)) if torch.cuda.is_available(): assert_backward_eq(reference, indexer) reference = torch.arange(0.0, 1296, dtype=dtype, device=device).view(3, 9, 8, 6) indices_to_test = [ [slice(None), slice(None), slice(None), [0, 3, 4]], [slice(None), slice(None), [2, 4, 5, 7], slice(None)], [slice(None), [2, 3], slice(None), slice(None)], [[1, 2], slice(None), slice(None), slice(None)], [slice(None), slice(None), [0, 2, 3], [1, 3, 4]], [slice(None), slice(None), [0], [1, 2, 4]], [slice(None), slice(None), [0, 1, 3], [4]], [slice(None), slice(None), [[0, 1], [1, 0]], [[2, 3]]], [slice(None), slice(None), [[0, 1], [2, 3]], [[0]]], [slice(None), slice(None), [[5, 6]], [[0, 3], [4, 4]]], [slice(None), [0, 2, 3], [1, 3, 4], slice(None)], [slice(None), [0], [1, 2, 4], slice(None)], [slice(None), [0, 1, 3], [4], slice(None)], [slice(None), [[0, 1], [3, 4]], [[2, 3], [0, 1]], slice(None)], [slice(None), [[0, 1], [3, 4]], [[2, 3]], slice(None)], [slice(None), [[0, 1], [3, 2]], [[0]], slice(None)], [slice(None), [[2, 1]], [[0, 3], [6, 4]], slice(None)], [slice(None), [[2]], [[0, 3], [4, 2]], slice(None)], [[0, 1, 2], [1, 3, 4], slice(None), slice(None)], [[0], [1, 2, 4], slice(None), slice(None)], [[0, 1, 2], [4], slice(None), slice(None)], [[[0, 1], [0, 2]], [[2, 4], [1, 5]], slice(None), slice(None)], [[[0, 1], [1, 2]], [[2, 0]], slice(None), slice(None)], [[[2, 2]], [[0, 3], [4, 5]], slice(None), slice(None)], [[[2]], [[0, 3], [4, 5]], slice(None), slice(None)], [slice(None), [3, 4, 6], [0, 2, 3], [1, 3, 4]], [slice(None), [2, 3, 4], [1, 3, 4], [4]], [slice(None), [0, 1, 3], [4], [1, 3, 4]], [slice(None), [6], [0, 2, 3], [1, 3, 4]], [slice(None), [2, 3, 5], [3], [4]], [slice(None), [0], [4], [1, 3, 4]], [slice(None), [6], [0, 2, 3], [1]], [slice(None), [[0, 3], [3, 6]], [[0, 1], [1, 3]], [[5, 3], [1, 2]]], [[2, 2, 1], [0, 2, 3], [1, 3, 4], slice(None)], [[2, 0, 1], [1, 2, 3], [4], slice(None)], [[0, 1, 2], [4], [1, 3, 4], slice(None)], [[0], [0, 2, 3], [1, 3, 4], slice(None)], [[0, 2, 1], [3], [4], slice(None)], [[0], [4], [1, 3, 4], slice(None)], [[1], [0, 2, 3], [1], slice(None)], [[[1, 2], [1, 2]], [[0, 1], [2, 3]], [[2, 3], [3, 5]], slice(None)], # less dim, ellipsis [Ellipsis, [0, 3, 4]], [Ellipsis, slice(None), [0, 3, 4]], [Ellipsis, slice(None), slice(None), [0, 3, 4]], [slice(None), Ellipsis, [0, 3, 4]], [slice(None), slice(None), Ellipsis, [0, 3, 4]], [slice(None), [0, 2, 3], [1, 3, 4]], [slice(None), [0, 2, 3], [1, 3, 4], Ellipsis], [Ellipsis, [0, 2, 3], [1, 3, 4], slice(None)], [[0], [1, 2, 4]], [[0], [1, 2, 4], slice(None)], [[0], [1, 2, 4], Ellipsis], [[0], [1, 2, 4], Ellipsis, slice(None)], [[1]], [[0, 2, 1], [3], [4]], [[0, 2, 1], [3], [4], slice(None)], [[0, 2, 1], [3], [4], Ellipsis], [Ellipsis, [0, 2, 1], [3], [4]], ] for indexer in indices_to_test: assert_get_eq(reference, indexer) assert_set_eq(reference, indexer, 1333) assert_set_eq(reference, indexer, get_set_tensor(reference, indexer)) indices_to_test += [ [slice(None), slice(None), [[0, 1], [1, 0]], [[2, 3], [3, 0]]], [slice(None), slice(None), [[2]], [[0, 3], [4, 4]]], ] for indexer in indices_to_test: assert_get_eq(reference, indexer) assert_set_eq(reference, indexer, 1333) if self.device_type != "cpu": assert_backward_eq(reference, indexer) def test_advancedindex_big(self, device): reference = torch.arange(0, 123344, dtype=torch.int, device=device) self.assertEqual( reference[[0, 123, 44488, 68807, 123343],], torch.tensor([0, 123, 44488, 68807, 123343], dtype=torch.int), ) def test_set_item_to_scalar_tensor(self, device): m = random.randint(1, 10) n = random.randint(1, 10) z = torch.randn([m, n], device=device) a = 1.0 w = torch.tensor(a, requires_grad=True, device=device) z[:, 0] = w z.sum().backward() self.assertEqual(w.grad, m * a) def test_single_int(self, device): v = torch.randn(5, 7, 3, device=device) self.assertEqual(v[4].shape, (7, 3)) def test_multiple_int(self, device): v = torch.randn(5, 7, 3, device=device) self.assertEqual(v[4].shape, (7, 3)) self.assertEqual(v[4, :, 1].shape, (7,)) def test_none(self, device): v = torch.randn(5, 7, 3, device=device) self.assertEqual(v[None].shape, (1, 5, 7, 3)) self.assertEqual(v[:, None].shape, (5, 1, 7, 3)) self.assertEqual(v[:, None, None].shape, (5, 1, 1, 7, 3)) self.assertEqual(v[..., None].shape, (5, 7, 3, 1)) def test_step(self, device): v = torch.arange(10, device=device) self.assertEqual(v[::1], v) self.assertEqual(v[::2].tolist(), [0, 2, 4, 6, 8]) self.assertEqual(v[::3].tolist(), [0, 3, 6, 9]) self.assertEqual(v[::11].tolist(), [0]) self.assertEqual(v[1:6:2].tolist(), [1, 3, 5]) def test_step_assignment(self, device): v = torch.zeros(4, 4, device=device) v[0, 1::2] = torch.tensor([3.0, 4.0], device=device) self.assertEqual(v[0].tolist(), [0, 3, 0, 4]) self.assertEqual(v[1:].sum(), 0) def test_bool_indices(self, device): v = torch.randn(5, 7, 3, device=device) boolIndices = torch.tensor( [True, False, True, True, False], dtype=torch.bool, device=device ) self.assertEqual(v[boolIndices].shape, (3, 7, 3)) self.assertEqual(v[boolIndices], torch.stack([v[0], v[2], v[3]])) v = torch.tensor([True, False, True], dtype=torch.bool, device=device) boolIndices = torch.tensor( [True, False, False], dtype=torch.bool, device=device ) uint8Indices = torch.tensor([1, 0, 0], dtype=torch.uint8, device=device) with warnings.catch_warnings(record=True) as w: v1 = v[boolIndices] v2 = v[uint8Indices] self.assertEqual(v1.shape, v2.shape) self.assertEqual(v1, v2) self.assertEqual( v[boolIndices], tensor([True], dtype=torch.bool, device=device) ) self.assertEqual(len(w), 1) def test_bool_indices_accumulate(self, device): mask = torch.zeros(size=(10,), dtype=torch.bool, device=device) y = torch.ones(size=(10, 10), device=device) y.index_put_((mask,), y[mask], accumulate=True) self.assertEqual(y, torch.ones(size=(10, 10), device=device)) def test_multiple_bool_indices(self, device): v = torch.randn(5, 7, 3, device=device) # note: these broadcast together and are transposed to the first dim mask1 = torch.tensor([1, 0, 1, 1, 0], dtype=torch.bool, device=device) mask2 = torch.tensor([1, 1, 1], dtype=torch.bool, device=device) self.assertEqual(v[mask1, :, mask2].shape, (3, 7)) def test_byte_mask(self, device): v = torch.randn(5, 7, 3, device=device) mask = torch.ByteTensor([1, 0, 1, 1, 0]).to(device) with warnings.catch_warnings(record=True) as w: res = v[mask] self.assertEqual(res.shape, (3, 7, 3)) self.assertEqual(res, torch.stack([v[0], v[2], v[3]])) self.assertEqual(len(w), 1) v = torch.tensor([1.0], device=device) self.assertEqual(v[v == 0], torch.tensor([], device=device)) def test_byte_mask_accumulate(self, device): mask = torch.zeros(size=(10,), dtype=torch.uint8, device=device) y = torch.ones(size=(10, 10), device=device) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") y.index_put_((mask,), y[mask], accumulate=True) self.assertEqual(y, torch.ones(size=(10, 10), device=device)) self.assertEqual(len(w), 2) @skipIfTorchDynamo( "This test causes SIGKILL when running with dynamo, https://github.com/pytorch/pytorch/issues/88472" ) @serialTest(TEST_CUDA) def test_index_put_accumulate_large_tensor(self, device): # This test is for tensors with number of elements >= INT_MAX (2^31 - 1). N = (1 << 31) + 5 dt = torch.int8 a = torch.ones(N, dtype=dt, device=device) indices = torch.tensor( [-2, 0, -2, -1, 0, -1, 1], device=device, dtype=torch.long ) values = torch.tensor([6, 5, 6, 6, 5, 7, 11], dtype=dt, device=device) a.index_put_((indices,), values, accumulate=True) self.assertEqual(a[0], 11) self.assertEqual(a[1], 12) self.assertEqual(a[2], 1) self.assertEqual(a[-3], 1) self.assertEqual(a[-2], 13) self.assertEqual(a[-1], 14) a = torch.ones((2, N), dtype=dt, device=device) indices0 = torch.tensor([0, -1, 0, 1], device=device, dtype=torch.long) indices1 = torch.tensor([-2, -1, 0, 1], device=device, dtype=torch.long) values = torch.tensor([12, 13, 10, 11], dtype=dt, device=device) a.index_put_((indices0, indices1), values, accumulate=True) self.assertEqual(a[0, 0], 11) self.assertEqual(a[0, 1], 1) self.assertEqual(a[1, 0], 1) self.assertEqual(a[1, 1], 12) self.assertEqual(a[:, 2], torch.ones(2, dtype=torch.int8)) self.assertEqual(a[:, -3], torch.ones(2, dtype=torch.int8)) self.assertEqual(a[0, -2], 13) self.assertEqual(a[1, -2], 1) self.assertEqual(a[-1, -1], 14) self.assertEqual(a[0, -1], 1) @onlyNativeDeviceTypes def test_index_put_accumulate_expanded_values(self, device): # checks the issue with cuda: https://github.com/pytorch/pytorch/issues/39227 # and verifies consistency with CPU result t = torch.zeros((5, 2)) t_dev = t.to(device) indices = [torch.tensor([0, 1, 2, 3]), torch.tensor([1])] indices_dev = [i.to(device) for i in indices] values0d = torch.tensor(1.0) values1d = torch.tensor([1.0]) out_cuda = t_dev.index_put_(indices_dev, values0d.to(device), accumulate=True) out_cpu = t.index_put_(indices, values0d, accumulate=True) self.assertEqual(out_cuda.cpu(), out_cpu) out_cuda = t_dev.index_put_(indices_dev, values1d.to(device), accumulate=True) out_cpu = t.index_put_(indices, values1d, accumulate=True) self.assertEqual(out_cuda.cpu(), out_cpu) t = torch.zeros(4, 3, 2) t_dev = t.to(device) indices = [ torch.tensor([0]), torch.arange(3)[:, None], torch.arange(2)[None, :], ] indices_dev = [i.to(device) for i in indices] values1d = torch.tensor([-1.0, -2.0]) values2d = torch.tensor([[-1.0, -2.0]]) out_cuda = t_dev.index_put_(indices_dev, values1d.to(device), accumulate=True) out_cpu = t.index_put_(indices, values1d, accumulate=True) self.assertEqual(out_cuda.cpu(), out_cpu) out_cuda = t_dev.index_put_(indices_dev, values2d.to(device), accumulate=True) out_cpu = t.index_put_(indices, values2d, accumulate=True) self.assertEqual(out_cuda.cpu(), out_cpu) @onlyCUDA def test_index_put_accumulate_non_contiguous(self, device): t = torch.zeros((5, 2, 2)) t_dev = t.to(device) t1 = t_dev[:, 0, :] t2 = t[:, 0, :] self.assertTrue(not t1.is_contiguous()) self.assertTrue(not t2.is_contiguous()) indices = [torch.tensor([0, 1])] indices_dev = [i.to(device) for i in indices] value = torch.randn(2, 2) out_cuda = t1.index_put_(indices_dev, value.to(device), accumulate=True) out_cpu = t2.index_put_(indices, value, accumulate=True) self.assertTrue(not t1.is_contiguous()) self.assertTrue(not t2.is_contiguous()) self.assertEqual(out_cuda.cpu(), out_cpu) @onlyCUDA @skipIfTorchDynamo("Not a suitable test for TorchDynamo") def test_index_put_accumulate_with_optional_tensors(self, device): # TODO: replace with a better solution. # Currently, here using torchscript to put None into indices. # on C++ it gives indices as a list of 2 optional tensors: first is null and # the second is a valid tensor. @torch.jit.script def func(x, i, v): idx = [None, i] x.index_put_(idx, v, accumulate=True) return x n = 4 t = torch.arange(n * 2, dtype=torch.float32).reshape(n, 2) t_dev = t.to(device) indices = torch.tensor([1, 0]) indices_dev = indices.to(device) value0d = torch.tensor(10.0) value1d = torch.tensor([1.0, 2.0]) out_cuda = func(t_dev, indices_dev, value0d.cuda()) out_cpu = func(t, indices, value0d) self.assertEqual(out_cuda.cpu(), out_cpu) out_cuda = func(t_dev, indices_dev, value1d.cuda()) out_cpu = func(t, indices, value1d) self.assertEqual(out_cuda.cpu(), out_cpu) @onlyNativeDeviceTypes def test_index_put_accumulate_duplicate_indices(self, device): for i in range(1, 512): # generate indices by random walk, this will create indices with # lots of duplicates interleaved with each other delta = torch.empty(i, dtype=torch.double, device=device).uniform_(-1, 1) indices = delta.cumsum(0).long() input = torch.randn(indices.abs().max() + 1, device=device) values = torch.randn(indices.size(0), device=device) output = input.index_put((indices,), values, accumulate=True) input_list = input.tolist() indices_list = indices.tolist() values_list = values.tolist() for i, v in zip(indices_list, values_list): input_list[i] += v self.assertEqual(output, input_list) @onlyNativeDeviceTypes def test_index_ind_dtype(self, device): x = torch.randn(4, 4, device=device) ind_long = torch.randint(4, (4,), dtype=torch.long, device=device) ind_int = ind_long.int() src = torch.randn(4, device=device) ref = x[ind_long, ind_long] res = x[ind_int, ind_int] self.assertEqual(ref, res) ref = x[ind_long, :] res = x[ind_int, :] self.assertEqual(ref, res) ref = x[:, ind_long] res = x[:, ind_int] self.assertEqual(ref, res) # no repeating indices for index_put ind_long = torch.arange(4, dtype=torch.long, device=device) ind_int = ind_long.int() for accum in (True, False): inp_ref = x.clone() inp_res = x.clone() torch.index_put_(inp_ref, (ind_long, ind_long), src, accum) torch.index_put_(inp_res, (ind_int, ind_int), src, accum) self.assertEqual(inp_ref, inp_res) @skipXLA def test_index_put_accumulate_empty(self, device): # Regression test for https://github.com/pytorch/pytorch/issues/94667 input = torch.rand([], dtype=torch.float32, device=device) with self.assertRaises(RuntimeError): input.index_put([], torch.tensor([1.0], device=device), True) def test_multiple_byte_mask(self, device): v = torch.randn(5, 7, 3, device=device) # note: these broadcast together and are transposed to the first dim mask1 = torch.ByteTensor([1, 0, 1, 1, 0]).to(device) mask2 = torch.ByteTensor([1, 1, 1]).to(device) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") self.assertEqual(v[mask1, :, mask2].shape, (3, 7)) self.assertEqual(len(w), 2) def test_byte_mask2d(self, device): v = torch.randn(5, 7, 3, device=device) c = torch.randn(5, 7, device=device) num_ones = (c > 0).sum() r = v[c > 0] self.assertEqual(r.shape, (num_ones, 3)) @skipIfTorchDynamo("Not a suitable test for TorchDynamo") def test_jit_indexing(self, device): def fn1(x): x[x < 50] = 1.0 return x def fn2(x): x[0:50] = 1.0 return x scripted_fn1 = torch.jit.script(fn1) scripted_fn2 = torch.jit.script(fn2) data = torch.arange(100, device=device, dtype=torch.float) out = scripted_fn1(data.detach().clone()) ref = torch.tensor( np.concatenate((np.ones(50), np.arange(50, 100))), device=device, dtype=torch.float, ) self.assertEqual(out, ref) out = scripted_fn2(data.detach().clone()) self.assertEqual(out, ref) def test_int_indices(self, device): v = torch.randn(5, 7, 3, device=device) self.assertEqual(v[[0, 4, 2]].shape, (3, 7, 3)) self.assertEqual(v[:, [0, 4, 2]].shape, (5, 3, 3)) self.assertEqual(v[:, [[0, 1], [4, 3]]].shape, (5, 2, 2, 3)) @dtypes( torch.cfloat, torch.cdouble, torch.float, torch.bfloat16, torch.long, torch.bool ) @dtypesIfCPU( torch.cfloat, torch.cdouble, torch.float, torch.long, torch.bool, torch.bfloat16 ) @dtypesIfCUDA( torch.cfloat, torch.cdouble, torch.half, torch.long, torch.bool, torch.bfloat16, torch.float8_e5m2, torch.float8_e4m3fn, ) def test_index_put_src_datatype(self, device, dtype): src = torch.ones(3, 2, 4, device=device, dtype=dtype) vals = torch.ones(3, 2, 4, device=device, dtype=dtype) indices = (torch.tensor([0, 2, 1]),) res = src.index_put_(indices, vals, accumulate=True) self.assertEqual(res.shape, src.shape) @dtypes(torch.float, torch.bfloat16, torch.long, torch.bool) @dtypesIfCPU(torch.float, torch.long, torch.bfloat16, torch.bool) @dtypesIfCUDA(torch.half, torch.long, torch.bfloat16, torch.bool) def test_index_src_datatype(self, device, dtype): src = torch.ones(3, 2, 4, device=device, dtype=dtype) # test index res = src[[0, 2, 1], :, :] self.assertEqual(res.shape, src.shape) # test index_put, no accum src[[0, 2, 1], :, :] = res self.assertEqual(res.shape, src.shape) def test_int_indices2d(self, device): # From the NumPy indexing example x = torch.arange(0, 12, device=device).view(4, 3) rows = torch.tensor([[0, 0], [3, 3]], device=device) columns = torch.tensor([[0, 2], [0, 2]], device=device) self.assertEqual(x[rows, columns].tolist(), [[0, 2], [9, 11]]) def test_int_indices_broadcast(self, device): # From the NumPy indexing example x = torch.arange(0, 12, device=device).view(4, 3) rows = torch.tensor([0, 3], device=device) columns = torch.tensor([0, 2], device=device) result = x[rows[:, None], columns] self.assertEqual(result.tolist(), [[0, 2], [9, 11]]) def test_empty_index(self, device): x = torch.arange(0, 12, device=device).view(4, 3) idx = torch.tensor([], dtype=torch.long, device=device) self.assertEqual(x[idx].numel(), 0) # empty assignment should have no effect but not throw an exception y = x.clone() y[idx] = -1 self.assertEqual(x, y) mask = torch.zeros(4, 3, device=device).bool() y[mask] = -1 self.assertEqual(x, y) def test_empty_ndim_index(self, device): x = torch.randn(5, device=device) self.assertEqual( torch.empty(0, 2, device=device), x[torch.empty(0, 2, dtype=torch.int64, device=device)], ) x = torch.randn(2, 3, 4, 5, device=device) self.assertEqual( torch.empty(2, 0, 6, 4, 5, device=device), x[:, torch.empty(0, 6, dtype=torch.int64, device=device)], ) x = torch.empty(10, 0, device=device) self.assertEqual(x[[1, 2]].shape, (2, 0)) self.assertEqual(x[[], []].shape, (0,)) with self.assertRaisesRegex(IndexError, "for dimension with size 0"): x[:, [0, 1]] def test_empty_ndim_index_bool(self, device): x = torch.randn(5, device=device) self.assertRaises( IndexError, lambda: x[torch.empty(0, 2, dtype=torch.uint8, device=device)] ) def test_empty_slice(self, device): x = torch.randn(2, 3, 4, 5, device=device) y = x[:, :, :, 1] z = y[:, 1:1, :] self.assertEqual((2, 0, 4), z.shape) # this isn't technically necessary, but matches NumPy stride calculations. self.assertEqual((60, 20, 5), z.stride()) self.assertTrue(z.is_contiguous()) def test_index_getitem_copy_bools_slices(self, device): true = torch.tensor(1, dtype=torch.uint8, device=device) false = torch.tensor(0, dtype=torch.uint8, device=device) tensors = [torch.randn(2, 3, device=device), torch.tensor(3.0, device=device)] for a in tensors: self.assertNotEqual(a.data_ptr(), a[True].data_ptr()) self.assertEqual(torch.empty(0, *a.shape), a[False]) self.assertNotEqual(a.data_ptr(), a[true].data_ptr()) self.assertEqual(torch.empty(0, *a.shape), a[false]) self.assertEqual(a.data_ptr(), a[None].data_ptr()) self.assertEqual(a.data_ptr(), a[...].data_ptr()) def test_index_setitem_bools_slices(self, device): true = torch.tensor(1, dtype=torch.uint8, device=device) false = torch.tensor(0, dtype=torch.uint8, device=device) tensors = [torch.randn(2, 3, device=device), torch.tensor(3, device=device)] for a in tensors: # prefix with a 1,1, to ensure we are compatible with numpy which cuts off prefix 1s # (some of these ops already prefix a 1 to the size) neg_ones = torch.ones_like(a) * -1 neg_ones_expanded = neg_ones.unsqueeze(0).unsqueeze(0) a[True] = neg_ones_expanded self.assertEqual(a, neg_ones) a[False] = 5 self.assertEqual(a, neg_ones) a[true] = neg_ones_expanded * 2 self.assertEqual(a, neg_ones * 2) a[false] = 5 self.assertEqual(a, neg_ones * 2) a[None] = neg_ones_expanded * 3 self.assertEqual(a, neg_ones * 3) a[...] = neg_ones_expanded * 4 self.assertEqual(a, neg_ones * 4) if a.dim() == 0: with self.assertRaises(IndexError): a[:] = neg_ones_expanded * 5 def test_index_scalar_with_bool_mask(self, device): a = torch.tensor(1, device=device) uintMask = torch.tensor(True, dtype=torch.uint8, device=device) boolMask = torch.tensor(True, dtype=torch.bool, device=device) self.assertEqual(a[uintMask], a[boolMask]) self.assertEqual(a[uintMask].dtype, a[boolMask].dtype) a = torch.tensor(True, dtype=torch.bool, device=device) self.assertEqual(a[uintMask], a[boolMask]) self.assertEqual(a[uintMask].dtype, a[boolMask].dtype) def test_setitem_expansion_error(self, device): true = torch.tensor(True, device=device) a = torch.randn(2, 3, device=device) # check prefix with non-1s doesn't work a_expanded = a.expand(torch.Size([5, 1]) + a.size()) # NumPy: ValueError with self.assertRaises(RuntimeError): a[True] = a_expanded with self.assertRaises(RuntimeError): a[true] = a_expanded def test_getitem_scalars(self, device): zero = torch.tensor(0, dtype=torch.int64, device=device) one = torch.tensor(1, dtype=torch.int64, device=device) # non-scalar indexed with scalars a = torch.randn(2, 3, device=device) self.assertEqual(a[0], a[zero]) self.assertEqual(a[0][1], a[zero][one]) self.assertEqual(a[0, 1], a[zero, one]) self.assertEqual(a[0, one], a[zero, 1]) # indexing by a scalar should slice (not copy) self.assertEqual(a[0, 1].data_ptr(), a[zero, one].data_ptr()) self.assertEqual(a[1].data_ptr(), a[one.int()].data_ptr()) self.assertEqual(a[1].data_ptr(), a[one.short()].data_ptr()) # scalar indexed with scalar r = torch.randn((), device=device) with self.assertRaises(IndexError): r[:] with self.assertRaises(IndexError): r[zero] self.assertEqual(r, r[...]) def test_setitem_scalars(self, device): zero = torch.tensor(0, dtype=torch.int64) # non-scalar indexed with scalars a = torch.randn(2, 3, device=device) a_set_with_number = a.clone() a_set_with_scalar = a.clone() b = torch.randn(3, device=device) a_set_with_number[0] = b a_set_with_scalar[zero] = b self.assertEqual(a_set_with_number, a_set_with_scalar) a[1, zero] = 7.7 self.assertEqual(7.7, a[1, 0]) # scalar indexed with scalars r = torch.randn((), device=device) with self.assertRaises(IndexError): r[:] = 8.8 with self.assertRaises(IndexError): r[zero] = 8.8 r[...] = 9.9 self.assertEqual(9.9, r) def test_basic_advanced_combined(self, device): # From the NumPy indexing example x = torch.arange(0, 12, device=device).view(4, 3) self.assertEqual(x[1:2, 1:3], x[1:2, [1, 2]]) self.assertEqual(x[1:2, 1:3].tolist(), [[4, 5]]) # Check that it is a copy unmodified = x.clone() x[1:2, [1, 2]].zero_() self.assertEqual(x, unmodified) # But assignment should modify the original unmodified = x.clone() x[1:2, [1, 2]] = 0 self.assertNotEqual(x, unmodified) def test_int_assignment(self, device): x = torch.arange(0, 4, device=device).view(2, 2) x[1] = 5 self.assertEqual(x.tolist(), [[0, 1], [5, 5]]) x = torch.arange(0, 4, device=device).view(2, 2) x[1] = torch.arange(5, 7, device=device) self.assertEqual(x.tolist(), [[0, 1], [5, 6]]) def test_byte_tensor_assignment(self, device): x = torch.arange(0.0, 16, device=device).view(4, 4) b = torch.ByteTensor([True, False, True, False]).to(device) value = torch.tensor([3.0, 4.0, 5.0, 6.0], device=device) with warnings.catch_warnings(record=True) as w: x[b] = value self.assertEqual(len(w), 1) self.assertEqual(x[0], value) self.assertEqual(x[1], torch.arange(4.0, 8, device=device)) self.assertEqual(x[2], value) self.assertEqual(x[3], torch.arange(12.0, 16, device=device)) def test_variable_slicing(self, device): x = torch.arange(0, 16, device=device).view(4, 4) indices = torch.IntTensor([0, 1]).to(device) i, j = indices self.assertEqual(x[i:j], x[0:1]) def test_ellipsis_tensor(self, device): x = torch.arange(0, 9, device=device).view(3, 3) idx = torch.tensor([0, 2], device=device) self.assertEqual(x[..., idx].tolist(), [[0, 2], [3, 5], [6, 8]]) self.assertEqual(x[idx, ...].tolist(), [[0, 1, 2], [6, 7, 8]]) def test_unravel_index_errors(self, device): with self.assertRaisesRegex(TypeError, r"expected 'indices' to be integer"): torch.unravel_index(torch.tensor(0.5, device=device), (2, 2)) with self.assertRaisesRegex(TypeError, r"expected 'indices' to be integer"): torch.unravel_index(torch.tensor([], device=device), (10, 3, 5)) with self.assertRaisesRegex( TypeError, r"expected 'shape' to be int or sequence" ): torch.unravel_index( torch.tensor([1], device=device, dtype=torch.int64), torch.tensor([1, 2, 3]), ) with self.assertRaisesRegex( TypeError, r"expected 'shape' sequence to only contain ints" ): torch.unravel_index( torch.tensor([1], device=device, dtype=torch.int64), (1, 2, 2.0) ) with self.assertRaisesRegex( ValueError, r"'shape' cannot have negative values, but got \(2, -3\)" ): torch.unravel_index(torch.tensor(0, device=device), (2, -3)) def test_invalid_index(self, device): x = torch.arange(0, 16, device=device).view(4, 4) self.assertRaisesRegex(TypeError, "slice indices", lambda: x["0":"1"]) def test_out_of_bound_index(self, device): x = torch.arange(0, 100, device=device).view(2, 5, 10) self.assertRaisesRegex( IndexError, "index 5 is out of bounds for dimension 1 with size 5", lambda: x[0, 5], ) self.assertRaisesRegex( IndexError, "index 4 is out of bounds for dimension 0 with size 2", lambda: x[4, 5], ) self.assertRaisesRegex( IndexError, "index 15 is out of bounds for dimension 2 with size 10", lambda: x[0, 1, 15], ) self.assertRaisesRegex( IndexError, "index 12 is out of bounds for dimension 2 with size 10", lambda: x[:, :, 12], ) def test_zero_dim_index(self, device): x = torch.tensor(10, device=device) self.assertEqual(x, x.item()) def runner(): print(x[0]) return x[0] self.assertRaisesRegex(IndexError, "invalid index", runner) @onlyCUDA def test_invalid_device(self, device): idx = torch.tensor([0, 1]) b = torch.zeros(5, device=device) c = torch.tensor([1.0, 2.0], device="cpu") for accumulate in [True, False]: self.assertRaises( RuntimeError, lambda: torch.index_put_(b, (idx,), c, accumulate=accumulate), ) @onlyCUDA def test_cpu_indices(self, device): idx = torch.tensor([0, 1]) b = torch.zeros(2, device=device) x = torch.ones(10, device=device) x[idx] = b # index_put_ ref = torch.ones(10, device=device) ref[:2] = 0 self.assertEqual(x, ref, atol=0, rtol=0) out = x[idx] # index self.assertEqual(out, torch.zeros(2, device=device), atol=0, rtol=0) @dtypes(torch.long, torch.float32) def test_take_along_dim(self, device, dtype): def _test_against_numpy(t, indices, dim): actual = torch.take_along_dim(t, indices, dim=dim) t_np = t.cpu().numpy() indices_np = indices.cpu().numpy() expected = np.take_along_axis(t_np, indices_np, axis=dim) self.assertEqual(actual, expected, atol=0, rtol=0) for shape in [(3, 2), (2, 3, 5), (2, 4, 0), (2, 3, 1, 4)]: for noncontiguous in [True, False]: t = make_tensor( shape, device=device, dtype=dtype, noncontiguous=noncontiguous ) for dim in list(range(t.ndim)) + [None]: if dim is None: indices = torch.argsort(t.view(-1)) else: indices = torch.argsort(t, dim=dim) _test_against_numpy(t, indices, dim) # test broadcasting t = torch.ones((3, 4, 1), device=device) indices = torch.ones((1, 2, 5), dtype=torch.long, device=device) _test_against_numpy(t, indices, 1) # test empty indices t = torch.ones((3, 4, 5), device=device) indices = torch.ones((3, 0, 5), dtype=torch.long, device=device) _test_against_numpy(t, indices, 1) @dtypes(torch.long, torch.float) def test_take_along_dim_invalid(self, device, dtype): shape = (2, 3, 1, 4) dim = 0 t = make_tensor(shape, device=device, dtype=dtype) indices = torch.argsort(t, dim=dim) # dim of `t` and `indices` does not match with self.assertRaisesRegex( RuntimeError, "input and indices should have the same number of dimensions" ): torch.take_along_dim(t, indices[0], dim=0) # invalid `indices` dtype with self.assertRaisesRegex(RuntimeError, r"dtype of indices should be Long"): torch.take_along_dim(t, indices.to(torch.bool), dim=0) with self.assertRaisesRegex(RuntimeError, r"dtype of indices should be Long"): torch.take_along_dim(t, indices.to(torch.float), dim=0) with self.assertRaisesRegex(RuntimeError, r"dtype of indices should be Long"): torch.take_along_dim(t, indices.to(torch.int32), dim=0) # invalid axis with self.assertRaisesRegex(IndexError, "Dimension out of range"): torch.take_along_dim(t, indices, dim=-7) with self.assertRaisesRegex(IndexError, "Dimension out of range"): torch.take_along_dim(t, indices, dim=7) @onlyCUDA @dtypes(torch.float) def test_gather_take_along_dim_cross_device(self, device, dtype): shape = (2, 3, 1, 4) dim = 0 t = make_tensor(shape, device=device, dtype=dtype) indices = torch.argsort(t, dim=dim) with self.assertRaisesRegex( RuntimeError, "Expected all tensors to be on the same device" ): torch.gather(t, 0, indices.cpu()) with self.assertRaisesRegex( RuntimeError, r"Expected tensor to have .* but got tensor with .* torch.take_along_dim()", ): torch.take_along_dim(t, indices.cpu(), dim=0) with self.assertRaisesRegex( RuntimeError, "Expected all tensors to be on the same device" ): torch.gather(t.cpu(), 0, indices) with self.assertRaisesRegex( RuntimeError, r"Expected tensor to have .* but got tensor with .* torch.take_along_dim()", ): torch.take_along_dim(t.cpu(), indices, dim=0) @onlyCUDA def test_cuda_broadcast_index_use_deterministic_algorithms(self, device): with DeterministicGuard(True): idx1 = torch.tensor([0]) idx2 = torch.tensor([2, 6]) idx3 = torch.tensor([1, 5, 7]) tensor_a = torch.rand(13, 11, 12, 13, 12).cpu() tensor_b = tensor_a.to(device=device) tensor_a[idx1] = 1.0 tensor_a[idx1, :, idx2, idx2, :] = 2.0 tensor_a[:, idx1, idx3, :, idx3] = 3.0 tensor_b[idx1] = 1.0 tensor_b[idx1, :, idx2, idx2, :] = 2.0 tensor_b[:, idx1, idx3, :, idx3] = 3.0 self.assertEqual(tensor_a, tensor_b.cpu(), atol=0, rtol=0) tensor_a = torch.rand(10, 11).cpu() tensor_b = tensor_a.to(device=device) tensor_a[idx3] = 1.0 tensor_a[idx2, :] = 2.0 tensor_a[:, idx2] = 3.0 tensor_a[:, idx1] = 4.0 tensor_b[idx3] = 1.0 tensor_b[idx2, :] = 2.0 tensor_b[:, idx2] = 3.0 tensor_b[:, idx1] = 4.0 self.assertEqual(tensor_a, tensor_b.cpu(), atol=0, rtol=0) tensor_a = torch.rand(10, 10).cpu() tensor_b = tensor_a.to(device=device) tensor_a[[8]] = 1.0 tensor_b[[8]] = 1.0 self.assertEqual(tensor_a, tensor_b.cpu(), atol=0, rtol=0) tensor_a = torch.rand(10).cpu() tensor_b = tensor_a.to(device=device) tensor_a[6] = 1.0 tensor_b[6] = 1.0 self.assertEqual(tensor_a, tensor_b.cpu(), atol=0, rtol=0) def test_index_limits(self, device): # Regression test for https://github.com/pytorch/pytorch/issues/115415 t = torch.tensor([], device=device) idx_min = torch.iinfo(torch.int64).min idx_max = torch.iinfo(torch.int64).max self.assertRaises(IndexError, lambda: t[idx_min]) self.assertRaises(IndexError, lambda: t[idx_max]) # The tests below are from NumPy test_indexing.py with some modifications to # make them compatible with PyTorch. It's licensed under the BDS license below: # # Copyright (c) 2005-2017, NumPy Developers. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # # * Neither the name of the NumPy Developers nor the names of any # contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. class NumpyTests(TestCase): def test_index_no_floats(self, device): a = torch.tensor([[[5.0]]], device=device) self.assertRaises(IndexError, lambda: a[0.0]) self.assertRaises(IndexError, lambda: a[0, 0.0]) self.assertRaises(IndexError, lambda: a[0.0, 0]) self.assertRaises(IndexError, lambda: a[0.0, :]) self.assertRaises(IndexError, lambda: a[:, 0.0]) self.assertRaises(IndexError, lambda: a[:, 0.0, :]) self.assertRaises(IndexError, lambda: a[0.0, :, :]) self.assertRaises(IndexError, lambda: a[0, 0, 0.0]) self.assertRaises(IndexError, lambda: a[0.0, 0, 0]) self.assertRaises(IndexError, lambda: a[0, 0.0, 0]) self.assertRaises(IndexError, lambda: a[-1.4]) self.assertRaises(IndexError, lambda: a[0, -1.4]) self.assertRaises(IndexError, lambda: a[-1.4, 0]) self.assertRaises(IndexError, lambda: a[-1.4, :]) self.assertRaises(IndexError, lambda: a[:, -1.4]) self.assertRaises(IndexError, lambda: a[:, -1.4, :]) self.assertRaises(IndexError, lambda: a[-1.4, :, :]) self.assertRaises(IndexError, lambda: a[0, 0, -1.4]) self.assertRaises(IndexError, lambda: a[-1.4, 0, 0]) self.assertRaises(IndexError, lambda: a[0, -1.4, 0]) # self.assertRaises(IndexError, lambda: a[0.0:, 0.0]) # self.assertRaises(IndexError, lambda: a[0.0:, 0.0,:]) def test_none_index(self, device): # `None` index adds newaxis a = tensor([1, 2, 3], device=device) self.assertEqual(a[None].dim(), a.dim() + 1) def test_empty_tuple_index(self, device): # Empty tuple index creates a view a = tensor([1, 2, 3], device=device) self.assertEqual(a[()], a) self.assertEqual(a[()].data_ptr(), a.data_ptr()) def test_empty_fancy_index(self, device): # Empty list index creates an empty array a = tensor([1, 2, 3], device=device) self.assertEqual(a[[]], torch.tensor([], dtype=torch.long, device=device)) b = tensor([], device=device).long() self.assertEqual(a[[]], torch.tensor([], dtype=torch.long, device=device)) b = tensor([], device=device).float() self.assertRaises(IndexError, lambda: a[b]) def test_ellipsis_index(self, device): a = tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], device=device) self.assertIsNot(a[...], a) self.assertEqual(a[...], a) # `a[...]` was `a` in numpy <1.9. self.assertEqual(a[...].data_ptr(), a.data_ptr()) # Slicing with ellipsis can skip an # arbitrary number of dimensions self.assertEqual(a[0, ...], a[0]) self.assertEqual(a[0, ...], a[0, :]) self.assertEqual(a[..., 0], a[:, 0]) # In NumPy, slicing with ellipsis results in a 0-dim array. In PyTorch # we don't have separate 0-dim arrays and scalars. self.assertEqual(a[0, ..., 1], torch.tensor(2, device=device)) # Assignment with `(Ellipsis,)` on 0-d arrays b = torch.tensor(1) b[(Ellipsis,)] = 2 self.assertEqual(b, 2) def test_single_int_index(self, device): # Single integer index selects one row a = tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], device=device) self.assertEqual(a[0], [1, 2, 3]) self.assertEqual(a[-1], [7, 8, 9]) # Index out of bounds produces IndexError self.assertRaises(IndexError, a.__getitem__, 1 << 30) # Index overflow produces Exception NB: different exception type self.assertRaises(Exception, a.__getitem__, 1 << 64) def test_single_bool_index(self, device): # Single boolean index a = tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], device=device) self.assertEqual(a[True], a[None]) self.assertEqual(a[False], a[None][0:0]) def test_boolean_shape_mismatch(self, device): arr = torch.ones((5, 4, 3), device=device) index = tensor([True], device=device) self.assertRaisesRegex(IndexError, "mask", lambda: arr[index]) index = tensor([False] * 6, device=device) self.assertRaisesRegex(IndexError, "mask", lambda: arr[index]) index = torch.ByteTensor(4, 4).to(device).zero_() self.assertRaisesRegex(IndexError, "mask", lambda: arr[index]) self.assertRaisesRegex(IndexError, "mask", lambda: arr[(slice(None), index)]) def test_boolean_indexing_onedim(self, device): # Indexing a 2-dimensional array with # boolean array of length one a = tensor([[0.0, 0.0, 0.0]], device=device) b = tensor([True], device=device) self.assertEqual(a[b], a) # boolean assignment a[b] = 1.0 self.assertEqual(a, tensor([[1.0, 1.0, 1.0]], device=device)) # https://github.com/pytorch/pytorch/issues/127003 @xfailIfTorchDynamo def test_boolean_assignment_value_mismatch(self, device): # A boolean assignment should fail when the shape of the values # cannot be broadcast to the subscription. (see also gh-3458) a = torch.arange(0, 4, device=device) def f(a, v): a[a > -1] = tensor(v).to(device) self.assertRaisesRegex(Exception, "shape mismatch", f, a, []) self.assertRaisesRegex(Exception, "shape mismatch", f, a, [1, 2, 3]) self.assertRaisesRegex(Exception, "shape mismatch", f, a[:1], [1, 2, 3]) def test_boolean_indexing_twodim(self, device): # Indexing a 2-dimensional array with # 2-dimensional boolean array a = tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], device=device) b = tensor( [[True, False, True], [False, True, False], [True, False, True]], device=device, ) self.assertEqual(a[b], tensor([1, 3, 5, 7, 9], device=device)) self.assertEqual(a[b[1]], tensor([[4, 5, 6]], device=device)) self.assertEqual(a[b[0]], a[b[2]]) # boolean assignment a[b] = 0 self.assertEqual(a, tensor([[0, 2, 0], [4, 0, 6], [0, 8, 0]], device=device)) def test_boolean_indexing_weirdness(self, device): # Weird boolean indexing things a = torch.ones((2, 3, 4), device=device) self.assertEqual((0, 2, 3, 4), a[False, True, ...].shape) self.assertEqual( torch.ones(1, 2, device=device), a[True, [0, 1], True, True, [1], [[2]]] ) self.assertRaises(IndexError, lambda: a[False, [0, 1], ...]) def test_boolean_indexing_weirdness_tensors(self, device): # Weird boolean indexing things false = torch.tensor(False, device=device) true = torch.tensor(True, device=device) a = torch.ones((2, 3, 4), device=device) self.assertEqual((0, 2, 3, 4), a[False, True, ...].shape) self.assertEqual( torch.ones(1, 2, device=device), a[true, [0, 1], true, true, [1], [[2]]] ) self.assertRaises(IndexError, lambda: a[false, [0, 1], ...]) def test_boolean_indexing_alldims(self, device): true = torch.tensor(True, device=device) a = torch.ones((2, 3), device=device) self.assertEqual((1, 2, 3), a[True, True].shape) self.assertEqual((1, 2, 3), a[true, true].shape) def test_boolean_list_indexing(self, device): # Indexing a 2-dimensional array with # boolean lists a = tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], device=device) b = [True, False, False] c = [True, True, False] self.assertEqual(a[b], tensor([[1, 2, 3]], device=device)) self.assertEqual(a[b, b], tensor([1], device=device)) self.assertEqual(a[c], tensor([[1, 2, 3], [4, 5, 6]], device=device)) self.assertEqual(a[c, c], tensor([1, 5], device=device)) def test_everything_returns_views(self, device): # Before `...` would return a itself. a = tensor([5], device=device) self.assertIsNot(a, a[()]) self.assertIsNot(a, a[...]) self.assertIsNot(a, a[:]) def test_broaderrors_indexing(self, device): a = torch.zeros(5, 5, device=device) self.assertRaisesRegex( IndexError, "shape mismatch", a.__getitem__, ([0, 1], [0, 1, 2]) ) self.assertRaisesRegex( IndexError, "shape mismatch", a.__setitem__, ([0, 1], [0, 1, 2]), 0 ) def test_trivial_fancy_out_of_bounds(self, device): a = torch.zeros(5, device=device) ind = torch.ones(20, dtype=torch.int64, device=device) if a.is_cuda: raise unittest.SkipTest("CUDA asserts instead of raising an exception") ind[-1] = 10 self.assertRaises(IndexError, a.__getitem__, ind) self.assertRaises(IndexError, a.__setitem__, ind, 0) ind = torch.ones(20, dtype=torch.int64, device=device) ind[0] = 11 self.assertRaises(IndexError, a.__getitem__, ind) self.assertRaises(IndexError, a.__setitem__, ind, 0) def test_index_is_larger(self, device): # Simple case of fancy index broadcasting of the index. a = torch.zeros((5, 5), device=device) a[[[0], [1], [2]], [0, 1, 2]] = tensor([2.0, 3.0, 4.0], device=device) self.assertTrue((a[:3, :3] == tensor([2.0, 3.0, 4.0], device=device)).all()) def test_broadcast_subspace(self, device): a = torch.zeros((100, 100), device=device) v = torch.arange(0.0, 100, device=device)[:, None] b = torch.arange(99, -1, -1, device=device).long() a[b] = v expected = b.float().unsqueeze(1).expand(100, 100) self.assertEqual(a, expected) def test_truncate_leading_1s(self, device): col_max = torch.randn(1, 4) kernel = col_max.T * col_max # [4, 4] tensor kernel2 = kernel.clone() # Set the diagonal kernel[range(len(kernel)), range(len(kernel))] = torch.square(col_max) torch.diagonal(kernel2).copy_(torch.square(col_max.view(4))) self.assertEqual(kernel, kernel2) instantiate_device_type_tests(TestIndexing, globals(), except_for="meta") instantiate_device_type_tests(NumpyTests, globals(), except_for="meta") if __name__ == "__main__": run_tests()