| # Owner(s): ["module: tests"] |
| import torch |
| import numpy as np |
| |
| import unittest |
| from itertools import product, permutations, combinations |
| from functools import partial |
| import random |
| |
| from torch.testing import make_tensor |
| from torch.testing._internal.common_utils import ( |
| IS_FBCODE, TestCase, run_tests, suppress_warnings, gradcheck, gradgradcheck, |
| numpy_to_torch_dtype_dict, skipIfTorchDynamo |
| ) |
| from torch.testing._internal.common_device_type import \ |
| (instantiate_device_type_tests, onlyCPU, dtypes, onlyNativeDeviceTypes, skipMeta) |
| from torch.testing._internal.common_dtype import ( |
| all_types_and_complex_and, complex_types, all_types_and, floating_and_complex_types_and, |
| ) |
| |
| # TODO: replace this with make_tensor() in common_utils.py |
| 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 |
| |
| # TODO: replace this with make_tensor() in common_utils.py |
| def _rand_shape(dim, min_size, max_size): |
| shape = [] |
| for i in range(dim): |
| shape.append(random.randint(min_size, max_size)) |
| return tuple(shape) |
| |
| # TODO: refactor tests to avoid this function |
| # Converts half/bfloat16 dtype to float when device is cpu |
| def _convert_t(dtype, device): |
| if device == 'cpu' and dtype in {torch.half, torch.bfloat16}: |
| return torch.float |
| return dtype |
| |
| # TODO: replace this with make_tensor() in common_utils.py |
| # Returns a tensor of the requested shape, dtype, and device |
| # Requesting a half CPU tensor returns a float CPU tensor with |
| # values representable by a half. |
| # Initialization uses randint for non-float types and randn for float types. |
| def _make_tensor(shape, dtype, device, fill_ones=False) -> torch.Tensor: |
| # Returns a tensor filled with ones |
| if fill_ones: |
| return torch.ones(*shape, dtype=_convert_t(dtype, device), device=device) |
| |
| # Returns a tensor with random integer values |
| if not (dtype.is_floating_point or dtype.is_complex): |
| t = torch.randint(0, 10, shape, device=device) |
| if dtype != torch.uint8: |
| t = t - 5 # generate negative values also |
| return t.to(_convert_t(dtype, device)) |
| |
| # Populates the CPU tensor with floats representable as half/bfloat16 |
| if dtype == torch.half and device == 'cpu': |
| return torch.randn(*shape, dtype=torch.float, device=device).half().float() |
| if dtype == torch.bfloat16 and device == 'cpu': |
| return torch.randn(*shape, dtype=torch.float, device=device).bfloat16().float() |
| |
| # Default: returns a tensor with random float values |
| return torch.randn(shape, dtype=dtype, device=device).to(dtype=dtype) |
| |
| # Tests ops and indexing to ensure they return views (and new tensors) as |
| # appropriate. |
| class TestViewOps(TestCase): |
| exact_dtype = True |
| |
| def is_view_of(self, base, other): |
| if (not other._is_view() or |
| other is base or |
| other._base is not base or |
| base.device != other.device): |
| return False |
| # Note: only validates storage on native device types |
| # because some accelerators, like XLA, do not expose storage |
| if base.device.type == 'cpu' or base.device.type == 'cuda': |
| if base.untyped_storage().data_ptr() != other.untyped_storage().data_ptr(): |
| return False |
| |
| return True |
| |
| # Returns true if v1 and v2 are views of the same base |
| def is_view_of_same_base(self, v1, v2): |
| if (not v1._is_view() or v1 is v2): |
| return False |
| return self.is_view_of(v1._base, v2) |
| |
| # Performs transpose if contiguous=True, else returns the input tensor as is |
| def _do_transpose(self, x, contiguous=False, dim0=0, dim1=1): |
| if contiguous: |
| return x |
| else: |
| return x.transpose(dim0, dim1) |
| |
| @dtypes(*all_types_and(torch.half, torch.bfloat16)) |
| def test_conj_self(self, device, dtype): |
| t = torch.ones(5, 5, device=device) |
| s = t.conj() |
| self.assertTrue(s is t) |
| |
| @skipIfTorchDynamo("TorchDynamo fails with unknown reason") |
| @onlyNativeDeviceTypes |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bool)) |
| def test_view_dtype_new(self, device, dtype): |
| dtypes = {value : key for (key, value) in numpy_to_torch_dtype_dict.items()} |
| del dtypes[torch.bool] |
| |
| def generate_inputs(): |
| yield make_tensor((4, 4, 64), dtype=dtype, device=device, low=-5, high=5) |
| yield make_tensor((4, 4, 64), dtype=dtype, device=device, low=-5, high=5).permute(1, 0, 2) |
| yield make_tensor((4, 64, 4), dtype=dtype, device=device, low=-5, high=5).permute(2, 0, 1) |
| yield make_tensor((1, 5, 1), dtype=dtype, device=device, low=-5, high=5).expand(5, 5, 64) |
| yield make_tensor((2, 5, 256), dtype=dtype, device=device, low=-5, high=5)[1::2, 1:, ::2] |
| yield make_tensor((0, 5, 64), dtype=dtype, device=device, low=-5, high=5) |
| yield make_tensor((), dtype=dtype, device=device, low=-5, high=5) |
| |
| def calc_expected_size_and_stride(a, view_dtype): |
| dtype_size = torch._utils._element_size(a.dtype) |
| view_dtype_size = torch._utils._element_size(view_dtype) |
| |
| if dtype_size == view_dtype_size: |
| return a.size(), a.stride() |
| |
| elif dtype_size > view_dtype_size: |
| size_ratio = dtype_size // view_dtype_size |
| |
| view_size = list(a.size()) |
| view_size[-1] = view_size[-1] * size_ratio |
| |
| view_stride = [stride * size_ratio for stride in a.stride()] |
| view_stride[-1] = 1 |
| return torch.Size(view_size), tuple(view_stride) |
| |
| else: |
| size_ratio = view_dtype_size // dtype_size |
| |
| view_size = list(a.size()) |
| view_size[-1] = view_size[-1] // size_ratio |
| |
| view_stride = [stride // size_ratio for stride in a.stride()] |
| view_stride[-1] = 1 |
| return torch.Size(view_size), tuple(view_stride) |
| |
| for a in generate_inputs(): |
| a_np = a.cpu().numpy() |
| a_np_contiguous = a.cpu().contiguous().numpy() |
| |
| for view_dtype, np_view_dtype in dtypes.items(): |
| equal_element_size = torch._utils._element_size(dtype) == torch._utils._element_size(view_dtype) |
| |
| if not equal_element_size and a.dim() == 0: |
| with self.assertRaisesRegex(RuntimeError, r"self.dim\(\) cannot be 0"): |
| a.view(view_dtype) |
| continue |
| |
| if not equal_element_size and a.stride(-1) != 1: |
| with self.assertRaisesRegex(RuntimeError, r"self.stride\(-1\) must be 1"): |
| a.view(view_dtype) |
| continue |
| |
| a_view = a.view(view_dtype) |
| self.assertEqual(a_view.dtype, view_dtype) |
| self.assertEqual(a.data_ptr(), a_view.data_ptr()) |
| |
| expected_size, expected_stride = calc_expected_size_and_stride(a, view_dtype) |
| self.assertEqual(a_view.size(), expected_size) |
| self.assertEqual(a_view.stride(), expected_stride) |
| |
| self.assertEqual(a_view.view(dtype), a, rtol=0, atol=0) |
| |
| # NumPy's dtype view requires contiguous input if target |
| # dtype is a different size |
| if equal_element_size: |
| a_np_view = a_np.view(np_view_dtype) |
| |
| else: |
| a_np_view = a_np_contiguous.view(np_view_dtype) |
| |
| self.assertEqual(a_view, a_np_view) |
| |
| # Test that requires_grad is dropped for floating point casts, |
| # because view(dtype) does not support backward yet |
| # TODO: Remove this when autograd support is added |
| if dtype.is_floating_point or dtype.is_complex: |
| for view_dtype in floating_and_complex_types_and(torch.half, torch.bfloat16): |
| t = make_tensor((5, 5, 64), dtype=dtype, device=device, low=-5, high=5, requires_grad=True) |
| self.assertFalse(t.view(view_dtype).requires_grad) |
| |
| # Test the extra error checks that happen when the view dtype |
| # has a greater element size than the original dtype |
| @onlyNativeDeviceTypes |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool)) |
| def test_view_dtype_upsize_errors(self, device, dtype): |
| dtype_size = torch._utils._element_size(dtype) |
| |
| for view_dtype in all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool): |
| view_dtype_size = torch._utils._element_size(view_dtype) |
| if view_dtype_size <= dtype_size: |
| continue |
| |
| size_ratio = view_dtype_size // dtype_size |
| a = make_tensor((4, 4, size_ratio + 1), dtype=dtype, device=device, low=-5, high=5) |
| with self.assertRaisesRegex( |
| RuntimeError, |
| rf"self.size\(-1\) must be divisible by {size_ratio}"): |
| a.view(view_dtype) |
| |
| with self.assertRaisesRegex( |
| RuntimeError, |
| rf"self.storage_offset\(\) must be divisible by {size_ratio}"): |
| a[:, :, 1:].view(view_dtype) |
| |
| a = make_tensor((4, 4, size_ratio), dtype=dtype, device=device, low=-5, high=5) |
| a = a.as_strided((4, 4, size_ratio), (size_ratio, 1, 1)) |
| with self.assertRaisesRegex( |
| RuntimeError, |
| rf"self.stride\(1\) must be divisible by {size_ratio}"): |
| a.view(view_dtype) |
| |
| @onlyNativeDeviceTypes |
| def test_view_as_complex(self, device): |
| def fn(contiguous_input=True, dim0=0, dim1=1): |
| t = torch.randn(3, 2, 2, device=device) |
| c_t = t[:, :, 0] + 1j * t[:, :, 1] |
| |
| input = self._do_transpose(t, contiguous_input, dim0, dim1) |
| |
| if input.size()[-1] != 2: |
| self.assertRaisesRegex( |
| RuntimeError, "Tensor must have a last dimension of size 2", |
| lambda: torch.view_as_complex(input)) |
| return |
| |
| if input.stride()[-1] != 1: |
| self.assertRaisesRegex( |
| RuntimeError, "Tensor must have a last dimension with stride 1", |
| lambda: torch.view_as_complex(input)) |
| return |
| |
| res = torch.view_as_complex(input) |
| self.assertEqual(res, self._do_transpose(c_t, contiguous_input, dim0, dim1)) |
| self.assertTrue(self.is_view_of(t, res)) |
| |
| fn() |
| fn(contiguous_input=False) |
| # RuntimeError since in this case the last dim of input would not be of size 2 |
| fn(contiguous_input=False, dim0=0, dim1=2) |
| # RuntimeError since in this case the last dim of input would not have stride 1 |
| fn(contiguous_input=False, dim0=1, dim1=2) |
| |
| |
| # RuntimeError since in this case the stride of non-last dim of input would not be of size 2 |
| x = torch.randn(3, 3, device=device) |
| t = torch.as_strided(x, (2, 2), (1, 1)) |
| self.assertRaisesRegex( |
| RuntimeError, "Tensor must have a stride divisible by 2 for all but last dimension", |
| lambda: torch.view_as_complex(t)) |
| |
| # tensor with zero elements |
| x = torch.tensor([], device=device) # torch.Size([0]) |
| self.assertRaisesRegex( |
| RuntimeError, "Tensor must have a last dimension of size 2", |
| lambda: torch.view_as_complex(x)) |
| |
| # zero dimension tensor |
| z = torch.tensor(2.0) |
| self.assertRaisesRegex( |
| RuntimeError, "Input tensor must have one or more dimensions", |
| lambda: torch.view_as_complex(z)) |
| |
| y = x.reshape(0, 2) # torch.Size([0, 2]) |
| res = torch.view_as_complex(y) |
| self.assertTrue(self.is_view_of(x, res)) |
| self.assertEqual(res.shape, torch.Size([0])) |
| |
| @onlyNativeDeviceTypes |
| @dtypes(*complex_types(), torch.complex32) |
| def test_view_as_real(self, device, dtype): |
| def fn(contiguous_input=True): |
| t = torch.randn(3, 4, dtype=dtype, device=device) |
| input = self._do_transpose(t, contiguous_input) |
| res = torch.view_as_real(input) |
| self.assertEqual(res[:, :, 0], input.real) |
| self.assertEqual(res[:, :, 1], input.imag) |
| self.assertTrue(self.is_view_of(t, res)) |
| |
| fn() |
| fn(contiguous_input=False) |
| |
| # tensor with zero elements |
| x = torch.tensor([], dtype=dtype, device=device) |
| res = torch.view_as_real(x) |
| self.assertTrue(self.is_view_of(x, res)) |
| self.assertEqual(res.shape, torch.Size([0, 2])) |
| |
| # tensor with zero dim |
| x = torch.tensor(2 + 3j, dtype=dtype, device=device) |
| res = torch.view_as_real(x) |
| self.assertTrue(self.is_view_of(x, res)) |
| self.assertEqual(res.shape, torch.Size([2])) |
| |
| @onlyNativeDeviceTypes |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool)) |
| def test_view_tensor_split(self, device, dtype): |
| a = make_tensor((40, 30), dtype=dtype, device=device, low=-9, high=9) |
| a_split_dim0 = a.tensor_split(7, 0) |
| for a_split_dim0_tensor in a_split_dim0: |
| self.assertTrue(self.is_view_of(a, a_split_dim0_tensor)) |
| a_split_dim1 = a.tensor_split(7, 1) |
| for a_split_dim1_tensor in a_split_dim1: |
| self.assertTrue(self.is_view_of(a, a_split_dim1_tensor)) |
| |
| @onlyNativeDeviceTypes |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool)) |
| def test_view_tensor_hsplit(self, device, dtype): |
| t = make_tensor((4, 4, 4), dtype=dtype, device=device, low=-9, high=9) |
| t_hsplit = torch.hsplit(t, 2) |
| for t_hsplit_tensor in t_hsplit: |
| self.assertTrue(self.is_view_of(t, t_hsplit_tensor)) |
| t[2, 2, 2] = 7 |
| self.assertEqual(t_hsplit[1][2, 0, 2], t[2, 2, 2]) |
| |
| @onlyNativeDeviceTypes |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool)) |
| def test_view_tensor_vsplit(self, device, dtype): |
| t = make_tensor((4, 4, 4), dtype=dtype, device=device, low=-9, high=9) |
| t_vsplit = torch.vsplit(t, 2) |
| for t_vsplit_tensor in t_vsplit: |
| self.assertTrue(self.is_view_of(t, t_vsplit_tensor)) |
| t[2, 2, 2] = 7 |
| self.assertEqual(t_vsplit[1][0, 2, 2], t[2, 2, 2]) |
| |
| @onlyNativeDeviceTypes |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool)) |
| def test_view_tensor_dsplit(self, device, dtype): |
| t = make_tensor((4, 4, 4), dtype=dtype, device=device, low=-9, high=9) |
| t_dsplit = torch.dsplit(t, 2) |
| for t_dsplit_tensor in t_dsplit: |
| self.assertTrue(self.is_view_of(t, t_dsplit_tensor)) |
| t[2, 2, 2] = 7 |
| self.assertEqual(t_dsplit[1][2, 2, 0], t[2, 2, 2]) |
| |
| @onlyNativeDeviceTypes |
| @dtypes(*all_types_and(torch.half, torch.bfloat16)) |
| def test_imag_noncomplex(self, device, dtype): |
| t = torch.ones((5, 5), dtype=dtype, device=device) |
| |
| with self.assertRaises(RuntimeError): |
| torch.imag(t) |
| |
| @onlyNativeDeviceTypes |
| @dtypes(*complex_types()) |
| def test_real_imag_view(self, device, dtype): |
| def compare_with_numpy(contiguous_input=True): |
| t = torch.randn(3, 3, dtype=dtype, device=device) |
| if not contiguous_input: |
| u = t.T |
| else: |
| u = t |
| |
| re = u.real |
| exp = torch.from_numpy(u.cpu().numpy().real).to(device=device) |
| self.assertEqual(re, exp) |
| # for the case of contiguous_input, t=u |
| # for the case of non contiguous_input, the base still remains |
| # t since we are performing a view operation to make the input non-contiguous |
| self.assertTrue(self.is_view_of(t, re)) |
| |
| im = u.imag |
| exp = torch.from_numpy(u.cpu().numpy().imag).to(device=device) |
| self.assertEqual(im, exp) |
| self.assertTrue(self.is_view_of(t, im)) |
| |
| compare_with_numpy() |
| compare_with_numpy(contiguous_input=False) |
| |
| # ensure storage offset is being correctly set |
| a = torch.randn(10, dtype=dtype) |
| self.assertEqual(a[5:].real, a.real[5:]) |
| self.assertEqual(a[5:].imag, a.imag[5:]) |
| |
| @onlyNativeDeviceTypes |
| @dtypes(*complex_types()) |
| def test_conj_imag_view(self, device, dtype) -> None: |
| t = _make_tensor((4, 5,), dtype, device) |
| t_numpy_conj = torch.from_numpy(t.cpu().numpy().conj()).to(device=device) |
| v = t.conj() |
| self.assertTrue(self.is_view_of(t, v)) |
| self.assertEqual(v, t_numpy_conj) |
| |
| if (t.is_complex()): |
| v_imag = v.imag |
| self.assertTrue(self.is_view_of(t, v_imag)) |
| self.assertEqual(v_imag, t_numpy_conj.imag) |
| self.assertTrue(v_imag.is_neg()) |
| |
| @onlyNativeDeviceTypes |
| def test_conj_view_with_shared_memory(self, device) -> None: |
| a = _make_tensor((4, 5,), torch.cfloat, device) |
| b = a.conj() |
| c = a.conj() |
| |
| self.assertEqual(torch.add(a, b), a.add_(b)) |
| self.assertEqual(torch.add(b, c), torch.add(b, c, out=a)) |
| self.assertEqual(torch.add(b, c), b.add_(c)) |
| |
| @onlyNativeDeviceTypes |
| @dtypes(*product(complex_types(), all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))) |
| @suppress_warnings |
| def test_set_real_imag(self, device, dtypes): |
| x = torch.randn(10, dtype=dtypes[0], device=device) |
| |
| new_real = _make_tensor((10,), dtypes[1], device) |
| new_imag = _make_tensor((10,), dtypes[1], device) |
| |
| x.real = new_real |
| x.imag = new_imag |
| |
| if dtypes[1].is_complex: |
| self.assertEqual(x.real, new_real.real, exact_dtype=False) |
| self.assertEqual(x.imag, new_imag.real, exact_dtype=False) |
| |
| else: |
| self.assertEqual(x.real, new_real, exact_dtype=False) |
| self.assertEqual(x.imag, new_imag, exact_dtype=False) |
| |
| def test_diagonal_view(self, device) -> None: |
| t = torch.ones((5, 5), device=device) |
| v = torch.diagonal(t) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0] = 0 |
| self.assertEqual(t[0, 0], v[0]) |
| |
| t = torch.ones((3, 3, 3), device=device) |
| v = torch.diagonal(t, offset=1, dim1=1, dim2=2) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0, 0] = 0 |
| self.assertEqual(t[0, 0, 1], v[0, 0]) |
| |
| def test_select_view(self, device) -> None: |
| t = torch.ones((5, 5), device=device) |
| v = t.select(0, 2) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0] = 0 |
| self.assertEqual(t[2, 0], v[0]) |
| |
| # Lazy hasn't implemented unbind yet. |
| @onlyNativeDeviceTypes |
| def test_unbind_view(self, device) -> None: |
| t = torch.zeros((5, 5), device=device) |
| tup = torch.unbind(t) |
| |
| for idx, v in enumerate(tup): |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0] = idx + 1 |
| self.assertEqual(t[idx, 0], v[0]) |
| |
| # TODO: opinfo this or move to unbind's test suite |
| def test_unbind(self): |
| stacked = torch.randn(3, 10, 10, requires_grad=True) |
| x, y, z = stacked.unbind() |
| grad = torch.randn(3, 10, 10) |
| torch.autograd.backward([x, y, z], grad.unbind()) |
| self.assertEqual(stacked.grad, grad) |
| # check that it works with only one gradient provided (#9977) |
| for i in range(3): |
| stacked = torch.randn(3, 10, 10, requires_grad=True) |
| outs = stacked.unbind() |
| gi = grad.unbind()[i] |
| g, = torch.autograd.grad(outs[i], stacked, gi) |
| g_expected = torch.stack([gi if j == i else torch.zeros_like(gi) |
| for j in range(3)], dim=0) |
| self.assertEqual(g, g_expected) |
| # Check with gradcheck |
| stacked = torch.randn(3, 10, 10, dtype=torch.double, requires_grad=True) |
| gradcheck(lambda x: x.unbind(), (stacked,), check_forward_ad=True) |
| |
| # TODO: Fix this test for LTC. There is an interaction with dynamic shapes here that is broken, |
| # causing asserts to trigger. |
| @onlyNativeDeviceTypes |
| def test_expand_view(self, device) -> None: |
| t = torch.ones((5, 1), device=device) |
| v = t.expand(5, 5) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[2, 2] = 0 |
| self.assertEqual(t[2, 0], v[2, 2]) |
| |
| def test_expand_as_view(self, device): |
| t = torch.ones((5, 1), device=device) |
| e = torch.empty((5, 5), device=device) |
| v = t.expand_as(e) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[2, 2] = 0 |
| self.assertEqual(t[2, 0], v[2, 2]) |
| |
| def test_narrow_view(self, device): |
| t = torch.ones((5, 5), device=device) |
| v = torch.narrow(t, 1, 2, 2) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0, 0] = 0 |
| self.assertEqual(t[0, 2], v[0, 0]) |
| |
| def test_permute_view(self, device) -> None: |
| t = torch.ones((5, 5), device=device) |
| v = t.permute(1, 0) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0, 1] = 0 |
| self.assertEqual(t[1, 0], v[0, 1]) |
| |
| def test_transpose_view(self, device): |
| for fn in (torch.swapdims, torch.swapaxes, torch.transpose): |
| t = torch.ones((5, 5), device=device) |
| v = fn(t, 0, 1) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0, 1] = 0 |
| self.assertEqual(t[1, 0], v[0, 1]) |
| |
| def test_transpose_inplace_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| v = t.view_as(t) |
| v = v.swapdims_(0, 1) |
| self.assertTrue(self.is_view_of(t, v)) |
| v[0, 1] = 0 |
| self.assertEqual(t[1, 0], v[0, 1]) |
| |
| t = torch.ones(5, 5, device=device) |
| v = t.view_as(t) |
| v = v.swapaxes_(0, 1) |
| self.assertTrue(self.is_view_of(t, v)) |
| v[0, 1] = 0 |
| self.assertEqual(t[1, 0], v[0, 1]) |
| |
| t = torch.ones(5, 5, device=device) |
| v = t.view_as(t) |
| v = v.transpose_(0, 1) |
| self.assertTrue(self.is_view_of(t, v)) |
| v[0, 1] = 0 |
| self.assertEqual(t[1, 0], v[0, 1]) |
| |
| def test_t_view(self, device): |
| t = torch.ones((5, 5), device=device) |
| v = t.t() |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0, 1] = 0 |
| self.assertEqual(t[1, 0], v[0, 1]) |
| |
| def test_t_inplace_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| v = t.view_as(t) |
| v = v.t_() |
| self.assertTrue(self.is_view_of(t, v)) |
| v[0, 1] = 0 |
| self.assertEqual(t[1, 0], v[0, 1]) |
| |
| def test_T_view(self, device): |
| for op in ("T", "H", "mT", "mH"): |
| t = torch.ones((5, 5), device=device) |
| v = getattr(t, op) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0, 1] = 0 |
| self.assertEqual(t[1, 0], v[0, 1]) |
| |
| def test_unfold_view(self, device): |
| t = torch.ones(10, device=device) |
| v = t.unfold(0, 3, 2) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[1, 0] = 0 |
| self.assertEqual(t[2], v[1, 0]) |
| |
| def test_squeeze_view(self, device): |
| t = torch.ones(5, 1, 5, device=device) |
| v = torch.squeeze(t) |
| self.assertTrue(self.is_view_of(t, v)) |
| v[0, 1] = 0 |
| self.assertEqual(t, v._base) |
| |
| def test_squeeze_inplace_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| v = t.view_as(t) |
| v = v.squeeze_() |
| self.assertTrue(self.is_view_of(t, v)) |
| v[0, 1] = 0 |
| self.assertEqual(t, v._base) |
| |
| def test_unsqueeze_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| v = torch.unsqueeze(t, 1) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0, 0, 1] = 0 |
| self.assertEqual(t[0, 1], v[0, 0, 1]) |
| |
| def test_unsqueeze_inplace_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| v = t.view_as(t) |
| v = v.unsqueeze_(1) |
| self.assertTrue(self.is_view_of(t, v)) |
| v[0, 0, 1] = 0 |
| self.assertEqual(t[0, 1], v[0, 0, 1]) |
| |
| def test_as_strided_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| v = torch.as_strided(t, (25,), (1,)) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[6] = 0 |
| self.assertEqual(t[1, 1], v[6]) |
| |
| def test_as_strided_inplace_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| v = t.view_as(t) |
| v = v.as_strided_((25,), (1,)) |
| self.assertTrue(self.is_view_of(t, v)) |
| v[6] = 0 |
| self.assertEqual(t[1, 1], v[6]) |
| |
| def test_as_strided_gradients(self): |
| def test(x, prepro_fn, size, strides, offset=None): |
| x = x.to(torch.double).detach().requires_grad_() |
| |
| # Check that forward will **not** resize storage because it may |
| # cause NaN in output and fail numerical Jacobian check consequently |
| with torch.no_grad(): |
| y = prepro_fn(x) if prepro_fn is not None else x |
| max_offset = sum((si - 1) * st for si, st in zip(size, strides)) |
| max_offset += offset if offset is not None else y.storage_offset() |
| assert max_offset < len(y.storage()), "test case resizes storage" |
| |
| def closure(x): |
| if prepro_fn is not None: |
| x = prepro_fn(x) |
| return x.as_strided(size, strides, offset) |
| |
| gradcheck(closure, [x], check_forward_ad=True) |
| gradgradcheck(closure, [x]) |
| |
| # test |
| test(torch.arange(0, 25), lambda x: x.view(5, 5), [3, 3], [6, 2], 2) |
| |
| # test crazy stride at dim with size 1 case |
| test(torch.randn(12), None, [1, 2, 1, 5], [0, 5, 100, 1], 2) |
| |
| # test expand case |
| test(torch.randn(5), None, [3, 3, 3], [0, 1, 0], 2) |
| test(torch.randn(5), None, [3, 3, 3], [0, 0, 0], 4) |
| test(torch.randn(5), lambda x: x.expand(5, 5), [5, 5], [0, 1], 0) |
| |
| # test non-expand overlapping case |
| test(torch.randn(35), None, [6, 6], [5, 1], 2) |
| test(torch.randn(15), None, [3, 2], [3, 6], 2) |
| |
| # test transpose case |
| test(torch.randn(3, 4), None, [4, 3], [1, 4]) |
| |
| # test "getting things outside the input" case |
| x = torch.randn(6, 2) |
| test(x[3:], None, [3, 2], [2, 1], 0) # should be all zeros |
| self.assertEqual(x[3:].as_strided([3, 2], [2, 1], 0), x[:3]) |
| |
| # test select on expanded input case |
| test(torch.randn(2, 3), lambda x: x.expand(10, 2, 3), [2, 3], [3, 1], 0) |
| |
| def test_view_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| v = t.view(25) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[6] = 0 |
| self.assertEqual(t[1, 1], v[6]) |
| |
| def test_view_as_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| e = torch.empty((25,)) |
| v = t.view_as(e) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[6] = 0 |
| self.assertEqual(t[1, 1], v[6]) |
| |
| def test_contiguous_self(self, device): |
| t = torch.ones(5, 5, device=device) |
| s = t.contiguous() |
| self.assertTrue(s is t) |
| |
| @skipMeta |
| # self.is_view_of reports false positives for lazy |
| @onlyNativeDeviceTypes |
| def test_contiguous_nonview(self, device): |
| t = torch.ones(5, 5, device=device) |
| nv = t.t().contiguous() |
| self.assertTrue(not self.is_view_of(t, nv)) |
| |
| nv[0, 0] = 0 |
| self.assertNotEqual(t[0, 0], nv[0, 0]) |
| |
| def test_reshape_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| v = torch.reshape(t, (25,)) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[6] = 0 |
| self.assertEqual(t[1, 1], v[6]) |
| |
| def test_reshape_as_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| e = torch.empty((25,), device=device) |
| v = t.reshape_as(e) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[6] = 0 |
| self.assertEqual(t[1, 1], v[6]) |
| |
| @skipMeta |
| # self.is_view_of reports false positives for lazy |
| @onlyNativeDeviceTypes |
| def test_reshape_nonview(self, device): |
| t = torch.ones(5, 5, device=device) |
| nv = torch.reshape(t.t(), (25,)) |
| self.assertTrue(not self.is_view_of(t, nv)) |
| |
| nv[6] = 0 |
| self.assertNotEqual(t[1, 1], nv[6]) |
| |
| # This test use as_strided to construct a tensor with overlapping memory, |
| # which is not handled by the functionalization pass. |
| @onlyNativeDeviceTypes |
| def test_flatten_view(self, device): |
| def test_writes_propagate(t, v): |
| idx_t = (0,) * t.ndim |
| idx_v = (0,) * v.ndim |
| v[idx_v] = 0 |
| self.assertEqual(t[idx_t], v[idx_v]) |
| |
| t = torch.ones(1, 2, 3, 4, device=device) |
| v = t.flatten() |
| self.assertTrue(self.is_view_of(t, v)) |
| test_writes_propagate(t, v) |
| |
| # zero-dimensional tensor |
| t = torch.tensor(1, device=device) |
| v = t.flatten() |
| test_writes_propagate(t, v) |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| t = torch.ones(1, 2, 3, 4, device=device).transpose(2, 3) |
| v = t.flatten(0, 1) |
| test_writes_propagate(t, v) |
| self.assertTrue(self.is_view_of_same_base(t, v)) |
| |
| # stride[i] = stride[i + 1] * size[i + 1] is satisfied for 3 groups: |
| t = torch.ones(720, device=device) \ |
| .as_strided((2, 3, 2, 3, 5, 4), (6, 2, 15, 5, 1, 0)) |
| # [--1--|---2---|-3-] [--1--|----2---|-3-] |
| v1 = t.flatten(0, 1) |
| v2 = v1.flatten(1, 3) |
| v3 = v2.flatten(2, 2) |
| test_writes_propagate(t, v1) |
| self.assertTrue(self.is_view_of_same_base(t, v1)) |
| test_writes_propagate(t, v2) |
| self.assertTrue(self.is_view_of_same_base(t, v2)) |
| test_writes_propagate(t, v3) |
| self.assertTrue(self.is_view_of_same_base(t, v3)) |
| |
| @onlyNativeDeviceTypes |
| def test_flatten_nonview(self, device): |
| def assert_is_nonview(t, nv): |
| idx_t = (0,) * t.ndim |
| idx_nv = (0,) * nv.ndim |
| self.assertTrue(not nv._is_view()) |
| nv[idx_nv] = 0 |
| if device != "meta": |
| self.assertNotEqual(t[idx_t], nv[idx_nv]) |
| t = torch.ones(2, 3, 2, 3, device=device).transpose(2, 3) |
| nv = t.flatten(1, 3) |
| assert_is_nonview(t, nv) |
| |
| t = torch.ones(2, 2, device=device).T |
| nv = t.flatten() |
| assert_is_nonview(t, nv) |
| |
| # flatten returns the original object if start_dim=end_dim |
| t = t = torch.ones(2, 2, device=device) |
| nv = t.flatten(1, 1) |
| self.assertTrue(t is nv) |
| |
| def test_basic_indexing_slice_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| v = t[:2, :3] |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0, 0] = 0 |
| self.assertEqual(t[0, 0], v[0, 0]) |
| |
| def test_basic_indexing_ellipses_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| v = t[..., :2] |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0, 0] = 0 |
| self.assertEqual(t[0, 0], v[0, 0]) |
| |
| def test_basic_indexing_newaxis_view(self, device): |
| t = torch.ones(5, 5, device=device) |
| v = t[None, :2, 3] |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0, 0] = 0 |
| self.assertEqual(t[0, 3], v[0, 0]) |
| |
| def test_advanced_indexing_nonview(self, device): |
| t = torch.ones(3, 3, device=device) |
| rows = torch.tensor([[0, 0], [2, 2]], device=device) |
| cols = torch.tensor([[0, 1], [2, 2]], device=device) |
| nv = t[rows, cols] |
| self.assertTrue(not self.is_view_of(t, nv)) |
| |
| nv[1, 1] = 0 |
| self.assertNotEqual(t[2, 2], nv[1, 1]) |
| |
| @unittest.skipIf(IS_FBCODE, "TorchScript backend not yet supported in FBCODE/OVRSOURCE builds") |
| def test_advanced_indexing_assignment(self, device): |
| t = torch.ones(3, 3, device=device) |
| rows = torch.tensor([[0, 0], [2, 2]], device=device) |
| cols = torch.tensor([[0, 1], [2, 2]], device=device) |
| t[rows, cols] = 0 |
| self.assertEqual(t[2, 2], 0) |
| |
| @unittest.skip("See https://github.com/pytorch/pytorch/pull/32720") |
| def test_chunk_view(self, device): |
| t = torch.zeros(3, 3, device=device) |
| l = torch.chunk(t, 3) |
| |
| for idx, v in enumerate(l): |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0, 0] = idx + 1 |
| self.assertEqual(t[idx, 0], v[0, 0]) |
| |
| @unittest.skip("See https://github.com/pytorch/pytorch/pull/32720") |
| def test_split_view(self, device): |
| t = torch.zeros(3, 3, device=device) |
| l = torch.split(t, [1, 1, 1]) |
| |
| for idx, v in enumerate(l): |
| self.assertTrue(self.is_view_of(t, v)) |
| |
| v[0, 0] = idx + 1 |
| self.assertEqual(t[idx, 0], v[0, 0]) |
| |
| def test_movedim_view(self, device): |
| def run_test(device, op): |
| t = torch.zeros(3, 3, device=device) |
| out = op(t) |
| |
| self.assertTrue(self.is_view_of(t, out)) |
| |
| # Randomly change values in output |
| # and verify that original is changed |
| # as well. |
| for _ in range(3): |
| idx_1, idx_2 = random.randint(0, 2), random.randint(0, 2) |
| out[idx_1, idx_2] = random.random() |
| self.assertEqual(t[idx_2, idx_1], out[idx_1, idx_2]) |
| |
| for fn in [torch.movedim, torch.moveaxis]: |
| op = partial(fn, source=(0, 1), destination=(1, 0)) |
| run_test(device, op) |
| |
| op = partial(fn, source=0, destination=1) |
| run_test(device, op) |
| |
| # Testing that the generated view_copy kernel and its derivative are implemented correctly |
| def test_view_copy(self, device): |
| a = torch.randn(4, device=device, requires_grad=True) |
| a_ref = a.clone().detach().requires_grad_() |
| a_view = a_ref.view(2, 2) |
| a_view_copy = torch.view_copy(a, (2, 2)) |
| |
| # view_copy ops don't preserve view relationship |
| self.assertTrue(self.is_view_of(a_ref, a_view)) |
| self.assertFalse(self.is_view_of(a, a_view_copy)) |
| |
| a_view_copy.sum().backward() |
| a_view.sum().backward() |
| |
| # forward and backward give the same shape + result |
| self.assertEqual(a_view_copy, a_view) |
| self.assertEqual(a.grad, a_ref.grad) |
| |
| # Testing that the output of a view_copy kernel (by default) is contiguous. |
| def test_view_copy_output_contiguous(self, device): |
| a = torch.randn(4, 4, 4, 4, device=device).to(memory_format=torch.channels_last) |
| b = torch.ops.aten.slice_copy(a, 0, 0, 2) |
| self.assertTrue(b.is_contiguous()) |
| |
| def test_view_copy_out(self, device): |
| a = torch.randn(2, 2, device=device) |
| out = torch.empty(2, device=device) |
| |
| torch.diagonal_copy(a, out=out) |
| expected = torch.diagonal_copy(a) |
| |
| self.assertEqual(expected, out) |
| |
| a = torch.randn(4, device=device) |
| out1 = torch.empty(2, device=device) |
| out2 = torch.empty(2, device=device) |
| |
| torch.split_copy(a, 2, out=(out1, out2)) |
| expected1, expected2 = torch.split_copy(a, 2) |
| |
| self.assertEqual(expected1, out1) |
| self.assertEqual(expected2, out2) |
| |
| class TestOldViewOps(TestCase): |
| def test_ravel(self, device): |
| |
| def _test_ravel(tensors, size, nc=False): |
| for src in tensors: |
| # Continuous Tensor -> View |
| flat = src.ravel() |
| self.assertEqual(flat.shape, torch.Size([size])) |
| self.assertEqual(src.view(-1), flat) |
| self.assertIs(flat._base, src) |
| self.assertTrue(flat.is_contiguous()) |
| |
| # Non-continuous Tensor -> Copy |
| if nc: |
| nc_src = src.t() |
| nc_flat = nc_src.ravel() |
| self.assertEqual(nc_flat.shape, torch.Size([size])) |
| self.assertEqual(nc_src.contiguous().view(-1), nc_flat) |
| self.assertIsNot(nc_flat._base, src) |
| self.assertTrue(nc_flat.is_contiguous()) |
| |
| # Test that flatten returns 1-dim tensor when given a 0-dim tensor |
| zero_dim_tensor = torch.tensor(123, device=device) |
| flat0 = zero_dim_tensor.ravel() |
| one_dim_tensor = torch.tensor([123], device=device) |
| flat1 = zero_dim_tensor.ravel() |
| nc_ones_tensor = torch.ones(10, device=device)[::2] |
| flat2 = nc_ones_tensor.ravel() |
| |
| self.assertEqual(zero_dim_tensor.shape, torch.Size([])) |
| self.assertEqual(flat0.shape, torch.Size([1])) |
| self.assertEqual(one_dim_tensor.shape, torch.Size([1])) |
| self.assertEqual(flat1.shape, torch.Size([1])) |
| self.assertEqual(nc_ones_tensor.shape, torch.Size([5])) |
| self.assertEqual(flat2.shape, torch.Size([5])) |
| self.assertEqual(flat0, one_dim_tensor) |
| self.assertEqual(flat0, flat1) |
| self.assertEqual(flat0.shape, flat1.shape) |
| self.assertTrue(flat0.is_contiguous()) |
| self.assertTrue(flat1.is_contiguous()) |
| self.assertTrue(flat2.is_contiguous()) |
| |
| # Test both float tensor and quantized tensor |
| tensors = [torch.randn(5, 5, 5, 5, device=device), |
| torch._empty_affine_quantized([5, 5, 5, 5], |
| scale=2, |
| zero_point=3, |
| dtype=torch.quint8, |
| device=device)] |
| _test_ravel(tensors, 625) |
| |
| tensors = [torch.randn(0, 2, 3, device=device), |
| torch.randn(3, 0, 2, device=device), |
| torch._empty_affine_quantized([0, 2, 3], |
| scale=2, |
| zero_point=3, |
| dtype=torch.quint8, |
| device=device), |
| torch._empty_affine_quantized([3, 0, 2], |
| scale=2, |
| zero_point=3, |
| dtype=torch.quint8, |
| device=device)] |
| _test_ravel(tensors, 0) |
| |
| tensors = [torch.randn(5, 5, device=device), |
| torch._empty_affine_quantized([5, 5], |
| scale=2, |
| zero_point=3, |
| dtype=torch.quint8, |
| device=device)] |
| _test_ravel(tensors, 25, True) |
| |
| # TODO: this should be refactored into the view ops test suite |
| def test_empty_reshape(self, device): |
| x = torch.randn(0, 6, device=device) |
| self.assertEqual((1, 0, 6, 1, 1), x.reshape(1, 0, 6, 1, 1).shape) |
| # should be viewable -- i.e. data_ptr is the same. |
| self.assertEqual(x.data_ptr(), x.reshape(1, 0, 6, 1, 1).data_ptr()) |
| |
| # match NumPy semantics -- don't infer the size of dimension with a degree of freedom |
| self.assertRaises(RuntimeError, lambda: x.reshape(0, -1)) |
| |
| @skipIfTorchDynamo("TorchDynamo fails with unknown reason") |
| def test_expand(self, device): |
| tensor = torch.rand(1, 8, 1, device=device) |
| tensor2 = torch.rand(5, device=device) |
| template = torch.rand(4, 8, 5, device=device) |
| target = template.size() |
| self.assertEqual(tensor.expand_as(template).size(), target) |
| self.assertEqual(tensor.expand(4, 8, 5).size(), target) |
| self.assertEqual(tensor.expand(target).size(), target) |
| self.assertEqual(tensor2.expand_as(template).size(), target) |
| self.assertEqual(tensor2.expand(4, 8, 5).size(), target) |
| self.assertEqual(tensor2.expand(target).size(), target) |
| |
| # test double expand |
| self.assertEqual(tensor2.expand(1, 5).expand(2, 2, 5), tensor2.repeat(2, 2, 1)) |
| |
| # test non-contiguous |
| noncontig = torch.randn(5, 2, 1, 3, device=device)[:, 0] |
| self.assertFalse(noncontig.is_contiguous()) |
| self.assertEqual(noncontig.expand(2, 5, 4, 3), noncontig.contiguous().repeat(2, 1, 4, 1)) |
| |
| # make sure it's compatible with unsqueeze |
| expanded = tensor2.expand(1, 1, 5) |
| unsqueezed = tensor2.unsqueeze(0).unsqueeze(1) |
| self.assertEqual(expanded, unsqueezed) |
| self.assertEqual(expanded.stride(), unsqueezed.stride()) |
| |
| # test -1 as target size |
| self.assertEqual(tensor.expand(4, -1, 5), tensor.expand(4, 8, 5)) |
| self.assertRaises(RuntimeError, lambda: tensor2.expand(-1, -1)) |
| |
| # test expanding empty to empty |
| self.assertEqual(torch.zeros(0, device=device).expand((0,)), torch.zeros(0, device=device)) |
| |
| # TODO: this should be refactored into the view ops test suite |
| def test_view_empty(self, device): |
| x = torch.randn(0, 6, device=device) |
| self.assertEqual((1, 0, 6, 1, 1), x.view(1, 0, 6, 1, 1).shape) |
| |
| # TODO: this should be refactored into the view ops test suite |
| @onlyNativeDeviceTypes |
| def test_reshape(self, device): |
| x = torch.randn(3, 3, device=device) |
| self.assertEqual(x.data_ptr(), x.reshape(-1).data_ptr()) |
| self.assertEqual(x.data_ptr(), x.reshape(1, 9, 1).data_ptr()) |
| self.assertEqual(torch.reshape(x, (9,)), x.reshape(9)) |
| self.assertRaises(RuntimeError, lambda: x.reshape(-1, -1)) |
| |
| y = torch.randn(4, 4, 4, device=device)[:, 0, :] |
| # .data_ptr() on meta tensors is always 0 so they are equal regardless of the reshape |
| if device != "meta": |
| self.assertNotEqual(y.data_ptr(), y.reshape(-1).data_ptr()) |
| self.assertEqual(y.contiguous().view(-1), y.reshape(-1)) |
| self.assertEqual(y.reshape(2, 2, 4).data_ptr(), y.data_ptr()) |
| |
| s = torch.randn((), device=device) |
| self.assertEqual(s.data_ptr(), s.reshape(()).data_ptr()) |
| self.assertEqual(s.reshape(-1).shape, (1,)) |
| self.assertRaises(RuntimeError, lambda: s.reshape(2)) |
| |
| empty = torch.tensor([], device=device) |
| self.assertEqual(empty, empty.reshape(-1)) |
| self.assertEqual(empty, empty.reshape([0])) |
| # TODO: fix these once we have multi-dimensional empty tensors |
| self.assertEqual(empty.reshape([0, 1]).shape, (0, 1)) |
| self.assertEqual(empty.reshape([1, -1]).shape, (1, 0)) |
| self.assertRaises(RuntimeError, lambda: empty.reshape(1)) |
| |
| x = torch.randn(3, 3, device=device) |
| self.assertEqual(x.data_ptr(), x.reshape_as(torch.rand(9)).data_ptr()) |
| self.assertEqual(x.data_ptr(), x.reshape_as(torch.rand(1, 9, 1)).data_ptr()) |
| self.assertRaises(RuntimeError, lambda: x.reshape_as(torch.rand(10, device=device))) |
| |
| def test_flatten(self, device): |
| # Test that flatten returns 1-dim tensor when given a 0-dim tensor |
| zero_dim_tensor = torch.tensor(123, device=device) |
| flat0 = zero_dim_tensor.flatten() |
| one_dim_tensor = torch.tensor([123], device=device) |
| flat1 = zero_dim_tensor.flatten() |
| |
| self.assertEqual(zero_dim_tensor.shape, torch.Size([])) |
| self.assertEqual(flat0.shape, torch.Size([1])) |
| self.assertEqual(one_dim_tensor.shape, torch.Size([1])) |
| self.assertEqual(flat1.shape, torch.Size([1])) |
| self.assertEqual(flat0, one_dim_tensor) |
| self.assertEqual(flat0, flat1) |
| self.assertEqual(flat0.shape, flat1.shape) |
| |
| # Test both float tensor and quantized tensor |
| tensors = [torch.randn(5, 5, 5, 5, device=device), |
| torch._empty_affine_quantized([5, 5, 5, 5], |
| scale=2, |
| zero_point=3, |
| dtype=torch.quint8, |
| device=device)] |
| for src in tensors: |
| flat = src.flatten(0, -1) |
| self.assertEqual(flat.shape, torch.Size([625])) |
| self.assertEqual(src.view(-1), flat.view(-1)) |
| |
| flat = src.flatten(0, 2) |
| self.assertEqual(flat.shape, torch.Size([125, 5])) |
| self.assertEqual(src.view(-1), flat.view(-1)) |
| |
| flat = src.flatten(0, 1) |
| self.assertEqual(flat.shape, torch.Size([25, 5, 5])) |
| self.assertEqual(src.view(-1), flat.view(-1)) |
| |
| flat = src.flatten(1, 2) |
| self.assertEqual(flat.shape, torch.Size([5, 25, 5])) |
| self.assertEqual(src.view(-1), flat.view(-1)) |
| |
| flat = src.flatten(2, 3) |
| self.assertEqual(flat.shape, torch.Size([5, 5, 25])) |
| self.assertEqual(src.view(-1), flat.view(-1)) |
| |
| flat = src.flatten(-2, -1) |
| self.assertEqual(flat.shape, torch.Size([5, 5, 25])) |
| self.assertEqual(src.view(-1), flat.view(-1)) |
| |
| flat = src.flatten(2, 2) |
| self.assertEqual(flat, src) |
| |
| # out of bounds index |
| with self.assertRaisesRegex(IndexError, 'Dimension out of range'): |
| src.flatten(5, 10) |
| |
| # invalid start and end |
| with self.assertRaisesRegex(RuntimeError, 'start_dim cannot come after end_dim'): |
| src.flatten(2, 0) |
| |
| # TODO: update to work on CUDA, too |
| @onlyCPU |
| def test_narrow(self, device): |
| x = torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) |
| self.assertEqual(x.narrow(0, 0, 1), torch.tensor([[0, 1, 2]])) |
| self.assertEqual(x.narrow(0, 0, 2), torch.tensor([[0, 1, 2], [3, 4, 5]])) |
| self.assertEqual(x.narrow(0, 1, 1), torch.tensor([[3, 4, 5]])) |
| self.assertEqual(x.narrow(0, -1, 1), torch.tensor([[6, 7, 8]])) |
| self.assertEqual(x.narrow(0, -2, 2), torch.tensor([[3, 4, 5], [6, 7, 8]])) |
| self.assertEqual(x.narrow(0, -3, 3), torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]])) |
| self.assertEqual(x.narrow(-1, -1, 1), torch.tensor([[2], [5], [8]])) |
| self.assertEqual(x.narrow(-2, -1, 1), torch.tensor([[6, 7, 8]])) |
| |
| # TODO: update to work on CUDA, too |
| @onlyCPU |
| def test_narrow_tensor(self, device): |
| x = torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) |
| self.assertEqual(x.narrow(0, torch.tensor(0), 1), torch.tensor([[0, 1, 2]])) |
| with self.assertRaises(Exception): |
| x.narrow(0, torch.tensor(0.), 1) |
| with self.assertRaises(Exception): |
| x.narrow(0, torch.tensor([0]), 1) |
| with self.assertRaises(Exception): |
| x.narrow(0, torch.tensor([0, 1]), 1) |
| |
| # TODO: make work on CUDA, too |
| @onlyCPU |
| def test_t(self, device): |
| # Test 0D tensors |
| x = torch.randn(()) |
| self.assertEqual(x, x.t()) |
| x = x.to_sparse() |
| self.assertEqual(x, x.t()) |
| |
| # Test 1D tensors |
| x = torch.arange(4) |
| self.assertEqual(x, x.t()) |
| x = x.to_sparse() |
| self.assertEqual(x, x.t()) |
| |
| # Test 2D tensors |
| x = torch.rand((2, 2)) |
| self.assertEqual(x.t(), x.transpose(0, 1)) |
| x = x.to_sparse() |
| self.assertEqual(x.t(), x.transpose(0, 1)) |
| |
| # Test 3D tensor |
| x = torch.rand((2, 2, 2)) |
| with self.assertRaisesRegex(RuntimeError, 'expects a tensor with <= 2 dimensions, but self is 3D'): |
| x.t() |
| x = x.to_sparse() |
| with self.assertRaisesRegex(RuntimeError, 'expects a tensor with <= 2 sparse and 0 dense dimensions'): |
| x.t() |
| |
| @onlyCPU |
| def test_split(self, device): |
| tensor = torch.rand(7, 4) |
| split_size = 3 |
| dim = 0 |
| target_sizes = ([3, 4], [3, 4], [1, 4]) |
| splits = tensor.split(split_size, dim) |
| start = 0 |
| for target_size, split in zip(target_sizes, splits): |
| self.assertEqual(split.size(), target_size) |
| self.assertEqual(tensor.narrow(dim, start, target_size[dim]), split, atol=0, rtol=0) |
| start = start + target_size[dim] |
| |
| # Variable sections split |
| tensor = torch.randn(20, 10) |
| dim = 0 |
| split_sizes = [5, 5, 10] |
| target_sizes = ([[5, 10], [5, 10], [10, 10]]) |
| splits = tensor.split(split_sizes, dim) |
| start = 0 |
| for target_size, split in zip(target_sizes, splits): |
| self.assertEqual(split.size(), target_size) |
| self.assertEqual(tensor.narrow(dim, start, target_size[dim]), split, atol=0, rtol=0) |
| start = start + target_size[dim] |
| |
| split_sizes = [2, 2, 6] |
| target_sizes = ([20, 2], [20, 2], [20, 6]) |
| dim = 1 |
| splits = tensor.split(split_sizes, dim) |
| start = 0 |
| for target_size, split in zip(target_sizes, splits): |
| self.assertEqual(split.size(), target_size) |
| self.assertEqual(tensor.narrow(dim, start, target_size[dim]), split, atol=0, rtol=0) |
| start = start + target_size[dim] |
| |
| @onlyCPU |
| def test_chunk(self, device): |
| tensor = torch.rand(4, 7) |
| num_chunks = 3 |
| dim = 1 |
| target_sizes = ([4, 3], [4, 3], [4, 1]) |
| splits = tensor.chunk(num_chunks, dim) |
| start = 0 |
| for target_size, split in zip(target_sizes, splits): |
| self.assertEqual(split.size(), target_size) |
| self.assertEqual(tensor.narrow(dim, start, target_size[dim]), split, |
| atol=0, rtol=0) |
| start = start + target_size[dim] |
| |
| # Invalid chunk sizes |
| error_regex = 'chunk expects.*greater than 0' |
| with self.assertRaisesRegex(RuntimeError, error_regex): |
| tensor.chunk(0) |
| with self.assertRaisesRegex(RuntimeError, error_regex): |
| tensor.chunk(-2) |
| |
| # TODO: make work on CUDA, too |
| @skipIfTorchDynamo("TorchDynamo fails with unknown reason") |
| @onlyCPU |
| def test_unsqueeze(self, device) -> None: |
| x = torch.randn(2, 3, 4) |
| y = x.unsqueeze(1) |
| self.assertEqual(y, x.view(2, 1, 3, 4)) |
| y = x.clone().unsqueeze_(2) |
| self.assertEqual(y, x.view(2, 3, 1, 4)) |
| |
| x = x[:, 1] |
| self.assertFalse(x.is_contiguous()) |
| y = x.unsqueeze(1) |
| self.assertEqual(y, x.contiguous().view(2, 1, 4)) |
| y = x.clone().unsqueeze_(2) |
| self.assertEqual(y, x.contiguous().view(2, 4, 1)) |
| |
| # unit test for special case transposed copy (see ATen/native/Copy.cpp for details) |
| def test_big_transpose(self, device): |
| t = torch.rand(456, 789, device=device) |
| t1 = t.t().contiguous() |
| t2 = torch.from_numpy(t.cpu().numpy().transpose()) |
| self.assertEqual(t1, t2) |
| |
| def test_T(self, device): |
| a = torch.randn(2, 3, 4, device=device) |
| t1 = a.T |
| t2 = a.permute(2, 1, 0) |
| self.assertEqual(t2, t1) |
| b = torch.randn(10, device=device) |
| self.assertEqual(b, b.T) |
| |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool)) |
| def test_transposes(self, device, dtype): |
| for op in ("T", "H", "mT", "mH", "adjoint"): |
| shapes = ((2, 3), (2, 3, 4)) if op[0] == "m" or op == "adjoint" else ((2, 3),) |
| for shape in shapes: |
| a = make_tensor(shape, device=device, dtype=dtype) |
| t1 = getattr(a, op) |
| if op == "adjoint": |
| t1 = t1() |
| t2 = a |
| t2 = t2.transpose(-2, -1) |
| if op[-1] == "H" or op == "adjoint": |
| t2 = t2.conj() |
| self.assertEqual(t2, t1) |
| |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool)) |
| def test_transposes_errors(self, device, dtype): |
| for op in ("H", "mT", "mH", "adjoint"): |
| shapes = ((2,), (2, 3, 4)) if op == "H" else ((2,),) |
| for shape in shapes: |
| a = make_tensor(shape, device=device, dtype=dtype) |
| with self.assertRaisesRegex(RuntimeError, "only supported on matrices"): |
| t1 = getattr(a, op) |
| if op == "adjoint": |
| t1 = t1() |
| |
| def test_python_types(self, device): |
| a1 = torch.randn((1, 2), device=device, dtype=torch.float64) |
| a2 = torch.randn((1, 2), device=device, dtype=float) |
| self.assertEqual(a1.dtype, a2.dtype) |
| |
| b1 = torch.arange(10, 20, dtype=torch.int64, device=device) |
| b2 = torch.arange(10, 20, dtype=int, device=device) |
| self.assertEqual(b1.dtype, b2.dtype) |
| |
| c1 = torch.tensor([True, False], dtype=torch.bool, device=device) |
| c2 = torch.tensor([True, False], dtype=bool, device=device) |
| self.assertEqual(c1.dtype, c2.dtype) |
| |
| # TODO: is resize best put in test_view_ops? |
| def test_resize_as_preserves_strides(self, device): |
| x = torch.empty(2, 3).t() |
| old_strides = x.stride() |
| x.resize_as_(x) |
| self.assertEqual(x.stride(), old_strides) |
| |
| def test_memory_format_resize_as(self, device): |
| def test_helper(shape, memory_format, device): |
| xc = torch.randn(shape, device=device).contiguous(memory_format=memory_format) |
| flat = torch.randn(xc.numel(), device=device) |
| flat.resize_as_(xc, memory_format=torch.preserve_format) |
| self.assertTrue(flat.is_contiguous(memory_format=memory_format)) |
| |
| test_helper((10, 3, 32, 32), torch.channels_last, device) |
| test_helper((3, 10, 3, 32, 32), torch.channels_last_3d, device) |
| |
| def test_memory_format_resize_(self, device): |
| def test_helper(shape, numel, memory_format, device): |
| flat = torch.randn(numel, device=device) |
| flat.resize_(shape, memory_format=memory_format) |
| self.assertTrue(flat.is_contiguous(memory_format=memory_format)) |
| |
| test_helper((10, 3, 32, 32), 10 * 3 * 32 * 32, torch.channels_last, device) |
| test_helper((3, 10, 3, 32, 32), 3 * 10 * 3 * 32 * 32, torch.channels_last_3d, device) |
| |
| @onlyNativeDeviceTypes |
| @dtypes(torch.int64, torch.float, torch.complex128) |
| def test_transpose_invalid(self, device, dtype): |
| for fn in (torch.swapdims, torch.swapaxes, torch.transpose): |
| shape = _rand_shape(4, min_size=5, max_size=10) |
| x = _generate_input(shape, dtype, device, False) |
| |
| # 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) |
| |
| @dtypes(torch.int64, torch.float, torch.complex128) |
| def test_transpose_vs_numpy(self, device, dtype): |
| for fn in (torch.swapdims, torch.swapaxes, torch.transpose): |
| for nd in range(5): |
| shape = _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 |
| |
| partial_map = { |
| torch.swapdims: partial(torch.swapdims, dim0=src_dim, dim1=dst_dim), |
| torch.swapaxes: partial(torch.swapaxes, axis0=src_dim, axis1=dst_dim), |
| torch.transpose: partial(torch.transpose, dim0=src_dim, dim1=dst_dim), |
| } |
| |
| torch_fn = partial_map[fn] |
| np_fn = partial(np.swapaxes, axis1=src_dim, axis2=dst_dim) |
| 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) |
| partial_map = { |
| torch.swapdims: partial(torch.swapdims, dim0=0, dim1=0), |
| torch.swapaxes: partial(torch.swapaxes, axis0=0, axis1=0), |
| torch.transpose: partial(torch.transpose, dim0=0, dim1=0), |
| } |
| torch_fn = partial_map[fn] |
| np_fn = partial(np.swapaxes, axis1=0, axis2=0) |
| self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None) |
| |
| def _test_atleast_dim(self, torch_fn, np_fn, device, dtype): |
| for ndims in range(0, 5): |
| shape = _rand_shape(ndims, min_size=5, max_size=10) |
| for n in range(ndims + 1): |
| for with_extremal in [False, True]: |
| for contiguous in [False, True]: |
| # Generate Input. |
| x = _generate_input(shape, dtype, device, with_extremal) |
| if contiguous: |
| x = x.T |
| self.compare_with_numpy(torch_fn, np_fn, x, device=None, dtype=None) |
| |
| # Compare sequence input |
| torch_sequence_x = (x,) * random.randint(3, 10) |
| np_sequence_x = tuple(np.array(x.detach().cpu().numpy()) for x in torch_sequence_x) |
| torch_res = torch_fn(*torch_sequence_x) |
| np_res = np_fn(*np_sequence_x) |
| |
| torch_res = tuple(x.cpu() for x in torch_res) |
| np_res = tuple(torch.from_numpy(x) for x in np_res) |
| self.assertEqual(np_res, torch_res) |
| |
| # TODO: are these view ops? |
| @dtypes(*all_types_and_complex_and(torch.half)) |
| def test_atleast(self, device, dtype): |
| self._test_atleast_dim(torch.atleast_1d, np.atleast_1d, device, dtype) |
| self._test_atleast_dim(torch.atleast_2d, np.atleast_2d, device, dtype) |
| self._test_atleast_dim(torch.atleast_3d, np.atleast_3d, device, dtype) |
| |
| # TODO: OpInfo this |
| def _test_atleast(self, device, torch_fn): |
| # 0-dim |
| s = torch.tensor(0.5, dtype=torch.double, requires_grad=True) |
| |
| gradcheck(lambda x: torch_fn(x), s) |
| gradgradcheck(lambda x: torch_fn(x), s) |
| |
| # 1-dim |
| a = torch.rand(4, dtype=torch.double, requires_grad=True) |
| |
| gradcheck(lambda x: torch_fn(x), a) |
| gradgradcheck(lambda x: torch_fn(x), a) |
| |
| # 2,3,4-dim |
| b = torch.rand(4, 3, dtype=torch.double, requires_grad=True) |
| c = torch.rand(4, 3, 2, dtype=torch.double, requires_grad=True) |
| d = torch.rand(4, 3, 2, 1, dtype=torch.double, requires_grad=True) |
| |
| input_tuple = (s, a, b, c, d) |
| gradcheck(lambda s, w, x, y, z: torch_fn(s, w, x, y, z), input_tuple) |
| gradgradcheck(lambda s, w, x, y, z: torch_fn(s, w, x, y, z), input_tuple) |
| |
| def test_atleast_gradient(self, device): |
| self._test_atleast(device, torch.atleast_1d) |
| self._test_atleast(device, torch.atleast_2d) |
| self._test_atleast(device, torch.atleast_3d) |
| |
| |
| @onlyCPU |
| @dtypes(torch.float) |
| def test_broadcast_tensors(self, device, dtype): |
| x0 = torch.randn(2, 1, 3, dtype=dtype, device=device) |
| x1 = torch.randn(3, dtype=dtype, device=device) |
| x2 = torch.randn(3, 1, dtype=dtype, device=device) |
| expected_size = (2, 3, 3) |
| |
| y0, y1, y2 = torch.broadcast_tensors(x0, x1, x2) |
| self.assertTrue(y0.size() == expected_size) |
| self.assertTrue(y1.size() == expected_size) |
| self.assertTrue(y2.size() == expected_size) |
| |
| |
| @onlyCPU |
| def test_broadcast_shapes(self, device): |
| examples = [(), (1,), (2,), (1, 1), (3, 1), (3, 2), (4, 1, 1), (4, 3, 2)] |
| for s0 in examples: |
| x0 = torch.randn(s0) |
| expected = torch.broadcast_tensors(x0)[0].shape |
| actual = torch.broadcast_shapes(s0) |
| self.assertEqual(expected, actual) |
| |
| for s1 in examples: |
| x1 = torch.randn(s1) |
| expected = torch.broadcast_tensors(x0, x1)[0].shape |
| actual = torch.broadcast_shapes(s0, s1) |
| self.assertEqual(expected, actual) |
| |
| inputs_list = [[1, 4], [4, 1], [1, 1, 3]] |
| for integral_inputs in inputs_list: |
| res1 = torch.broadcast_shapes(*integral_inputs) |
| res2 = torch.broadcast_tensors(*map(torch.empty, integral_inputs))[0].shape |
| self.assertEqual(res1, res2) |
| |
| inputs_with_neg_vals = [[1, 1, -12], [-1, 1], [-11, ]] |
| for integral_inputs_with_neg_vals in inputs_with_neg_vals: |
| with self.assertRaisesRegex(RuntimeError, "Trying to create tensor with negative dimension"): |
| torch.broadcast_shapes(*integral_inputs_with_neg_vals) |
| |
| integral_inputs_error_case = [(3, 5), (2, 4, 1)] |
| for error_input in integral_inputs_error_case: |
| with self.assertRaisesRegex(RuntimeError, "Shape mismatch: objects cannot be broadcast to a single shape"): |
| torch.broadcast_shapes(*error_input) |
| |
| negative_inputs = [(-1,), (1, -12), (4, -11), (-4, 1), (1, 1, -2)] |
| for s0 in negative_inputs: |
| with self.assertRaisesRegex(RuntimeError, "Trying to create tensor with negative dimension"): |
| torch.broadcast_shapes(s0) |
| |
| for s1 in negative_inputs: |
| with self.assertRaisesRegex(RuntimeError, "Trying to create tensor with negative dimension"): |
| torch.broadcast_shapes(s0, s1) |
| |
| float_inputs_error_case = [(1.1, 2.0), (1.1, 1.0)] |
| for error_case in float_inputs_error_case: |
| for float_input in error_case: |
| with self.assertRaisesRegex(RuntimeError, "Input shapes " |
| "should be of type ints, a tuple of ints, or a list of ints"): |
| torch.broadcast_shapes(float_input) |
| |
| diff_input_types = [(1, (5,)), (3, (1,)), (1, (3, 4))] |
| for s0 in diff_input_types: |
| res1 = torch.broadcast_shapes(*s0) |
| res2 = torch.broadcast_tensors(*map(torch.empty, s0))[0].shape |
| self.assertEqual(res1, res2) |
| |
| # Skip BFloat16 since numpy does not support it |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bool)) |
| def test_broadcast_to(self, device, dtype): |
| def can_broadcast(s0, s1): |
| # s0.dim() <= s1.dim(), reverse s0 and s1 to compare trailing dimension |
| s0 = tuple(reversed(s0)) |
| s1 = tuple(reversed(s1)) |
| for i in range(len(s0)): |
| if s0[i] != 1 and s0[i] != s1[i]: |
| return False |
| return True |
| |
| sizes = ( |
| (), (1,), (2,), (1, 1), (3, 1), (3, 2), (4, 1, 1), (4, 3, 2) |
| ) |
| for s0, s1 in combinations(sizes, r=2): |
| t = make_tensor(s0, dtype=dtype, device=device, low=-9, high=9) |
| t_np = t.cpu().numpy() |
| |
| if can_broadcast(s0, s1): |
| res = torch.broadcast_to(t, s1) |
| np_res = np.broadcast_to(t_np, s1) |
| self.assertEqual(res, np_res) |
| else: |
| with self.assertRaisesRegex(RuntimeError, |
| r"The expanded size of the tensor \(\d\) " |
| r"must match the existing size \(\d\)"): |
| torch.broadcast_to(t, s1) |
| |
| def test_view(self, device): |
| tensor = torch.rand(15, device=device) |
| template = torch.rand(3, 5, device=device) |
| empty = torch.empty(0, device=device) |
| target = template.size() |
| self.assertEqual(tensor.view_as(template).size(), target) |
| self.assertEqual(tensor.view(3, 5).size(), target) |
| self.assertEqual(tensor.view(torch.Size([3, 5])).size(), target) |
| self.assertEqual(tensor.view(-1, 5).size(), target) |
| self.assertEqual(tensor.view(3, -1).size(), target) |
| tensor_view = tensor.view(5, 3) |
| tensor_view.fill_(random.uniform(0, 1)) |
| self.assertEqual(empty.view_as(empty), empty) |
| self.assertEqual(empty.view(0), empty) |
| self.assertEqual(empty.view(0, 3, 0, 1).size(), torch.Size([0, 3, 0, 1])) |
| self.assertEqual(empty.view(0, 3, 0, 1).view(0), empty) |
| |
| # test size inference with empty tensors |
| self.assertEqual(empty.view(-1).size(), torch.Size([0])) |
| self.assertEqual(empty.view(10, 3, -1).size(), torch.Size([10, 3, 0])) |
| |
| with self.assertRaisesRegex(RuntimeError, r"because the unspecified dimension size -1 can be any value"): |
| empty.view(-1, 0) |
| |
| with self.assertRaisesRegex(RuntimeError, r"because the unspecified dimension size -1 can be any value"): |
| empty.view(3, 0, -1, 0) |
| |
| self.assertRaises(RuntimeError, lambda: tensor.view(15, 0)) |
| self.assertRaises(RuntimeError, lambda: tensor.view(7, -1)) |
| self.assertRaises(RuntimeError, lambda: tensor.view(15, -1, -1)) |
| |
| # test view when tensor is not contiguous in every dimension, but only |
| # contiguous dimensions are touched. |
| tensor = torch.rand(4, 2, 5, 1, 6, 2, 9, 3, device=device).transpose(-1, 2).transpose(-2, 3) |
| # size: [ 4, 2, 3, 9, 6, 2, 1, 5] |
| # stride: [3840, 1620, 1, 3, 54, 27, 324, 324] |
| # contiguous dim chunks: [__________, ____, ____, __________, ____, ____] |
| # merging 1 to chunk after: [__________, ____, ____, __________, __________] |
| contig_tensor = tensor.clone() |
| # [4, 2] => [8, 1] |
| # [3] => [3] |
| # [9] => [3, 3] |
| # [6, 2] => [4, 1, 3] |
| # [1, 5] => [5] |
| view_size = [8, 1, 3, 3, 3, 4, 1, 3, 5] |
| self.assertEqual(tensor.view(*view_size), contig_tensor.view(*view_size)) |
| # [4, 2] => [2, 4] |
| # [3] => [3] |
| # [9] => [1, 9] |
| # [6, 2] => [2, 2, 3] |
| # [1, 5] => [5, 1] |
| view_size = [2, 4, 3, 1, 9, 2, 2, 3, 5, 1] |
| self.assertEqual(tensor.view(*view_size), contig_tensor.view(*view_size)) |
| # adding size 1 dims |
| view_size = [1, 1, 2, 1, 4, 3, 1, 1, 9, 1, 2, 1, 2, 3, 1, 5, 1, 1] |
| self.assertEqual(tensor.view(*view_size), contig_tensor.view(*view_size)) |
| |
| # invalid views |
| self.assertRaises(RuntimeError, lambda: tensor.view(-1)) |
| # crossing [4, 2], [3] |
| self.assertRaises(RuntimeError, lambda: tensor.view(24, 9, 6, 2, 1, 5)) |
| # crossing [6, 2], [1, 5] |
| self.assertRaises(RuntimeError, lambda: tensor.view(8, 3, 9, 6, 10)) |
| # crossing [9], [6, 2] |
| self.assertRaises(RuntimeError, lambda: tensor.view(8, 3, 54, 2, 1, 5)) |
| |
| # view with stride 0 dims |
| tensor = torch.empty(1, 1, device=device).expand(3, 4) # all dims are contiguous |
| contig_tensor = tensor.clone() |
| self.assertEqual(tensor.view(-1), contig_tensor.view(-1)) |
| self.assertEqual(tensor.view(1, -1, 1), contig_tensor.view(1, -1, 1)) |
| self.assertEqual(tensor.view(-1, 1), contig_tensor.view(-1, 1)) |
| self.assertEqual(tensor.view(6, 2, 1), contig_tensor.view(6, 2, 1)) |
| self.assertEqual(tensor.view(1, 6, 2, 1), contig_tensor.view(1, 6, 2, 1)) |
| |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool)) |
| def test_reshape_view_semantics(self, device, dtype): |
| tensor = make_tensor((15, 4), dtype=dtype, device=device) |
| target = (20, 3) |
| |
| # Cases where the tensor can be returned as a view. |
| view_tensor = tensor.reshape(target) |
| self.assertEqual((view_tensor.size()), target) |
| self.assertEqual(tensor.storage().data_ptr(), view_tensor.storage().data_ptr()) |
| |
| # Cases where the tensor must be copied (transpose makes it non-contiguous forcing |
| # the copy). |
| copy_tensor = tensor.transpose(0, 1).reshape(target) |
| self.assertEqual(copy_tensor.size(), target) |
| self.assertNotEqual(tensor.storage().data_ptr(), copy_tensor.storage().data_ptr()) |
| |
| def test_contiguous(self, device): |
| x = torch.randn(1, 16, 5, 5, device=device) |
| self.assertTrue(x.is_contiguous()) |
| stride = list(x.stride()) |
| stride[0] = 20 |
| # change the stride in dimension 0. the tensor is still contiguous because size[0] is 1 |
| x.set_(x.storage(), 0, x.size(), stride) |
| self.assertTrue(x.is_contiguous()) |
| |
| @onlyNativeDeviceTypes |
| # Skip BFloat16 since numpy does not support it |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bool)) |
| def test_tensor_split_sections(self, device, dtype): |
| input_sizes = [ |
| (0,), |
| (10,), |
| (10, 0), |
| (0, 10), |
| (4, 10), |
| (12, 3), |
| ] |
| for input_size in input_sizes: |
| a_base = make_tensor(input_size, dtype=dtype, device=device, low=-9, high=9) |
| # Run tests on transposed input if it has at least 2 dims |
| for a in [a_base, a_base.t()] if a_base.dim() > 2 else [a_base]: |
| a_n = a.cpu().numpy() |
| for dim in range(-a.dim(), a.dim()): |
| for sections in range(1, 2 * a.size(dim)): |
| msg = f'input_size {input_size}, sections {sections}, dim {dim}' |
| result1 = torch.tensor_split(a, sections, dim) |
| result2 = torch.tensor_split(a, torch.tensor(sections, dtype=torch.int64), dim) |
| for r1, r2 in zip(result1, result2): |
| self.assertEqual(r1.device, torch.device(device), msg=msg) |
| self.assertEqual(r1.dtype, dtype, msg=msg) |
| self.assertEqual(r2.device, torch.device(device), msg=msg) |
| self.assertEqual(r2.dtype, dtype, msg=msg) |
| result_n = np.array_split(a_n, sections, dim) |
| self.assertEqual(result_n, result1, msg=msg) |
| self.assertEqual(result_n, result2, msg=msg) |
| |
| @onlyNativeDeviceTypes |
| # Skip BFloat16 since numpy does not support it |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bool)) |
| def test_tensor_split_indices(self, device, dtype): |
| input_sizes = [ |
| (0,), |
| (10,), |
| (10, 0), |
| (0, 10), |
| (4, 10), |
| (12, 3), |
| ] |
| indices_args = [ |
| (), |
| (0,), |
| (3,), |
| (10,), |
| (-1,), |
| (-10,), |
| (2, -1), |
| (3, 4, 10), |
| (0, -1, 0, 10), |
| (1, 5, 2, 8), |
| ] |
| for input_size in input_sizes: |
| a_base = make_tensor(input_size, dtype=dtype, device=device, low=-9, high=9) |
| # Run tests on transposed input if it has at least 2 dims |
| for a in [a_base, a_base.t()] if a_base.dim() > 2 else [a_base]: |
| a_n = a.cpu().numpy() |
| for dim in range(-a.dim(), a.dim()): |
| for indices in indices_args: |
| result_1 = torch.tensor_split(a, indices, dim) |
| result_2 = torch.tensor_split(a, torch.tensor(indices, dtype=torch.int64), dim) |
| |
| msg = f'input_size {input_size}, indices {indices}, dim {dim}' |
| for r1, r2 in zip(result_1, result_2): |
| self.assertEqual(r1.device, torch.device(device), msg=msg) |
| self.assertEqual(r1.dtype, dtype, msg=msg) |
| self.assertEqual(r2.device, torch.device(device), msg=msg) |
| self.assertEqual(r2.dtype, dtype, msg=msg) |
| |
| result_n = np.array_split(a_n, indices, dim) |
| self.assertEqual(result_n, result_1, msg=msg) |
| self.assertEqual(result_n, result_2, msg=msg) |
| |
| @onlyNativeDeviceTypes |
| def test_tensor_split_errors(self, device): |
| S = 10 |
| test_cases = [ |
| # input size, sections or indices, dim, error type, error message, numpy error type |
| [(S,), 10, 1, IndexError, r'Dimension out of range', IndexError], |
| [(), 10, 0, RuntimeError, r'tensor_split expected at least a 1-dimensional tensor, ' |
| + 'but got a tensor with 0 dims', IndexError], |
| [(S,), (10,), 1, IndexError, r'Dimension out of range', IndexError], |
| [(), (10,), 0, RuntimeError, r'tensor_split expected at least a 1-dimensional tensor, ' |
| + 'but got a tensor with 0 dims', IndexError], |
| [(S,), 0, 0, RuntimeError, r'number of sections must be larger than 0, got 0', ValueError], |
| [(S,), -1, 0, RuntimeError, r'number of sections must be larger than 0, got -1', ValueError], |
| ] |
| for input_size, sections_or_indices, dim, err, err_msg, numpy_err in test_cases: |
| a = torch.randn(input_size, device=device) |
| msg = f'input_size {input_size}, sections_or_indices {sections_or_indices}, dim {dim}' |
| with self.assertRaisesRegex(err, err_msg, msg=msg): |
| torch.tensor_split(a, sections_or_indices, dim) |
| with self.assertRaisesRegex(err, err_msg, msg=msg): |
| torch.tensor_split(a, torch.tensor(sections_or_indices), dim) |
| with self.assertRaises(numpy_err, msg=msg): |
| np.array_split(a.cpu().numpy(), sections_or_indices, dim) |
| |
| # addtional tests for tensor_split with tensor_indices_or_sections |
| with self.assertRaisesRegex(RuntimeError, |
| r'tensor_split expected tensor_indices_or_sections to have dtype of long, but got Float'): |
| torch.tensor_split(a, torch.tensor(1.1), dim) |
| |
| with self.assertRaisesRegex(RuntimeError, |
| r'tensor_split expected tensor_indices_or_sections to be a' |
| + ' zero-dimensional or one-dimensional tensor, but got a tensor with 2 dims'): |
| torch.tensor_split(torch.rand(S, device=device), torch.tensor(((1,),)), 0) |
| |
| def test_resize_all_dtypes_and_devices(self, device): |
| shape = (2, 2) |
| for dt in all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool): |
| x = torch.tensor([[1, 2], [3, 4], [5, 6]], dtype=dt, device=device) |
| x.resize_(shape) |
| self.assertEqual(shape, x.shape) |
| |
| def test_resize_as_all_dtypes_and_devices(self, device): |
| for dt in all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool): |
| x = torch.tensor([[1, 2], [3, 4], [5, 6]], dtype=dt, device=device) |
| y = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=dt, device=device) |
| x.resize_as_(y) |
| self.assertEqual(y.shape, x.shape) |
| |
| @onlyNativeDeviceTypes |
| def test_resize_overflow(self, device): |
| x = torch.empty((), dtype=torch.float64) |
| with self.assertRaisesRegex(RuntimeError, 'Storage size calculation overflowed'): |
| x.resize_([2, 4, 2**29, 2**29]) |
| with self.assertRaisesRegex(RuntimeError, 'overflow'): |
| x.resize_([8, 8, 2**29, 2**29]) |
| with self.assertRaisesRegex(RuntimeError, 'Stride calculation overflowed'): |
| x.resize_([0, 4, 2305843009213693952]) |
| |
| def test_view_all_dtypes_and_devices(self, device): |
| for dt in all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool): |
| x = torch.tensor([[1, 2], [3, 4], [5, 6]], dtype=dt, device=device) |
| self.assertEqual(x.view(6).shape, [6]) |
| |
| @onlyCPU |
| def test_conj_neg_view_numpy_error(self, device): |
| self.assertRaisesRegex(RuntimeError, "has conjugate bit set", lambda: torch.tensor([1 + 2j]).conj().numpy()) |
| self.assertRaisesRegex(RuntimeError, "has negative bit set", lambda: torch.tensor([1 + 2j]).conj().imag.numpy()) |
| self.assertRaisesRegex(RuntimeError, "not supported for conjugate view tensors", |
| lambda: torch.tensor([1 + 2j]).conj().view(torch.float64)) |
| self.assertRaisesRegex(RuntimeError, "not supported for tensors with negative bit set", |
| lambda: torch.tensor([1 + 2j]).conj().imag.view(torch.int32)) |
| |
| @onlyCPU |
| def test_crow_col_indices(self, device): |
| crow_indices = (0, 1, 2) |
| col_indices = (1, 0) |
| values = (1, 2) |
| t = torch.sparse_csr_tensor(crow_indices, col_indices, values, size=(2, 2)) |
| # This is the test. If crow_indices is not a view op it'll |
| # trigger an internal assert due to use count greater than 1 |
| # in debug build. |
| t.crow_indices() |
| t.col_indices() |
| |
| instantiate_device_type_tests(TestViewOps, globals(), include_lazy=True) |
| instantiate_device_type_tests(TestOldViewOps, globals()) |
| |
| if __name__ == '__main__': |
| run_tests() |
