| # Owner(s): ["module: tests"] |
| |
| import torch |
| import numpy as np |
| |
| import math |
| from numbers import Number |
| import random |
| import unittest |
| |
| from torch import inf, nan |
| from torch.testing._internal.common_utils import ( |
| TestCase, |
| run_tests, |
| torch_to_numpy_dtype_dict, |
| numpy_to_torch_dtype_dict, |
| suppress_warnings, |
| TEST_SCIPY, |
| slowTest, |
| skipIfNoSciPy, |
| IS_WINDOWS, |
| gradcheck, |
| ) |
| from torch.testing._internal.common_methods_invocations import ( |
| unary_ufuncs, |
| generate_elementwise_unary_tensors, |
| generate_elementwise_unary_small_value_tensors, |
| generate_elementwise_unary_large_value_tensors, |
| generate_elementwise_unary_extremal_value_tensors, |
| ) |
| from torch.testing._internal.common_device_type import ( |
| instantiate_device_type_tests, |
| ops, |
| dtypes, |
| onlyCPU, |
| onlyNativeDeviceTypes, |
| onlyCUDA, |
| dtypesIfCUDA, |
| precisionOverride, |
| dtypesIfCPU, |
| ) |
| |
| from torch.testing import make_tensor |
| from torch.testing._internal.common_dtype import ( |
| floating_types_and, |
| all_types_and_complex_and, |
| integral_types_and, |
| get_all_math_dtypes, |
| complex_types, |
| all_types_and, |
| floating_and_complex_types_and, |
| ) |
| |
| if TEST_SCIPY: |
| import scipy |
| |
| # Refer [scipy reference filter] |
| # Filter operators for which the reference function |
| # is available in the current environment (for reference_numerics tests). |
| reference_filtered_ops = list(filter(lambda op: op.ref is not None, unary_ufuncs)) |
| |
| # Tests for unary "universal functions (ufuncs)" that accept a single |
| # tensor and have common properties like: |
| # - they are elementwise functions |
| # - the input shape is the output shape |
| # - they typically have method and inplace variants |
| # - they typically support the out kwarg |
| # - they typically have NumPy or SciPy references |
| |
| # See NumPy's universal function documentation |
| # (https://numpy.org/doc/1.18/reference/ufuncs.html) for more details |
| # about the concept of ufuncs. |
| |
| |
| # TODO: port test_unary_out_op_mem_overlap |
| # TODO: add test for inplace variants erroring on broadcasted inputs |
| class TestUnaryUfuncs(TestCase): |
| exact_dtype = True |
| |
| @ops( |
| [_fn for _fn in unary_ufuncs if _fn.domain != (None, None)], |
| allowed_dtypes=floating_types_and(torch.bfloat16, torch.half), |
| ) |
| def test_float_domains(self, device, dtype, op): |
| eps = (1e-5, 1e-3, 1e-1, 1, 2, 10, 20, 50, 100) |
| |
| low, high = op.domain |
| # NOTE: the following two loops are separated for readability |
| if low is not None: |
| low_tensor = torch.tensor(low, device=device, dtype=dtype) |
| for epsilon in eps: |
| lower_tensor = low_tensor - epsilon |
| |
| # Skips the test if the difference is not representable, |
| # which can occur if, for example, the difference is small |
| # and the dtype is imprecise (like bfloat16 is) |
| if lower_tensor.item() == low_tensor.item(): |
| continue |
| |
| result = op(lower_tensor) |
| self.assertEqual( |
| result.item(), |
| float("nan"), |
| msg=( |
| "input of {} outside lower domain boundary" |
| " {} produced {}, not nan!" |
| ).format(lower_tensor.item(), low, result.item()), |
| ) |
| |
| if high is not None: |
| high_tensor = torch.tensor(high, device=device, dtype=dtype) |
| for epsilon in eps: |
| higher_tensor = high_tensor + epsilon |
| |
| # See above comment |
| if higher_tensor.item() == high_tensor.item(): |
| continue |
| |
| result = op(higher_tensor) |
| self.assertEqual( |
| result.item(), |
| float("nan"), |
| msg=( |
| "input of {} outside upper domain boundary" |
| " {} produced {}, not nan!" |
| ).format(higher_tensor.item(), high, result.item()), |
| ) |
| |
| # Helper for comparing torch tensors and numpy arrays |
| # TODO: should this or assertEqual also validate that strides are equal? |
| def assertEqualHelper( |
| self, actual, expected, msg, *, dtype, exact_dtype=True, **kwargs |
| ): |
| assert isinstance(actual, torch.Tensor) |
| |
| # Some NumPy functions return scalars, not arrays |
| if isinstance(expected, Number): |
| self.assertEqual(actual.item(), expected, msg, **kwargs) |
| elif isinstance(expected, np.ndarray): |
| # Handles exact dtype comparisons between arrays and tensors |
| if exact_dtype: |
| if ( |
| actual.dtype is torch.bfloat16 |
| or expected.dtype != torch_to_numpy_dtype_dict[actual.dtype] |
| ): |
| # Allows array dtype to be float32 when comparing with bfloat16 tensors |
| # since NumPy doesn't support the bfloat16 dtype |
| # Also ops like scipy.special.erf, scipy.special.erfc, etc, promote float16 |
| # to float32 |
| if expected.dtype == np.float32: |
| assert actual.dtype in ( |
| torch.float16, |
| torch.bfloat16, |
| torch.float32, |
| ) |
| elif expected.dtype == np.float64: |
| assert actual.dtype in ( |
| torch.float16, |
| torch.bfloat16, |
| torch.float32, |
| torch.float64, |
| ) |
| else: |
| self.fail( |
| f"Expected dtype {expected.dtype} but got {actual.dtype}!" |
| ) |
| |
| self.assertEqual( |
| actual, |
| torch.from_numpy(expected).to(actual.dtype), |
| msg, |
| exact_device=False, |
| **kwargs |
| ) |
| else: |
| self.assertEqual(actual, expected, msg, exact_device=False, **kwargs) |
| |
| # Tests that the function and its (array-accepting) reference produce the same |
| # values on given tensors |
| def _test_reference_numerics(self, dtype, op, tensors, equal_nan=True): |
| def _helper_reference_numerics( |
| expected, actual, msg, exact_dtype, equal_nan=True |
| ): |
| if not torch.can_cast( |
| numpy_to_torch_dtype_dict[expected.dtype.type], dtype |
| ): |
| exact_dtype = False |
| |
| if dtype in [torch.uint8, torch.int8, torch.bool]: |
| # NOTE: For these dtypes, PyTorch computes in the default scalar type (float) |
| # while NumPy computes in float16 |
| self.assertEqualHelper( |
| actual, |
| expected, |
| msg, |
| dtype=dtype, |
| exact_dtype=exact_dtype, |
| rtol=1e-3, |
| atol=1e-2, |
| ) |
| elif dtype is torch.bfloat16: |
| # Ref: https://github.com/pytorch/pytorch/blob/master/torch/testing/_internal/common_utils.py#L1149 |
| self.assertEqualHelper( |
| actual, |
| expected, |
| msg, |
| dtype=dtype, |
| exact_dtype=exact_dtype, |
| rtol=16e-3, |
| atol=1e-5, |
| ) |
| elif dtype is torch.half: |
| self.assertEqualHelper( |
| actual, |
| expected, |
| msg, |
| dtype=dtype, |
| exact_dtype=exact_dtype, |
| rtol=1.2e-03, |
| atol=1e-03, |
| ) |
| else: |
| self.assertEqualHelper( |
| actual, |
| expected, |
| msg, |
| dtype=dtype, |
| equal_nan=equal_nan, |
| exact_dtype=exact_dtype, |
| ) |
| |
| for t in tensors: |
| t = t.input |
| torch_kwargs, numpy_kwargs = op.sample_kwargs(t.device, dtype, t) |
| if dtype is torch.bfloat16: |
| a = t.cpu().to(torch.float32).numpy() |
| elif dtype is torch.complex32: |
| a = t.cpu().to(torch.complex64).numpy() |
| else: |
| a = t.cpu().numpy() |
| |
| actual = op(t, **torch_kwargs) |
| expected = op.ref(a, **numpy_kwargs) |
| |
| # Crafts a custom error message for smaller, printable tensors |
| if t.numel() < 10: |
| msg = ( |
| "Failed to produce expected results! Input tensor was" |
| " {}, torch result is {}, and reference result is" |
| " {}." |
| ).format(t, actual, expected) |
| else: |
| msg = None |
| |
| exact_dtype = True |
| if isinstance(actual, torch.Tensor): |
| _helper_reference_numerics( |
| expected, actual, msg, exact_dtype, equal_nan |
| ) |
| else: |
| for x, y in zip(expected, actual): |
| # testing multi-outputs results |
| _helper_reference_numerics(x, y, msg, exact_dtype, equal_nan) |
| |
| # Tests that the function and its (array-accepting) reference produce the same |
| # values on a range of tensors, including empty tensors, scalar tensors, |
| # 1D tensors and a large 2D tensor with interesting and extremal values |
| # and noncontiguities. |
| @suppress_warnings |
| @ops(reference_filtered_ops) |
| def test_reference_numerics_normal(self, device, dtype, op): |
| tensors = generate_elementwise_unary_tensors( |
| op, device=device, dtype=dtype, requires_grad=False |
| ) |
| self._test_reference_numerics(dtype, op, tensors) |
| |
| @suppress_warnings |
| @ops(reference_filtered_ops) |
| def test_reference_numerics_small(self, device, dtype, op): |
| if dtype in (torch.bool,): |
| raise self.skipTest("bool has no small values") |
| |
| tensors = generate_elementwise_unary_small_value_tensors( |
| op, device=device, dtype=dtype, requires_grad=False |
| ) |
| self._test_reference_numerics(dtype, op, tensors) |
| |
| @suppress_warnings |
| @ops(reference_filtered_ops) |
| def test_reference_numerics_large(self, device, dtype, op): |
| if dtype in (torch.bool, torch.uint8, torch.int8): |
| raise self.skipTest("bool, uint8, and int8 dtypes have no large values") |
| |
| tensors = generate_elementwise_unary_large_value_tensors( |
| op, device=device, dtype=dtype, requires_grad=False |
| ) |
| self._test_reference_numerics(dtype, op, tensors) |
| |
| @suppress_warnings |
| @ops( |
| reference_filtered_ops, |
| allowed_dtypes=floating_and_complex_types_and(torch.bfloat16, torch.half), |
| ) |
| def test_reference_numerics_extremal(self, device, dtype, op): |
| tensors = generate_elementwise_unary_extremal_value_tensors( |
| op, device=device, dtype=dtype, requires_grad=False |
| ) |
| self._test_reference_numerics(dtype, op, tensors) |
| |
| # Tests for testing (non)contiguity consistency |
| @ops(unary_ufuncs) |
| def test_contig_vs_every_other(self, device, dtype, op): |
| contig = make_tensor( |
| (1026,), device=device, dtype=dtype, low=op.domain[0], high=op.domain[1] |
| ) |
| non_contig = contig[::2] |
| |
| self.assertTrue(contig.is_contiguous()) |
| self.assertFalse(non_contig.is_contiguous()) |
| |
| torch_kwargs, _ = op.sample_kwargs(device, dtype, non_contig) |
| self.assertEqual( |
| op(contig, **torch_kwargs)[::2], op(non_contig, **torch_kwargs) |
| ) |
| |
| @ops(unary_ufuncs) |
| def test_contig_vs_transposed(self, device, dtype, op): |
| contig = make_tensor( |
| (789, 357), device=device, dtype=dtype, low=op.domain[0], high=op.domain[1] |
| ) |
| non_contig = contig.T |
| |
| self.assertTrue(contig.is_contiguous()) |
| self.assertFalse(non_contig.is_contiguous()) |
| |
| torch_kwargs, _ = op.sample_kwargs(device, dtype, contig) |
| self.assertEqual(op(contig, **torch_kwargs).T, op(non_contig, **torch_kwargs)) |
| |
| @ops(unary_ufuncs) |
| def test_non_contig(self, device, dtype, op): |
| shapes = [(5, 7), (1024,)] |
| for shape in shapes: |
| contig = make_tensor( |
| shape, dtype=dtype, device=device, low=op.domain[0], high=op.domain[1] |
| ) |
| non_contig = torch.empty(shape + (2,), device=device, dtype=dtype)[..., 0] |
| non_contig.copy_(contig) |
| |
| self.assertTrue(contig.is_contiguous()) |
| self.assertFalse(non_contig.is_contiguous()) |
| |
| torch_kwargs, _ = op.sample_kwargs(device, dtype, contig) |
| self.assertEqual(op(contig, **torch_kwargs), op(non_contig, **torch_kwargs)) |
| |
| @ops(unary_ufuncs) |
| def test_non_contig_index(self, device, dtype, op): |
| contig = make_tensor( |
| (2, 2, 1, 2), |
| dtype=dtype, |
| device=device, |
| low=op.domain[0], |
| high=op.domain[1], |
| ) |
| non_contig = contig[:, 1, ...] |
| contig = non_contig.contiguous() |
| |
| self.assertTrue(contig.is_contiguous()) |
| self.assertFalse(non_contig.is_contiguous()) |
| |
| torch_kwargs, _ = op.sample_kwargs(device, dtype, contig) |
| self.assertEqual(op(contig, **torch_kwargs), op(non_contig, **torch_kwargs)) |
| |
| @ops(unary_ufuncs) |
| def test_non_contig_expand(self, device, dtype, op): |
| shapes = [(1, 3), (1, 7), (5, 7)] |
| for shape in shapes: |
| contig = make_tensor( |
| shape, dtype=dtype, device=device, low=op.domain[0], high=op.domain[1] |
| ) |
| non_contig = contig.clone().expand(3, -1, -1) |
| |
| self.assertTrue(contig.is_contiguous()) |
| self.assertFalse(non_contig.is_contiguous()) |
| |
| torch_kwargs, _ = op.sample_kwargs(device, dtype, contig) |
| contig = op(contig, **torch_kwargs) |
| non_contig = op(non_contig, **torch_kwargs) |
| for i in range(3): |
| self.assertEqual( |
| contig, non_contig[i], msg="non-contiguous expand[" + str(i) + "]" |
| ) |
| |
| @ops(unary_ufuncs) |
| def test_contig_size1(self, device, dtype, op): |
| contig = make_tensor( |
| (5, 100), dtype=dtype, device=device, low=op.domain[0], high=op.domain[1] |
| ) |
| contig = contig[:1, :50] |
| contig2 = torch.empty(contig.size(), device=device, dtype=dtype) |
| contig2.copy_(contig) |
| |
| self.assertTrue(contig.is_contiguous()) |
| self.assertTrue(contig2.is_contiguous()) |
| |
| torch_kwargs, _ = op.sample_kwargs(device, dtype, contig) |
| self.assertEqual(op(contig, **torch_kwargs), op(contig2, **torch_kwargs)) |
| |
| @ops(unary_ufuncs) |
| def test_contig_size1_large_dim(self, device, dtype, op): |
| contig = make_tensor( |
| (5, 2, 3, 1, 4, 5, 3, 2, 1, 2, 3, 4), |
| dtype=dtype, |
| device=device, |
| low=op.domain[0], |
| high=op.domain[1], |
| ) |
| contig = contig[:1, :, :, :, :, :, :, :, :, :, :, :] |
| contig2 = torch.empty(contig.size(), device=device, dtype=dtype) |
| contig2.copy_(contig) |
| |
| self.assertTrue(contig.is_contiguous()) |
| self.assertTrue(contig2.is_contiguous()) |
| |
| torch_kwargs, _ = op.sample_kwargs(device, dtype, contig) |
| self.assertEqual(op(contig, **torch_kwargs), op(contig2, **torch_kwargs)) |
| |
| # Tests that computation on a multiple batches is the same as |
| # per-batch computation. |
| @ops(unary_ufuncs) |
| def test_batch_vs_slicing(self, device, dtype, op): |
| input = make_tensor( |
| (1024, 512), dtype=dtype, device=device, low=op.domain[0], high=op.domain[1] |
| ) |
| |
| torch_kwargs, _ = op.sample_kwargs(device, dtype, input) |
| actual = op(input, **torch_kwargs) |
| expected = torch.stack([op(slice, **torch_kwargs) for slice in input]) |
| |
| self.assertEqual(actual, expected) |
| |
| @dtypes(*all_types_and(torch.bool, torch.half)) |
| def test_nan_to_num(self, device, dtype): |
| for contiguous in [False, True]: |
| x = make_tensor((64, 64), low=0.0, high=100.0, dtype=dtype, device=device) |
| |
| if dtype.is_floating_point: |
| # Add extremal values. |
| extremals = [float("nan"), float("inf"), -float("inf")] |
| for idx, extremal in zip(torch.randint(0, 63, (3,)), extremals): |
| x[idx, :] = extremal |
| |
| if not contiguous: |
| x = x.T |
| |
| # With args |
| nan = random.random() |
| posinf = random.random() * 5 |
| neginf = random.random() * 10 |
| |
| self.compare_with_numpy( |
| lambda x: x.nan_to_num(nan=nan, posinf=posinf), |
| lambda x: np.nan_to_num(x, nan=nan, posinf=posinf), |
| x, |
| ) |
| self.compare_with_numpy( |
| lambda x: x.nan_to_num(posinf=posinf, neginf=neginf), |
| lambda x: np.nan_to_num(x, posinf=posinf, neginf=neginf), |
| x, |
| ) |
| |
| # Out Variant |
| out = torch.empty_like(x) |
| result = torch.nan_to_num(x) |
| torch.nan_to_num(x, out=out) |
| self.assertEqual(result, out) |
| |
| result = torch.nan_to_num(x, nan=nan, posinf=posinf, neginf=neginf) |
| torch.nan_to_num(x, out=out, nan=nan, posinf=posinf, neginf=neginf) |
| self.assertEqual(result, out) |
| |
| @onlyCPU |
| def test_nan_to_num_bfloat16(self, device): |
| def test_dtype(fn, input, dtype): |
| input = input.detach().clone().to(dtype=dtype).requires_grad_(True) |
| input2 = input.detach().clone().float().requires_grad_(True) |
| out = fn(input) |
| out.sum().backward() |
| out2 = fn(input2) |
| out2.sum().backward() |
| self.assertEqual(out.dtype, dtype) |
| self.assertEqual(input.grad.dtype, dtype) |
| self.assertEqual(out, out2, exact_dtype=False) |
| self.assertEqual(input.grad, input2.grad, exact_dtype=False) |
| |
| def func(): |
| return torch.nan_to_num |
| |
| shapes = [[1, 3, 6, 6], [1, 3, 6, 128], [1, 3, 256, 256]] |
| for shape in shapes: |
| x = torch.randn(shape, device=device) |
| extremals = [float('nan'), float('inf'), -float('inf')] |
| for id1, id2, extremal in zip(torch.randint(0, 2, (3,)), torch.randint(0, 5, (3,)), extremals): |
| x[0, id1, id2, :] = extremal |
| test_dtype(func(), x, torch.bfloat16) |
| |
| @dtypes(torch.cdouble) |
| def test_complex_edge_values(self, device, dtype): |
| # sqrt Test Reference: https://github.com/pytorch/pytorch/pull/47424 |
| x = torch.tensor(0.0 - 1.0e20j, dtype=dtype, device=device) |
| self.compare_with_numpy(torch.sqrt, np.sqrt, x) |
| # acos test reference: https://github.com/pytorch/pytorch/issue/42952 |
| # Skip on Windows, as CUDA acos returns conjugate value |
| # see https://github.com/pytorch/pytorch/issues/52299 |
| if not (IS_WINDOWS and dtype == torch.cdouble and "cuda" in device): |
| self.compare_with_numpy(torch.acos, np.arccos, x) |
| |
| x = torch.tensor( |
| (-1.0e60 if dtype == torch.cdouble else -1.0e20) - 4988429.2j, |
| dtype=dtype, |
| device=device, |
| ) |
| self.compare_with_numpy(torch.sqrt, np.sqrt, x) |
| |
| @unittest.skipIf(not TEST_SCIPY, "Requires SciPy") |
| @dtypes(torch.float, torch.double) |
| def test_digamma_special(self, device, dtype): |
| # Based on SciPy test for the following special values. |
| # Reference: |
| # https://github.com/scipy/scipy/blob/3a8a3a1d4657254a6611e77e9c28feafa26e6645/scipy/special/tests/test_digamma.py#L22 |
| euler = 0.57721566490153286 |
| dataset = [ |
| (0.0, -0.0), |
| (1, -euler), |
| (0.5, -2 * math.log(2) - euler), |
| (1 / 3, -math.pi / (2 * math.sqrt(3)) - 3 * math.log(3) / 2 - euler), |
| (1 / 4, -math.pi / 2 - 3 * math.log(2) - euler), |
| ( |
| 1 / 6, |
| -math.pi * math.sqrt(3) / 2 |
| - 2 * math.log(2) |
| - 3 * math.log(3) / 2 |
| - euler, |
| ), |
| ( |
| 1 / 8, |
| -math.pi / 2 |
| - 4 * math.log(2) |
| - (math.pi + math.log(2 + math.sqrt(2)) - math.log(2 - math.sqrt(2))) |
| / math.sqrt(2) |
| - euler, |
| ), |
| ] |
| x = torch.tensor(dataset, device=device, dtype=dtype) |
| self.compare_with_numpy(torch.digamma, scipy.special.digamma, x) |
| |
| @unittest.skipIf(not TEST_SCIPY, "Requires SciPy") |
| @dtypes(torch.float, torch.double) |
| def test_digamma(self, device, dtype): |
| # Tests pole behavior |
| tensor = torch.tensor( |
| [ |
| -0.999999994, |
| -1.999999994, |
| -2.0000000111, |
| -100.99999994, |
| 0.000000111, |
| -1931.99999994, |
| -0.000000111, |
| 0, |
| -0, |
| -1, |
| -2, |
| -931, |
| ], |
| dtype=dtype, |
| device=device, |
| ) |
| self.compare_with_numpy(torch.digamma, scipy.special.digamma, tensor) |
| |
| @dtypes(*floating_types_and(torch.half)) |
| def test_frexp(self, device, dtype): |
| input = make_tensor((50, 50), dtype=dtype, device=device) |
| mantissa, exponent = torch.frexp(input) |
| np_mantissa, np_exponent = np.frexp(input.cpu().numpy()) |
| |
| self.assertEqual(mantissa, np_mantissa) |
| self.assertEqual(exponent, np_exponent) |
| |
| # torch.frexp returns exponent in int32 to be compatible with np.frexp |
| self.assertTrue(exponent.dtype == torch.int32) |
| self.assertTrue(torch_to_numpy_dtype_dict[exponent.dtype] == np_exponent.dtype) |
| |
| def test_frexp_assert_raises(self, device): |
| invalid_input_dtypes = integral_types_and(torch.bool) + complex_types() |
| for dtype in invalid_input_dtypes: |
| input = make_tensor((50, 50), dtype=dtype, device=device) |
| with self.assertRaisesRegex( |
| RuntimeError, r"torch\.frexp\(\) only supports floating-point dtypes" |
| ): |
| torch.frexp(input) |
| |
| for dtype in floating_types_and(torch.half): |
| input = make_tensor((50, 50), dtype=dtype, device=device) |
| |
| dtypes = list( |
| all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16) |
| ) |
| dtypes.remove(dtype) |
| for mantissa_dtype in dtypes: |
| mantissa = torch.empty_like(input, dtype=mantissa_dtype) |
| exponent = torch.empty_like(input, dtype=torch.int) |
| with self.assertRaisesRegex( |
| RuntimeError, |
| r"torch\.frexp\(\) expects mantissa to have dtype .+ but got .+", |
| ): |
| torch.frexp(input, out=(mantissa, exponent)) |
| |
| dtypes.append(dtype) |
| dtypes.remove(torch.int) |
| for exponent_dtype in dtypes: |
| mantissa = torch.empty_like(input) |
| exponent = torch.empty_like(input, dtype=exponent_dtype) |
| with self.assertRaisesRegex( |
| RuntimeError, |
| r"torch\.frexp\(\) expects exponent to have int dtype but got .+", |
| ): |
| torch.frexp(input, out=(mantissa, exponent)) |
| |
| def test_polygamma_neg(self, device): |
| with self.assertRaisesRegex( |
| RuntimeError, r"polygamma\(n, x\) does not support negative n\." |
| ): |
| torch.polygamma(-1, torch.tensor([1.0, 2.0], device=device)) |
| |
| # TODO resolve with opinfos |
| @onlyCPU |
| def test_op_invert(self, device): |
| res = 0xFFFF - torch.arange(127, dtype=torch.int8) |
| for dtype in (torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64): |
| a = torch.arange(127, dtype=dtype) |
| self.assertEqual(res.to(dtype), ~a) |
| |
| self.assertEqual(torch.tensor([True, False]), ~torch.tensor([False, True])) |
| |
| # test exceptions |
| for dtype in (torch.half, torch.float, torch.double): |
| a = torch.zeros(10, dtype=dtype) |
| with self.assertRaises(TypeError): |
| b = ~a |
| |
| @dtypes(torch.complex64, torch.complex128) |
| def test_abs_angle_complex_to_float(self, device, dtype): |
| # Constructs random complex values |
| from random import random |
| |
| random_vals = [] |
| for multiplier in (-1, 1, -10, 10, -100, 100): |
| for _ in range(10): |
| random_vals.append( |
| complex(random() * multiplier, random() * multiplier) |
| ) |
| |
| for vals in (random_vals, []): |
| a = np.array(vals, dtype=torch_to_numpy_dtype_dict[dtype]) |
| t = torch.tensor(vals, device=device, dtype=dtype) |
| |
| for fn_name in ("abs", "angle"): |
| torch_fn = getattr(torch, fn_name) |
| np_fn = getattr(np, fn_name) |
| |
| # Tests function |
| np_result = torch.from_numpy(np_fn(a)) |
| torch_result = torch_fn(t).cpu() |
| self.assertEqual(np_result, torch_result, exact_dtype=True) |
| |
| # Tests float out |
| float_dtype = ( |
| torch.float32 if dtype is torch.complex64 else torch.float64 |
| ) |
| np_float_out = np_fn(a).astype(torch_to_numpy_dtype_dict[float_dtype]) |
| float_out = torch.empty_like(t, dtype=float_dtype) |
| torch_fn(t, out=float_out) |
| self.assertEqual(torch.from_numpy(np_float_out), float_out.cpu()) |
| |
| # Tests float out (resized out) |
| float_out = torch.empty(1, device=device, dtype=float_dtype) |
| torch_fn(t, out=float_out) |
| self.assertEqual(torch.from_numpy(np_float_out), float_out.cpu()) |
| |
| # Tests complex out |
| np_complex_out = np_fn(a).astype(torch_to_numpy_dtype_dict[dtype]) |
| complex_out = torch.empty_like(t) |
| torch_fn(t, out=complex_out) |
| self.assertEqual(torch.from_numpy(np_complex_out), complex_out.cpu()) |
| |
| # Tests complex out (resized out) |
| complex_out = torch.empty(0, device=device, dtype=dtype) |
| torch_fn(t, out=complex_out) |
| self.assertEqual(torch.from_numpy(np_complex_out), complex_out.cpu()) |
| |
| # Tests long out behavior (expected failure) |
| long_out = torch.empty(0, device=device, dtype=torch.long) |
| with self.assertRaises(RuntimeError): |
| torch_fn(t, out=long_out) |
| |
| # Tests inplace |
| if fn_name == "abs": |
| torch_inplace_method = getattr(torch.Tensor, fn_name + "_") |
| np_fn(a, out=a) |
| if dtype.is_complex: |
| with self.assertRaisesRegex( |
| RuntimeError, |
| "In-place abs is not supported for complex tensors.", |
| ): |
| torch_inplace_method(t) |
| return |
| torch_inplace_method(t) |
| self.assertEqual(torch.from_numpy(a), t.cpu()) |
| |
| # Note: angle does not have an in-place variant |
| if fn_name == "angle": |
| with self.assertRaises(AttributeError): |
| torch_inplace_method = getattr(torch.Tensor, fn_name + "_") |
| |
| def check_internal_mem_overlap( |
| self, inplace_op, num_inputs, dtype, device, expected_failure=False |
| ): |
| if isinstance(inplace_op, str): |
| inplace_op = getattr(torch.Tensor, inplace_op) |
| input = torch.randn(1, dtype=dtype, device=device).expand(3, 3) |
| inputs = [input] + [torch.randn_like(input) for i in range(num_inputs - 1)] |
| if not expected_failure: |
| with self.assertRaisesRegex(RuntimeError, "single memory location"): |
| inplace_op(*inputs) |
| else: |
| with self.assertRaises(AssertionError): |
| with self.assertRaisesRegex(RuntimeError, "single memory location"): |
| inplace_op(*inputs) |
| |
| def unary_check_input_output_mem_overlap( |
| self, data, sz, op, expected_failure=False |
| ): |
| def _test(op, output, input): |
| output_exp = torch.empty_like(output) |
| op(input, out=output_exp) |
| self.assertEqual(op(input, out=output), output_exp, msg=op.__name__) |
| |
| # output is identical to input: |
| _test(op, output=data[0:sz], input=data[0:sz]) |
| # output and input are independent: |
| _test(op, output=data[0:sz], input=data[sz : 2 * sz]) |
| # output partially overlaps with input: |
| if not expected_failure: |
| with self.assertRaisesRegex(RuntimeError, "unsupported operation"): |
| _test(op, data[0:sz], data[1 : sz + 1]) |
| else: |
| with self.assertRaises(AssertionError): |
| with self.assertRaisesRegex(RuntimeError, "unsupported operation"): |
| _test(op, data[0:sz], data[1 : sz + 1]) |
| |
| # TODO: run on non-native device types |
| @dtypes(torch.double) |
| def test_unary_out_op_mem_overlap(self, device, dtype): |
| sz = 3 |
| doubles = torch.randn(2 * sz, dtype=dtype, device=device) |
| positives = torch.randint(1, 100, (2 * sz,), device=device).double() |
| ints = torch.randint(-100, 100, (2 * sz,), device=device) |
| unary_mem_overlap_cases = [ |
| ("abs", doubles, True, True, "cpu"), |
| ("abs", doubles, True, True, "cuda"), |
| ("acos", doubles, True, True, "cpu"), |
| ("acos", doubles, True, True, "cuda"), |
| ("asin", doubles, True, True, "cpu"), |
| ("asin", doubles, True, True, "cuda"), |
| ("atan", doubles, True, True, "cpu"), |
| ("atan", doubles, True, True, "cuda"), |
| ("acosh", doubles, True, True, "cpu"), |
| ("acosh", doubles, True, True, "cuda"), |
| ("asinh", doubles, True, True, "cpu"), |
| ("asinh", doubles, True, True, "cuda"), |
| ("atanh", doubles, True, True, "cpu"), |
| ("atanh", doubles, True, True, "cuda"), |
| ("bitwise_not", ints, True, True, "cpu"), |
| ("bitwise_not", ints, True, True, "cuda"), |
| ("ceil", doubles, True, True, "cpu"), |
| ("ceil", doubles, True, True, "cuda"), |
| ("cos", doubles, True, True, "cpu"), |
| ("cos", doubles, True, True, "cuda"), |
| ("cosh", doubles, True, True, "cpu"), |
| ("cosh", doubles, True, True, "cuda"), |
| ("digamma", doubles, True, True, "cpu"), |
| ("erf", doubles, True, True, "cpu"), |
| ("erf", doubles, True, True, "cuda"), |
| ("erfc", doubles, True, True, "cpu"), |
| ("erfc", doubles, True, True, "cuda"), |
| ("erfinv", doubles, True, True, "cpu"), |
| ("erfinv", doubles, True, True, "cuda"), |
| ("exp", doubles, True, True, "cpu"), |
| ("exp", doubles, True, True, "cuda"), |
| ("exp2", doubles, True, True, "cpu"), |
| ("exp2", doubles, True, True, "cuda"), |
| ("expm1", doubles, True, True, "cpu"), |
| ("expm1", doubles, True, True, "cuda"), |
| ("floor", doubles, True, True, "cpu"), |
| ("floor", doubles, True, True, "cuda"), |
| ("frac", doubles, True, True, "cpu"), |
| ("frac", doubles, True, True, "cuda"), |
| ("i0", doubles, True, True, "cpu"), |
| ("i0", doubles, True, True, "cuda"), |
| ("log", positives, True, True, "cpu"), |
| ("log", positives, True, True, "cuda"), |
| ("log10", positives, True, True, "cpu"), |
| ("log10", positives, True, True, "cuda"), |
| ("log1p", positives, True, True, "cpu"), |
| ("log1p", positives, True, True, "cuda"), |
| ("log2", positives, True, True, "cpu"), |
| ("log2", positives, True, True, "cuda"), |
| ("neg", doubles, True, True, "cpu"), |
| ("neg", doubles, True, True, "cuda"), |
| ("reciprocal", doubles, True, True, "cpu"), |
| ("reciprocal", doubles, True, True, "cuda"), |
| ("round", doubles, True, True, "cpu"), |
| ("round", doubles, True, True, "cuda"), |
| ("rsqrt", positives, True, True, "cpu"), |
| ("rsqrt", positives, True, True, "cuda"), |
| ("sin", doubles, True, True, "cpu"), |
| ("sin", doubles, True, True, "cuda"), |
| ("sinh", doubles, True, True, "cpu"), |
| ("sinh", doubles, False, True, "cuda"), |
| ("sigmoid", doubles, True, True, "cpu"), |
| ("sigmoid", doubles, True, True, "cuda"), |
| ("logit", doubles, True, True, "cpu"), |
| ("logit", doubles, True, True, "cuda"), |
| ("sqrt", doubles, True, True, "cpu"), |
| ("sqrt", doubles, False, True, "cuda"), |
| ("tan", doubles, True, True, "cpu"), |
| ("tan", doubles, True, True, "cuda"), |
| ("tanh", doubles, True, True, "cpu"), |
| ("tanh", doubles, True, True, "cuda"), |
| ("trunc", doubles, True, True, "cpu"), |
| ("trunc", doubles, True, True, "cuda"), |
| ] |
| |
| for ( |
| fn, |
| inputs, |
| has_input_output_mem_overlap_check, |
| has_internal_mem_overlap_check, |
| dev, |
| ) in unary_mem_overlap_cases: |
| if dev != device: |
| continue |
| out_fn = getattr(torch, fn) |
| in_fn = getattr(torch.Tensor, fn + "_") |
| |
| self.unary_check_input_output_mem_overlap( |
| inputs, |
| sz, |
| out_fn, |
| expected_failure=not has_input_output_mem_overlap_check, |
| ) |
| |
| self.check_internal_mem_overlap( |
| in_fn, |
| 1, |
| dtype, |
| dev, |
| expected_failure=not has_internal_mem_overlap_check, |
| ) |
| |
| # TODO: opinfo hardshrink |
| @onlyCPU |
| @dtypes(torch.float, torch.double, torch.bfloat16) |
| def test_hardshrink(self, device, dtype): |
| data = torch.tensor([1, 0.5, 0.3, 0.6], dtype=dtype, device=device).view(2, 2) |
| self.assertEqual( |
| torch.tensor([1, 0.5, 0, 0.6], dtype=dtype, device=device).view(2, 2), |
| data.hardshrink(0.3), |
| ) |
| self.assertEqual( |
| torch.tensor([1, 0, 0, 0.6], dtype=dtype, device=device).view(2, 2), |
| data.hardshrink(0.5), |
| ) |
| |
| # test default lambd=0.5 |
| self.assertEqual(data.hardshrink(), data.hardshrink(0.5)) |
| |
| # test non-contiguous case |
| self.assertEqual( |
| torch.tensor([1, 0, 0.5, 0.6], dtype=dtype, device=device).view(2, 2), |
| data.t().hardshrink(0.3), |
| ) |
| |
| @onlyCPU |
| @dtypes(torch.float, torch.double, torch.bfloat16) |
| def test_hardshrink_edge_cases(self, device, dtype) -> None: |
| def h(values, l_expected): |
| for l, expected in l_expected.items(): |
| values_tensor = torch.tensor( |
| [float(v) for v in values], dtype=dtype, device=device |
| ) |
| expected_tensor = torch.tensor( |
| [float(v) for v in expected], dtype=dtype, device=device |
| ) |
| self.assertEqual( |
| expected_tensor == values_tensor.hardshrink(l), |
| torch.ones_like(values_tensor, dtype=torch.bool), |
| ) |
| |
| def test_helper(min, max): |
| h( |
| [0.0, min, -min, 0.1, -0.1, 1.0, -1.0, max, -max, inf, -inf], |
| { |
| 0.0: [0.0, min, -min, 0.1, -0.1, 1.0, -1.0, max, -max, inf, -inf], |
| min: [0.0, 0.0, 0.0, 0.1, -0.1, 1.0, -1.0, max, -max, inf, -inf], |
| 0.1: [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, -1.0, max, -max, inf, -inf], |
| 1.0: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, max, -max, inf, -inf], |
| max: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, inf, -inf], |
| inf: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], |
| }, |
| ) |
| |
| test_helper(torch.finfo(dtype).tiny, torch.finfo(dtype).max) |
| |
| @onlyCPU |
| @slowTest |
| @dtypes(torch.float) |
| @unittest.skipIf(True, "Insufficient memory on linux.(2|4)xlarge") |
| def test_exp_slow(self, device, dtype): |
| # Test for https://github.com/pytorch/pytorch/issues/17271 |
| # This is pretty slow on my Macbook but it only takes a few |
| # seconds on a beefy Xeon server |
| a = torch.exp(torch.ones(2**31, dtype=dtype, device=device)) |
| b = torch.exp(torch.ones(1, dtype=dtype, device=device)) |
| self.assertEqual(a, b.expand(2**31)) |
| |
| @precisionOverride( |
| {torch.bfloat16: 1e-2, torch.float: 0.0002, torch.double: 0.0002} |
| ) |
| @dtypes(torch.float, torch.double, torch.bfloat16) |
| def test_hardswish(self, device, dtype): |
| inputValues = [-1000, -4, -3, -2, 0, 2, 3, 4, 1000] |
| expectedOutput = np.multiply( |
| inputValues, np.minimum(np.maximum((np.add(inputValues, 3)), 0), 6) / 6.0 |
| ) |
| |
| inputTensor = torch.tensor(inputValues, dtype=dtype, device=device) |
| expectedOutputTensor = torch.tensor(expectedOutput, dtype=dtype, device=device) |
| |
| # normal |
| self.assertEqual( |
| torch.nn.functional.hardswish(inputTensor), expectedOutputTensor |
| ) |
| |
| # inplace |
| inputTensorCpy = inputTensor.clone().detach() |
| torch.nn.functional.hardswish(inputTensorCpy, inplace=True) |
| self.assertEqual(inputTensorCpy, expectedOutputTensor) |
| |
| @precisionOverride( |
| {torch.bfloat16: 1e-2, torch.float: 0.0002, torch.double: 0.0002} |
| ) |
| @dtypes(torch.float, torch.double, torch.bfloat16) |
| def test_hardsigmoid(self, device, dtype): |
| inputValues = [-1000, -4, -3, -2, 0, 2, 3, 4, 1000] |
| expectedOutput = np.minimum(np.maximum((np.add(inputValues, 3)), 0), 6) / 6.0 |
| |
| inputTensor = torch.tensor(inputValues, dtype=dtype, device=device) |
| |
| # normal |
| self.assertEqual( |
| torch.nn.functional.hardsigmoid(inputTensor), |
| torch.tensor(expectedOutput, dtype=dtype, device=device), |
| ) |
| |
| # inplace |
| inputTensorCpy = inputTensor.clone().detach() |
| self.assertEqual( |
| torch.nn.functional.hardsigmoid(inputTensorCpy, inplace=True), |
| torch.tensor(expectedOutput, dtype=dtype, device=device), |
| ) |
| |
| @precisionOverride( |
| {torch.bfloat16: 1e-2, torch.float: 0.0002, torch.double: 0.0002} |
| ) |
| @dtypes(torch.float, torch.double, torch.bfloat16) |
| def test_hardsigmoid_backward(self, device, dtype): |
| inputValues = [-3.0, 3.0, -2.0, 2.0, -6.0, 6.0] |
| expectedValues = [0.0, 0.0, 1.0 / 6.0, 1.0 / 6.0, 0.0, 0.0] |
| inputTensor = torch.tensor( |
| inputValues, dtype=dtype, device=device |
| ).requires_grad_() |
| expetedTensor = torch.tensor(expectedValues, dtype=dtype, device=device) |
| out = torch.nn.functional.hardsigmoid(inputTensor) |
| out.backward(torch.ones_like(inputTensor)) |
| self.assertEqual(inputTensor.grad, expetedTensor) |
| |
| @skipIfNoSciPy |
| @dtypes(torch.float, torch.double) |
| def test_silu(self, device, dtype): |
| input_np = np.random.randn(5, 8) |
| special_input = [[-1000, -1, -0.1, 0, 0.5, 1, 2, 1000]] |
| input_np = np.concatenate((input_np, special_input), axis=0).astype( |
| torch_to_numpy_dtype_dict[dtype] |
| ) |
| expected_output_np = input_np * scipy.special.expit(input_np) |
| |
| expected_output = torch.from_numpy(expected_output_np).to(device) |
| expected_output_noncontig = expected_output.transpose(0, 1) |
| |
| atol = 1e-6 |
| rtol = 1e-6 |
| |
| input = torch.from_numpy(input_np).clone().contiguous().to(device) |
| self.assertEqual( |
| torch.nn.functional.silu(input), expected_output, atol=atol, rtol=rtol |
| ) |
| self.assertEqual( |
| torch.nn.functional.silu(input, inplace=True), |
| expected_output, |
| atol=atol, |
| rtol=rtol, |
| ) |
| |
| input = torch.from_numpy(input_np).clone().to(device) |
| input_noncontig = input.transpose(0, 1) |
| self.assertEqual( |
| torch.nn.functional.silu(input_noncontig), |
| expected_output_noncontig, |
| atol=atol, |
| rtol=rtol, |
| ) |
| self.assertEqual( |
| torch.nn.functional.silu(input_noncontig, inplace=True), |
| expected_output_noncontig, |
| atol=atol, |
| rtol=rtol, |
| ) |
| |
| # It is not obvious how to merge this into OpInfo becuase these inputs |
| # succeed for gradcheck but are expected to fail for gradgradcheck |
| @dtypes(torch.double) |
| def test_sinc(self, device, dtype): |
| # The derivative of sinc(x) at x=0 has to be special cased. |
| # A naive computation will result in 0/0 -> NaN. |
| # We also need to be careful when we are very close to 0, as the |
| # derivative's denominator is squared, and there are some floats |
| # that are positive and whose squares are zero. |
| a = torch.tensor( |
| [0.0, torch.finfo(torch.double).tiny, 1.0], |
| dtype=dtype, |
| requires_grad=True, |
| device=device, |
| ) |
| gradcheck(torch.sinc, a) |
| |
| @skipIfNoSciPy |
| @dtypes(torch.float, torch.double) |
| def test_mish(self, device, dtype): |
| input_np = np.random.randn(5, 8) |
| special_input = [[-1000, -1, -0.1, 0, 0.5, 1, 2, 1000]] |
| input_np = np.concatenate((input_np, special_input), axis=0).astype( |
| torch_to_numpy_dtype_dict[dtype] |
| ) |
| expected_output_np = input_np * np.tanh(np.log1p(np.exp(input_np))) |
| |
| expected_output = torch.from_numpy(expected_output_np).to(device) |
| expected_output_noncontig = expected_output.transpose(0, 1) |
| |
| atol = 1e-6 |
| rtol = 1e-6 |
| |
| input = torch.from_numpy(input_np).clone().contiguous().to(device) |
| self.assertEqual( |
| torch.nn.functional.mish(input), expected_output, atol=atol, rtol=rtol |
| ) |
| self.assertEqual( |
| torch.nn.functional.mish(input, inplace=True), |
| expected_output, |
| atol=atol, |
| rtol=rtol, |
| ) |
| |
| input = torch.from_numpy(input_np).clone().to(device) |
| input_noncontig = input.transpose(0, 1) |
| self.assertEqual( |
| torch.nn.functional.mish(input_noncontig), |
| expected_output_noncontig, |
| atol=atol, |
| rtol=rtol, |
| ) |
| self.assertEqual( |
| torch.nn.functional.mish(input_noncontig, inplace=True), |
| expected_output_noncontig, |
| atol=atol, |
| rtol=rtol, |
| ) |
| |
| @dtypes(torch.complex64, torch.complex128) |
| def test_log1p_complex(self, device, dtype): |
| # The output values here were obtained using arbitrary precision math (mpmath) |
| # and double checked with WolframAlpha. |
| # Not using numpy's log1p here because by the time of writing this, |
| # np.log1p has precision problems for small complex input values, see here: |
| # https://github.com/numpy/numpy/issues/22609 |
| inouts = [ |
| (0.2 + 0.3j, 0.21263386770217202 + 0.24497866312686414j), |
| (1e-19 + 1e-18j, 1e-19 + 1e-18j), |
| (1e-18 + 0.1j, 0.00497517 + 0.0996687j), |
| (0.1 + 1e-18j, 0.0953102 + 9.090909090909090909e-19j), |
| (0.5 + 0j, 0.40546510810816 + 0j), |
| (0.0 + 0.5j, 0.111571776 + 0.463647609j), |
| (2.0 + 1.0j, 1.151292546497023 + 0.3217505543966422j), |
| (-1.0 + 2.0j, 0.6931471805599453 + 1.570796326794897j), |
| (2.0j, 0.80471895621705014 + 1.1071487177940904j), |
| (-2.0j, 0.80471895621705014 - 1.1071487177940904j), |
| ] |
| # test the extreme values |
| if dtype == torch.complex128: |
| inouts += [ |
| (-1 + 1e250j, 575.6462732485114 + 1.5707963267948966j), |
| (1e250 + 1j, 575.6462732485114 + 1e-250j), |
| (1e250 + 1e250j, 575.9928468387914 + 0.7853981633974483j), |
| (1e-250 + 1e250j, 575.6462732485114 + 1.5707963267948966j), |
| (1e-250 + 2e-250j, 1e-250 + 2e-250j), |
| (1e250 + 1e-250j, 575.6462732485114 + 0.0j), |
| ] |
| elif dtype == torch.complex64: |
| inouts += [ |
| (-1 + 1e30j, 69.07755278982137 + 1.5707963267948966j), |
| (1e30 + 1j, 69.07755278982137 + 1e-30j), |
| (1e30 + 1e30j, 69.42412638010134 + 0.7853981633974483j), |
| (1e-30 + 1e30j, 69.07755278982137 + 1.5707963267948966j), |
| (1e-30 + 2e-30j, 1e-30 + 2e-30j), |
| (1e30 + 1e-30j, 69.07755278982137 + 0.0j), |
| ] |
| |
| # test the log1p individually |
| for inp, out in inouts: |
| res = torch.log1p(torch.tensor(inp, dtype=dtype, device=device)) |
| self.assertFalse(torch.any(torch.isnan(res))) |
| # setting up atol == 0.0 because some part has very small values |
| self.assertEqual(res.real, out.real, atol=0.0, rtol=1e-6) |
| self.assertEqual(res.imag, out.imag, atol=0.0, rtol=1e-6) |
| |
| # test the log1p in tensor |
| inp_lst, out_lst = (list(elmt) for elmt in zip(*inouts)) |
| inp_tens = torch.tensor(inp_lst, dtype=dtype, device=device) |
| out_tens = torch.tensor(out_lst, dtype=dtype, device=device) |
| res_tens = torch.log1p(inp_tens) |
| self.assertEqual(res_tens.real, out_tens.real, atol=0.0, rtol=1e-6) |
| self.assertEqual(res_tens.imag, out_tens.imag, atol=0.0, rtol=1e-6) |
| |
| # do ops like threshold need a test_unary(_nonufunc) test suite? |
| @onlyCPU |
| @dtypes(*get_all_math_dtypes("cpu")) |
| def test_threshold(self, device, dtype): |
| if dtype != torch.uint8 and dtype != torch.float16 and not dtype.is_complex: |
| # 100 is wide enough to use AVX2 instructions for all types |
| x = ( |
| torch.randn(100, dtype=torch.float, device=device) |
| .sign() |
| .to(dtype=dtype) |
| ) |
| y = torch.threshold(x, 0, 0) |
| self.assertTrue(y.le(0).any()) |
| |
| def _helper_test_igamma(self, loglo, loghi, device, dtype, torch_fcn, scipy_fcn): |
| exp1 = 2.71828182846 |
| vec1 = torch.logspace( |
| loglo, loghi, steps=500, base=exp1, dtype=torch.float64, device=device |
| ).unsqueeze(-1) |
| vec1 = vec1.to(dtype) |
| inputs = [ |
| (vec1, vec1.transpose(0, 1)), |
| (vec1, vec1), # for large number, it should approach 0.5 |
| (vec1, 0.5 * vec1), # test for considerable ratio |
| (vec1, 2.0 * vec1), |
| (vec1[::2, :], vec1[::2, :]), # contiguous/noncontiguous tests |
| (vec1[::2, :], vec1[: vec1.shape[0] // 2, :]), |
| (vec1[: vec1.shape[0] // 2, :], vec1[::2, :]), |
| ] |
| half_prec = dtype in [torch.bfloat16, torch.float16] |
| for input0, input1 in inputs: |
| actual = torch_fcn(input0, input1) |
| if half_prec: |
| input0 = input0.to(torch.float) |
| input1 = input1.to(torch.float) |
| expected = scipy_fcn(input0.cpu().numpy(), input1.cpu().numpy()) |
| expected = torch.from_numpy(expected).to(dtype) |
| self.assertEqual(actual, expected) |
| |
| @dtypesIfCPU(torch.float16, torch.bfloat16, torch.float32, torch.float64) |
| @dtypes(torch.float32, torch.float64) |
| @unittest.skipIf(not TEST_SCIPY, "SciPy not found") |
| @onlyNativeDeviceTypes |
| def test_igamma_common(self, device, dtype): |
| # test igamma for reasonable range of values |
| loglo = -4 # approx 0.018 |
| loghi = 4 # approx 54.6 |
| self._helper_test_igamma( |
| loglo, loghi, device, dtype, torch.igamma, scipy.special.gammainc |
| ) |
| |
| @dtypesIfCPU(torch.float16, torch.bfloat16, torch.float32, torch.float64) |
| @dtypes(torch.float32, torch.float64) |
| @unittest.skipIf(not TEST_SCIPY, "SciPy not found") |
| @onlyNativeDeviceTypes |
| def test_igammac_common(self, device, dtype): |
| # test igammac for reasonable range of values |
| loglo = -4 # approx 0.018 |
| loghi = 4 # approx 54.6 |
| self._helper_test_igamma( |
| loglo, loghi, device, dtype, torch.igammac, scipy.special.gammaincc |
| ) |
| |
| @dtypesIfCPU(torch.float16, torch.bfloat16, torch.float32, torch.float64) |
| @dtypes(torch.float32, torch.float64) |
| @onlyNativeDeviceTypes |
| def test_igamma_edge_cases(self, device, dtype): |
| tkwargs = {"dtype": dtype, "device": device} |
| infs = torch.zeros((3,), **tkwargs) + float("inf") |
| zeros = torch.zeros((3,), **tkwargs) |
| ones = torch.ones((3,), **tkwargs) |
| zero_to_large = torch.tensor([0.0, 1.0, 1e3], **tkwargs) |
| small_to_inf = torch.tensor([1e-3, 1.0, float("inf")], **tkwargs) |
| nans = torch.zeros((3,), **tkwargs) + float("nan") |
| inpouts = [ |
| # (a , x), out |
| ((zeros, small_to_inf), ones), |
| ((small_to_inf, zeros), zeros), |
| ((infs, zero_to_large), zeros), |
| ((zero_to_large, infs), ones), |
| ((zeros, zeros), nans), |
| ((infs, infs), nans), |
| ((-small_to_inf, small_to_inf), nans), |
| ] |
| for inputs, output in inpouts: |
| input0, input1 = inputs |
| calc = torch.igamma(input0, input1) |
| if torch.all(torch.isnan(output)): |
| self.assertTrue(torch.all(torch.isnan(calc))) |
| else: |
| self.assertEqual(calc, output) |
| |
| @dtypesIfCPU(torch.float16, torch.bfloat16, torch.float32, torch.float64) |
| @dtypes(torch.float32, torch.float64) |
| @onlyNativeDeviceTypes |
| def test_igammac_edge_cases(self, device, dtype): |
| tkwargs = {"dtype": dtype, "device": device} |
| infs = torch.zeros((3,), **tkwargs) + float("inf") |
| zeros = torch.zeros((3,), **tkwargs) |
| ones = torch.ones((3,), **tkwargs) |
| zero_to_large = torch.tensor([0.0, 1.0, 1e3], **tkwargs) |
| small_to_inf = torch.tensor([1e-3, 1.0, float("inf")], **tkwargs) |
| nans = torch.zeros((3,), **tkwargs) + float("nan") |
| inpouts = [ |
| # (a , x), out |
| ((zeros, small_to_inf), zeros), |
| ((small_to_inf, zeros), ones), |
| ((infs, zero_to_large), ones), |
| ((zero_to_large, infs), zeros), |
| ((zeros, zeros), nans), |
| ((infs, infs), nans), |
| ((-small_to_inf, small_to_inf), nans), |
| ] |
| for inputs, output in inpouts: |
| input0, input1 = inputs |
| calc = torch.igammac(input0, input1) |
| if torch.all(torch.isnan(output)): |
| self.assertTrue(torch.all(torch.isnan(calc))) |
| else: |
| self.assertEqual(calc, output) |
| |
| def _i0_helper(self, t): |
| # Test by comparing to scipy |
| dtype = t.dtype |
| actual = torch.i0(t) |
| if dtype is torch.bfloat16: |
| t = t.to(torch.float32) |
| expected = scipy.special.i0(t.cpu().numpy()) |
| # Casting down for dtype float16 is required since scipy upcasts to float32 |
| if dtype is torch.bfloat16 or dtype is torch.float16: |
| expected = torch.from_numpy(expected).to(dtype) |
| self.assertEqual(actual, expected) |
| |
| def _i0_range_helper(self, range, device, dtype): |
| # i0 tests are broken up by the domain for which the function does not overflow for each dtype |
| # This is done to ensure that the function performs well across all possible input values, without worrying |
| # about inf or nan possibilities |
| for r in (range, -range): |
| t = torch.rand(1000, device=device).to(dtype) * r |
| self._i0_helper(t) |
| |
| @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16)) |
| @dtypes(torch.bfloat16, torch.float32, torch.float64) |
| @unittest.skipIf(not TEST_SCIPY, "SciPy not found") |
| def test_i0_range1(self, device, dtype): |
| # This tests the domain for i0 for which float16 does not overflow |
| # The domain is (-13.25, 13.25) |
| self._i0_range_helper(13.25, device, dtype) |
| |
| @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16)) |
| @dtypes(torch.bfloat16, torch.float32, torch.float64) |
| @unittest.skipIf(not TEST_SCIPY, "SciPy not found") |
| def test_i0_range2(self, device, dtype): |
| # This tests the domain for i0 for which float32 and bfloat16 does not overflow |
| # The domain is (-88.5, 88.5) |
| self._i0_range_helper(88.5, device, dtype) |
| |
| @dtypes(torch.float64) |
| @unittest.skipIf(not TEST_SCIPY, "SciPy not found") |
| def test_i0_range3(self, device, dtype): |
| # This tests the domain for i0 for which float64 does not overflow |
| # The domain is (-709.75, 709.75) |
| self._i0_range_helper(709.75, device, dtype) |
| |
| @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16)) |
| @dtypes(torch.bfloat16, torch.float32, torch.float64) |
| @unittest.skipIf(not TEST_SCIPY, "SciPy not found") |
| def test_i0_special(self, device, dtype): |
| t = torch.tensor([], device=device, dtype=dtype) |
| self._i0_helper(t) |
| |
| t = torch.tensor([inf, -inf, nan], device=device, dtype=dtype) |
| self.assertTrue(torch.i0(t).isnan().all()) |
| |
| @dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16)) |
| @dtypes(torch.bfloat16, torch.float32, torch.float64) |
| @unittest.skipIf(not TEST_SCIPY, "SciPy not found") |
| def test_special_i0_i1_vs_scipy(self, device, dtype): |
| def check_equal(t, torch_fn, scipy_fn): |
| # Test by comparing to scipy |
| actual = torch_fn(t) |
| if dtype is torch.bfloat16: |
| t = t.to(torch.float32) |
| expected = scipy_fn(t.cpu().numpy()) |
| |
| # Casting down for dtype float16 is required since scipy upcasts to float32 |
| if dtype is torch.bfloat16 or dtype is torch.float16: |
| expected = torch.from_numpy(expected).to(dtype) |
| self.assertEqual(actual, expected) |
| |
| t = torch.tensor([], device=device, dtype=dtype) |
| check_equal(t, torch.i0, scipy.special.i0) |
| check_equal(t, torch.special.i0e, scipy.special.i0e) |
| if dtype not in [torch.half, torch.bfloat16]: |
| check_equal(t, torch.special.i1, scipy.special.i1) |
| check_equal(t, torch.special.i1e, scipy.special.i1e) |
| |
| range = (-1e7, 1e7) |
| if dtype == torch.half: |
| range = (-65000, 65000) |
| |
| t = torch.linspace(*range, int(1e4), device=device, dtype=dtype) |
| check_equal(t, torch.i0, scipy.special.i0) |
| check_equal(t, torch.special.i0e, scipy.special.i0e) |
| if dtype not in [torch.half, torch.bfloat16]: |
| check_equal(t, torch.special.i1, scipy.special.i1) |
| check_equal(t, torch.special.i1e, scipy.special.i1e) |
| |
| # NaN, inf, -inf are tested in reference_numerics tests. |
| info = torch.finfo(dtype) |
| min, max, eps, tiny = info.min, info.max, info.eps, info.tiny |
| t = torch.tensor([min, max, eps, tiny], dtype=dtype, device=device) |
| check_equal(t, torch.i0, scipy.special.i0) |
| check_equal(t, torch.special.i0e, scipy.special.i0e) |
| if dtype not in [torch.half, torch.bfloat16]: |
| check_equal(t, torch.special.i1, scipy.special.i1) |
| check_equal(t, torch.special.i1e, scipy.special.i1e) |
| |
| @dtypes(torch.float32, torch.float64) |
| @unittest.skipIf(not TEST_SCIPY, "SciPy not found") |
| def test_special_ndtr_vs_scipy(self, device, dtype): |
| def check_equal(t): |
| # Test by comparing to scipy |
| actual = torch.special.ndtr(t) |
| expected = scipy.special.ndtr(t.cpu().numpy()) |
| self.assertEqual(actual, expected) |
| |
| range = (-10, 10) |
| t = torch.linspace(*range, 1, device=device, dtype=dtype) |
| check_equal(t) |
| |
| # Skip testing NaN, inf, -inf since they are tested in reference_numerics tests. |
| info = torch.finfo(dtype) |
| min, max, eps, tiny = info.min, info.max, info.eps, info.tiny |
| t = torch.tensor([min, max, eps, tiny], dtype=dtype, device=device) |
| check_equal(t) |
| |
| @dtypes(torch.float32, torch.float64) |
| @unittest.skipIf(not TEST_SCIPY, "SciPy not found") |
| def test_special_log_ndtr_vs_scipy(self, device, dtype): |
| def check_equal(t): |
| # Test by comparing with scipy |
| actual = torch.special.log_ndtr(t) |
| expected = scipy.special.log_ndtr(t.cpu().numpy()) |
| self.assertEqual(actual, expected) |
| |
| # Skip testing NaN, inf, -inf since they are tested in reference_numerics tests. |
| info = torch.finfo(dtype) |
| min, max, eps, tiny = info.min, info.max, info.eps, info.tiny |
| t = torch.tensor([min, max, eps, tiny], dtype=dtype, device=device) |
| check_equal(t) |
| |
| # TODO: allow large opinfo values to be opted-into via metadata |
| @dtypes(torch.long) |
| def test_abs_big_number(self, device, dtype): |
| bignumber = 2**31 + 1 |
| res = torch.tensor([bignumber], device=device, dtype=dtype) |
| self.assertGreater(res.abs()[0], 0) |
| |
| # TODO: add signed zero testing to opinfos |
| @dtypes(torch.float, torch.double) |
| def test_abs_signed_zero(self, device, dtype): |
| # Both abs(0.0) and abs(-0.0) should result in 0.0 |
| size = 128 + 1 # pick a large enough number with remainder so that |
| # both vectorized and nonvectorized op is tested |
| inp = torch.zeros(size, device=device, dtype=dtype) |
| inp[::2] = -0.0 |
| inp = inp.abs() |
| for v in inp: |
| self.assertGreater(math.copysign(1.0, v), 0.0) |
| |
| # TODO: update to compare against NumPy by rationalizing with OpInfo |
| @onlyCUDA |
| @dtypes(torch.float, torch.double) |
| def test_abs_zero(self, device, dtype): |
| # Both abs(0.0) and abs(-0.0) should result in 0.0 |
| abs_zeros = torch.tensor([0.0, -0.0], device=device, dtype=dtype).abs().tolist() |
| for num in abs_zeros: |
| self.assertGreater(math.copysign(1.0, num), 0.0) |
| |
| @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16)) |
| def test_isposinf_isneginf_non_boolean_output(self, device, dtype): |
| # test non-boolean tensors as the `out=` parameters |
| # boolean outputs are tested in the above testcases |
| vals = (float("inf"), -float("inf"), 1.2) |
| t = torch.tensor(vals, device=device) |
| for torch_op in (torch.isposinf, torch.isneginf): |
| out = torch.empty_like(t, dtype=dtype) |
| with self.assertRaisesRegex( |
| RuntimeError, "does not support non-boolean outputs" |
| ): |
| torch_op(t, out=out) |
| |
| def test_nonzero_empty(self, device): |
| def assert_tuple_empty(tup, dim): |
| self.assertEqual(dim, len(tup)) |
| for t in tup: |
| self.assertEqual(torch.Size([0]), t.shape) |
| |
| x = torch.randn(0, 2, 0, 5, 0, device=device) |
| y = torch.nonzero(x) |
| z = torch.nonzero(x, as_tuple=True) |
| |
| self.assertEqual(0, y.numel()) |
| self.assertEqual(torch.Size([0, 5]), y.shape) |
| assert_tuple_empty(z, 5) |
| |
| x = torch.tensor(0.5, device=device) |
| y = torch.nonzero(x) |
| # nonzero with as_tuple returns a |
| # tuple of len 1 for a zero-dim tensor. |
| # This is done to match Numpy behavior. |
| z = torch.nonzero(x, as_tuple=True) |
| self.assertEqual(1, len(z)) |
| self.assertEqual(torch.zeros(1, dtype=torch.long), z[0]) |
| |
| x = torch.zeros((), device=device) |
| y = torch.nonzero(x) |
| z = torch.nonzero(x, as_tuple=True) |
| self.assertEqual(torch.Size([0, 0]), y.shape) |
| self.assertEqual(1, len(z)) |
| self.assertEqual(torch.empty(0, dtype=torch.long), z[0]) |
| |
| # TODO: rationalize with exp OpInfo |
| @dtypes(*floating_and_complex_types_and(torch.bfloat16)) |
| @dtypesIfCUDA(*floating_and_complex_types_and(torch.half, torch.bfloat16)) |
| def test_exp(self, device, dtype): |
| for v in (2, -2) + ((1j, 1 + 1j) if dtype.is_complex else ()): |
| a = ( |
| torch.tensor(v, dtype=dtype, device=device) |
| * torch.arange(18, device=device) |
| / 3 |
| * math.pi |
| ) |
| a = a.to(dtype) |
| # bfloat16 overflows |
| if dtype == torch.bfloat16: |
| return |
| self.compare_with_numpy(torch.exp, np.exp, a) |
| |
| if dtype.is_complex: |
| inf_real_zero_imag_in = torch.tensor( |
| complex(float("inf"), 0), device=device, dtype=dtype |
| ) |
| inf_real_zero_imag_out = torch.exp(inf_real_zero_imag_in).item() |
| self.assertTrue(math.isinf(inf_real_zero_imag_out.real)) |
| if self.device_type == "cpu": |
| pass |
| # These are commented out because it cannot be consistently reproduced. |
| # This is incorrect. It should be zero. Need fix! |
| # https://github.com/pytorch/pytorch/issues/40590 |
| # self.assertNotEqual(inf_real_zero_imag_out.imag, 0) |
| # This is incorrect. They should equal. Need fix! |
| # https://github.com/pytorch/pytorch/issues/40590 |
| # with self.assertRaises(AssertionError): |
| # self.compare_with_numpy(torch.exp, np.exp, inf_real_zero_imag_in) |
| else: |
| self.assertEqual(inf_real_zero_imag_out.imag, 0, atol=0, rtol=0) |
| self.compare_with_numpy(torch.exp, np.exp, inf_real_zero_imag_in) |
| |
| zero_real_inf_imag_in = torch.tensor( |
| complex(0, float("inf")), device=device, dtype=dtype |
| ) |
| zero_real_inf_imag_out = torch.exp(zero_real_inf_imag_in).item() |
| self.assertTrue(math.isnan(zero_real_inf_imag_out.real)) |
| self.assertTrue(math.isnan(zero_real_inf_imag_out.imag)) |
| # Ensure we are notified when NumPy changes its behavior |
| self.compare_with_numpy(torch.exp, np.exp, zero_real_inf_imag_in) |
| |
| inf_real_imag_in = torch.tensor( |
| complex(float("inf"), float("inf")), device=device, dtype=dtype |
| ) |
| inf_real_imag_out = torch.exp(inf_real_imag_in).item() |
| if self.device_type == "cpu": |
| pass |
| # This is incorrect. Need fix! https://github.com/pytorch/pytorch/issues/40590 |
| # This is commented out because it cannot be consistently reproduced. |
| # with self.assertRaises(AssertionError): |
| # self.compare_with_numpy(torch.exp, np.exp, inf_real_imag_in) |
| else: |
| self.assertTrue(math.isinf(inf_real_imag_out.real)) |
| self.assertTrue(math.isnan(inf_real_imag_out.imag)) |
| self.compare_with_numpy(torch.exp, np.exp, inf_real_imag_in) |
| |
| inf_real_nan_imag_in = torch.tensor( |
| complex(float("inf"), float("nan")), device=device, dtype=dtype |
| ) |
| inf_real_nan_imag_out = torch.exp(inf_real_nan_imag_in).item() |
| if self.device_type == "cpu": |
| pass |
| # This is incorrect. It should be inf. Need fix! https://github.com/pytorch/pytorch/issues/40590 |
| # This is commented out because it cannot be consistently reproduced. |
| # with self.assertRaises(AssertionError): |
| # self.compare_with_numpy(torch.exp, np.exp, inf_real_nan_imag_in) |
| else: |
| self.assertTrue(math.isinf(inf_real_nan_imag_out.real)) |
| self.assertTrue(math.isnan(inf_real_nan_imag_out.imag)) |
| self.compare_with_numpy(torch.exp, np.exp, inf_real_nan_imag_in) |
| |
| nan_real_inf_imag_in = torch.tensor( |
| complex(float("nan"), float("inf")), device=device, dtype=dtype |
| ) |
| nan_real_inf_imag_out = torch.exp(nan_real_inf_imag_in).item() |
| self.assertTrue(math.isnan(nan_real_inf_imag_out.real)) |
| self.assertTrue(math.isnan(nan_real_inf_imag_out.imag)) |
| # Ensure we are notified when NumPy changes its behavior |
| self.compare_with_numpy(torch.exp, np.exp, nan_real_inf_imag_in) |
| |
| |
| instantiate_device_type_tests(TestUnaryUfuncs, globals()) |
| |
| if __name__ == "__main__": |
| run_tests() |
