| import torch |
| import unittest |
| import itertools |
| import warnings |
| from math import inf, nan, isnan |
| from random import randrange |
| |
| from torch.testing._internal.common_utils import \ |
| (TestCase, run_tests, TEST_NUMPY, IS_MACOS, IS_WINDOWS, TEST_WITH_ASAN, make_tensor) |
| from torch.testing._internal.common_device_type import \ |
| (instantiate_device_type_tests, dtypes, dtypesIfCUDA, |
| onlyCUDA, skipCUDAIfNoMagma, skipCPUIfNoLapack, precisionOverride) |
| from torch.testing._internal.jit_metaprogramming_utils import gen_script_fn_and_args |
| from torch.autograd import gradcheck |
| |
| if TEST_NUMPY: |
| import numpy as np |
| |
| class TestLinalg(TestCase): |
| exact_dtype = True |
| |
| # Tests torch.outer, and its alias, torch.ger, vs. NumPy |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| @precisionOverride({torch.bfloat16: 1e-1}) |
| @dtypes(*(torch.testing.get_all_dtypes())) |
| def test_outer(self, device, dtype): |
| def run_test_case(a, b): |
| if dtype == torch.bfloat16: |
| a_np = a.to(torch.double).cpu().numpy() |
| b_np = b.to(torch.double).cpu().numpy() |
| else: |
| a_np = a.cpu().numpy() |
| b_np = b.cpu().numpy() |
| expected = np.outer(a_np, b_np) |
| |
| self.assertEqual(torch.outer(a, b), expected) |
| self.assertEqual(torch.Tensor.outer(a, b), expected) |
| |
| self.assertEqual(torch.ger(a, b), expected) |
| self.assertEqual(torch.Tensor.ger(a, b), expected) |
| |
| # test out variant |
| out = torch.empty(a.size(0), b.size(0), device=device, dtype=dtype) |
| torch.outer(a, b, out=out) |
| self.assertEqual(out, expected) |
| |
| out = torch.empty(a.size(0), b.size(0), device=device, dtype=dtype) |
| torch.ger(a, b, out=out) |
| self.assertEqual(out, expected) |
| |
| a = torch.randn(50).to(device=device, dtype=dtype) |
| b = torch.randn(50).to(device=device, dtype=dtype) |
| run_test_case(a, b) |
| |
| # test 0 strided tensor |
| zero_strided = torch.randn(1).to(device=device, dtype=dtype).expand(50) |
| run_test_case(zero_strided, b) |
| run_test_case(a, zero_strided) |
| |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| @precisionOverride({torch.bfloat16: 1e-1}) |
| @dtypes(*(torch.testing.get_all_dtypes())) |
| def test_addr(self, device, dtype): |
| def run_test_case(m, a, b, beta=1, alpha=1): |
| if dtype == torch.bfloat16: |
| a_np = a.to(torch.double).cpu().numpy() |
| b_np = b.to(torch.double).cpu().numpy() |
| m_np = m.to(torch.double).cpu().numpy() |
| else: |
| a_np = a.cpu().numpy() |
| b_np = b.cpu().numpy() |
| m_np = m.cpu().numpy() |
| |
| if beta == 0: |
| expected = alpha * np.outer(a_np, b_np) |
| else: |
| expected = beta * m_np + alpha * np.outer(a_np, b_np) |
| |
| self.assertEqual(torch.addr(m, a, b, beta=beta, alpha=alpha), expected) |
| self.assertEqual(torch.Tensor.addr(m, a, b, beta=beta, alpha=alpha), expected) |
| |
| result_dtype = torch.addr(m, a, b, beta=beta, alpha=alpha).dtype |
| out = torch.empty_like(m, dtype=result_dtype) |
| torch.addr(m, a, b, beta=beta, alpha=alpha, out=out) |
| self.assertEqual(out, expected) |
| |
| a = torch.randn(50).to(device=device, dtype=dtype) |
| b = torch.randn(50).to(device=device, dtype=dtype) |
| m = torch.randn(50, 50).to(device=device, dtype=dtype) |
| |
| # when beta is zero |
| run_test_case(m, a, b, beta=0., alpha=2) |
| |
| # when beta is not zero |
| run_test_case(m, a, b, beta=0.5, alpha=2) |
| |
| # test transpose |
| m_transpose = torch.transpose(m, 0, 1) |
| run_test_case(m_transpose, a, b, beta=0.5, alpha=2) |
| |
| # test 0 strided tensor |
| zero_strided = torch.randn(1).to(device=device, dtype=dtype).expand(50) |
| run_test_case(m, zero_strided, b, beta=0.5, alpha=2) |
| |
| # test scalar |
| m_scalar = torch.tensor(1, device=device, dtype=dtype) |
| run_test_case(m_scalar, a, b) |
| |
| @dtypes(*itertools.product(torch.testing.get_all_dtypes(), |
| torch.testing.get_all_dtypes())) |
| def test_outer_type_promotion(self, device, dtypes): |
| a = torch.randn(5).to(device=device, dtype=dtypes[0]) |
| b = torch.randn(5).to(device=device, dtype=dtypes[1]) |
| for op in (torch.outer, torch.Tensor.outer, torch.ger, torch.Tensor.ger): |
| result = op(a, b) |
| self.assertEqual(result.dtype, torch.result_type(a, b)) |
| |
| @dtypes(*itertools.product(torch.testing.get_all_dtypes(), |
| torch.testing.get_all_dtypes())) |
| def test_addr_type_promotion(self, device, dtypes): |
| a = torch.randn(5).to(device=device, dtype=dtypes[0]) |
| b = torch.randn(5).to(device=device, dtype=dtypes[1]) |
| m = torch.randn(5, 5).to(device=device, |
| dtype=torch.result_type(a, b)) |
| for op in (torch.addr, torch.Tensor.addr): |
| # pass the integer 1 to the torch.result_type as both |
| # the default values of alpha and beta are integers (alpha=1, beta=1) |
| desired_dtype = torch.result_type(m, 1) |
| result = op(m, a, b) |
| self.assertEqual(result.dtype, desired_dtype) |
| |
| desired_dtype = torch.result_type(m, 2.) |
| result = op(m, a, b, beta=0, alpha=2.) |
| self.assertEqual(result.dtype, desired_dtype) |
| |
| # Tests migrated from test_torch.py |
| # 1) test the shape of the result tensor when there is empty input tensor |
| # 2) test the Runtime Exception when there is scalar input tensor |
| def test_outer_ger_addr_legacy_tests(self, device): |
| for size in ((0, 0), (0, 5), (5, 0)): |
| a = torch.rand(size[0], device=device) |
| b = torch.rand(size[1], device=device) |
| |
| self.assertEqual(torch.outer(a, b).shape, size) |
| self.assertEqual(torch.ger(a, b).shape, size) |
| |
| m = torch.empty(size, device=device) |
| self.assertEqual(torch.addr(m, a, b).shape, size) |
| |
| m = torch.randn(5, 6, device=device) |
| a = torch.randn(5, device=device) |
| b = torch.tensor(6, device=device) |
| self.assertRaises(RuntimeError, lambda: torch.outer(a, b)) |
| self.assertRaises(RuntimeError, lambda: torch.outer(b, a)) |
| self.assertRaises(RuntimeError, lambda: torch.ger(a, b)) |
| self.assertRaises(RuntimeError, lambda: torch.ger(b, a)) |
| self.assertRaises(RuntimeError, lambda: torch.addr(m, a, b)) |
| self.assertRaises(RuntimeError, lambda: torch.addr(m, b, a)) |
| |
| # Tests torch.det and its alias, torch.linalg.det, vs. NumPy |
| @skipCUDAIfNoMagma |
| @skipCPUIfNoLapack |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| @dtypes(torch.double) |
| def test_det(self, device, dtype): |
| tensors = ( |
| torch.randn((2, 2), device=device, dtype=dtype), |
| torch.randn((129, 129), device=device, dtype=dtype), |
| torch.randn((3, 52, 52), device=device, dtype=dtype), |
| torch.randn((4, 2, 26, 26), device=device, dtype=dtype)) |
| |
| |
| ops = (torch.det, torch.Tensor.det, |
| torch.linalg.det) |
| for t in tensors: |
| expected = np.linalg.det(t.cpu().numpy()) |
| for op in ops: |
| actual = op(t) |
| self.assertEqual(actual, expected) |
| |
| # NOTE: det requires a 2D+ tensor |
| t = torch.randn(1, device=device, dtype=dtype) |
| with self.assertRaises(RuntimeError): |
| op(t) |
| |
| # This test confirms that torch.linalg.norm's dtype argument works |
| # as expected, according to the function's documentation |
| @skipCUDAIfNoMagma |
| def test_norm_dtype(self, device): |
| def run_test_case(input_size, ord, keepdim, from_dtype, to_dtype, compare_dtype): |
| msg = ( |
| f'input_size={input_size}, ord={ord}, keepdim={keepdim}, ' |
| f'from_dtype={from_dtype}, to_dtype={to_dtype}') |
| input = torch.randn(*input_size, dtype=from_dtype, device=device) |
| result = torch.linalg.norm(input, ord, keepdim=keepdim, dtype=from_dtype) |
| self.assertEqual(result.dtype, from_dtype, msg=msg) |
| result_converted = torch.linalg.norm(input, ord, keepdim=keepdim, dtype=to_dtype) |
| self.assertEqual(result_converted.dtype, to_dtype, msg=msg) |
| self.assertEqual(result.to(compare_dtype), result_converted.to(compare_dtype), msg=msg) |
| |
| result_out_converted = torch.empty_like(result_converted) |
| torch.linalg.norm(input, ord, keepdim=keepdim, dtype=to_dtype, out=result_out_converted) |
| self.assertEqual(result_out_converted.dtype, to_dtype, msg=msg) |
| self.assertEqual(result_converted, result_out_converted, msg=msg) |
| |
| ord_vector = [0, 1, -1, 2, -2, 3, -3, 4.5, -4.5, inf, -inf, None] |
| ord_matrix = ['fro', 'nuc', 1, -1, 2, -2, inf, -inf, None] |
| S = 10 |
| test_cases = [ |
| ((S, ), ord_vector), |
| ((S, S), ord_matrix), |
| ] |
| for keepdim in [True, False]: |
| for input_size, ord_settings in test_cases: |
| for ord in ord_settings: |
| # float to double |
| run_test_case(input_size, ord, keepdim, torch.float, torch.double, torch.float) |
| # double to float |
| run_test_case(input_size, ord, keepdim, torch.double, torch.double, torch.float) |
| |
| # Make sure that setting dtype != out.dtype raises an error |
| dtype_pairs = [ |
| (torch.float, torch.double), |
| (torch.double, torch.float), |
| ] |
| for keepdim in [True, False]: |
| for input_size, ord_settings in test_cases: |
| for ord in ord_settings: |
| for dtype, out_dtype in dtype_pairs: |
| input = torch.rand(*input_size) |
| result = torch.Tensor().to(out_dtype) |
| with self.assertRaisesRegex(RuntimeError, r'provided dtype must match dtype of result'): |
| torch.linalg.norm(input, ord=ord, keepdim=keepdim, dtype=dtype, out=result) |
| |
| # This test compares torch.linalg.norm and numpy.linalg.norm to ensure that |
| # their vector norm results match |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| @dtypes(torch.float, torch.double) |
| def test_norm_vector(self, device, dtype): |
| def run_test_case(input, p, dim, keepdim): |
| result = torch.linalg.norm(input, ord, dim, keepdim) |
| input_numpy = input.cpu().numpy() |
| result_numpy = np.linalg.norm(input_numpy, ord, dim, keepdim) |
| |
| msg = f'input.size()={input.size()}, ord={ord}, dim={dim}, keepdim={keepdim}, dtype={dtype}' |
| self.assertEqual(result, result_numpy, msg=msg) |
| |
| result_out = torch.empty_like(result) |
| torch.linalg.norm(input, ord, dim, keepdim, out=result_out) |
| self.assertEqual(result, result_out, msg=msg) |
| |
| ord_vector = [0, 1, -1, 2, -2, 3, -3, 4.5, -4.5, inf, -inf, None] |
| S = 10 |
| test_cases = [ |
| # input size, p settings, dim |
| ((S, ), ord_vector, None), |
| ((S, ), ord_vector, 0), |
| ((S, S, S), ord_vector, 0), |
| ((S, S, S), ord_vector, 1), |
| ((S, S, S), ord_vector, 2), |
| ((S, S, S), ord_vector, -1), |
| ((S, S, S), ord_vector, -2), |
| ] |
| L = 1_000_000 |
| if dtype == torch.double: |
| test_cases.append(((L, ), ord_vector, None)) |
| for keepdim in [True, False]: |
| for input_size, ord_settings, dim in test_cases: |
| input = torch.randn(*input_size, dtype=dtype, device=device) |
| for ord in ord_settings: |
| run_test_case(input, ord, dim, keepdim) |
| |
| # This test compares torch.linalg.norm and numpy.linalg.norm to ensure that |
| # their matrix norm results match |
| @skipCUDAIfNoMagma |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| @dtypes(torch.float, torch.double) |
| def test_norm_matrix(self, device, dtype): |
| def run_test_case(input, p, dim, keepdim): |
| result = torch.linalg.norm(input, ord, dim, keepdim) |
| input_numpy = input.cpu().numpy() |
| result_numpy = np.linalg.norm(input_numpy, ord, dim, keepdim) |
| |
| msg = f'input.size()={input.size()}, ord={ord}, dim={dim}, keepdim={keepdim}, dtype={dtype}' |
| self.assertEqual(result, result_numpy, msg=msg) |
| |
| result_out = torch.empty_like(result) |
| torch.linalg.norm(input, ord, dim, keepdim, out=result_out) |
| self.assertEqual(result, result_out, msg=msg) |
| |
| ord_matrix = [1, -1, 2, -2, inf, -inf, 'nuc', 'fro', None] |
| S = 10 |
| test_cases = [ |
| # input size, p settings, dim |
| ((S, S), ord_matrix, None), |
| ((S, S), ord_matrix, (0, 1)), |
| ((S, S), ord_matrix, (1, 0)), |
| ((S, S, S, S), ord_matrix, (2, 0)), |
| ((S, S, S, S), ord_matrix, (-1, -2)), |
| ((S, S, S, S), ord_matrix, (-1, -3)), |
| ((S, S, S, S), ord_matrix, (-3, 2)), |
| ] |
| L = 1_000 |
| if dtype == torch.double: |
| test_cases.append(((L, L), ord_matrix, None)) |
| for keepdim in [True, False]: |
| for input_size, ord_settings, dim in test_cases: |
| input = torch.randn(*input_size, dtype=dtype, device=device) |
| for ord in ord_settings: |
| run_test_case(input, ord, dim, keepdim) |
| |
| # Test autograd and jit functionality for linalg functions. |
| # TODO: Once support for linalg functions is added to method_tests in common_methods_invocations.py, |
| # the `test_cases` entries below should be moved there. These entries are in a similar format, |
| # so they should work with minimal changes. |
| @dtypes(torch.float, torch.double) |
| def test_autograd_and_jit(self, device, dtype): |
| torch.manual_seed(0) |
| S = 10 |
| NO_ARGS = None # NOTE: refer to common_methods_invocations.py if you need this feature |
| test_cases = [ |
| # NOTE: Not all the features from common_methods_invocations.py are functional here, since this |
| # is only a temporary solution. |
| # ( |
| # method name, |
| # input size/constructing fn, |
| # args (tuple represents shape of a tensor arg), |
| # test variant name (will be used at test name suffix), // optional |
| # (should_check_autodiff[bool], nonfusible_nodes, fusible_nodes) for autodiff, // optional |
| # indices for possible dim arg, // optional |
| # fn mapping output to part that should be gradcheck'ed, // optional |
| # kwargs // optional |
| # ) |
| ('norm', (S,), (), 'default_1d'), |
| ('norm', (S, S), (), 'default_2d'), |
| ('norm', (S, S, S), (), 'default_3d'), |
| ('norm', (S,), (inf,), 'vector_inf'), |
| ('norm', (S,), (3.5,), 'vector_3_5'), |
| ('norm', (S,), (0.5,), 'vector_0_5'), |
| ('norm', (S,), (2,), 'vector_2'), |
| ('norm', (S,), (1,), 'vector_1'), |
| ('norm', (S,), (0,), 'vector_0'), |
| ('norm', (S,), (-inf,), 'vector_neg_inf'), |
| ('norm', (S,), (-3.5,), 'vector_neg_3_5'), |
| ('norm', (S,), (-0.5,), 'vector_neg_0_5'), |
| ('norm', (S,), (2,), 'vector_neg_2'), |
| ('norm', (S,), (1,), 'vector_neg_1'), |
| ('norm', (S, S), (inf,), 'matrix_inf'), |
| ('norm', (S, S), (2,), 'matrix_2', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), |
| ('norm', (S, S), (1,), 'matrix_1'), |
| ('norm', (S, S), (-inf,), 'matrix_neg_inf'), |
| ('norm', (S, S), (-2,), 'matrix_neg_2', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), |
| ('norm', (S, S), (-1,), 'matrix_neg_1'), |
| ('norm', (S, S), ('fro',), 'fro'), |
| ('norm', (S, S), ('fro', [0, 1]), 'fro_dim'), |
| ('norm', (S, S), ('nuc',), 'nuc', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), |
| ('norm', (S, S), ('nuc', [0, 1]), 'nuc_dim', (), NO_ARGS, [skipCPUIfNoLapack, skipCUDAIfNoMagma]), |
| ] |
| for test_case in test_cases: |
| func_name = test_case[0] |
| func = getattr(torch.linalg, func_name) |
| input_size = test_case[1] |
| args = list(test_case[2]) |
| test_case_name = test_case[3] if len(test_case) >= 4 else None |
| mapping_funcs = list(test_case[6]) if len(test_case) >= 7 else None |
| |
| # Skip a test if a decorator tells us to |
| if mapping_funcs is not None: |
| def decorated_func(self, device, dtype): |
| pass |
| for mapping_func in mapping_funcs: |
| decorated_func = mapping_func(decorated_func) |
| try: |
| decorated_func(self, device, dtype) |
| except unittest.SkipTest: |
| continue |
| |
| msg = f'function name: {func_name}, case name: {test_case_name}' |
| |
| # Test JIT |
| input = torch.randn(*input_size, dtype=dtype, device=device) |
| input_script = input.clone().detach() |
| script_method, tensors = gen_script_fn_and_args("linalg.norm", "functional", input_script, *args) |
| self.assertEqual( |
| func(input, *args), |
| script_method(input_script), |
| msg=msg) |
| |
| # Test autograd |
| # gradcheck is only designed to work with torch.double inputs |
| if dtype == torch.double: |
| input = torch.randn(*input_size, dtype=dtype, device=device, requires_grad=True) |
| |
| def run_func(input): |
| return func(input, *args) |
| self.assertTrue(gradcheck(run_func, input), msg=msg) |
| |
| # This test calls torch.linalg.norm and numpy.linalg.norm with illegal arguments |
| # to ensure that they both throw errors |
| @unittest.skipIf(not TEST_NUMPY, "NumPy not found") |
| @dtypes(torch.float, torch.double) |
| def test_norm_errors(self, device, dtype): |
| def run_error_test_case(input, ord, dim, keepdim, error_type, error_regex): |
| test_case_info = ( |
| f'test case input.size()={input.size()}, ord={ord}, dim={dim}, ' |
| f'keepdim={keepdim}, dtype={dtype}') |
| |
| with self.assertRaisesRegex(error_type, error_regex, msg=test_case_info): |
| torch.linalg.norm(input, ord, dim, keepdim) |
| |
| input_numpy = input.cpu().numpy() |
| |
| msg = f'numpy does not raise error but pytorch does, for case "{test_case_info}"' |
| with self.assertRaises(Exception, msg=test_case_info): |
| np.linalg.norm(input_numpy, ord, dim, keepdim) |
| |
| S = 10 |
| error_test_cases = [ |
| # input size, p settings, dim, error type, error regex |
| ((S, ), ['fro'], None, RuntimeError, r'order "fro" can only be used if either len\(dim\) == 2'), |
| ((S, ), ['nuc'], None, RuntimeError, r'order "nuc" can only be used if either len\(dim\) == 2'), |
| ((S, S), [3.5], None, RuntimeError, r'Order 3.5 not supported for matrix norm'), |
| ((S, S), [0], None, RuntimeError, r'Order 0 not supported for matrix norm'), |
| ((S, S), ['nuc'], 0, RuntimeError, r'order "nuc" can only be used if either len\(dim\) == 2'), |
| ((S, S), ['fro'], 0, RuntimeError, r'order "fro" can only be used if either len\(dim\) == 2'), |
| ((S, S), ['nuc'], (0, 0), RuntimeError, r'duplicate or invalid dimensions'), |
| ((S, S), ['fro', 0], (0, 0), RuntimeError, r'Expected dims to be different'), |
| ((S, S), ['fro', 'nuc', 0], (0, 4), IndexError, r'Dimension out of range'), |
| ((S, ), [0], (4, ), IndexError, r'Dimension out of range'), |
| ((S, ), [None], (0, 0), RuntimeError, r'Expected dims to be different, got this instead'), |
| ((S, S, S), [1], (0, 1, 2), RuntimeError, r"'dim' must specify 1 or 2 dimensions"), |
| ((S, S, S), [1], None, RuntimeError, r"'dim' must specify 1 or 2 dimensions"), |
| ((S, S), ['garbage'], (0, 1), RuntimeError, r'Invalid norm order: garbage'), |
| ] |
| for keepdim in [True, False]: |
| for input_size, ord_settings, dim, error_type, error_regex in error_test_cases: |
| input = torch.randn(*input_size, dtype=dtype, device=device) |
| for ord in ord_settings: |
| run_error_test_case(input, ord, dim, keepdim, error_type, error_regex) |
| |
| # Test complex number inputs for linalg.norm. Some cases are not supported yet, so |
| # this test also verifies that those cases raise an error. |
| @skipCUDAIfNoMagma |
| @skipCPUIfNoLapack |
| @unittest.skipIf(not TEST_NUMPY, "Numpy not found") |
| @dtypes(torch.cfloat, torch.cdouble) |
| def test_norm_complex(self, device, dtype): |
| def gen_error_message(input_size, ord, keepdim, dim=None): |
| return "complex norm failed for input size %s, ord=%s, keepdim=%s, dim=%s" % ( |
| input_size, ord, keepdim, dim) |
| |
| if self.device_type == 'cpu': |
| supported_vector_ords = [0, 1, 3, inf, -1, -2, -3, -inf] |
| supported_matrix_ords = ['nuc', 1, 2, inf, -1, -2, -inf] |
| unsupported_vector_ords = [ |
| (2, r'norm with p=2 not supported for complex tensors'), |
| (None, r'norm with p=2 not supported for complex tensors'), |
| ] |
| unsupported_matrix_ords = [ |
| ('fro', r'frobenius norm not supported for complex tensors'), |
| (None, r'norm with p=2 not supported for complex tensors'), |
| ] |
| |
| elif self.device_type == 'cuda': |
| supported_vector_ords = [inf, -inf] |
| supported_matrix_ords = [1, inf, -1, -inf] |
| unsupported_vector_ords = [ |
| (0, r'norm_cuda" not implemented for \'Complex'), |
| (1, r'norm_cuda" not implemented for \'Complex'), |
| (2, r'norm with p=2 not supported for complex tensors'), |
| (-1, r'norm_cuda" not implemented for \'Complex'), |
| (-2, r'norm_cuda" not implemented for \'Complex'), |
| (None, r'norm with p=2 not supported for complex tensors'), |
| ] |
| unsupported_matrix_ords = [ |
| (None, r'norm with p=2 not supported for complex tensors'), |
| ('fro', r'frobenius norm not supported for complex tensors'), |
| ] |
| |
| # Test supported ords |
| for keepdim in [False, True]: |
| # vector norm |
| x = torch.randn(25, device=device, dtype=dtype) |
| xn = x.cpu().numpy() |
| for ord in supported_vector_ords: |
| res = torch.linalg.norm(x, ord, keepdim=keepdim).cpu() |
| expected = np.linalg.norm(xn, ord, keepdims=keepdim) |
| msg = gen_error_message(x.size(), ord, keepdim) |
| self.assertEqual(res.shape, expected.shape, msg=msg) |
| self.assertEqual(res, expected, msg=msg) |
| |
| # matrix norm |
| x = torch.randn(25, 25, device=device, dtype=dtype) |
| xn = x.cpu().numpy() |
| for ord in supported_matrix_ords: |
| # TODO: Need to fix abort when nuclear norm is given cdouble input: |
| # "double free or corruption (!prev) Aborted (core dumped)" |
| if ord == 'nuc' and dtype == torch.cdouble: |
| continue |
| res = torch.linalg.norm(x, ord, keepdim=keepdim).cpu() |
| expected = np.linalg.norm(xn, ord, keepdims=keepdim) |
| msg = gen_error_message(x.size(), ord, keepdim) |
| self.assertEqual(res.shape, expected.shape, msg=msg) |
| self.assertEqual(res, expected, msg=msg) |
| |
| # Test unsupported ords |
| # vector norm |
| x = torch.randn(25, device=device, dtype=dtype) |
| for ord, error_msg in unsupported_vector_ords: |
| with self.assertRaisesRegex(RuntimeError, error_msg): |
| torch.linalg.norm(x, ord) |
| |
| # matrix norm |
| x = torch.randn(25, 25, device=device, dtype=dtype) |
| for ord, error_msg in unsupported_matrix_ords: |
| with self.assertRaisesRegex(RuntimeError, error_msg): |
| torch.linalg.norm(x, ord) |
| |
| # Test that linal.norm gives the same result as numpy when inputs |
| # contain extreme values (inf, -inf, nan) |
| @unittest.skipIf(IS_WINDOWS, "Skipped on Windows!") |
| @unittest.skipIf(IS_MACOS, "Skipped on MacOS!") |
| @skipCUDAIfNoMagma |
| @skipCPUIfNoLapack |
| @unittest.skipIf(not TEST_NUMPY, "Numpy not found") |
| def test_norm_extreme_values(self, device): |
| vector_ords = [0, 1, 2, 3, inf, -1, -2, -3, -inf] |
| matrix_ords = ['fro', 'nuc', 1, 2, inf, -1, -2, -inf] |
| vectors = [] |
| matrices = [] |
| for pair in itertools.product([inf, -inf, 0.0, nan, 1.0], repeat=2): |
| vectors.append(list(pair)) |
| matrices.append([[pair[0], pair[1]]]) |
| matrices.append([[pair[0]], [pair[1]]]) |
| for vector in vectors: |
| x = torch.tensor(vector).to(device) |
| x_n = x.cpu().numpy() |
| for ord in vector_ords: |
| msg = f'ord={ord}, vector={vector}' |
| result = torch.linalg.norm(x, ord=ord) |
| result_n = np.linalg.norm(x_n, ord=ord) |
| self.assertEqual(result, result_n, msg=msg) |
| |
| # TODO: Remove this function once the broken cases are fixed |
| def is_broken_matrix_norm_case(ord, x): |
| if self.device_type == 'cuda': |
| if x.size() == torch.Size([1, 2]): |
| if ord in ['nuc', 2, -2] and isnan(x[0][0]) and x[0][1] == 1: |
| # These cases are broken because of an issue with svd |
| # https://github.com/pytorch/pytorch/issues/43567 |
| return True |
| return False |
| |
| for matrix in matrices: |
| x = torch.tensor(matrix).to(device) |
| x_n = x.cpu().numpy() |
| for ord in matrix_ords: |
| msg = f'ord={ord}, matrix={matrix}' |
| result = torch.linalg.norm(x, ord=ord) |
| result_n = np.linalg.norm(x_n, ord=ord) |
| |
| if is_broken_matrix_norm_case(ord, x): |
| continue |
| else: |
| self.assertEqual(result, result_n, msg=msg) |
| |
| # Test degenerate shape results match numpy for linalg.norm vector norms |
| @skipCUDAIfNoMagma |
| @skipCPUIfNoLapack |
| @unittest.skipIf(TEST_WITH_ASAN, "Skipped on ASAN since it checks for undefined behavior.") |
| @dtypes(torch.float, torch.double, torch.cfloat, torch.cdouble) |
| def test_norm_vector_degenerate_shapes(self, device, dtype): |
| def run_test_case(input, ord, dim, keepdim, should_error): |
| msg = f'input.size()={input.size()}, ord={ord}, dim={dim}, keepdim={keepdim}, dtype={dtype}' |
| input_numpy = input.cpu().numpy() |
| if should_error: |
| with self.assertRaises(ValueError): |
| np.linalg.norm(input_numpy, ord, dim, keepdim) |
| with self.assertRaises(RuntimeError): |
| torch.linalg.norm(input, ord, dim, keepdim) |
| else: |
| if dtype in [torch.cfloat, torch.cdouble] and ord in [2, None]: |
| # TODO: Once these ord values have support for complex numbers, |
| # remove this error test case |
| with self.assertRaises(RuntimeError): |
| torch.linalg.norm(input, ord, dim, keepdim) |
| return |
| result_numpy = np.linalg.norm(input_numpy, ord, dim, keepdim) |
| result = torch.linalg.norm(input, ord, dim, keepdim) |
| self.assertEqual(result, result_numpy, msg=msg) |
| |
| ord_vector = [0, 0.5, 1, 2, 3, inf, -0.5, -1, -2, -3, -inf, None] |
| S = 10 |
| test_cases = [ |
| # input size, p settings that cause error, dim |
| ((0, ), [inf, -inf], None), |
| ((0, S), [inf, -inf], 0), |
| ((0, S), [], 1), |
| ((S, 0), [], 0), |
| ((S, 0), [inf, -inf], 1), |
| ] |
| for keepdim in [True, False]: |
| for input_size, error_ords, dim in test_cases: |
| input = torch.randn(*input_size, dtype=dtype, device=device) |
| for ord in ord_vector: |
| run_test_case(input, ord, dim, keepdim, ord in error_ords) |
| |
| # Test degenerate shape results match numpy for linalg.norm matrix norms |
| @skipCUDAIfNoMagma |
| @skipCPUIfNoLapack |
| @unittest.skipIf(not TEST_NUMPY, "Numpy not found") |
| @dtypes(torch.float, torch.double, torch.cfloat, torch.cdouble) |
| def test_norm_matrix_degenerate_shapes(self, device, dtype): |
| def run_test_case(input, ord, dim, keepdim, should_error): |
| if dtype in [torch.cfloat, torch.cdouble] and ord in ['fro', None]: |
| # TODO: Once these ord values have support for complex numbers, |
| # remove this error test case |
| with self.assertRaises(RuntimeError): |
| torch.linalg.norm(input, ord, dim, keepdim) |
| return |
| msg = f'input.size()={input.size()}, ord={ord}, dim={dim}, keepdim={keepdim}, dtype={dtype}' |
| input_numpy = input.cpu().numpy() |
| if should_error: |
| with self.assertRaises(ValueError): |
| np.linalg.norm(input_numpy, ord, dim, keepdim) |
| with self.assertRaises(RuntimeError): |
| torch.linalg.norm(input, ord, dim, keepdim) |
| else: |
| result_numpy = np.linalg.norm(input_numpy, ord, dim, keepdim) |
| result = torch.linalg.norm(input, ord, dim, keepdim) |
| self.assertEqual(result, result_numpy, msg=msg) |
| |
| ord_matrix = ['fro', 'nuc', 1, 2, inf, -1, -2, -inf, None] |
| S = 10 |
| test_cases = [ |
| # input size, p settings that cause error, dim |
| ((0, 0), [1, 2, inf, -1, -2, -inf], None), |
| ((0, S), [2, inf, -2, -inf], None), |
| ((S, 0), [1, 2, -1, -2], None), |
| ((S, S, 0), [], (0, 1)), |
| ((1, S, 0), [], (0, 1)), |
| ((0, 0, S), [1, 2, inf, -1, -2, -inf], (0, 1)), |
| ((0, 0, S), [1, 2, inf, -1, -2, -inf], (1, 0)), |
| ] |
| for keepdim in [True, False]: |
| for input_size, error_ords, dim in test_cases: |
| input = torch.randn(*input_size, dtype=dtype, device=device) |
| for ord in ord_matrix: |
| run_test_case(input, ord, dim, keepdim, ord in error_ords) |
| |
| def test_norm_fastpaths(self, device): |
| x = torch.randn(3, 5, device=device) |
| |
| # slow path |
| result = torch.linalg.norm(x, 4.5, 1) |
| expected = torch.pow(x.abs().pow(4.5).sum(1), 1.0 / 4.5) |
| self.assertEqual(result, expected) |
| |
| # fast 0-norm |
| result = torch.linalg.norm(x, 0, 1) |
| expected = (x != 0).type_as(x).sum(1) |
| self.assertEqual(result, expected) |
| |
| # fast 1-norm |
| result = torch.linalg.norm(x, 1, 1) |
| expected = x.abs().sum(1) |
| self.assertEqual(result, expected) |
| |
| # fast 2-norm |
| result = torch.linalg.norm(x, 2, 1) |
| expected = torch.sqrt(x.pow(2).sum(1)) |
| self.assertEqual(result, expected) |
| |
| # fast 3-norm |
| result = torch.linalg.norm(x, 3, 1) |
| expected = torch.pow(x.pow(3).abs().sum(1), 1.0 / 3.0) |
| self.assertEqual(result, expected) |
| |
| @skipCUDAIfNoMagma |
| @skipCPUIfNoLapack |
| @unittest.skipIf(not TEST_NUMPY, "Numpy not found") |
| def test_norm_old(self, device): |
| def gen_error_message(input_size, p, keepdim, dim=None): |
| return "norm failed for input size %s, p=%s, keepdim=%s, dim=%s" % ( |
| input_size, p, keepdim, dim) |
| |
| for keepdim in [False, True]: |
| # full reduction |
| x = torch.randn(25, device=device) |
| xn = x.cpu().numpy() |
| for p in [0, 1, 2, 3, 4, inf, -inf, -1, -2, -3, 1.5]: |
| res = x.norm(p, keepdim=keepdim).cpu() |
| expected = np.linalg.norm(xn, p, keepdims=keepdim) |
| self.assertEqual(res, expected, atol=1e-5, rtol=0, msg=gen_error_message(x.size(), p, keepdim)) |
| |
| # one dimension |
| x = torch.randn(25, 25, device=device) |
| xn = x.cpu().numpy() |
| for p in [0, 1, 2, 3, 4, inf, -inf, -1, -2, -3]: |
| dim = 1 |
| res = x.norm(p, dim, keepdim=keepdim).cpu() |
| expected = np.linalg.norm(xn, p, dim, keepdims=keepdim) |
| msg = gen_error_message(x.size(), p, keepdim, dim) |
| self.assertEqual(res.shape, expected.shape, msg=msg) |
| self.assertEqual(res, expected, msg=msg) |
| |
| # matrix norm |
| for p in ['fro', 'nuc']: |
| res = x.norm(p, keepdim=keepdim).cpu() |
| expected = np.linalg.norm(xn, p, keepdims=keepdim) |
| msg = gen_error_message(x.size(), p, keepdim) |
| self.assertEqual(res.shape, expected.shape, msg=msg) |
| self.assertEqual(res, expected, msg=msg) |
| |
| # zero dimensions |
| x = torch.randn((), device=device) |
| xn = x.cpu().numpy() |
| res = x.norm(keepdim=keepdim).cpu() |
| expected = np.linalg.norm(xn, keepdims=keepdim) |
| msg = gen_error_message(x.size(), None, keepdim) |
| self.assertEqual(res.shape, expected.shape, msg=msg) |
| self.assertEqual(res, expected, msg=msg) |
| |
| # larger tensor sanity check |
| self.assertEqual( |
| 2 * torch.norm(torch.ones(10000), keepdim=keepdim), |
| torch.norm(torch.ones(40000), keepdim=keepdim)) |
| |
| # matrix norm with non-square >2-D tensors, all combinations of reduction dims |
| x = torch.randn(5, 6, 7, 8, device=device) |
| xn = x.cpu().numpy() |
| for p in ['fro', 'nuc']: |
| for dim in itertools.product(*[list(range(4))] * 2): |
| if dim[0] == dim[1]: |
| continue |
| res = x.norm(p=p, dim=dim, keepdim=keepdim).cpu() |
| expected = np.linalg.norm(xn, ord=p, axis=dim, keepdims=keepdim) |
| msg = gen_error_message(x.size(), p, keepdim, dim) |
| self.assertEqual(res.shape, expected.shape, msg=msg) |
| self.assertEqual(res, expected, msg=msg) |
| |
| @skipCUDAIfNoMagma |
| @skipCPUIfNoLapack |
| @unittest.skipIf(not TEST_NUMPY, "Numpy not found") |
| def test_norm_complex_old(self, device): |
| def gen_error_message(input_size, p, keepdim, dim=None): |
| return "complex norm failed for input size %s, p=%s, keepdim=%s, dim=%s" % ( |
| input_size, p, keepdim, dim) |
| |
| if device == 'cpu': |
| for keepdim in [False, True]: |
| # vector norm |
| x = torch.randn(25, device=device) + 1j * torch.randn(25, device=device) |
| xn = x.cpu().numpy() |
| for p in [0, 1, 3, inf, -1, -2, -3, -inf]: |
| res = x.norm(p, keepdim=keepdim).cpu() |
| expected = np.linalg.norm(xn, p, keepdims=keepdim) |
| msg = gen_error_message(x.size(), p, keepdim) |
| self.assertEqual(res.shape, expected.shape, msg=msg) |
| self.assertEqual(res, expected, msg=msg) |
| |
| # matrix norm |
| x = torch.randn(25, 25, device=device) + 1j * torch.randn(25, 25, device=device) |
| xn = x.cpu().numpy() |
| for p in ['nuc']: |
| res = x.norm(p, keepdim=keepdim).cpu() |
| expected = np.linalg.norm(xn, p, keepdims=keepdim) |
| msg = gen_error_message(x.size(), p, keepdim) |
| self.assertEqual(res.shape, expected.shape, msg=msg) |
| self.assertEqual(res, expected, msg=msg) |
| |
| # TODO: remove error test and add functionality test above when 2-norm support is added |
| with self.assertRaisesRegex(RuntimeError, r'norm with p=2 not supported for complex tensors'): |
| x = torch.randn(2, device=device, dtype=torch.complex64).norm(p=2) |
| |
| # TODO: remove error test and add functionality test above when frobenius support is added |
| with self.assertRaisesRegex(RuntimeError, r'frobenius norm not supported for complex tensors'): |
| x = torch.randn(2, 2, device=device, dtype=torch.complex64).norm(p='fro') |
| |
| elif device == 'cuda': |
| with self.assertRaisesRegex(RuntimeError, r'"norm_cuda" not implemented for \'ComplexFloat\''): |
| (1j * torch.randn(25)).norm() |
| |
| # Ensure torch.norm with p='fro' and p=2 give the same results for mutually supported input combinations |
| @dtypes(torch.float) |
| def test_norm_fro_2_equivalence_old(self, device, dtype): |
| input_sizes = [ |
| (0,), |
| (10,), |
| (0, 0), |
| (4, 30), |
| (0, 45), |
| (100, 0), |
| (45, 10, 23), |
| (0, 23, 59), |
| (23, 0, 37), |
| (34, 58, 0), |
| (0, 0, 348), |
| (0, 3434, 0), |
| (0, 0, 0), |
| (5, 3, 8, 1, 3, 5)] |
| |
| for input_size in input_sizes: |
| a = make_tensor(input_size, device, dtype, low=-9, high=9) |
| |
| # Try full reduction |
| dim_settings = [None] |
| |
| # Try all possible 1-D reductions |
| dim_settings += list(range(-a.dim(), a.dim())) |
| |
| def wrap_dim(dim, ndims): |
| assert (dim < ndims) and (dim >= -ndims) |
| if dim >= 0: |
| return dim |
| else: |
| return dim + ndims |
| |
| # Try all possible 2-D reductions |
| dim_settings += [ |
| (d0, d1) for d0, d1 in itertools.combinations(range(-a.dim(), a.dim()), 2) |
| if wrap_dim(d0, a.dim()) != wrap_dim(d1, a.dim())] |
| |
| for dim in dim_settings: |
| for keepdim in [True, False]: |
| a_norm_2 = torch.norm(a, p=2, dim=dim, keepdim=keepdim) |
| a_norm_fro = torch.norm(a, p='fro', dim=dim, keepdim=keepdim) |
| self.assertEqual(a_norm_fro, a_norm_2) |
| |
| @skipCUDAIfNoMagma |
| @unittest.skipIf(not TEST_NUMPY, "Numpy not found") |
| def test_nuclear_norm_axes_small_brute_force_old(self, device): |
| def check_single_nuclear_norm(x, axes): |
| if self.device_type != 'cpu' and randrange(100) < 95: |
| return # too many cpu <==> device copies |
| |
| a = np.array(x.cpu(), copy=False) |
| expected = np.linalg.norm(a, "nuc", axis=axes) |
| |
| ans = torch.norm(x, "nuc", dim=axes) |
| self.assertTrue(ans.is_contiguous()) |
| self.assertEqual(ans.shape, expected.shape) |
| self.assertEqual(ans.cpu(), expected, rtol=1e-02, atol=1e-03, equal_nan=True) |
| |
| out = torch.zeros(expected.shape, dtype=x.dtype, device=x.device) |
| ans = torch.norm(x, "nuc", dim=axes, out=out) |
| self.assertIs(ans, out) |
| self.assertTrue(ans.is_contiguous()) |
| self.assertEqual(ans.shape, expected.shape) |
| self.assertEqual(ans.cpu(), expected, rtol=1e-02, atol=1e-03, equal_nan=True) |
| |
| for n in range(1, 3): |
| for m in range(1, 3): |
| for axes in itertools.permutations([0, 1], 2): |
| # 2d, inner dimensions C |
| x = torch.randn(n, m, device=device) |
| check_single_nuclear_norm(x, axes) |
| |
| # 2d, inner dimensions Fortran |
| x = torch.randn(m, n, device=device).transpose(-1, -2) |
| check_single_nuclear_norm(x, axes) |
| |
| # 2d, inner dimensions non-contiguous |
| x = torch.randn(n, 2 * m, device=device)[:, ::2] |
| check_single_nuclear_norm(x, axes) |
| |
| # 2d, all dimensions non-contiguous |
| x = torch.randn(7 * n, 2 * m, device=device)[::7, ::2] |
| check_single_nuclear_norm(x, axes) |
| |
| for o in range(1, 3): |
| for axes in itertools.permutations([0, 1, 2], 2): |
| # 3d, inner dimensions C |
| x = torch.randn(o, n, m, device=device) |
| check_single_nuclear_norm(x, axes) |
| |
| # 3d, inner dimensions Fortran |
| x = torch.randn(o, m, n, device=device).transpose(-1, -2) |
| check_single_nuclear_norm(x, axes) |
| |
| # 3d, inner dimensions non-contiguous |
| x = torch.randn(o, n, 2 * m, device=device)[:, :, ::2] |
| check_single_nuclear_norm(x, axes) |
| |
| # 3d, all dimensions non-contiguous |
| x = torch.randn(7 * o, 5 * n, 2 * m, device=device)[::7, ::5, ::2] |
| check_single_nuclear_norm(x, axes) |
| |
| for r in range(1, 3): |
| for axes in itertools.permutations([0, 1, 2, 3], 2): |
| # 4d, inner dimensions C |
| x = torch.randn(r, o, n, m, device=device) |
| check_single_nuclear_norm(x, axes) |
| |
| # 4d, inner dimensions Fortran |
| x = torch.randn(r, o, n, m, device=device).transpose(-1, -2) |
| check_single_nuclear_norm(x, axes) |
| |
| # 4d, inner dimensions non-contiguous |
| x = torch.randn(r, o, n, 2 * m, device=device)[:, :, :, ::2] |
| check_single_nuclear_norm(x, axes) |
| |
| # 4d, all dimensions non-contiguous |
| x = torch.randn(7 * r, 5 * o, 11 * n, 2 * m, device=device)[::7, ::5, ::11, ::2] |
| check_single_nuclear_norm(x, axes) |
| |
| @skipCUDAIfNoMagma |
| def test_nuclear_norm_exceptions_old(self, device): |
| for lst in [], [1], [1, 2]: |
| x = torch.tensor(lst, dtype=torch.double, device=device) |
| for axes in (), (0,): |
| self.assertRaises(RuntimeError, torch.norm, x, "nuc", axes) |
| self.assertRaises(IndexError, torch.norm, x, "nuc", (0, 1)) |
| |
| x = torch.tensor([[0, 1, 2], [3, 4, 5]], dtype=torch.double, device=device) |
| self.assertRaisesRegex(RuntimeError, "duplicate or invalid", torch.norm, x, "nuc", (0, 0)) |
| self.assertRaisesRegex(IndexError, "Dimension out of range", torch.norm, x, "nuc", (0, 2)) |
| |
| @skipCUDAIfNoMagma |
| @skipCPUIfNoLapack |
| @dtypes(torch.float, torch.double, torch.cfloat, torch.cdouble) |
| @dtypesIfCUDA(torch.float, torch.double) |
| @precisionOverride({torch.float: 1e-4, torch.cfloat: 1e-4}) |
| def test_tensorsolve(self, device, dtype): |
| def run_test(a_shape, dims): |
| a = torch.randn(a_shape, dtype=dtype, device=device) |
| b = torch.randn(a_shape[:2], dtype=dtype, device=device) |
| result = torch.linalg.tensorsolve(a, b, dims=dims) |
| expected = np.linalg.tensorsolve(a.cpu().numpy(), b.cpu().numpy(), axes=dims) |
| self.assertEqual(result, expected) |
| |
| # check the out= variant |
| out = torch.empty_like(result) |
| ans = torch.linalg.tensorsolve(a, b, dims=dims, out=out) |
| self.assertEqual(ans, out) |
| self.assertEqual(ans, result) |
| |
| a_shapes = [(2, 3, 6), (3, 4, 4, 3)] |
| dims = [None, (0, 2)] |
| for a_shape, d in itertools.product(a_shapes, dims): |
| run_test(a_shape, d) |
| |
| @skipCUDAIfNoMagma |
| @skipCPUIfNoLapack |
| @dtypes(torch.float, torch.double, torch.cfloat, torch.cdouble) |
| @dtypesIfCUDA(torch.float, torch.double) |
| def test_tensorsolve_empty(self, device, dtype): |
| # Check for empty inputs. NumPy does not work for these cases. |
| a = torch.empty(0, 0, 1, 2, 3, 0, dtype=dtype, device=device) |
| b = torch.empty(a.shape[:2], dtype=dtype, device=device) |
| x = torch.linalg.tensorsolve(a, b) |
| self.assertEqual(torch.tensordot(a, x, dims=len(x.shape)), b) |
| |
| # TODO: once "solve_cuda" supports complex dtypes, they shall be added to above tests |
| @unittest.expectedFailure |
| @onlyCUDA |
| @skipCUDAIfNoMagma |
| @dtypes(torch.cfloat, torch.cdouble) |
| def test_tensorsolve_xfailed(self, device, dtype): |
| a_shape = (2, 3, 6) |
| a = torch.randn(a_shape, dtype=dtype, device=device) |
| b = torch.randn(a_shape[:2], dtype=dtype, device=device) |
| result = torch.linalg.tensorsolve(a, b) |
| expected = np.linalg.tensorsolve(a.cpu().numpy(), b.cpu().numpy()) |
| self.assertEqual(result, expected) |
| |
| @skipCUDAIfNoMagma |
| @skipCPUIfNoLapack |
| @dtypes(torch.float, torch.double, torch.cfloat, torch.cdouble) |
| @dtypesIfCUDA(torch.float, torch.double) |
| @precisionOverride({torch.float: 1e-4, torch.cfloat: 1e-4}) |
| def test_tensorsolve_non_contiguous(self, device, dtype): |
| def run_test_permuted(a_shape, dims): |
| # check for permuted / transposed inputs |
| a = torch.randn(a_shape, dtype=dtype, device=device) |
| a = a.movedim((0, 2), (-2, -1)) |
| self.assertFalse(a.is_contiguous()) |
| b = torch.randn(a.shape[:2], dtype=dtype, device=device) |
| b = b.t() |
| self.assertFalse(b.is_contiguous()) |
| result = torch.linalg.tensorsolve(a, b, dims=dims) |
| expected = np.linalg.tensorsolve(a.cpu().numpy(), b.cpu().numpy(), axes=dims) |
| self.assertEqual(result, expected) |
| |
| def run_test_skipped_elements(a_shape, dims): |
| # check for inputs with skipped elements |
| a = torch.randn(a_shape, dtype=dtype, device=device) |
| a = a[::2] |
| self.assertFalse(a.is_contiguous()) |
| b = torch.randn(a_shape[:2], dtype=dtype, device=device) |
| b = b[::2] |
| self.assertFalse(b.is_contiguous()) |
| result = torch.linalg.tensorsolve(a, b, dims=dims) |
| expected = np.linalg.tensorsolve(a.cpu().numpy(), b.cpu().numpy(), axes=dims) |
| self.assertEqual(result, expected) |
| |
| # check non-contiguous out |
| out = torch.empty(2 * result.shape[0], *result.shape[1:], dtype=dtype, device=device)[::2] |
| self.assertFalse(out.is_contiguous()) |
| ans = torch.linalg.tensorsolve(a, b, dims=dims, out=out) |
| self.assertEqual(ans, out) |
| self.assertEqual(ans, result) |
| |
| a_shapes = [(2, 3, 6), (3, 4, 4, 3)] |
| dims = [None, (0, 2)] |
| for a_shape, d in itertools.product(a_shapes, dims): |
| run_test_permuted(a_shape, d) |
| |
| a_shapes = [(4, 3, 6), (6, 4, 4, 3)] |
| dims = [None, (0, 2)] |
| for a_shape, d in itertools.product(a_shapes, dims): |
| run_test_skipped_elements(a_shape, d) |
| |
| @skipCUDAIfNoMagma |
| @skipCPUIfNoLapack |
| @dtypes(torch.float32) |
| def test_tensorsolve_errors_and_warnings(self, device, dtype): |
| # tensorsolve expects the input that can be reshaped to a square matrix |
| a = torch.eye(2 * 3 * 4).reshape((2 * 3, 4, 2, 3, 4)) |
| b = torch.randn(8, 4) |
| self.assertTrue(np.prod(a.shape[2:]) != np.prod(b.shape)) |
| with self.assertRaisesRegex(RuntimeError, r'Expected self to satisfy the requirement'): |
| torch.linalg.tensorsolve(a, b) |
| |
| # if non-empty out tensor with wrong shape is passed a warning is given |
| out = torch.empty_like(a) |
| b = torch.randn(6, 4) |
| with warnings.catch_warnings(record=True) as w: |
| # Trigger warning |
| torch.linalg.tensorsolve(a, b, out=out) |
| # Check warning occurs |
| self.assertEqual(len(w), 1) |
| self.assertTrue("An output with one or more elements was resized" in str(w[-1].message)) |
| |
| # dtypes should match |
| out = torch.empty_like(a).to(torch.int) |
| with self.assertRaisesRegex(RuntimeError, "result dtype Int does not match self dtype"): |
| torch.linalg.tensorsolve(a, b, out=out) |
| |
| instantiate_device_type_tests(TestLinalg, globals()) |
| |
| if __name__ == '__main__': |
| run_tests() |
