diff --git a/test/test_autograd.py b/test/test_autograd.py index 0642e87399c676..965fdab9c8b546 100644 --- a/test/test_autograd.py +++ b/test/test_autograd.py @@ -1990,7 +1990,6 @@ def test_cat_empty(self): lambda a, b: torch.cat((a, b)), True, f_args_variable, f_args_tensor) - @skipIfRocm def test_potrf(self): root = Variable(torch.tril(torch.rand(S, S)), requires_grad=True) @@ -2150,7 +2149,6 @@ def run_test(input_size, exponent): run_test((10, 10), torch.zeros(10, 10)) run_test((10,), 0) - @skipIfRocm def test_pinverse(self): # Why is pinverse tested this way, and not ordinarily as other linear algebra methods? # 1. Pseudo-inverses are not generally continuous, which means that they are not differentiable @@ -2546,7 +2544,6 @@ def backward(ctx, gO): out.backward() self.assertIn('MyFunc.apply', str(w[0].message)) - @skipIfRocm def test_symeig_no_eigenvectors(self): A = torch.tensor([[1., 2.], [2., 4.]], dtype=torch.float32, requires_grad=True) w, v = torch.symeig(A, eigenvectors=False) @@ -3185,13 +3182,13 @@ class dont_convert(tuple): 'large', NO_ARGS, [skipIfNoLapack]), ('gesv', (S, S), (random_fullrank_matrix_distinct_singular_value(S),), '', NO_ARGS, [skipIfNoLapack]), ('gesv', (S, S, S), (random_fullrank_matrix_distinct_singular_value(S, S),), - 'batched', NO_ARGS, [skipIfNoLapack, skipIfRocm]), + 'batched', NO_ARGS, [skipIfNoLapack]), ('gesv', (2, 3, S, S), (random_fullrank_matrix_distinct_singular_value(S, 2, 3),), - 'batched_dims', NO_ARGS, [skipIfNoLapack, skipIfRocm]), + 'batched_dims', NO_ARGS, [skipIfNoLapack]), ('gesv', (2, 2, S, S), (random_fullrank_matrix_distinct_singular_value(S, 1),), - 'batched_broadcast_A', NO_ARGS, [skipIfNoLapack, skipIfRocm]), + 'batched_broadcast_A', NO_ARGS, [skipIfNoLapack]), ('gesv', (1, S, S), (random_fullrank_matrix_distinct_singular_value(S, 2, 2),), - 'batched_broadcast_b', NO_ARGS, [skipIfNoLapack, skipIfRocm]), + 'batched_broadcast_b', NO_ARGS, [skipIfNoLapack]), ('fill_', (S, S, S), (1,), 'number'), ('fill_', (), (1,), 'number_scalar'), # FIXME: we should compute the derivative w.r.t torch.tensor(1) diff --git a/test/test_cuda.py b/test/test_cuda.py index 2c647b08cbd601..cf21208c77aabe 100644 --- a/test/test_cuda.py +++ b/test/test_cuda.py @@ -353,25 +353,23 @@ def tmp(t): ('kthvalue', small_3d_unique, lambda t: [3],), ('kthvalue', small_3d_unique, lambda t: [3, 1], 'dim'), ('kthvalue', small_3d_unique, lambda t: [3, -1], 'neg_dim'), - ('lerp', small_3d, lambda t: [small_3d(t), 0.3], '', types, False, "skipIfRocm:HalfTensor"), - ('max', small_3d_unique, lambda t: [], '', types, False, "skipIfRocm:HalfTensor"), - ('max', small_3d_unique, lambda t: [1], 'dim', types, False, skipIfRocm), - ('max', small_3d_unique, lambda t: [-1], 'neg_dim', types, False, skipIfRocm), + ('lerp', small_3d, lambda t: [small_3d(t), 0.3]), + ('max', small_3d_unique, lambda t: []), + ('max', small_3d_unique, lambda t: [1], 'dim'), + ('max', small_3d_unique, lambda t: [-1], 'neg_dim'), ('max', medium_2d, lambda t: [medium_2d(t)], 'elementwise'), ('min', small_3d_unique, lambda t: [], '', types, False, "skipIfRocm:HalfTensor"), ('min', small_3d_unique, lambda t: [1], 'dim', types, False, skipIfRocm), ('min', small_3d_unique, lambda t: [-1], 'neg_dim', types, False, skipIfRocm), ('min', medium_2d, lambda t: [medium_2d(t)], 'elementwise'), - ('mean', small_3d, lambda t: [], '', types, False, "skipIfRocm:HalfTensor"), - ('mean', small_3d, lambda t: [-1], 'neg_dim', types, False, "skipIfRocm:DoubleTensor,FloatTensor,HalfTensor"), - ('mean', small_3d, lambda t: [1], 'dim', types, False, "skipIfRocm:DoubleTensor,FloatTensor,HalfTensor"), - ('mode', small_3d, lambda t: [], '', types, False, skipIfRocm), - ('mode', small_3d, lambda t: [1], 'dim', types, False, skipIfRocm), - ('mode', small_3d, lambda t: [-1], 'neg_dim', types, False, skipIfRocm), - ('mvlgamma', lambda t: tensor_clamp(small_2d(t), 0.1, 10), lambda t: [1], '2d_p=1', float_types_no_half, - False, "skipIfRocm:DoubleTensor,FloatTensor"), - ('mvlgamma', lambda t: tensor_clamp(small_2d(t), 0.6, 10), lambda t: [2], '2d_p=2', float_types_no_half, - False, "skipIfRocm:DoubleTensor,FloatTensor"), + ('mean', small_3d, lambda t: []), + ('mean', small_3d, lambda t: [-1], 'neg_dim'), + ('mean', small_3d, lambda t: [1], 'dim'), + ('mode', small_3d, lambda t: []), + ('mode', small_3d, lambda t: [1], 'dim'), + ('mode', small_3d, lambda t: [-1], 'neg_dim'), + ('mvlgamma', lambda t: tensor_clamp(small_2d(t), 0.1, 10), lambda t: [1], '2d_p=1', float_types_no_half), + ('mvlgamma', lambda t: tensor_clamp(small_2d(t), 0.6, 10), lambda t: [2], '2d_p=2', float_types_no_half), ('remainder', small_3d, lambda t: [3], 'value', types, False, "skipIfRocm:HalfTensor"), ('remainder', small_3d, lambda t: [-3], 'negative_value', signed_types), ('remainder', small_3d, lambda t: [small_3d_positive(t)], 'tensor'), @@ -977,7 +975,6 @@ def test_broadcast_cpu(self): def test_broadcast_gpu(self): self._test_broadcast(torch.randn(5, 5).cuda()) - @skipIfRocm def test_min_max_nan(self): tests = [(lambda x: x.min(), 'min'), (lambda x: x.max(), 'max'), @@ -1743,7 +1740,6 @@ def test_tensor_scatterAdd(self): def test_tensor_scatterFill(self): TestTorch._test_scatter_base(self, lambda t: t.cuda(), 'scatter_', True, test_bounds=False) - @skipIfRocm def test_min_max_inits(self): # Testing if THC_reduceAll received the correct index initialization. # This affects the result of THC_reduceAll operations at extreme values @@ -1757,11 +1753,9 @@ def test_min_max_inits(self): _, v = y.min(dim=0) self.assertEqual(v, expected) - @skipIfRocm def test_max_with_inf(self): TestTorch._test_max_with_inf(self, (torch.half, torch.float, torch.double), 'cuda') - @skipIfRocm def test_min_with_inf(self): TestTorch._test_min_with_inf(self, (torch.half, torch.float, torch.double), 'cuda')