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Reenable test_sparse unit tests that are now passing in ROCm #208

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Merged
merged 2 commits into from
Oct 3, 2018
Merged

Reenable test_sparse unit tests that are now passing in ROCm #208

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493afc3
Reenable test_sparse unit tests that are now passing
jithunnair-amd Sep 13, 2018
e29f7ed
Skip this test - it fails on the CI.
iotamudelta Oct 3, 2018
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13 changes: 0 additions & 13 deletions test/test_sparse.py
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Original file line number Diff line number Diff line change
Expand Up @@ -394,7 +394,6 @@ def test_contig_hybrid(self):
self.assertEqual(exp_i, x._indices())
self.assertEqual(exp_v, x._values())

@skipIfRocm
def test_clone(self):
x, _, _ = self._gen_sparse(4, 20, 5)
if self.is_uncoalesced:
Expand Down Expand Up @@ -618,7 +617,6 @@ def test_spadd_hybrid(self):
self._test_spadd_shape([50, 30, 20], [2])
self._test_spadd_shape([5, 5, 5, 5, 5, 5], [2])

@skipIfRocm
def test_norm(self):
x, _, _ = self._gen_sparse(3, 10, 100)
y = x.coalesce()
Expand Down Expand Up @@ -701,7 +699,6 @@ def test_basic_ops_hybrid(self):
self._test_basic_ops_shape([50, 30, 20], [2])
self._test_basic_ops_shape([5, 5, 5, 5, 5, 5], [2])

@skipIfRocm
def test_add_dense_sparse_mismatch(self):
x = torch.zeros([3, 4], dtype=self.value_dtype, device=self.device)
sparse_y = self.SparseTensor(torch.zeros(1, 4, dtype=torch.int64, device=self.device),
Expand Down Expand Up @@ -949,7 +946,6 @@ def test_sparse_mask_hybrid(self):
self._test_sparse_mask_shape([50, 30, 20], [2])
self._test_sparse_mask_shape([5, 5, 5, 5, 5, 5], [2])

@skipIfRocm
def test_sparse_add_coalesce(self):
i = self.IndexTensor([[1, 2, 1]])
v = self.ValueTensor([3, 4, 5])
Expand All @@ -966,7 +962,6 @@ def test_storage_not_null(self):

@cuda_only
@unittest.skipIf(torch.cuda.device_count() < 2, "only one GPU detected")
@skipIfRocm
def test_same_gpu(self):
i = self.IndexTensor([[2]]).cuda(1)
v = self.ValueTensor([5]).cuda(1)
Expand Down Expand Up @@ -1044,7 +1039,6 @@ def test_factory(self):
self.assertEqual(device, sparse_tensor._values().device)
self.assertEqual(True, sparse_tensor.requires_grad)

@skipIfRocm
def test_factory_size_check(self):
indices = self.IndexTensor([[1, 2], [0, 2]])
values = self.ValueTensor([.5, .5])
Expand Down Expand Up @@ -1087,7 +1081,6 @@ def test_factory_empty_indices(self):
expected_indices = torch.empty((4, 0), dtype=torch.long, device=device)
self.assertEqual(tensor._indices(), expected_indices)

@skipIfRocm
def test_factory_nnz(self):
indices = self.IndexTensor([[0]]) # (sparseDims, nnz): (1, 1)
values = self.ValueTensor([[1, 1], [1, 1]]) # (nnz, ...): (2, 2)
Expand Down Expand Up @@ -1115,7 +1108,6 @@ def test_factory_nnz_zero(self):
self._test_factory_tensor_shape([3, 0], [0, 2, 4, 0], [0, 0, 0, 2, 4, 0], [0, 0, 0, 2, 4, 0])
self._test_factory_tensor_shape([3, 0], [0, 2, 4, 0], [1, 2, 3, 2, 4, 0], [1, 2, 3, 2, 4, 0])

@skipIfRocm
def test_factory_dense_dims(self):
indices = self.IndexTensor([[0]])
values = self.ValueTensor([[[1, 1, 1], [1, 1, 1]]])
Expand All @@ -1133,7 +1125,6 @@ def test_factory_type_inference(self):
self.assertEqual(torch.int64, t.dtype)

@cuda_only
@skipIfRocm
def test_factory_device_type_inference(self):
# both indices/values are CUDA
shape = (1, 3)
Expand Down Expand Up @@ -1191,7 +1182,6 @@ def test_empty_full(self):
TestTorch._test_empty_full(self, all_sparse_dtypes, torch.sparse_coo, None)
TestTorch._test_empty_full(self, all_sparse_dtypes, torch.sparse_coo, torch.device('cuda:0'))

@skipIfRocm
def test_is_sparse(self):
x = torch.randn(3, 3)
self.assertFalse(x.is_sparse)
Expand Down Expand Up @@ -1233,7 +1223,6 @@ def _test_resize_shape(self, x_i, x_v, x_size, y_i, y_v, y_size):
self.assertEqual(x.to_dense().view(-1)[0:x_v_numel].view(x_v),
x_dense.view(-1)[0:x_v_numel].view(x_v))

@skipIfRocm
def test_resize(self):
# 1. Increase the size of some dense dimensions [Supported]
self._test_resize_shape([1, 1], [1, 2, 3], [2, 2, 3],
Expand Down Expand Up @@ -1307,7 +1296,6 @@ def setUp(self):

class TestSparseOneOff(TestCase):
@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
@skipIfRocm
def test_cuda_from_cpu(self):
self.assertExpectedRaises(
RuntimeError,
Expand All @@ -1316,7 +1304,6 @@ def test_cuda_from_cpu(self):
[3, 4, 4]))

@unittest.skipIf(not TEST_CUDA, 'CUDA not available')
@skipIfRocm
def test_cuda_sparse_cpu_dense_add(self):
x = torch.zeros(3, 4, 4)
sparse_y = torch.cuda.sparse.FloatTensor(torch.zeros(1, 4).long().cuda(),
Expand Down