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Merged
merged 8 commits into from
Sep 14, 2018
Merged

re-enable tests that pass in test_cuda and test_torch. #194

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2f8b44b
re-enabled few tests in test_cuda
lcskrishna Sep 7, 2018
b8f4324
Merge branch 'master' of https://github.com/ROCmSoftwarePlatform/pytorch
lcskrishna Sep 7, 2018
4b59028
reenabled tests that pass in test_torch
lcskrishna Sep 7, 2018
c65c7ab
re-enabled tests that pass after compiler fixes
lcskrishna Sep 11, 2018
c4f6544
enabled fp16 tests for test_torch
lcskrishna Sep 13, 2018
4caf0d4
skipped tests that fail on CI
lcskrishna Sep 14, 2018
b570dec
skipped tests in test_torch that fails on CI
lcskrishna Sep 14, 2018
5c98f39
fixed test_cuda failures in CI
lcskrishna Sep 14, 2018
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118 changes: 51 additions & 67 deletions test/test_cuda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -248,60 +248,47 @@ def tmp(t):
# - disable inplace test, if set to True, no inplace test will be done (default=False)
# - decorator, e.g., unittest.skipIf (default is no decorator)
tests = [
('add', small_3d, lambda t: [number(3.14, 3, t)], '', types, False,
"skipIfRocm:ByteTensor,CharTensor,HalfTensor,ShortTensor"),
('add', small_3d, lambda t: [number(3.14, 3, t)]),
('add', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
('add', small_3d, lambda t: [number(0.2, 2, t), small_3d_positive(t)], 'scalar_tensor'),
('sub', small_3d, lambda t: [number(3.14, 3, t)], '', types, False,
"skipIfRocm:ByteTensor,CharTensor,HalfTensor,ShortTensor"),
('sub', small_3d, lambda t: [number(3.14, 3, t)]),
('sub', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
('mul', small_3d, lambda t: [number(3.14, 3, t)], '', types, False,
"skipIfRocm:ByteTensor,CharTensor,HalfTensor,ShortTensor"),
"skipIfRocm:CharTensor,ShortTensor"),
('mul', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
('div', small_3d, lambda t: [number(3.14, 3, t)], '', types, False,
"skipIfRocm:ByteTensor,CharTensor,FloatTensor,HalfTensor,ShortTensor"),
('div', small_3d, lambda t: [number(3.14, 3, t)]),
('div', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
('pow', small_3d, lambda t: [number(3.14, 3, t)], None, float_types, False, "skipIfRocm:HalfTensor"),
('pow', small_3d, lambda t: [number(1., 1, t)], 'pow1', types, False, "skipIfRocm:HalfTensor"),
('pow', small_3d, lambda t: [number(2., 2, t)], 'pow2', types, False, "skipIfRocm:HalfTensor"),
('pow', small_3d, lambda t: [number(3., 3, t)], 'pow3', types, False, "skipIfRocm:HalfTensor"),
('pow', small_3d, lambda t: [number(-1., -1, t)], 'pow-1', float_types, False, "skipIfRocm:HalfTensor"),
('pow', small_3d, lambda t: [number(3.14, 3, t)], None, float_types),
('pow', small_3d, lambda t: [number(1., 1, t)], 'pow1'),
('pow', small_3d, lambda t: [number(2., 2, t)], 'pow2'),
('pow', small_3d, lambda t: [number(3., 3, t)], 'pow3'),
('pow', small_3d, lambda t: [number(-1., -1, t)], 'pow-1', float_types),
# HalfTensor gives bad result at pow-2 with data sampled from torch.randn
('pow', small_3d, lambda t: [number(-2., -2, t)], 'pow-2', float_types_no_half,
False, "skipIfRocm:HalfTensor,FloatTensor"),
('pow', small_3d, lambda t: [tensor_abs_(small_3d(t))], 'tensor', float_types, False, "skipIfRocm:HalfTensor"),
('addbmm', small_2d, lambda t: [small_3d(t), small_3d(t)], None, float_types, False, "skipIfRocm:HalfTensor"),
('addbmm', small_2d, lambda t: [number(0.4, 2, t), small_3d(t), small_3d(t)], 'scalar',
types, False, "skipIfRocm:HalfTensor"),
('addbmm', small_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), small_3d(t), small_3d(t)], 'two_scalars',
types, False, "skipIfRocm:HalfTensor"),
('pow', small_3d, lambda t: [number(-2., -2, t)], 'pow-2', float_types_no_half, False,
"skipIfRocm:FloatTensor"),
('pow', small_3d, lambda t: [tensor_abs_(small_3d(t))], 'tensor', float_types),
('addbmm', small_2d, lambda t: [small_3d(t), small_3d(t)], None, float_types),
('addbmm', small_2d, lambda t: [number(0.4, 2, t), small_3d(t), small_3d(t)], 'scalar'),
('addbmm', small_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), small_3d(t), small_3d(t)], 'two_scalars'),
('baddbmm', small_3d, lambda t: [small_3d(t), small_3d(t)], '', types, False, "skipIfRocm:HalfTensor"),
('baddbmm', small_3d, lambda t: [number(0.4, 2, t), small_3d(t), small_3d(t)], 'scalar',
types, False, "skipIfRocm:HalfTensor"),
('baddbmm', small_3d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), small_3d(t), small_3d(t)], 'two_scalars',
types, False, "skipIfRocm:HalfTensor"),
('addcdiv', small_2d_lapack, lambda t: [tensor_mul(small_2d_lapack(t), 2), small_2d_lapack(t)], '',
types, False, "skipIfRocm:HalfTensor"),
('baddbmm', small_3d, lambda t: [number(0.4, 2, t), small_3d(t), small_3d(t)], 'scalar'),
('baddbmm', small_3d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), small_3d(t), small_3d(t)], 'two_scalars'),
('addcdiv', small_2d_lapack, lambda t: [tensor_mul(small_2d_lapack(t), 2), small_2d_lapack(t)]),
('addcdiv', small_2d_lapack, lambda t: [number(2.8, 1, t), tensor_mul(small_2d_lapack(t), 2), small_2d_lapack(t)],
'scalar', types, False, "skipIfRocm:HalfTensor"),
('addcmul', small_3d, lambda t: [small_3d(t), small_3d(t)], '', types, False, "skipIfRocm:HalfTensor"),
('addcmul', small_3d, lambda t: [number(0.4, 2, t), small_3d(t), small_3d(t)], 'scalar',
types, False, "skipIfRocm:HalfTensor"),
('addmm', medium_2d, lambda t: [medium_2d(t), medium_2d(t)], '', types, False, "skipIfRocm:HalfTensor"),
('addmm', medium_2d, lambda t: [number(0.4, 2, t), medium_2d(t), medium_2d(t)], 'scalar',
types, False, "skipIfRocm:HalfTensor"),
('addmm', medium_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_2d(t), medium_2d(t)], 'two_scalars',
types, False, "skipIfRocm:HalfTensor"),
'scalar'),
('addcmul', small_3d, lambda t: [small_3d(t), small_3d(t)]),
('addcmul', small_3d, lambda t: [number(0.4, 2, t), small_3d(t), small_3d(t)], 'scalar'),
('addmm', medium_2d, lambda t: [medium_2d(t), medium_2d(t)]),
('addmm', medium_2d, lambda t: [number(0.4, 2, t), medium_2d(t), medium_2d(t)], 'scalar'),
('addmm', medium_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_2d(t), medium_2d(t)], 'two_scalars'),
('addmv', medium_1d, lambda t: [medium_2d(t), medium_1d(t)], '', types, False, "skipIfRocm:HalfTensor"),
('addmv', medium_1d, lambda t: [number(0.4, 2, t), medium_2d(t), medium_1d(t)], 'scalar',
types, False, "skipIfRocm:HalfTensor"),
('addmv', medium_1d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_2d(t), medium_1d(t)], 'two_scalars',
types, False, "skipIfRocm:HalfTensor"),
('addr', medium_2d, lambda t: [medium_1d(t), medium_1d(t)], '', types, False, "skipIfRocm:HalfTensor"),
('addr', medium_2d, lambda t: [number(0.4, 2, t), medium_1d(t), medium_1d(t)], 'scalar',
types, False, "skipIfRocm:HalfTensor"),
('addr', medium_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_1d(t), medium_1d(t)], 'two_scalars',
types, False, "skipIfRocm:HalfTensor"),
('addr', medium_2d, lambda t: [medium_1d(t), medium_1d(t)]),
('addr', medium_2d, lambda t: [number(0.4, 2, t), medium_1d(t), medium_1d(t)], 'scalar'),
('addr', medium_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_1d(t), medium_1d(t)], 'two_scalars'),
('atan2', medium_2d, lambda t: [medium_2d(t)], None, float_types + [torch.HalfTensor]),
('fmod', small_3d, lambda t: [3], 'value', types, False, "skipIfRocm:HalfTensor"),
('fmod', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
Expand All @@ -313,14 +300,14 @@ def tmp(t):
('clone', medium_2d, lambda t: [],),
('contiguous', medium_2d, lambda t: [],),
('cross', new_t(M, 3, M), lambda t: [new_t(M, 3, M)(t)],),
('cumprod', small_3d, lambda t: [1], '', types, False, "skipIfRocm:HalfTensor"),
('cumprod', small_3d, lambda t: [-1], 'neg_dim', types, False, "skipIfRocm:HalfTensor"),
('cumsum', small_3d, lambda t: [1], '', types, False, "skipIfRocm:HalfTensor"),
('cumsum', small_3d, lambda t: [-1], 'neg_dim', types, False, "skipIfRocm:HalfTensor"),
('cumprod', small_3d, lambda t: [1]),
('cumprod', small_3d, lambda t: [-1], 'neg_dim'),
('cumsum', small_3d, lambda t: [1]),
('cumsum', small_3d, lambda t: [-1], 'neg_dim'),
('dim', small_3d, lambda t: [],),
('dist', small_2d, lambda t: [small_2d(t)], '', types, False, "skipIfRocm:HalfTensor"),
('dist', small_2d, lambda t: [small_2d(t), 3], '3_norm', types, False, "skipIfRocm:HalfTensor"),
('dist', small_2d, lambda t: [small_2d(t), 2.5], '2_5_norm', types, False, "skipIfRocm:HalfTensor"),
('dist', small_2d, lambda t: [small_2d(t)]),
('dist', small_2d, lambda t: [small_2d(t), 3], '3_norm'),
('dist', small_2d, lambda t: [small_2d(t), 2.5], '2_5_norm'),
('dot', medium_1d, lambda t: [medium_1d(t)], '', types, False, "skipIfRocm:HalfTensor"),
('element_size', medium_1d, lambda t: [],),
('eq', small_3d_ones, lambda t: [small_3d(t)],),
Expand All @@ -331,7 +318,7 @@ def tmp(t):
('equal', small_3d_ones, lambda t: [small_3d(t)],),
('expand', new_t(M, 1, M), lambda t: [M, 4, M],),
('expand_as', new_t(M, 1, M), lambda t: [new_t(M, 4, M)(t)],),
('fill', medium_2d, lambda t: [number(3.14, 3, t)], '', types, False, "skipIfRocm:HalfTensor"),
('fill', medium_2d, lambda t: [number(3.14, 3, t)]),
('ge', medium_2d, lambda t: [medium_2d(t)],),
('le', medium_2d, lambda t: [medium_2d(t)],),
('gt', medium_2d, lambda t: [medium_2d(t)],),
Expand All @@ -345,33 +332,31 @@ 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"),
('lerp', small_3d, lambda t: [small_3d(t), 0.3]),
('max', small_3d_unique, lambda t: []),
('max', small_3d_unique, lambda t: [1], 'dim', types, False, skipIfRocm),
('max', small_3d_unique, lambda t: [-1], 'neg_dim', types, False, skipIfRocm),
('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"),
('mean', small_3d, lambda t: []),
('mean', small_3d, lambda t: [-1], 'neg_dim', types, False, "skipIfRocm:DoubleTensor,FloatTensor"),
('mean', small_3d, lambda t: [1], 'dim', types, False, "skipIfRocm:DoubleTensor,FloatTensor"),
('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"),
('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'),
('remainder', small_3d, lambda t: [constant_tensor_sub(0, small_3d_positive(t))], 'negative_tensor', signed_types),
('std', small_3d, lambda t: [], '', types, False, "skipIfRocm:HalfTensor"),
('std', small_3d, lambda t: []),
('std', small_3d, lambda t: [1], 'dim'),
('std', small_3d, lambda t: [-1], 'neg_dim'),
('var', small_3d, lambda t: [], '', types, False, "skipIfRocm:HalfTensor"),
('var', small_3d, lambda t: []),
('var', small_3d, lambda t: [1], 'dim'),
('var', small_3d, lambda t: [-1], 'neg_dim'),
('ndimension', small_3d, lambda t: [],),
Expand All @@ -380,20 +365,19 @@ def tmp(t):
('narrow', small_3d, lambda t: [1, 3, 2],),
('narrow', small_3d, lambda t: [-1, 3, 2], 'neg_dim'),
('nonzero', small_3d, lambda t: [], '', types, False, skipIfRocm),
('norm', small_3d, lambda t: [], '', types, False, "skipIfRocm:HalfTensor"),
('norm', small_3d, lambda t: [3], '3_norm', types, False, "skipIfRocm:HalfTensor"),
('norm', small_3d, lambda t: [3, 0], '3_norm_dim', types, False, "skipIfRocm:HalfTensor,DoubleTensor,FloatTensor"),
('norm', small_3d, lambda t: [3, -2], '3_norm_neg_dim', types,
False, "skipIfRocm:HalfTensor,DoubleTensor,FloatTensor"),
('norm', small_3d, lambda t: []),
('norm', small_3d, lambda t: [3], '3_norm'),
('norm', small_3d, lambda t: [3, 0], '3_norm_dim'),
('norm', small_3d, lambda t: [3, -2], '3_norm_neg_dim'),
('ones', small_3d, lambda t: [1, 2, 3, 4, 5],),
('permute', new_t(1, 2, 3, 4), lambda t: [2, 1, 3, 0],),
('put_', new_t(2, 5, 3), lambda t: [long_type(t)([[0], [-2]]), t([[3], [4]])], '', types, False, skipIfRocm),
('put_', new_t(2, 3), lambda t: [long_type(t)([]), t([])], 'empty'),
('put_', new_t(2, 2), lambda t: [long_type(t)([[1], [-3]]), t([[1], [2]]), True], 'accumulate'),
('prod', small_2d_oneish, lambda t: [], '', types, False, "skipIfRocm:HalfTensor"),
('prod', small_2d_oneish, lambda t: []),
('prod', small_3d, lambda t: [1], 'dim', types, False, skipIfRocm),
('prod', small_3d, lambda t: [-1], 'neg_dim', types, False, skipIfRocm),
('sum', small_2d, lambda t: [], '', types, False, "skipIfRocm:HalfTensor"),
('sum', small_2d, lambda t: []),
('sum', small_3d, lambda t: [1], 'dim', types, False, skipIfRocm),
('sum', small_3d, lambda t: [-1], 'neg_dim', types, False, skipIfRocm),
('renorm', small_3d, lambda t: [2, 1, 1], '2_norm', types, False, "skipIfRocm:HalfTensor,DoubleTensor,FloatTensor"),
Expand Down Expand Up @@ -424,7 +408,7 @@ def tmp(t):
('topk', small_3d_unique, lambda t: [2, 1, False, True], 'dim_sort', types, False, skipIfRocm),
('topk', small_3d_unique, lambda t: [2, -1, False, True], 'neg_dim_sort', types, False, skipIfRocm),
('topk', small_3d_unique, lambda t: [2, 1, True, True], 'dim_desc_sort', types, False, skipIfRocm),
('trace', medium_2d, lambda t: [], '', types, False, "skipIfRocm:HalfTensor"),
('trace', medium_2d, lambda t: []),
('tril', medium_2d, lambda t: [],),
('tril', medium_2d_expanded, lambda t: [], 'zero_stride', types, True),
('tril', medium_2d, lambda t: [2], 'positive'),
Expand Down
7 changes: 1 addition & 6 deletions test/test_torch.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1078,7 +1078,6 @@ def test_pairwise_distance_empty(self):
self.assertEqual(torch.zeros(0, device=device), torch.pairwise_distance(x, y))
self.assertEqual(torch.zeros((0, 1), device=device), torch.pairwise_distance(x, y, keepdim=True))

@skipIfRocm
def test_pdist_empty(self):
devices = ['cpu']
for device in devices:
Expand All @@ -1094,7 +1093,6 @@ def test_pdist_empty(self):
x = torch.randn(shape, device=device)
self.assertEqual(torch.zeros(3, device=device), torch.pdist(x))

@skipIfRocm
@unittest.skipIf(not TEST_SCIPY, "Scipy not found")
def test_pdist_scipy(self):
from scipy.spatial.distance import pdist
Expand Down Expand Up @@ -2277,7 +2275,6 @@ def get_int64_dtype(dtype):
dtype=int64_dtype, layout=layout, device=device, requires_grad=False),
int64_dtype, layout, device, fv + 5, False)

@skipIfRocm
def test_empty_full(self):
self._test_empty_full(self, torch.testing.get_all_dtypes(), torch.strided, torch.device('cpu'))
if torch.cuda.device_count() > 0:
Expand Down Expand Up @@ -3479,6 +3476,7 @@ def test_topk_arguments(self):
self.assertRaises(TypeError, lambda: q.topk(4, True))

@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
@skipIfRocm
def test_topk_noncontiguous_gpu(self):
t = torch.randn(20, device="cuda")[::2]
top1, idx1 = t.topk(5)
Expand Down Expand Up @@ -3731,7 +3729,6 @@ def test_narrow(self):
self.assertEqual(x.narrow(-1, -1, 1), torch.Tensor([[2], [5], [8]]))
self.assertEqual(x.narrow(-2, -1, 1), torch.Tensor([[6, 7, 8]]))

@skipIfRocm
def test_narrow_empty(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
Expand Down Expand Up @@ -4022,7 +4019,6 @@ def _test_gesv_batched_dims(self, cast):
self.assertEqual(x.data, cast(x_exp))

@skipIfNoLapack
@skipIfRocm
def test_gesv_batched_dims(self):
self._test_gesv_batched_dims(self, lambda t: t)

Expand Down Expand Up @@ -7496,7 +7492,6 @@ def test_serialize_device(self):
self.assertEqual(device, device_copied)

@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
@skipIfRocm
def test_half_tensor_cuda(self):
x = torch.randn(5, 5).half()
self.assertEqual(x.cuda(), x)
Expand Down