def test_from_sequence(self):
seq = [list(range(i * 4, i * 4 + 4)) for i in range(5)]
reference = torch.arange(0, 20).resize_(5, 4)
for t in types:
cuda_type = get_gpu_type(t)
self.assertEqual(cuda_type(seq), reference)
def test_is_tensor(self):
for t in types:
tensor = get_gpu_type(t)()
self.assertTrue(torch.is_tensor(tensor))
self.assertTrue(torch.is_tensor(torch.cuda.HalfTensor()))
after1 = torch.cuda.FloatTensor(100, device=1).normal_()
self.assertEqual(before0, after0, 0)
self.assertEqual(before1, after1, 0)
def test_nvtx(self):
# Just making sure we can see the symbols
torch.cuda.nvtx.range_push("foo")
torch.cuda.nvtx.mark("bar")
torch.cuda.nvtx.range_pop()
if HAS_CUDA:
for decl in tests:
for t in types:
tensor = t()
gpu_tensor = get_gpu_type(t)()
if len(decl) == 3:
name, constr, arg_constr = decl
desc = ''
elif len(decl) == 4:
name, constr, arg_constr, desc = decl
elif len(decl) == 5:
name, constr, arg_constr, desc, type_subset = decl
if t not in type_subset:
continue
precision = custom_precision.get(name, TestCuda.precision)
for inplace in (True, False):
if inplace:
name_inner = name + '_'
else:
