def test_tensordot_recurring_dim_runtime_error(test_case):
with test_case.assertRaises(Exception) as context:
a = flow.randn(1, 2, 3)
b = flow.randn(1, 2, 3)
flow.tensordot(a, b, dims=[[1, 1], [1, 1]])
test_case.assertTrue("dim 1 appears multiple times in the list of dims"
in str(context.exception))
def test_tensordot_dims_different_length_runtime_error(test_case):
with test_case.assertRaises(Exception) as context:
a = flow.randn(1, 2, 3)
b = flow.randn(1, 2, 3)
flow.tensordot(a, b, dims=[[1], [1, 2]])
test_case.assertTrue("both dimension lists should have same length" in
str(context.exception))
def test_tensordot_neg_dims_runtime_error(test_case):
with test_case.assertRaises(Exception) as context:
a = flow.randn(1, 2, 3)
b = flow.randn(1, 2, 3)
flow.tensordot(a, b, dims=-1)
test_case.assertTrue("tensordot expects dims >= 0, but got dims=-1" in
str(context.exception))
def test_tensordot_unmatch_dims_runtime_error(test_case):
with test_case.assertRaises(Exception) as context:
a = flow.randn(1, 2, 3)
b = flow.randn(1, 2, 3)
flow.tensordot(a, b, dims=[[1], [2]])
test_case.assertTrue(
"contracted dimensions need to match, but first has size 2 in dim 1 and second has size 3 in dim 2"
in str(context.exception))
def test_tensordot_out_of_range_dims_runtime_error(test_case):
with test_case.assertRaises(Exception) as context:
a = flow.randn(1, 2, 3)
b = flow.randn(1, 2, 3)
flow.tensordot(a, b, dims=[[3], [2]])
test_case.assertTrue(
"Dimension out of range (expected to be in range of [-3, 2], but got 3)"
in str(context.exception))
def test_tensordot_too_large_int_dims_runtime_error(test_case):
with test_case.assertRaises(Exception) as context:
a = flow.randn(1, 2, 3)
b = flow.randn(1, 2, 3)
flow.tensordot(a, b, dims=100)
test_case.assertTrue(
"tensordot expects dims <= a.ndim which is 3, but got 100" in str(
context.exception))
def _test_tensor_dim(test_case, device):
np_dim = np.array([[1, 2, 3], [1, 2, 3]], dtype=np.int)
flow_dim = flow.tensor(np_dim).to(device)
torch_dim = torch.tensor(np_dim).to(device)
np_random_array = np.random.randn(2, 3, 4, 5)
flow_tensor = flow.tensor(np_random_array).to(device)
torch_tensor = torch.tensor(np_random_array).to(device)
flow_result = flow.tensordot(flow_tensor,
flow_tensor,
dims=flow_dim)
torch_result = torch.tensordot(torch_tensor,
torch_tensor,
dims=torch_dim)
test_case.assertTrue(
np.allclose(
flow_result.numpy(),
torch_result.cpu().numpy(),
rtol=0.0001,
atol=0.0001,
))