def test_cdgmm_forward(self, data, backend, device, inplace):
if device == 'cpu' and backend.NAME == 'skcuda':
pytest.skip("skcuda backend can only run on gpu")
x, filt, y = data
# move to device
x, filt, y = x.to(device), filt.to(device), y.to(device)
# call cdgmm
if inplace:
x = x.clone()
z = backend.cdgmm(x, filt, inplace=inplace)
if inplace:
z = x
# compare
assert (y - z).abs().max() < 1e-6
def test_Cublas():
for device in devices:
if device == 'gpu':
for backend in backends:
x = torch.rand(100, 128, 128, 2).cuda()
filter = torch.rand(128, 128, 2).cuda()
filter[..., 1] = 0
y = torch.ones(100, 128, 128, 2).cuda()
z = torch.Tensor(100, 128, 128, 2).cuda()
for i in range(100):
y[i, :, :, 0] = x[i, :, :, 0] * filter[:, :, 0] - x[
i, :, :, 1] * filter[:, :, 1]
y[i, :, :, 1] = x[i, :, :, 1] * filter[:, :, 0] + x[
i, :, :, 0] * filter[:, :, 1]
z = backend.cdgmm(x, filter)
assert (y - z).abs().max() < 1e-6
elif device == 'cpu':
for backend in backends:
if backend.NAME == 'skcuda':
continue
x = torch.rand(100, 128, 128, 2)
filter = torch.rand(128, 128, 2)
filter[..., 1] = 0
y = torch.ones(100, 128, 128, 2)
z = torch.Tensor(100, 128, 128, 2)
for i in range(100):
y[i, :, :, 0] = x[i, :, :, 0] * filter[:, :, 0] - x[
i, :, :, 1] * filter[:, :, 1]
y[i, :, :, 1] = x[i, :, :, 1] * filter[:, :, 0] + x[
i, :, :, 0] * filter[:, :, 1]
z = backend.cdgmm(x, filter)
assert (y - z).abs().max() < 1e-6
def test_cdgmm_exceptions(self, backend):
with pytest.raises(RuntimeError) as exc:
backend.cdgmm(torch.empty(3, 4, 5, 2), torch.empty(4, 3, 2))
assert "not compatible" in exc.value.args[0]
with pytest.raises(TypeError) as exc:
backend.cdgmm(torch.empty(3, 4, 5, 1), torch.empty(4, 5, 1))
assert "input must be complex" in exc.value.args[0]
with pytest.raises(TypeError) as exc:
backend.cdgmm(torch.empty(3, 4, 5, 2), torch.empty(4, 5, 3))
assert "filter must be complex or real" in exc.value.args[0]
with pytest.raises(RuntimeError) as exc:
backend.cdgmm(torch.empty(3, 4, 5, 2), torch.empty(3, 4, 5, 2))
assert "filter must be a 3-tensor" in exc.value.args[0]
with pytest.raises(RuntimeError) as exc:
backend.cdgmm(torch.empty(3, 4, 5, 2),
torch.empty(4, 5, 1).double())
assert "must be of the same dtype" in exc.value.args[0]
if 'gpu' in devices:
with pytest.raises(RuntimeError) as exc:
backend.cdgmm(torch.empty(3, 4, 5, 2),
torch.empty(4, 5, 1).cuda())
assert "must be on the same device" in exc.value.args[0]
class TestCDGMM:
@pytest.fixture(params=(False, True))
def data(self, request):
real_filter = request.param
x = torch.rand(100, 128, 128, 2)
filt = torch.rand(128, 128, 2)
y = torch.ones(100, 128, 128, 2)
if real_filter:
filt[..., 1] = 0
y[..., 0] = x[..., 0] * filt[..., 0] - x[..., 1] * filt[..., 1]
y[..., 1] = x[..., 1] * filt[..., 0] + x[..., 0] * filt[..., 1]
if real_filter:
filt = filt[..., :1]
return x, filt, y
if 'gpu' in devices:
x, filt, y = data
x, filt = x.to('cpu'), filt.to('gpu')
with pytest.raises(RuntimeError) as exc:
backend.cdgmm(x, filt)
assert ('device' in exc.value.args[0])
@pytest.mark.parametrize("backend", backends)
@pytest.mark.parametrize("device", devices)
@pytest.mark.parametrize("inplace", (False, True))
def test_cdgmm_forward(self, data, backend, device, inplace):
if device == 'cpu' and backend.NAME == 'skcuda':
pytest.skip("skcuda backend can only run on gpu")
x, filt, y = data
# move to device
device = 'cuda' if device == 'gpu' else device
x, filt, y = x.to(device), filt.to(device), y.to(device)
# call cdgmm
if inplace:
x = x.clone()
z = backend.cdgmm(x, filt, inplace=inplace)
if inplace:
z = x
# compare
assert (y - z).abs().max() < 1e-6
@pytest.mark.parametrize("backend", backends)
def test_cdgmm_exceptions(self, backend):
with pytest.raises(RuntimeError) as exc:
backend.cdgmm(torch.empty(3, 4, 5, 2), torch.empty(4, 3, 2))
assert "not compatible" in exc.value.args[0]
with pytest.raises(TypeError) as exc:
backend.cdgmm(torch.empty(3, 4, 5, 1), torch.empty(4, 5, 1))
assert "input must be complex" in exc.value.args[0]
with pytest.raises(TypeError) as exc:
backend.cdgmm(torch.empty(3, 4, 5, 2), torch.empty(4, 5, 3))
assert "filter must be complex or real" in exc.value.args[0]
with pytest.raises(RuntimeError) as exc:
backend.cdgmm(torch.empty(3, 4, 5, 2), torch.empty(3, 4, 5, 2))
assert "filter must be a 3-tensor" in exc.value.args[0]
with pytest.raises(RuntimeError) as exc:
backend.cdgmm(torch.empty(3, 4, 5, 2), torch.empty(4, 5, 1).double())
assert "must be of the same dtype" in exc.value.args[0]
if 'gpu' in devices:
with pytest.raises(RuntimeError) as exc:
backend.cdgmm(torch.empty(3, 4, 5, 2), torch.empty(4, 5, 1).cuda())
assert "must be on the same device" in exc.value.args[0]