Ensure TorchScript output tensors are contiguous (#554)

### Summary:

Ensures that all tensors returned from a TorchScript model are contiguous.

Fixes #553

### Test Plan:

Added a test. The test did not pass before the fix and it does pass after the fix.

source/neuropod/backends/torchscript/torch_backend.cc

@@ -130,7 +130,8 @@ void insert_value_in_output(NeuropodValueMap & output,
  // Torch tensor
  // Transfer it to CPU
  // .to(device) is a no-op if the tensor is already transferred
- auto tensor = value.toTensor().to(torch::kCPU);
+ // .contiguous() is a no-op if the tensor is already contiguous
+ auto tensor = value.toTensor().to(torch::kCPU).contiguous();
 
  // Get the type and make a TorchNeuropodTensor
  auto neuropod_tensor_type = get_neuropod_type_from_torch_type(tensor.scalar_type());

source/python/neuropod/backends/torchscript/test/test_torchscript_packaging.py

@@ -136,6 +136,18 @@ def forward(self, x, y):
  return SomeNamedTuple(sum=x + y, difference=x - y, product=x * y)
 
 
+class SplitterModel(torch.jit.ScriptModule):
+ """
+ This model returns a non-contiguous output
+ """
+
+ @torch.jit.script_method
+ def forward(self, x):
+ x1 = x[:, :2]
+ x2 = x[:, 2:]
+ return {"x1": x1, "x2": x2}
+
+
 @requires_frameworks("torchscript")
 class TestTorchScriptPackaging(unittest.TestCase):
  def package_simple_addition_model(self, do_fail=False):
@@ -280,6 +292,26 @@ def test_named_tuple_model_failure(self):
  with self.assertRaises(ValueError):
  self.package_named_tuple_model(do_fail=True)
 
+ def test_noncontiguous_array(self):
+ # Test a non-contiguous output
+ x = np.arange(16).astype(np.int64).reshape(4, 4)
+
+ with TemporaryDirectory() as test_dir:
+ neuropod_path = os.path.join(test_dir, "test_neuropod")
+
+ create_torchscript_neuropod(
+ neuropod_path=neuropod_path,
+ model_name="splitter",
+ module=SplitterModel(),
+ input_spec=[{"name": "x", "dtype": "int64", "shape": (4, 4)}],
+ output_spec=[
+ {"name": "x1", "dtype": "int64", "shape": (4, 2)},
+ {"name": "x2", "dtype": "int64", "shape": (4, 2)},
+ ],
+ test_input_data={"x": x},
+ test_expected_out={"x1": x[:, :2], "x2": x[:, 2:]},
+ )
+
 
 if __name__ == "__main__":
  unittest.main()