Simple TorchScript test fails

[RaulPPelaez]

This test will run exactly 4 iterations and then print an error:

def test_really_simple():
    n_atoms=10
    zs = torch.tensor([1, 6, 7, 8, 9], dtype=torch.long)
    z = zs[torch.randint(0, len(zs), (n_atoms,))]
    pos = torch.randn(len(z), 3)
    batch = torch.zeros(len(z), dtype=torch.long)
    batch[len(batch) // 2 :] = 1
    args = {"model": "tensornet",
            "embedding_dimension": 128,
            "num_layers": 2,
            "num_rbf": 32,
            "rbf_type": "expnorm",
            "trainable_rbf": False,
            "activation": "silu",
            "cutoff_lower": 0.0,
            "cutoff_upper": 5.0,
            "max_z": 100,
            "max_num_neighbors": 128,
            "equivariance_invariance_group": "O(3)",
            "prior_model": None,
            "atom_filter": -1,
            "derivative": True,
            "output_model": "Scalar",
            "reduce_op": "sum",
            "precision": 32 }
    model = create_model(args).to(device="cuda")
    z = z.to("cuda")
    pos = pos.to("cuda").requires_grad_(True)
    batch = batch.to("cuda")
    model = torch.jit.script(model).to(device="cuda")
    for i in range(0, 10):
        print("Running iteration {}".format(i))
        y, neg_dy = model(z, pos, batch)

The error:

self = RecursiveScriptModule(
  original_name=TorchMD_Net
  (representation_model): RecursiveScriptModule(
    original_name=...      (1): RecursiveScriptModule(original_name=SiLU)
      (2): RecursiveScriptModule(original_name=Linear)
    )
  )
)
args = (tensor([7, 6, 7, 7, 8, 7, 8, 7, 8, 8], device='cuda:0'), tensor([[-0.6791,  0.2550, -0.8304],
        [-0.4754,  0.60...107, -1.6600,  0.7560]], device='cuda:0', requires_grad=True), tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1], device='cuda:0')), kwargs = {}, forward_call = <torch.ScriptMethod object at 0x7f9d363cd4e0>

    def _call_impl(self, *args, **kwargs):
        forward_call = (self._slow_forward if torch._C._get_tracing_state() else self.forward)
        # If we don't have any hooks, we want to skip the rest of the logic in
        # this function, and just call forward.
        if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
                or _global_backward_pre_hooks or _global_backward_hooks
                or _global_forward_hooks or _global_forward_pre_hooks):
>           return forward_call(*args, **kwargs)
E           RuntimeError: The following operation failed in the TorchScript interpreter.
E           Traceback of TorchScript (most recent call last):
E             File "/shared/raul/torchmd-net/torchmdnet/models/model.py", line 292, in forward
E                       print("Shape of pos")
E                       print(pos.shape)
E                       dy = grad(
E                            ~~~~ <--- HERE
E                           [y],
E                           [pos],
E           RuntimeError: The following operation failed in the TorchScript interpreter.
E           Traceback of TorchScript (most recent call last):
E           RuntimeError: The following operation failed in the TorchScript interpreter.
E           Traceback of TorchScript (most recent call last):
E           RuntimeError: The size of tensor a (128) must match the size of tensor b (3) at non-singleton dimension 3

../../mambaforge/envs/openmmtorch-test/lib/python3.10/site-packages/torch/nn/modules/module.py:1501: RuntimeError

• If one does not jit.script the model then no error is produced.
• The number of atoms does not seem to affect this and I only see it with tensornet.
• Tried to use the old Distance module to check for problems related to C++ extensions, but the error is the same.
• Even for something like 10 atoms, each iteration takes increasingly more time, in the order of 5 seconds.
• 128 is the embedding dimension, changing it to 32 does indeed change the error to 32.

[RaulPPelaez]

Saving and loading the model each iteration solves the issue, and it is in fact faster than not doing it:

    for i in range(0, 10):
        print("Running iteration {}".format(i))
        import io
        buffer = io.BytesIO()
        torch.jit.save(model, buffer)
        buffer.seek(0)
        model = torch.jit.load(buffer).to(device="cuda")
        y, neg_dy = model(z, pos, batch)

[RaulPPelaez]

This is the offending line. Making tensor_m = tensor or using 0 layers makes the problem go away.

https://github.com/torchmd/torchmd-net/blob/fd8395463370c6b0510d4fe24b25d937382247a1/torchmdnet/models/tensornet.py#L331 Replacing it by this does NOT solve the issue:

tensor_m = torch.zeros_like(tensor).scatter_add(0, (edge_index[0])[:,None, None, None].expand_as(msg), msg)

But my suspicion is scatter_add is the same thing being used by scatter anyway... EDIT: Not saving the graph when calling grad also makes the error go away:

        if self.derivative:
            grad_outputs: List[Optional[torch.Tensor]] = [torch.ones_like(y)]
            dy = grad(
                [y],
                [pos],
                grad_outputs=grad_outputs,
                create_graph=False,
                retain_graph=False,
            )[0]

[RaulPPelaez]

5 is a magic number we have encountered before when dealing with CUDA torch models. See for instance https://github.com/openmm/openmm-torch/pull/122

I just made the connection with this issue now while reading about torch.jit fusion operation mechanism with NVFuser: https://github.com/pytorch/pytorch/blob/main/torch/csrc/jit/codegen/cuda/README.md#general-ideas-of-debug-no-fusion

What I believe is going on here is that there is some kind of collision between jit.script, the nvfuser backend and autograd.

[RaulPPelaez]

This issue goes away with pytorch>2.0.0, so I am going to assume it is a bug there. Will leave this open until there is a conda-forge package for pytorch 2.1.

[RaulPPelaez]

Closing this as there is a package for 2.1 already.