torch.export failed while exporting alpamayo model (Qwen3_vl) with symbolic error

In export the Alpamayo-R1 model (which internally uses Qwen3-VL) using torch.export and then lower it to torch-mlir (ATen dialect).

The export fails due to torch.linspace being called with a data-dependent length, which becomes symbolic during export.

Error message :

During torch.export.export(...), the following error is raised:

torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not extract specialized integer from data-dependent expression u0

The error originates from a call to torch.linspace.

Problematic code:

`def fast_pos_embed_interpolate(self, grid_thw):
grid_ts, grid_hs, grid_ws = grid_thw[:, 0], grid_thw[:, 1], grid_thw[:, 2]

for t, h, w in zip(grid_ts, grid_hs, grid_ws):
    h_idxs = torch.linspace(0, self.num_grid_per_side - 1, h)
    w_idxs = torch.linspace(0, self.num_grid_per_side - 1, w)`

Here, h and w are derived from grid_thw and become symbolic integers during export, which causes torch.linspace(..., steps=h) to fail

Related issue:

This appears similar to the following PyTorch issue:

https://github.com/pytorch/pytorch/issues/138116

@zjgarvey , @penguin-wwy , @angelayi , @agunapal
Any guidance on how to resolve this would be very helpful and is the suggestable to modify the code in model

Start with fast_pos_embed_interpolate in the Alpamayo-R1/Qwen3-VL model code and reproduce the failure during torch.export.export(...). Read the related PyTorch issue 138116 to compare handling of symbolic steps in torch.linspace. Done means the model exports successfully and can be lowered to the torch-mlir ATen dialect.