[Relax][PyTorch] Bind symbolic scalar inputs in from_fx

[guan404ming]

Why

• torch.compile(backend=relax_dynamo(), dynamic=True) lifts SymInt scalars as scalar graph inputs
• from_fx skips these placeholders, so ops referencing one, e.g. view(x.size(0), -1), fail with KeyError

How

• Bind sym placeholders to the same-named tir.Var from the input tensors' symbolic shapes; skip as before when none exists
• Add test_relax_dynamo_dynamic_sym_input_reference; fails with KeyError without the fix

[gemini-code-assist]

Code Review

This pull request enables symbolic shape variables lifted by Dynamo as scalar graph inputs to be bound to matching shape variables, allowing operations like view that reference them to resolve correctly. A test case is added to verify this behavior. The feedback recommends explicitly importing tirx locally to prevent potential runtime errors from fragile transitive imports.

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[gemini-code-assist]

Relying on transitive imports for submodules (like tvm.tirx) can be fragile and may lead to AttributeError if the import structure of other modules changes. It is safer and more robust to explicitly import tirx locally within the function.

        from tvm import tirx

        sym_vars = {
            v.name: v for shape, _ in input_info for v in shape if isinstance(v, tirx.Var)
        }