Iterate over fwd_args for hopefully more precise new_fwd_args

in the lookaside of `torch.ops.higher_order.autograd_function_apply`
that could take `torch.SymInt` and/or `torch.SymFloat` as its arguments
passed to GraphModule's forward/backward.

Signed-off-by: Masaki Kozuki <mkozuki@nvidia.com>

thunder/core/jit_ext.py

@@ -792,7 +792,23 @@ def _generate_random_str_id() -> str:
     # note that this key is quite new: https://github.com/pytorch/pytorch/pull/134087
     # non_differentiable_idx = fwd_kwargs.get("non_differentiable_idx")
     length_of_tensor_args = sum(args_tensor_mask)
-    new_fwd_args = (wrap_const(None),) + fwd_args[:length_of_tensor_args]
+
+    # N.B.(crcrpar) When `torch.compile(..., dynamic=True)`,
+    # GraphModules' forward seem to take `SymInt` and other values
+    # as its argument with some probability. Though that piece of information unfortunately
+    # does not seem to be indicated in ``args_tensor_`` nor ``non_differentiable_idx``.
+    # Thus we optimistically iterate over ``fwd_args`` and gather non-tensor values to ``fwd_args``.
+    new_fwd_args = []
+    for i, v in enumerate(fwd_args):
+        if i < length_of_tensor_args:
+            print(unwrap(v))
+            new_fwd_args.append(v)
+        else:
+            # note(crcrpar): we might want to include `FutureTensorProxy` and
+            # a proxy of tensor subclass in the near future.
+            if not isinstance(unwrap(v), TensorProxy):
+                new_fwd_args.append(v)
+    new_fwd_args = (wrap_const(None),) + tuple(new_fwd_args)
 
     aug_fwd_trace, aug_fwd_provenance = _convert_pytorchfunc_to_thundertrace(fwd, False, *new_fwd_args)
     if aug_fwd_trace is INTERPRETER_SIGNALS.EXCEPTION_RAISED: