Skip shape inference when outputs already have static type/shape

When constant propagation has already determined the type and static
shape of all node outputs, skip the expensive ONNX shape inference
call. Only skip when shapes are fully static (all dims are concrete
integers); dynamic/symbolic dims still benefit from inference.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Author: gramalingam

onnxscript/_internal/_inference.py

@@ -93,7 +93,18 @@ def get_type(index: int, value: ir.Value) -> onnx.TypeProto:
             output.type = ir.serde.deserialize_type_proto_for_type(inferred_type)
 
 
+def _has_complete_type_and_shape(output: ir.Value) -> bool:
+    """Check if an output already has fully determined type and static shape."""
+    if output.type is None or output.shape is None:
+        return False
+    return output.shape.is_static()
+
+
 def infer_outputs(node: ir.Node) -> None:
+    # Skip inference if all outputs already have type and fully static shape
+    # (e.g., from constant propagation setting const_value).
+    if all(_has_complete_type_and_shape(output) for output in node.outputs):
+        return
     try:
         _do_onnx_inference(node)
     except Exception as e:  # pylint: disable=broad-exception-caught