[Relax][ONNX] Refactor constant Unsqueeze scalar path and add regress…

…ion test

- refactor constant Unsqueeze lowering to build target shape then reshape
  instead of scalar special-casing plus repeated expand_dims
- add Unsqueeze scalar-input regression test to cover the new path
- restore helper used by structural ONNX frontend IR checks

Validation:
- python -m ruff check python/tvm/relax/frontend/onnx/onnx_frontend.py tests/python/relax/test_frontend_onnx.py
- python -m pre_commit run --files python/tvm/relax/frontend/onnx/onnx_frontend.py tests/python/relax/test_frontend_onnx.py
- python -m pytest -n 1 tests/python/relax/test_frontend_onnx.py -k "unsqueeze_scalar_input or unsqueeze_dynamic_axes or unsqueeze_duplicate_axes_validation or slice_dynamic_inputs_ir or slice_dynamic_inputs_length_validation or slice_zero_step_validation" -v

Result:
- 8 passed

python/tvm/relax/frontend/onnx/onnx_frontend.py

@@ -707,11 +707,15 @@ def _impl_v13(cls, bb, inputs, attr, params):
             )
             constant_axes = sorted(constant_axes)
             expanded = data.data.numpy()
-            if len(expanded.shape) == 0:
-                expanded = [expanded]
-                constant_axes = [axis - 1 for axis in constant_axes if axis != 0]
-            for axis in constant_axes:
-                expanded = _np.expand_dims(expanded, axis=axis)
+            output_rank = expanded.ndim + len(constant_axes)
+            new_shape = []
+            input_dims_iter = iter(expanded.shape)
+            for i in range(output_rank):
+                if i in constant_axes:
+                    new_shape.append(1)
+                else:
+                    new_shape.append(next(input_dims_iter))
+            expanded = expanded.reshape(new_shape)
             return relax.const(expanded, data.struct_info.dtype)
 
         if isinstance(axes, relax.Constant):

tests/python/relax/test_frontend_onnx.py

@@ -197,6 +197,17 @@ def _check_output(tvm_out, ort_out):
             _check_output(tvm_out, ort_out)
 
 
+def collect_relax_call_ops(relax_func: relax.Function) -> set[str]:
+    call_ops = set()
+
+    def _visit(expr):
+        if isinstance(expr, relax.Call) and isinstance(expr.op, tvm.ir.Op):
+            call_ops.add(expr.op.name)
+
+    relax.analysis.post_order_visit(relax_func.body, _visit)
+    return call_ops
+
+
 @pytest.mark.parametrize(
     "input_names, expected_names",
     [
@@ -904,6 +915,22 @@ def test_unsqueeze():
     check_correctness(model)
 
 
+def test_unsqueeze_scalar_input():
+    unsqueeze_node = helper.make_node("Unsqueeze", ["a", "axes"], ["b"])
+
+    graph = helper.make_graph(
+        [unsqueeze_node],
+        "unsqueeze_scalar_input",
+        inputs=[helper.make_tensor_value_info("a", TensorProto.FLOAT, [])],
+        initializer=[helper.make_tensor("axes", TensorProto.INT64, [2], vals=[0, 1])],
+        outputs=[helper.make_tensor_value_info("b", TensorProto.FLOAT, [1, 1])],
+    )
+
+    model = helper.make_model(graph, producer_name="unsqueeze_scalar_input_test")
+    inputs = {"a": np.array(3.0, dtype="float32")}
+    check_correctness(model, inputs, opset=13)
+
+
 def test_unsqueeze_dynamic_axes():
     unsqueeze_node = helper.make_node("Unsqueeze", ["a", "axes"], ["b"])