diff --git a/iree/turbine/kernel/wave/expansion.py b/iree/turbine/kernel/wave/expansion.py
index 726b0c96..517ea733 100644
--- a/iree/turbine/kernel/wave/expansion.py
+++ b/iree/turbine/kernel/wave/expansion.py
@@ -553,6 +553,53 @@ def get_dim_scaling(
     return dim_scaling
 
 
+def _expand_mma_tiled_reduction(
+    mma: MMA,
+    trace: CapturedTrace,
+    dim_query: dict[IndexSymbol, int],
+    dim_scaling: dict[IndexSymbol, int],
+    context: ExpandedNodeMap,
+    get_node_dim_scaling: Callable[[fx.Node], dict[IndexSymbol, int]],
+    res_idx: int,
+) -> CustomOp:
+    latest_reduced_op = mma
+    for scale_idx in range(1, dim_scaling[mma.reduction_dim]):
+        dim_query[mma.reduction_dim] = scale_idx
+        # Temporarily replace the loop carried arg here to avoid
+        # duplicated expansion. Otherwise we have the following situation:
+        # Suppose we have:
+        #   mma_0_0_0(..., acc_0_0_0)
+        #   mma_0_0_1(..., mma_0_0_0)
+        # Expanding mma_0_0_1 to mma_0_0_2 will trigger expansion of its arg
+        # mma_0_0_0 in dims 0_0_2 as well, effectively duplicating the new node.
+        # To avoid this we temporarily replace the use of it with a dummy
+        # placeholder which will not trigger further expansion.
+        dummy = Placeholder("dummy").add_to_graph(latest_reduced_op.graph)
+        dummy.type = None
+
+        saved_acc = latest_reduced_op.acc
+        latest_reduced_op.update_arg("acc", dummy)
+        new_node = _expand_node(
+            latest_reduced_op,
+            trace,
+            dim_query,
+            dim_scaling,
+            context,
+            get_node_dim_scaling,
+            res_idx,
+        )
+
+        # This expansion always happens, user should never be reused
+        assert new_node != latest_reduced_op
+        # Update MMA_{t} to accumulate on MMA_{t-1}, and then save
+        # current MMA_{t} to outputs for use in next loop.
+        latest_reduced_op.update_arg("acc", saved_acc)
+        new_node.update_arg("acc", latest_reduced_op)
+        latest_reduced_op.graph.erase_node(dummy)
+        latest_reduced_op = new_node
+    return latest_reduced_op
+
+
 def _handle_reduction_dim(
     reduction: Reduction,
     output: Output,
@@ -596,39 +643,14 @@ def get_output_index(custom: CustomOp):
         op_output_index = get_output_index(root_op)
         if dim_scaling[reduction.axis] <= 1:
             continue
-        for scale_idx in range(1, dim_scaling[reduction.axis]):
-            dims[reduction.axis] = scale_idx
-            # Temporarily replace the loop carried arg here to avoid
-            # duplicated expansion. Otherwise we have the following situation:
-            # Suppose we have:
-            #   mma_0_0_0(..., acc_0_0_0)
-            #   mma_0_0_1(..., mma_0_0_0)
-            # Expanding mma_0_0_1 to mma_0_0_2 will trigger expansion of its arg
-            # mma_0_0_0 in dims 0_0_2 as well, effectively duplicating the new node.
-            # To avoid this we temporarily replace the use of it with a dummy
-            # placeholder which will not trigger further expansion.
-            dummy = Placeholder("dummy").add_to_graph(latest_reduced_op.graph)
-            dummy.type = None
-
-            saved_acc = latest_reduced_op.acc
-            latest_reduced_op.update_arg("acc", dummy)
-            new_node = _expand_node(
-                latest_reduced_op,
-                trace,
-                dims,
-                dim_scaling,
-                context,
-                get_node_dim_scaling,
-                res_idx,
-            )
-
-            # This expansion always happens, user should never be reused
-            assert new_node != latest_reduced_op
-            # Update MMA_{t} to accumulate on MMA_{t-1}, and then save
-            # current MMA_{t} to outputs for use in next loop.
-            latest_reduced_op.update_arg("acc", saved_acc)
-            new_node.update_arg("acc", latest_reduced_op)
-            latest_reduced_op.graph.erase_node(dummy)
-            latest_reduced_op = new_node
+        latest_reduced_op = _expand_mma_tiled_reduction(
+            root_op,
+            trace,
+            dims,
+            dim_scaling,
+            context,
+            get_node_dim_scaling,
+            res_idx,
+        )
         new_outputs[op_output_index] = latest_reduced_op.fx_node
     output.update_arg("return_vals", new_outputs)