[BugFix][Relax]: handle ONNX ScatterElements reduction

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

@@ -1140,6 +1140,19 @@ def _impl_v11(cls, bb, inputs, attr, params):
         raise ValueError("Scatter is deprecated in ONNX 11")
 
 
+def _get_onnx_reduction(attr, valid_reductions: list[str]):
+    reduction = attr.get("reduction", None)
+    reduction = reduction or b"update"
+    if isinstance(reduction, bytes):
+        reduction = reduction.decode("utf-8")
+    reduction = "update" if reduction == "none" else reduction
+    assert reduction in valid_reductions, (
+        f"Only {valid_reductions} reductions are supported, but {reduction} is gotten"
+    )
+
+    return reduction
+
+
 class ScatterElements(OnnxOpConverter):
     """Convert an onnx ScatterElements node into an equivalent Relax expression."""
 
@@ -1148,21 +1161,29 @@ def _impl_v11(cls, bb, inputs, attr, params):
         axis = attr.get("axis", 0)
         return relax.op.scatter_elements(inputs[0], inputs[1], inputs[2], axis=axis)
 
+    @classmethod
+    def _impl_v16(cls, bb, inputs, attr, params):
+        axis = attr.get("axis", 0)
+        reduction = _get_onnx_reduction(attr, ["update", "add", "mul"])
+        return relax.op.scatter_elements(
+            inputs[0], inputs[1], inputs[2], axis=axis, reduction=reduction
+        )
+
+    @classmethod
+    def _impl_v18(cls, bb, inputs, attr, params):
+        axis = attr.get("axis", 0)
+        reduction = _get_onnx_reduction(attr, ["update", "add", "mul", "min", "max"])
+        return relax.op.scatter_elements(
+            inputs[0], inputs[1], inputs[2], axis=axis, reduction=reduction
+        )
+
 
 class ScatterND(OnnxOpConverter):
     """Convert an onnx ScatterND node into an equivalent Relax expression."""
 
     @staticmethod
     def _reduction_check(attr, valid_reductions: list[str]):
-        reduction = attr.get("reduction", None)
-        reduction = reduction or b"update"
-        reduction = reduction.decode("utf-8")
-        reduction = "update" if reduction == "none" else reduction
-        assert reduction in valid_reductions, (
-            f"Only {valid_reductions} reductions are supported, but {reduction} is gotten"
-        )
-
-        return reduction
+        return _get_onnx_reduction(attr, valid_reductions)
 
     @classmethod
     def _impl_v11(cls, bb, inputs, attr, params):

tests/python/relax/test_frontend_onnx.py

@@ -961,6 +961,80 @@ def test_scatter(axis: int, name: str, opset: int):
     check_correctness(model, inputs={"indices": indices}, opset=opset)
 
 
+@pytest.mark.parametrize(
+    "reduction, opset, data, indices, updates",
+    [
+        (
+            None,
+            11,
+            np.array([[1, 2, 3], [4, 5, 6]], dtype="float32"),
+            np.array([[2, 0, 1], [1, 2, 0]], dtype="int64"),
+            np.array([[30, 10, 20], [50, 60, 40]], dtype="float32"),
+        ),
+        (
+            "none",
+            18,
+            np.array([[1, 2, 3], [4, 5, 6]], dtype="float32"),
+            np.array([[2, 0, 1], [1, 2, 0]], dtype="int64"),
+            np.array([[30, 10, 20], [50, 60, 40]], dtype="float32"),
+        ),
+        (
+            "add",
+            16,
+            np.full((2, 3), 10, dtype="float32"),
+            np.array([[0, 0, 2], [1, 1, 2]], dtype="int64"),
+            np.array([[2, 5, 7], [20, 3, 4]], dtype="float32"),
+        ),
+        (
+            "mul",
+            16,
+            np.full((2, 3), 10, dtype="float32"),
+            np.array([[0, 0, 2], [1, 1, 2]], dtype="int64"),
+            np.array([[2, 5, 7], [20, 3, 4]], dtype="float32"),
+        ),
+        (
+            "min",
+            18,
+            np.full((2, 3), 10, dtype="float32"),
+            np.array([[0, 0, 2], [1, 1, 2]], dtype="int64"),
+            np.array([[2, 5, 7], [20, 3, 4]], dtype="float32"),
+        ),
+        (
+            "max",
+            18,
+            np.full((2, 3), 10, dtype="float32"),
+            np.array([[0, 0, 2], [1, 1, 2]], dtype="int64"),
+            np.array([[2, 5, 7], [20, 3, 4]], dtype="float32"),
+        ),
+    ],
+)
+def test_scatter_elements_reduction(reduction, opset, data, indices, updates):
+    attrs = {"axis": 1}
+    if reduction is not None:
+        attrs["reduction"] = reduction
+    scatter_elements_node = helper.make_node(
+        "ScatterElements", ["data", "indices", "updates"], ["output"], **attrs
+    )
+
+    graph = helper.make_graph(
+        [scatter_elements_node],
+        "scatter_elements_reduction_test",
+        inputs=[
+            helper.make_tensor_value_info("data", TensorProto.FLOAT, list(data.shape)),
+            helper.make_tensor_value_info("indices", TensorProto.INT64, list(indices.shape)),
+            helper.make_tensor_value_info("updates", TensorProto.FLOAT, list(updates.shape)),
+        ],
+        outputs=[helper.make_tensor_value_info("output", TensorProto.FLOAT, list(data.shape))],
+    )
+    model = helper.make_model(graph, producer_name="scatter_elements_reduction_test")
+
+    check_correctness(
+        model,
+        inputs={"data": data, "indices": indices, "updates": updates},
+        opset=opset,
+    )
+
+
 @pytest.mark.parametrize("reduction", ["none", "add", "mul"])
 def test_scatter_nd(reduction):
     def verify_scatter_nd(data_shape, indices_shape, updates_shape):