Fix scatter_reduce(reduce="mean") producing incorrect ONNX export results

[Copilot]

aten::scatter_reduce.two with reduce="mean" silently produced wrong results after ONNX export because the implementation mapped "mean" → ONNX reduction="none" (a plain overwrite), with a comment acknowledging it was unsupported but no correct fallback.

Root cause

ScatterElements in ONNX has no native mean reduction. The previous code fell through to reduction="none", which simply overwrites each output position with the last scattered value instead of averaging.

Fix

Implements mean explicitly as sum / count using two ScatterElements(reduction="add") passes:

• scatter_sum: accumulates src values at each output index
• scatter_count: accumulates ones to get the number of contributions per position
• include_self=True: adds self to both sum (+self) and count (+1)
• include_self=False: uses max(count, 1) to avoid 0/0 — positions with no incoming values have sum=0 so 0/1=0 matches PyTorch semantics

# Before fix
h = [[1,10],[3,30],[5,50],[7,70],[2,20],[4,40]]
batch = [0,0,1,1,2,2]
# PyTorch:      [[2,20],[6,60],[3,30]]
# ONNX (wrong): [[3,30],[7,70],[4,40]]  ← last-write wins

# After fix
# ONNX (fixed): [[2,20],[6,60],[3,30]]  ← correct mean

Test changes

• Removed xfail(variant_name="mean") from ops_test_data.py — this reduction mode is now correctly supported
• Added two e2e tests covering include_self=False and include_self=True

Original prompt

---

This section details on the original issue you should resolve

<issue_title>torch.export + torch.onnx.export(dynamo=True) gives incorrect results for scatter_reduce_(reduce="mean")</issue_title>
<issue_description>### 🐛 Describe the bug

Observed behaviour:

• scatter_reduce(mean) shows a large mismatch after export:
  • max_abs_diff: 10.0
  • mean_abs_diff: 5.5
• Equivalent sum/count control matches exactly:
  • max_abs_diff: 0.0
  • mean_abs_diff: 0.0

Expected behaviour:

• ONNX output should match eager PyTorch semantics (within normal floating-point tolerance) for:
  • scatter_reduce_(reduce="mean", include_self=False)

Impact:

• Silent numerical correctness issue (wrong predictions without a crash).

Environment:

• torch: 2.7.1
• onnxruntime: 1.24.1
• python: 3.12
• os: macOS (darwin 24.6.0)

Code example:

import numpy as np
import onnxruntime as ort
import torch


class ScatterMeanModel(torch.nn.Module):
    def forward(self, h: torch.Tensor, batch: torch.Tensor) -> torch.Tensor:
        # h: [N, F], batch: [N] with group IDs in [0, G-1]
        index = batch.unsqueeze(1).repeat(1, h.shape[1])
        groups = batch.max().int() + 1
        out = torch.zeros(groups, h.shape[1], dtype=h.dtype, device=h.device)
        out = out.scatter_reduce_(0, index, h, reduce="mean", include_self=False)
        return out


class ScatterSumDivCountModel(torch.nn.Module):
    # Mathematically equivalent grouped mean = sum / count.
    def forward(self, h: torch.Tensor, batch: torch.Tensor) -> torch.Tensor:
        index = batch.unsqueeze(1).repeat(1, h.shape[1])
        groups = batch.max().int() + 1

        sums = torch.zeros(groups, h.shape[1], dtype=h.dtype, device=h.device)
        sums = sums.scatter_reduce_(0, index, h, reduce="sum", include_self=False)

        ones = torch.ones(h.shape[0], 1, dtype=h.dtype, device=h.device)
        counts = torch.zeros(groups, 1, dtype=h.dtype, device=h.device)
        counts = counts.scatter_reduce_(
            0, batch.unsqueeze(1), ones, reduce="sum", include_self=False
        )
        return sums / counts


def run(model: torch.nn.Module) -> tuple[np.ndarray, np.ndarray, float, float]:
    model.eval()
    h = torch.tensor(
        [
            [1.0, 10.0],
            [3.0, 30.0],
            [5.0, 50.0],
            [7.0, 70.0],
            [2.0, 20.0],
            [4.0, 40.0],
        ],
        dtype=torch.float32,
    )
    batch = torch.tensor([0, 0, 1, 1, 2, 2], dtype=torch.int64)

    with torch.inference_mode():
        pt = model(h, batch).cpu().numpy()

    exported = torch.export.export(model, (h, batch), strict=False)
    onnx_program = torch.onnx.export(exported, f=None, dynamo=True)

    sess = ort.InferenceSession(
        onnx_program.model_proto.SerializeToString(),
        providers=["CPUExecutionProvider"],
    )
    input_names = [i.name for i in sess.get_inputs()]
    ort_out = sess.run(
        None, {input_names[0]: h.numpy(), input_names[1]: batch.numpy()}
    )[0]

    diff = np.abs(pt - ort_out)
    return pt, ort_out, float(diff.max()), float(diff.mean())


print("torch:", torch.__version__)
print("onnxruntime:", ort.__version__)

pt, ort_out, max_abs, mean_abs = run(ScatterMeanModel())
print("\n=== scatter_reduce(mean) ===")
print("PyTorch output:\n", pt)
print("ONNX Runtime output:\n", ort_out)
print("max_abs_diff:", max_abs)
print("mean_abs_diff:", mean_abs)

pt2, ort_out2, max_abs2, mean_abs2 = run(ScatterSumDivCountModel())
print("\n=== sum/count control ===")
print("PyTorch output:\n", pt2)
print("ONNX Runtime output:\n", ort_out2)
print("max_abs_diff:", max_abs2)
print("mean_abs_diff:", mean_abs2)

Example output:

python tmp/repro_pytorch_scatter_reduce_mean_onnx.py
torch: 2.7.1
onnxruntime: 1.24.1
W0225 11:07:22.309000 94867 torch/onnx/_internal/exporter/_registration.py:103] torchvision is not installed. Skipping torchvision::nms
W0225 11:07:22.310000 94867 torch/onnx/_internal/exporter/_registration.py:103] torchvision is not installed. Skipping torchvision::roi_align
W0225 11:07:22.310000 94867 torch/onnx/_internal/exporter/_registration.py:103] torchvision is not installed. Skipping torchvision::roi_pool
[torch.onnx] Run decomposition...
[torch.onnx] Run decomposition... ✅
[torch.onnx] Translate the graph into ONNX...
[torch.onnx] Translate the graph into ONNX... ✅

=== scatter_reduce(mean) ===
PyTorch output:
 [[ 2. 20.]
 [ 6. 60.]
 [ 3. 30.]]
ONNX Runtime output:
 [[ 3. 30.]
 [ 7. 70.]
 [ 4. 40.]]
max_abs_diff: 10.0
mean_abs_diff: 5.5
W0225 11:07:22.633000 94867 torch/onnx/_internal/exporter/_registration.py:103] torchvision is not installed. Skipping torchvision::nms
W0225 11:07:22.634000 94867 torch/onnx/_internal/exporter/_registration.py:103] torchvision is not installed. Skipping torchvision::roi_align
W0225 11:07:22.634000 94867 torch/onnx/_internal/exporter/_registration.py:103] torchvision is not installed. Skipping torchvision::roi_pool
[tor...

</details>



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- Fixes pytorch/pytorch#175738

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[codecov]

❌ 4 Tests Failed:

Tests completed	Failed	Passed	Skipped
12741	4	12737	939

View the top 3 failed test(s) by shortest run time

tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU::test_output_match_opinfo__scatter_reduce_mean_cpu_float32

Stack Traces | 0.356s run time

.../function_libs/torch_lib/ops_test.py:243: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not close!
E   
E   Mismatched elements: 30 / 50 (60.0%)
E   Greatest absolute difference: 8.478373527526855 at index (0, 0) (up to 1e-05 allowed)
E   Greatest relative difference: 1.0 at index (0, 0) (up to 1.3e-06 allowed)

tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU::test_output_match_opinfo__scatter_reduce_mean_cpu_int32

Stack Traces | 0.826s run time

.../function_libs/torch_lib/ops_test.py:243: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not equal!
E   
E   Mismatched elements: 30 / 50 (60.0%)
E   Greatest absolute difference: 9 at index (0, 2)
E   Greatest relative difference: 1.0 at index (0, 0)

tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU::test_output_match_opinfo__scatter_reduce_mean_cpu_float16

Stack Traces | 2.28s run time

.../function_libs/torch_lib/ops_test_common.py:593: in _capture_graph_and_evaluate_torch_script_evaluator
    return _ort_session_run(model_proto.SerializeToString(), ort_inputs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.../function_libs/torch_lib/ops_test_common.py:349: in _ort_session_run
    return session.run(None, ort_inputs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
..../test_onnx_ir_git/lib/python3.11.../onnxruntime/capi/onnxruntime_inference_collection.py:275: in run
    return self._sess.run(output_names, input_feed, run_options)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
E   onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running ScatterElements node. Name:'node_ScatterElements_11' Status Message: CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.

The above exception was the direct cause of the following exception:
.../function_libs/torch_lib/ops_test.py:206: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
.../function_libs/torch_lib/ops_test_common.py:607: in _capture_graph_and_evaluate_torch_script_evaluator
    raise RuntimeError(
E   RuntimeError: ONNX Runtime failed to evaluate:
E   Inputs:
E   {'input_0': array(-1.837, dtype=float16),
E    'input_2': array(0),
E    'input_3': array(-2.645, dtype=float16)}
E   Model:
E   <
E      ir_version: 10,
E      opset_import: ["" : 18, "pkg.torch.onnx" : 1, "pkg.onnxscript.torch_lib.common" : 1, "pkg.onnxscript.torch_lib" : 1],
E      producer_name: "torch_test"
E   >
E   main_graph (float16 input_0, int64 input_2, float16 input_3) => (float16 val_16) 
E      <int64[1] val_0, float16[1] val_1, int64[1] val_2, float16[1] val_3, int64[1] val_4, float16[1] val_5, float16[1] val_6, int64[1] val_7, float16[1] val_8, int64[1] val_9, float16[1] val_10, float16[1] val_11, int64[1] val_12, float16[1] val_13, float16[1] val_14, float16[1] val_15>
E   {
E      val_0 = Constant <value_ints: ints = [-1]> ()
E      val_1 = Reshape <allowzero: int = 0> (input_0, val_0)
E      val_2 = Reshape <allowzero: int = 0> (input_2, val_0)
E      val_3 = Reshape <allowzero: int = 0> (input_3, val_0)
E      val_4 = Shape <start: int = 0> (val_1)
E      val_5 = ConstantOfShape <value: tensor = float16[1]...> (val_4)
E      val_6 = ScatterElements <axis: int = 0, reduction: string = "add"> (val_5, val_2, val_3)
E      val_7 = Shape <start: int = 0> (val_1)
E      val_8 = ConstantOfShape <value: tensor = float16[1]...> (val_7)
E      val_9 = Shape <start: int = 0> (val_3)
E      val_10 = ConstantOfShape <value: tensor = float16[1]...> (val_9)
E      val_11 = ScatterElements <axis: int = 0, reduction: string = "add"> (val_8, val_2, val_10)
E      val_12 = Shape <start: int = 0> (val_11)
E      val_13 = ConstantOfShape <value: tensor = float16[1]...> (val_12)
E      val_14 = Max (val_11, val_13)
E      val_15 = Div (val_6, val_14)
E      val_16 = Squeeze (val_15)
E   }

View the full list of 1 ❄️ flaky test(s)

tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU::test_output_match_opinfo__scatter_reduce_mean_cpu_int64

Flake rate in main: 8.33% (Passed 11 times, Failed 1 times)

Stack Traces | 0.495s run time

.../function_libs/torch_lib/ops_test.py:243: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not equal!
E   
E   Mismatched elements: 30 / 50 (60.0%)
E   Greatest absolute difference: 9 at index (0, 2)
E   Greatest relative difference: 1.0 at index (0, 0)

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