ai.onnx.CumProd
ai.onnx · standard ONNX operator · ONNX opset ≥ 26
Description
Computes the cumulative product of input elements along a given axis. By default the product is inclusive (each output element is the product of all preceding elements including itself); setting exclusive to 1 shifts the window so the current element is excluded. Setting reverse to 1 performs the scan in the opposite direction along the axis.
See the ONNX CumProd spec for the reference semantics.
Inputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
x |
x |
T |
— | — | Input tensor to be scanned along the specified axis. | required |
Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
|---|---|---|---|---|---|---|
y |
y |
T |
same as x |
same as x |
Output tensor of the same shape and type as x containing the cumulative products. |
required |
Runtime arguments
| Name | Kind | Semantic | Description | Presence |
|---|---|---|---|---|
axis |
i32 |
axis |
Axis along which to accumulate; negative values count from the back. | required |
Attributes
Default values (overridable per request):
| Attribute | Default | Description |
|---|---|---|
exclusive |
0 |
If 1, returns the exclusive product: each output element is the product of all elements before it (not including itself); if 0, the current element is included. |
reverse |
0 |
If 1, performs the cumulative product in the reverse direction along the axis; if 0, proceeds in the forward direction. |
Type constraints
| Variable | Allowed dtypes |
|---|---|
T |
float32, float16, int32, uint32 |
Files
metadata.json— kernel metadata (id, digests, provenance)manifest.json— the op contract (source of truth)test.json— correctness casesbench.json— benchmark + tuning casescumprod-tiled-short-rows.wgsl.jinjascan-row-chunked-vec4.wgsl.jinjascan-row-chunked.wgsl.jinjascan-thread-row.wgsl.jinja
Use with @huggingface/kernels
The loader derives every required output's shape and logical dtype from the manifest contract and this call. It then allocates the result tensors automatically.
The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model version.
Replace each *Data placeholder with a typed array containing the corresponding input data.
import { getKernel } from "@huggingface/kernels";
const kernel = await getKernel("webgpu-kernels/ai.onnx.CumProd", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [3] }, axis: 0 });
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Requires WebGPU support. See the compatibility table.