| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # 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](https://onnx.ai/onnx/operators/onnx__CumProd.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `x` | `T` | — | — | Input tensor to be scanned along the specified axis. | required | |
|
|
| ## Outputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `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 | Description | Presence | |
| | --- | --- | --- | --- | |
| | `axis` | `i32` | 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`](build/webgpu/metadata.json) — kernel metadata (id, digests, per-variant templates, provenance) |
| - [`manifest.json`](build/webgpu/manifest.json) — the op contract (source of truth) |
| - [`test.json`](build/webgpu/test.json) — correctness cases |
| - [`bench.json`](build/webgpu/bench.json) — benchmark cases |
| - [`cumprod-tiled-short-rows.wgsl.jinja`](build/webgpu/cumprod-tiled-short-rows.wgsl.jinja) |
| - [`scan-row-chunked-vec4.wgsl.jinja`](build/webgpu/scan-row-chunked-vec4.wgsl.jinja) |
| - [`scan-row-chunked.wgsl.jinja`](build/webgpu/scan-row-chunked.wgsl.jinja) |
| - [`scan-thread-row.wgsl.jinja`](build/webgpu/scan-thread-row.wgsl.jinja) |
|
|
| ## Use with `@huggingface/kernels` |
|
|
| ```sh |
| npm install --save-exact @huggingface/kernels@0.0.1-preview.3 |
| ``` |
|
|
| Required output shapes and logical data types are inferred from the supplied inputs and attributes; result tensors are allocated automatically. |
|
|
| The `version: 1` option selects the published kernel contract; it is independent of any operator opset, contrib `since_version`, or model version. |
| It follows the `v1` branch as fixes land. To pin exact artifact bytes, pass a 40-character commit `revision` instead of `version`. |
|
|
| Replace each `*Data` placeholder with a typed array containing the corresponding input data. |
|
|
| ```js |
| 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 }); |
| ``` |
|
|
