| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.Scan |
|
|
| `ai.onnx` · internal tensor lowering (non-standard) · reviewed against ONNX opset 25 |
|
|
| ## Description |
|
|
| Internal additive prefix-scan lowering over one rank-1 state and one rank-2 input, with forward and reverse traversal. This package does not implement ONNX `Scan` body-graph semantics. |
|
|
| See the [standard ONNX `Scan` spec](https://onnx.ai/onnx/operators/onnx__Scan.html) for the contract this internal lowering does not implement. |
|
|
| ## Inputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `initial_state` | `T` | `1` | — | Initial rank-1 additive state of shape `[dim]`. | required | |
| | `scan_input` | `T` | `2` | — | Rank-2 input of shape `[steps, dim]`. Each row is added to the running state in forward or reverse traversal order. | required | |
|
|
| ## Outputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `final_state` | `T` | `1` | same as `initial_state` | Final rank-1 state of shape `[dim]` after all input rows have been accumulated. | required | |
| | `scan_output` | `T` | `2` | same as `scan_input` | Inclusive additive prefix results with the same `[steps, dim]` shape as `scan_input`. Reverse traversal still writes each result at its corresponding input row. | required | |
|
|
| ## Attributes |
|
|
| Default values (overridable per request): |
|
|
| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `reverse` | `0` | When non-zero, the scan input sequence is traversed in reverse order (equivalent to `scan_input_directions=1`); default `0` scans forward. | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32` | |
|
|
| ## Implementation variants |
|
|
| One implementation is selected per call from the device capabilities, the request shapes and the dtypes; these notes say what each one covers. |
|
|
| - `vector_channel_prefix_sum` — Scan four adjacent state channels together with float32 accumulation; bound the power-of-two workgroup by the scan length, tuning and device resources, and use subgroups only when it contains complete groups. |
|
|
| ## 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 |
| - [`scan-coop-channel.wgsl.jinja`](build/webgpu/scan-coop-channel.wgsl.jinja) |
| - [`scan-multichunk-apply.wgsl.jinja`](build/webgpu/scan-multichunk-apply.wgsl.jinja) |
| - [`scan-multichunk-carries.wgsl.jinja`](build/webgpu/scan-multichunk-carries.wgsl.jinja) |
| - [`scan-multichunk-local.wgsl.jinja`](build/webgpu/scan-multichunk-local.wgsl.jinja) |
| - [`scan-prefix-sum.wgsl.jinja`](build/webgpu/scan-prefix-sum.wgsl.jinja) |
| - [`scan-strided-row.wgsl.jinja`](build/webgpu/scan-strided-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.Scan", { version: 1 }); |
| const { final_state, scan_output } = await kernel({ |
| initial_state: { data: initial_stateData, shape: [1] }, |
| scan_input: { data: scan_inputData, shape: [3, 1] }, |
| }); |
| ``` |
|
|
