| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.Loop |
|
|
| `ai.onnx` · internal tensor lowering (non-standard) · reviewed against ONNX opset 25 |
|
|
| ## Description |
|
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| Support status: the standard ONNX `Loop` control-flow operator is not implemented because standalone kernel packages cannot carry or execute its body graph. This internal lowering performs one fixed recurrence—adding `step` to a rank-1 loop-carried tensor for up to `M` iterations while writing a dense scan tensor; `step` is not an ONNX `Loop` input, and this lowering must not be treated as ONNX `Loop`. |
|
|
| See the [standard ONNX `Loop` spec](https://onnx.ai/onnx/operators/onnx__Loop.html) for the contract this internal lowering does not implement. |
|
|
| ## Inputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `m` | `M` | `I` | — | — | Required uint32 trip-count limit encoded as either a scalar tensor or a one-element rank-1 tensor. The lowering executes at most `min(M, scan_output.shape[0])` iterations. | required | |
| | `cond` | — | `B` | — | — | Required initial condition encoded as either a scalar tensor or a one-element rank-1 tensor. A false value executes zero iterations; a true value permits all iterations selected by `M`. This fixed lowering does not update the condition inside the loop. | required | |
| | `v_initial` | — | `T` | `1` | — | Initial rank-1 loop-carried state of shape `[dim]`. | required | |
| | `step` | — | `T` | `1` | — | Implementation-specific rank-1 increment of shape `[dim]`, added elementwise to the state on every executed iteration. This is not a standard ONNX `Loop` input. | required | |
|
|
| ## Outputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `v_final` | `T` | `1` | same as `v_initial` | Final rank-1 state of shape `[dim]` after the executed additions. | required | |
| | `scan_output` | `T` | `2` | — | Dense tensor of shape `[scan_steps, dim]`. Each executed row contains the updated state for that iteration; rows beyond the executed trip count are zero-filled. | required | |
|
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| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32` | |
| | `I` | `uint32` | |
| | `B` | `uint32`, `bool` | |
|
|
| ## 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 |
| - [`loop-add-step.wgsl.jinja`](build/webgpu/loop-add-step.wgsl.jinja) |
|
|
| ## Use with `@huggingface/kernels` |
|
|
| ```sh |
| npm install --save-exact @huggingface/kernels@0.0.1-preview.3 |
| ``` |
|
|
| Outputs with inferable metadata are allocated automatically. Explicit `outputs` entries request optional results or provide metadata that cannot be inferred from the supplied inputs and attributes. |
|
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| This example supplies explicit metadata for: |
|
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| - `scan_output` |
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| 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.Loop", { version: 1 }); |
| // Explicit destinations request optional results or supply metadata that cannot be inferred. |
| const { v_final, scan_output } = await kernel({ |
| m: { data: mData, shape: [1] }, |
| cond: { data: condData, shape: [1] }, |
| v_initial: { data: v_initialData, shape: [1] }, |
| step: { data: stepData, shape: [1] }, |
| }, { |
| outputs: { scan_output: { shape: [1, 1], dtype: "float32" } }, |
| }); |
| ``` |
|
|
