| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.HammingWindow |
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| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 17 |
|
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| ## Description |
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| Generates a Hamming window of a given length using the cosine-sum formula `a0 - a1 * cos(2π * n / denom)`, where `a0 ≈ 0.5435` and `a1 ≈ 0.4565`. The window can be periodic (for use in spectral analysis) or symmetric (for filter design). |
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| See the [ONNX `HammingWindow` spec](https://onnx.ai/onnx/operators/onnx__HammingWindow.html) for the reference semantics. |
|
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| ## Inputs |
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| | Name | Bind key | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `size` | `size` | `T1` | `0` | — | Scalar length of the window to generate. | required | |
|
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| ## Outputs |
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| | Name | Bind key | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `output` | `y` | `T2` | `1` | — | 1-D Hamming window tensor of shape `[size]`. | required | |
|
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| ## Attributes |
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| Default values (overridable per request): |
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| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `output_datatype` | `1` | Data type of the output tensor, specified as a TensorProto DataType enum value; default `1` (FLOAT). | |
| | `periodic` | `1` | When `1` (default), returns a periodic window of length `size` (suitable for spectral analysis); when `0`, returns a symmetric window of length `size`. | |
|
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| ## Type constraints |
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| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T1` | `int32` | |
| | `T2` | `float32`, `float16`, `uint32`, `int32`, `uint8`, `int8`, `int16` | |
|
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| ## Files |
|
|
| - [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, 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 + tuning cases |
| - [`window-cosine-sum.wgsl.jinja`](build/webgpu/window-cosine-sum.wgsl.jinja) |
|
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| ## Use with `@huggingface/kernels` |
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| The loader automatically allocates outputs whose metadata it can derive from the manifest contract and this call. |
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| The explicit `outputs` entries provide shape and logical dtype metadata for the results listed below: |
|
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| - `y` |
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| Each entry either requests an optional result or supplies metadata that cannot be inferred from the inputs. |
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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. |
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| Replace each `*Data` placeholder with a typed array containing the corresponding input data. |
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| ```js |
| import { getKernel } from "@huggingface/kernels"; |
| |
| const kernel = await getKernel("webgpu-kernels/ai.onnx.HammingWindow", { version: 1 }); |
| // Explicit destinations request optional results or supply metadata that cannot be inferred. |
| const { y } = await kernel({ size: { data: sizeData, shape: [] } }, { |
| outputs: { y: { shape: [1], dtype: "float32" } }, |
| }); |
| ``` |
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|
