| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.DFT |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 20 |
|
|
| ## Description |
|
|
| Computes the discrete Fourier Transform (DFT) of the input along a specified axis. For a signal of length N, output bin k is `sum_{n=0}^{N-1} exp(-2*pi*j*k*n/N) * x[n]`; the inverse divides by N and negates the exponent sign. Supports forward/inverse, real-to-complex (RFFT), and complex-to-real (IRFFT) modes via the `onesided` and `inverse` attributes. |
|
|
| See the [ONNX `DFT` spec](https://onnx.ai/onnx/operators/onnx__DFT.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Logical dtype | WebGPU storage | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `input` | `T` | same as logical dtype | — | — | Input signal tensor; the last dimension is 1 for real values or 2 for complex `(real, imaginary)` pairs. | required | |
| | `dft_length` | `L` | same as logical dtype | `0` | — | Optional int32 scalar controlling the signal length used for the transform; input is zero-padded or truncated to this length. | optional | |
| | `axis` | `I` | `int32` | `0` | — | Optional logical int64 scalar specifying the dimension over which to compute the DFT (the last axis is reserved for the real/imaginary component and is excluded); signed axes use int32 WebGPU storage and default to `-2` when omitted. | optional | |
|
|
| ## Outputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `output` | `T` | same as `input` | — | DFT result tensor; last dimension is 2 (complex) for forward DFT and RFFT, or 1 (real) for IRFFT. | required | |
|
|
| ## Attributes |
|
|
| Default values (overridable per request): |
|
|
| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `inverse` | `0` | When set to 1, computes the inverse DFT (IDFT/IRFFT) instead of the forward transform; default is 0 (forward). | |
| | `onesided` | `0` | When set to 1, exploits conjugate symmetry to return only the non-redundant half of the spectrum (RFFT for forward, IRFFT for inverse); default is 0 (full spectrum). | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32` | |
| | `L` | `int32` | |
| | `I` | `int64` | |
|
|
| ## 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 + tuning cases |
| - [`dft-contiguous-naive.wgsl.jinja`](build/webgpu/dft-contiguous-naive.wgsl.jinja) |
| - [`dft-general.wgsl.jinja`](build/webgpu/dft-general.wgsl.jinja) |
| - [`dft-rank4.wgsl.jinja`](build/webgpu/dft-rank4.wgsl.jinja) |
| - [`dft-runtime-axis-fft-shared.wgsl.jinja`](build/webgpu/dft-runtime-axis-fft-shared.wgsl.jinja) |
| - [`dft-tiled-real.wgsl.jinja`](build/webgpu/dft-tiled-real.wgsl.jinja) |
| - [`fft-radix2-dit-storage.wgsl.jinja`](build/webgpu/fft-radix2-dit-storage.wgsl.jinja) |
| - [`fft-stockham-shared.wgsl.jinja`](build/webgpu/fft-stockham-shared.wgsl.jinja) |
|
|
| ## Use with `@huggingface/kernels` |
|
|
| ```sh |
| npm install --save-exact @huggingface/kernels@0.0.1-preview.2 |
| ``` |
|
|
| 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. |
|
|
| This example supplies explicit metadata for: |
|
|
| - `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`. |
|
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| 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.DFT", { version: 1 }); |
| // Explicit destinations request optional results or supply metadata that cannot be inferred. |
| const { output } = await kernel({ input: { data: inputData, shape: [1, 1, 1, 1, 1, 1, 2, 1] } }, { |
| outputs: { output: { shape: [1, 1, 1, 1, 1, 1, 2, 2], dtype: "float32" } }, |
| }); |
| ``` |
|
|
