ai.onnx.DFT / README.md

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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 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 — kernel metadata (id, digests, per-variant templates, provenance)
manifest.json — the op contract (source of truth)
test.json — correctness cases
bench.json — benchmark + tuning cases
dft-contiguous-naive.wgsl.jinja
dft-general.wgsl.jinja
dft-rank4.wgsl.jinja
dft-runtime-axis-fft-shared.wgsl.jinja
dft-tiled-real.wgsl.jinja
fft-radix2-dit-storage.wgsl.jinja
fft-stockham-shared.wgsl.jinja

Use with `@huggingface/kernels`

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

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.

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" } },
});