| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.Mod |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 13 |
|
|
| ## Description |
|
|
| Performs elementwise binary modulo on tensors `A` and `B` with multidirectional broadcasting. When `fmod` is `0` (default), applies Python-style `%` with the sign of the divisor; when `fmod` is `1`, applies C-style `fmod` with the sign of the dividend. |
|
|
| See the [ONNX `Mod` spec](https://onnx.ai/onnx/operators/onnx__Mod.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `a` | `A` | `T` | — | — | Dividend tensor. | required | |
| | `b` | `B` | `T` | — | — | Divisor tensor. | required | |
|
|
| ## Outputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `c` | `C` | `T` | derived | broadcast result of `a` and `b` | Remainder tensor; same shape as the broadcast result of A and B. | required | |
|
|
| ## Attributes |
|
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| Default values (overridable per request): |
|
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| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `fmod` | `0` | Controls the modulo mode: `0` (default) uses Python-style integer mod (sign of divisor); `1` uses C-style `fmod` (sign of dividend, floating-point types only). | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32`, `float16`, `int32`, `uint32`, `int16`, `int8`, `uint8` | |
|
|
| ## 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 |
| - [`mod-vec4.wgsl.jinja`](build/webgpu/mod-vec4.wgsl.jinja) |
| - [`mod.wgsl.jinja`](build/webgpu/mod.wgsl.jinja) |
|
|
| ## Use with `@huggingface/kernels` |
|
|
| ```sh |
| npm install --save-exact @huggingface/kernels@0.0.1-preview.2 |
| ``` |
|
|
| 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`. |
|
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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.Mod", { version: 1 }); |
| const { c } = await kernel({ a: { data: aData, shape: [3] }, b: { data: bData, shape: [3] } }); |
| ``` |
|
|
