| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.MeanVarianceNormalization |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 13 |
|
|
| ## Description |
|
|
| Normalizes each group as `(X - mean) / sqrt(variance)`, reducing over `axes` (default `[0, 2, 3]`). |
|
|
| See the [ONNX `MeanVarianceNormalization` spec](https://onnx.ai/onnx/operators/onnx__MeanVarianceNormalization.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `x` | `X` | `T` | — | — | Input tensor to normalize. | required | |
|
|
| ## Outputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `y` | `Y` | `T` | same as `x` | same as `x` | Normalized tensor with the same shape as `X`. | required | |
|
|
| ## Attributes |
|
|
| Default values (overridable per request): |
|
|
| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `axes` | `[0,2,3]` | Axes that share a mean and variance; negative values count from the back. | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32`, `float16` | |
|
|
| ## 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 |
| - [`mean-variance-normalization-serial-rows.wgsl.jinja`](build/webgpu/mean-variance-normalization-serial-rows.wgsl.jinja) |
| - [`mean-variance-normalization-subgroup.wgsl.jinja`](build/webgpu/mean-variance-normalization-subgroup.wgsl.jinja) |
| - [`noop.wgsl.jinja`](build/webgpu/noop.wgsl.jinja) |
| - [`norm-flat-apply.wgsl.jinja`](build/webgpu/norm-flat-apply.wgsl.jinja) |
| - [`norm-flat-splitk-combine.wgsl.jinja`](build/webgpu/norm-flat-splitk-combine.wgsl.jinja) |
| - [`norm-flat-splitk-partials.wgsl.jinja`](build/webgpu/norm-flat-splitk-partials.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`. |
|
|
| 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.MeanVarianceNormalization", { version: 1 }); |
| const { y } = await kernel({ x: { data: xData, shape: [2, 2, 1, 2] } }); |
| ``` |
|
|
