| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.BatchNormalization |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 15 |
|
|
| ## Description |
|
|
| Applies inference-mode batch normalization: `Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B`. This package supports `training_mode=0`, rank-2-or-higher inputs, and a common float16 or float32 dtype for every tensor. ONNX training mode is intentionally not implemented because this inference-only release does not expose its required running-mean and running-variance outputs. |
|
|
| See the [ONNX `BatchNormalization` spec](https://onnx.ai/onnx/operators/onnx__BatchNormalization.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `x` | `X` | `T` | — | — | Input data tensor with shape `(N, C, D1, ..., Dn)`, normalized independently per channel using the supplied estimated statistics. | required | |
| | `scale` | — | `T` | `1` | — | Per-channel scale tensor with shape `(C)`. | required | |
| | `b` | `B` | `T` | `1` | — | Per-channel bias tensor with shape `(C)`. | required | |
| | `inputMean` | `input_mean` | `T` | `1` | — | Precomputed estimated mean tensor with shape `(C)` used for inference. | required | |
| | `inputVar` | `input_var` | `T` | `1` | — | Precomputed estimated variance tensor with shape `(C)` used for inference. | required | |
|
|
| ## Outputs |
|
|
| | Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `y` | `Y` | `T` | same as `x` | same as `x` | Batch-normalized output tensor with the same shape as `X`. | required | |
|
|
| ## Attributes |
|
|
| Default values (overridable per request): |
|
|
| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `epsilon` | `0.00001` | Small value added to the variance before taking the square root to avoid division by zero. | |
| | `momentum` | `0.9` | Standard ONNX running-statistics momentum. This inference-only package accepts the default `0.9`; non-default values are reserved for the unsupported training-state update. | |
| | `training_mode` | `0` | Execution mode. This inference-only package supports the default value 0; value 1 is rejected because the ONNX training outputs are not exposed. | |
|
|
| ## 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 cases |
| - [`batch-normalization-nc-vec4.wgsl.jinja`](build/webgpu/batch-normalization-nc-vec4.wgsl.jinja) |
| - [`batch-normalization-nchw-flat-vec4.wgsl.jinja`](build/webgpu/batch-normalization-nchw-flat-vec4.wgsl.jinja) |
| - [`batch-normalization-nchw-vec4.wgsl.jinja`](build/webgpu/batch-normalization-nchw-vec4.wgsl.jinja) |
| - [`batch-normalization-nchw.wgsl.jinja`](build/webgpu/batch-normalization-nchw.wgsl.jinja) |
|
|
| ## Use with `@huggingface/kernels` |
|
|
| ```sh |
| npm install --save-exact @huggingface/kernels@0.0.1-preview.3 |
| ``` |
|
|
| 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.BatchNormalization", { version: 1 }); |
| const { y } = await kernel({ |
| x: { data: xData, shape: [2, 3] }, |
| scale: { data: scaleData, shape: [3] }, |
| b: { data: bData, shape: [3] }, |
| inputMean: { data: inputMeanData, shape: [3] }, |
| inputVar: { data: inputVarData, shape: [3] }, |
| }); |
| ``` |
|
|
