| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.LogSoftmax |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 13 |
|
|
| ## Description |
|
|
| Computes `log(softmax(input, axis))` along a single axis using a numerically stable shifted reduction. The output has the same shape as the input. |
|
|
| See the [ONNX `LogSoftmax` spec](https://onnx.ai/onnx/operators/onnx__LogSoftmax.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Bind key | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `input` | `x` | `T` | — | — | The input tensor of rank >= 1. | required | |
|
|
| ## Outputs |
|
|
| | Name | Bind key | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `output` | `y` | `T` | same as `input` | same as `input` | The log-softmax values; same shape as the input. | required | |
|
|
| ## Attributes |
|
|
| Default values (overridable per request): |
|
|
| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `axis` | `-1` | The axis along which log-softmax is computed. Negative values count from the end; the default `-1` operates over the last dimension. Accepted range is `[-r, r-1]` where `r` is the input rank. | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32`, `float16` | |
|
|
| ## Files |
|
|
| - [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, 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 |
| - [`softmax-longrow-normalize.wgsl.jinja`](build/webgpu/softmax-longrow-normalize.wgsl.jinja) |
| - [`softmax-longrow-stats.wgsl.jinja`](build/webgpu/softmax-longrow-stats.wgsl.jinja) |
| - [`softmax-normalize.wgsl.jinja`](build/webgpu/softmax-normalize.wgsl.jinja) |
| - [`softmax-online-packed-rows.wgsl.jinja`](build/webgpu/softmax-online-packed-rows.wgsl.jinja) |
| - [`softmax-online.wgsl.jinja`](build/webgpu/softmax-online.wgsl.jinja) |
| - [`softmax-row-stage-strided-vec4.wgsl.jinja`](build/webgpu/softmax-row-stage-strided-vec4.wgsl.jinja) |
| - [`softmax-row-stage.wgsl.jinja`](build/webgpu/softmax-row-stage.wgsl.jinja) |
| - [`softmax-strided-online-coop.wgsl.jinja`](build/webgpu/softmax-strided-online-coop.wgsl.jinja) |
| - [`softmax-strided-packed4-tail.wgsl.jinja`](build/webgpu/softmax-strided-packed4-tail.wgsl.jinja) |
|
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| ## Use with `@huggingface/kernels` |
|
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| The loader derives every required output's shape and logical dtype from the manifest contract and this call. |
| It then allocates the result tensors automatically. |
|
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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. |
|
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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.LogSoftmax", { version: 1 }); |
| const { y } = await kernel({ x: { data: xData, shape: [1, 3] } }); |
| ``` |
|
|
