ai.onnx.Softmax

ai.onnx · standard ONNX operator · ONNX opset ≥ 13

Description

Computes the normalized exponential (softmax) of the input along a single axis: exp(x) / sum(exp(x)) reduced over that axis. For finite rows, the result has the same shape as the input, with values in [0, 1] that sum to 1 along the softmax axis. The implementation uses max subtraction for numerical stability while preserving the literal ONNX result: a row containing only negative infinity produces NaN values.

See the ONNX Softmax spec for the reference semantics.

Inputs

Outputs

Attributes

Default values (overridable per request):

Type constraints

Device requirements

Some implementation variants require subgroups. These are route-specific capabilities, not package-wide requirements; availability also depends on the request shape and dtype.

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
• softmax-longrow-normalize.wgsl.jinja
• softmax-longrow-stats.wgsl.jinja
• softmax-normalize.wgsl.jinja
• softmax-online-packed-rows.wgsl.jinja
• softmax-online.wgsl.jinja
• softmax-row-stage.wgsl.jinja
• softmax-strided-online-coop.wgsl.jinja
• softmax-strided-online-lane.wgsl.jinja
• softmax-subgroup-rows.wgsl.jinja

Use with @huggingface/kernels

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.

import { getKernel } from "@huggingface/kernels";

const kernel = await getKernel("webgpu-kernels/ai.onnx.Softmax", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [1, 3] } });