ai.onnx.Max

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

Description

Computes the elementwise maximum across one or more input tensors with NumPy-style multidirectional broadcasting. All inputs must share the same data type, and the output has the broadcasted shape.

See the ONNX Max spec for the reference semantics.

Inputs

Outputs

Type constraints

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
• datamove-elementwise-copy.wgsl.jinja
• minmax-broadcast.wgsl.jinja
• minmax-vec4.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.Max", { version: 1 });
const { y } = await kernel({ a: { data: aData, shape: [] } });