ai.onnx.Clip

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

Description

Limits each element of the input tensor to the interval [min, max], equivalent to Min(max, Max(input, min)). Each bound is an optional scalar input tensor; an omitted bound defaults to the input dtype's lowest or highest numeric value. When min exceeds max, all elements are set to max.

See the ONNX Clip spec for the reference semantics.

Inputs

Name Logical dtype Rank Shape Description Presence
input T Input tensor whose elements are to be clipped. required
min T 0 [] Scalar lower bound; elements below this value are replaced by it. optional
max T 0 [] Scalar upper bound; elements above this value are replaced by it. optional

Outputs

Name Logical dtype Rank Shape Description Presence
output T same as input same as input Output tensor with each element clipped to the specified interval. required

Type constraints

Variable Allowed dtypes
T float32, float16, int32, uint32, int8, uint8

Files

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.Clip", { version: 1 });
const { output } = await kernel({ input: { data: inputData, shape: [3] } });
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Requires WebGPU support. See the compatibility table.