ai.onnx.CumSum

ai.onnx · standard ONNX operator · ONNX opset ≥ 14

Description

Computes the cumulative sum of elements along a specified axis. By default, the sum is inclusive (each output element includes itself); setting exclusive to 1 excludes the current element, and setting reverse to 1 scans in the opposite direction.

See the ONNX CumSum spec for the reference semantics.

Inputs

Outputs

Runtime arguments

Attributes

Default values (overridable per request):

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
• cumsum-splitk-carry.wgsl.jinja
• cumsum-splitk-scan-vec4.wgsl.jinja
• cumsum-splitk-scan.wgsl.jinja
• cumsum-splitk-totals-vec4.wgsl.jinja
• cumsum-splitk-totals.wgsl.jinja
• cumsum-strided-row.wgsl.jinja
• scan-row-chunked-vec4.wgsl.jinja
• scan-row-chunked.wgsl.jinja
• scan-thread-row.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.CumSum", { version: 1 });
const { y } = await kernel({ x: { data: xData, shape: [5] }, axis: 0 });