ai.onnx.Trilu

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

Description

Returns the upper or lower triangular part of a 2-D matrix or batch of 2-D matrices of shape [*, N, M], zeroing all other elements. The diagonal offset k shifts the boundary: positive values move it above the main diagonal, negative values below it.

See the ONNX Trilu spec for the reference semantics.

Inputs

Name	Bind key	Logical dtype	WebGPU storage	Rank	Shape	Description	Presence
`input`	`input`	`T`	runtime-selected; narrow integers and bool use 32-bit slots	—	—	Input tensor of rank 2 or higher whose triangular part is extracted.	required
`k`	`k`	`I`	`int32`	`0`	`[]`	Optional logical int64 scalar specifying the signed diagonal offset; WebGPU stores it as int32 and defaults to 0 (main diagonal).	optional

Outputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`output`	`output`	`T`	same as `input`	same as `input`	Output tensor of the same type and shape as the input, with non-triangular elements set to zero.	required

Attributes

Default values (overridable per request):

Attribute	Default	Description
`upper`	`1`	When 1 (true), retains the upper triangular part; when 0 (false), retains the lower triangular part. Default is 1.

Type constraints

Variable	Allowed dtypes
`T`	`float32`, `float16`, `int32`, `int16`, `int8`, `uint32`, `uint8`, `bool`
`I`	`int64`

Files

metadata.json — kernel metadata (id, digests, provenance)
manifest.json — the op contract (source of truth)
test.json — correctness cases
bench.json — benchmark + tuning cases
trilu-vec4.wgsl.jinja
trilu.wgsl.jinja

Use with `@huggingface/kernels`

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.

The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model 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.Trilu", { version: 1 });
const { output } = await kernel({ input: { data: inputData, shape: [2, 2] } });

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.