ai.onnx.Conv

ai.onnx · standard ONNX operator · ONNX opset ≥ 11

Description

Applies an N-dimensional convolution to the input tensor X using filter weights W and an optional bias B. Supports grouped convolution, explicit per-axis padding, dilation, and stride along each spatial dimension.

See the ONNX Conv spec for the reference semantics.

Inputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`X`	`x`	`T`	—	—	Input data tensor of shape `(N x C x D1 x ... x Dn)`, where `N` is the batch size and `C` is the number of channels.	required
`W`	`w`	`T`	—	—	Convolution filter weights of shape `(M x C/group x k1 x ... x kn)`, where `M` is the number of output feature maps.	required
`B`	`bias`	`T`	`1`	—	Optional 1D bias of length M added to each output channel.	optional

Outputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`Y`	`y`	`T`	same as `X`	derived; see description	Output tensor whose spatial dimensions are determined by the kernel size, strides, dilations, and padding.	required

Attributes

Attributes and default values (overridable per request):

Attribute	Default	Description
`auto_pad`	`"NOTSET"`	Automatic padding mode. `NOTSET` uses `pads`; `SAME_UPPER` and `SAME_LOWER` choose padding so each output spatial size is `ceil(input / stride)`; `VALID` uses no padding.
`group`	`1`	Number of groups that input and output channels are split into; defaults to 1.
`dilations`	—	Optional dilation factors, one positive integer per spatial axis. Omission means all ones.
`kernel_shape`	—	Optional kernel shape, one positive integer per spatial axis. When present, it must match the spatial dimensions of the weight tensor; omission infers the shape from the weights.
`pads`	—	Optional explicit padding in ONNX order `[begin_axis_0, ..., begin_axis_n, end_axis_0, ..., end_axis_n]`. Omission means all zeros; it cannot be combined with an automatic padding mode.
`strides`	—	Optional stride factors, one positive integer per spatial axis. Omission means all ones.

Type constraints

Variable	Allowed dtypes
`T`	`float32`, `float16`

Device requirements

Some implementation variants require subgroup-matrix and 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, provenance)
manifest.json — the op contract (source of truth)
test.json — correctness cases
bench.json — benchmark + tuning cases
conv-1x1-channel-reduce.wgsl.jinja
conv-1x1-gemm-tiled-reg.wgsl.jinja
conv-1x1-gemm-tiled.wgsl.jinja
conv-1x1-subgroup-matrix.wgsl.jinja
conv-direct-nd.wgsl.jinja
conv-direct-unrolled.wgsl.jinja
conv-im2col-ncdhw.wgsl.jinja
conv-im2col-nchw.wgsl.jinja
conv-splitk-reduce.wgsl.jinja
conv1d-tiled-reg.wgsl.jinja
conv2d-grouped-large-w4.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.Conv", { version: 1 });
const { y } = await kernel({
  x: { data: xData, shape: [1, 1, 7] },
  w: { data: wData, shape: [1, 1, 1] },
});

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.