ai.onnx.ConvInteger

ai.onnx · standard ONNX operator · ONNX opset ≥ 10

Description

Performs integer convolution on quantized inputs x and filter w, each with an optional zero point, producing an int32 output. Zero-point subtraction is applied before accumulation; the result must not overflow 32 bits during accumulation.

See the ONNX ConvInteger spec for the reference semantics.

Inputs

Name	Logical dtype	Rank	Shape	Description	Presence
`x`	`TX`	—	—	Input data tensor of shape `(N x C x D1 x ... x Dn)`.	required
`w`	`TW`	—	—	Convolution weight tensor of shape `(M x C/group x k1 x ... x kn)`.	required
`x_zero_point`	`TX`	—	—	Optional scalar zero point for `x`; defaults to 0.	optional
`w_zero_point`	`TW`	—	—	Optional scalar or per-output-channel zero point for `w`; defaults to 0.	optional

Outputs

Name	Logical dtype	Rank	Shape	Description	Presence
`y`	`TY`	same as `x`	derived	Output tensor containing `int32` convolution results.	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.
`dilations`	—	Optional dilation factors, one positive integer per spatial axis. Omission means all ones.
`group`	`1`	Number of groups that input and output channels are split into; defaults to 1.
`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
`TX`	`uint8`, `int8`
`TW`	`uint8`, `int8`
`TY`	`int32`

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
conv-int-accumulate-spatial.wgsl.jinja
conv-int-im2col-spatial.wgsl.jinja
quant-dp4a-matmul.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.ConvInteger", { version: 1 });
const { y } = await kernel({
  x: { data: xData, shape: [1, 1, 2, 1, 1] },
  w: { data: wData, shape: [1, 1, 1, 1, 1] },
});

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.