---
library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# 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](https://onnx.ai/onnx/operators/onnx__Conv.html) for the reference semantics.

## Inputs

| Name | Upstream name | 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 |
| `bias` | `B` | `T` | `1` | — | Optional 1D bias of length M added to each output channel. | optional |

## Outputs

| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `y` | `Y` | `T` | same as `x` | derived | 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. |
| `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 |
| --- | --- |
| `T` | `float32`, `float16` |

## Implementation variants

One implementation is selected per call from the device capabilities, the request shapes and the dtypes; these notes say what each one covers.

- `pointwise_channel_reduce` — Handles pointwise unit-stride convolution with few output channels by assigning one invocation to each spatial position and accumulating every output channel in registers. It does not materialize im2col scratch.
- `pointwise_channel_reduce_bias` — Handles pointwise unit-stride convolution with few output channels by assigning one invocation to each spatial position and accumulating every output channel in registers. It does not materialize im2col scratch.
- `gemm_1x1_subgroup_matrix` — Multiplies 1x1 weights directly by NCHW input. Complete output-channel tiles use direct subgroup-matrix loads, eliminating input staging and reduction-loop barriers; partial tiles retain guarded staging. Tile geometry follows the device's workgroup limits.
- `gemm_1x1_subgroup_matrix_bias` — Multiplies 1x1 weights directly by NCHW input. Complete output-channel tiles use direct subgroup-matrix loads, eliminating input staging and reduction-loop barriers; partial tiles retain guarded staging. Tile geometry follows the device's workgroup limits.
- `im2col_gemm_subgroup_matrix` — Materializes convolution windows as a column matrix, then multiplies it by the weights with subgroup-matrix operations. It applies when reduction and spatial extents are tile-aligned and column scratch fits.
- `im2col_gemm_subgroup_matrix_bias` — Materializes convolution windows as a column matrix, then multiplies it by the weights with subgroup-matrix operations. It applies when reduction and spatial extents are tile-aligned and column scratch fits.
- `im2col_direct_f32_subgroup_matrix` — Materialize tile-aligned convolution columns and load weights and columns directly into subgroup matrices, removing operand staging and K-loop barriers. Retain f32 accumulation, existing split-K reduction order, and the implicit gather preference below the measured channel-tile reuse threshold.
- `im2col_direct_f16_subgroup_matrix` — Materialize tile-aligned convolution columns and load weights and columns directly into subgroup matrices, removing operand staging and K-loop barriers. Retain f32 accumulation, existing split-K reduction order, and the implicit gather preference below the measured channel-tile reuse threshold.
- `im2col_direct_f32_subgroup_matrix_bias` — Materialize tile-aligned convolution columns and load weights and columns directly into subgroup matrices, removing operand staging and K-loop barriers. Retain f32 accumulation, existing split-K reduction order, and the implicit gather preference below the measured channel-tile reuse threshold.
- `im2col_direct_f16_subgroup_matrix_bias` — Materialize tile-aligned convolution columns and load weights and columns directly into subgroup matrices, removing operand staging and K-loop barriers. Retain f32 accumulation, existing split-K reduction order, and the implicit gather preference below the measured channel-tile reuse threshold.
- `implicit_im2col_subgroup_matrix` — Gathers each logical im2col tile directly from X instead of materializing a column matrix, trading extra address arithmetic for the scratch and bandwidth that materialization would cost. It applies when the output has few output-channel tiles, so the gather is not repeated across many tiles.
- `implicit_im2col_subgroup_matrix_bias` — Gathers each logical im2col tile directly from X instead of materializing a column matrix, trading extra address arithmetic for the scratch and bandwidth that materialization would cost. It applies when the output has few output-channel tiles, so the gather is not repeated across many tiles.
- `implicit_im2col3d_subgroup_matrix` — Gathers each logical 3-D im2col tile directly from X instead of materializing a column matrix, trading extra address arithmetic for the scratch and bandwidth a volumetric materialization would cost. It applies when the output has few output-channel tiles, so the gather is not repeated across many tiles.
- `implicit_im2col3d_subgroup_matrix_bias` — Gathers each logical 3-D im2col tile directly from X instead of materializing a column matrix, trading extra address arithmetic for the scratch and bandwidth a volumetric materialization would cost. It applies when the output has few output-channel tiles, so the gather is not repeated across many tiles.
- `implicit_im2col_subgroup_matrix_splitk` — Partitions the implicit-im2col reduction across workgroups when the unsplit output grid is too small, then combines raw partial sums from scratch. The combine reassociates floating-point addition relative to the unsplit route.
- `implicit_im2col_subgroup_matrix_bias_splitk` — Partitions the implicit-im2col reduction across workgroups when the unsplit output grid is too small, then combines raw partial sums from scratch. The combine reassociates floating-point addition relative to the unsplit route.
- `im2col_gemm_subgroup_matrix_padded` — Materializes a zero-padded column matrix before a subgroup-matrix multiply, allowing logical reduction and spatial extents that are not tile-aligned. Padded values contribute zero and are excluded from the logical output.
- `im2col_gemm_subgroup_matrix_padded_bias` — Materializes a zero-padded column matrix before a subgroup-matrix multiply, allowing logical reduction and spatial extents that are not tile-aligned. Padded values contribute zero and are excluded from the logical output.
- `im2col_gemm_subgroup_matrix_padded_splitk` — Materializes a zero-padded column matrix, partitions its subgroup-matrix reduction across workgroups, and combines raw partial sums from scratch. It preserves the padded route for low-parallelism outputs; the combine reassociates floating-point addition.
- `im2col_gemm_subgroup_matrix_padded_bias_splitk` — Materializes a zero-padded column matrix, partitions its subgroup-matrix reduction across workgroups, and combines raw partial sums from scratch. It preserves the padded route for low-parallelism outputs; the combine reassociates floating-point addition.
- `implicit_im2col_tiled_bias_reg_m32` — Gathers logical im2col elements directly from X while filling register-blocked GEMM tiles. It avoids column scratch and provides the portable implicit route without subgroup-matrix support.
- `implicit_im2col_tiled_reg` — Gathers logical im2col elements directly from X while filling register-blocked GEMM tiles. It avoids column scratch and provides the portable implicit route without subgroup-matrix support.
- `implicit_im2col_tiled_bias_reg` — Gathers logical im2col elements directly from X while filling register-blocked GEMM tiles. It avoids column scratch and provides the portable implicit route without subgroup-matrix support.
- `implicit_im2col_tiled_reg_splitk` — Partitions the register-blocked implicit-im2col reduction across workgroups when the unsplit output grid is too small, then combines raw partial sums. The combine reassociates floating-point addition relative to the unsplit route.
- `implicit_im2col_tiled_bias_reg_splitk` — Partitions the register-blocked implicit-im2col reduction across workgroups when the unsplit output grid is too small, then combines raw partial sums. The combine reassociates floating-point addition relative to the unsplit route.
- `gemm_1x1_tiled_reg_splitk` — Partitions the register-blocked 1x1 GEMM reduction across additional workgroups when the output grid is small, then combines raw partial sums. The combine reassociates floating-point addition relative to the unsplit route.
- `gemm_1x1_tiled_bias_reg_splitk` — Partitions the register-blocked 1x1 GEMM reduction across additional workgroups when the output grid is small, then combines raw partial sums. The combine reassociates floating-point addition relative to the unsplit route.

## Device requirements

Some implementation variants require `subgroup-matrix`, `shader-f16`, and `subgroups`. These are route-specific capabilities, not package-wide requirements; availability also depends on the request shape and dtype.

## Files

- [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, per-variant templates, provenance)
- [`manifest.json`](build/webgpu/manifest.json) — the op contract (source of truth)
- [`test.json`](build/webgpu/test.json) — correctness cases
- [`bench.json`](build/webgpu/bench.json) — benchmark + tuning cases
- [`conv-1x1-channel-reduce.wgsl.jinja`](build/webgpu/conv-1x1-channel-reduce.wgsl.jinja)
- [`conv-1x1-gemm-tiled-reg.wgsl.jinja`](build/webgpu/conv-1x1-gemm-tiled-reg.wgsl.jinja)
- [`conv-1x1-gemm-tiled.wgsl.jinja`](build/webgpu/conv-1x1-gemm-tiled.wgsl.jinja)
- [`conv-1x1-subgroup-matrix.wgsl.jinja`](build/webgpu/conv-1x1-subgroup-matrix.wgsl.jinja)
- [`conv-direct-nd.wgsl.jinja`](build/webgpu/conv-direct-nd.wgsl.jinja)
- [`conv-direct-unrolled.wgsl.jinja`](build/webgpu/conv-direct-unrolled.wgsl.jinja)
- [`conv-im2col-ncdhw.wgsl.jinja`](build/webgpu/conv-im2col-ncdhw.wgsl.jinja)
- [`conv-im2col-nchw.wgsl.jinja`](build/webgpu/conv-im2col-nchw.wgsl.jinja)
- [`conv-splitk-reduce.wgsl.jinja`](build/webgpu/conv-splitk-reduce.wgsl.jinja)
- [`conv1d-tiled-reg.wgsl.jinja`](build/webgpu/conv1d-tiled-reg.wgsl.jinja)
- [`conv2d-grouped-large-w4.wgsl.jinja`](build/webgpu/conv2d-grouped-large-w4.wgsl.jinja)

## Use with `@huggingface/kernels`

```sh
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.

```js
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] },
});
```