ai.onnx.ConvTranspose

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

Description

Computes the transpose of a convolution, also known as a fractionally strided convolution or deconvolution, from input tensor X, filter weights W, and an optional bias B. Output spatial dimensions follow the stride, dilation, padding, and optional output_padding attributes. Supports grouped convolution through group.

See the ONNX ConvTranspose spec for the reference semantics.

Inputs

Outputs

Attributes

Attributes and default values (overridable per request):

Type constraints

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.

• ncdhw3d_implicit_sgmat — Stride-1 transposed convolution as a forward convolution of the unchanged input with tap-reversed weights and begin pads of kernel-1-pad: the implicit-gather GEMM reads input windows directly and stores output elements directly, so no column matrix is materialized and no scatter pass runs, while the reduction spans the full in-channel-by-tap depth.
• ncdhw3d_implicit_sgmat_bias — Stride-1 transposed convolution as a forward convolution of the unchanged input with tap-reversed weights and begin pads of kernel-1-pad: the implicit-gather GEMM reads input windows directly and stores output elements directly, so no column matrix is materialized and no scatter pass runs, while the reduction spans the full in-channel-by-tap depth.
• nchw2d_grouped_stride_phase_blocked — Grouped stride-phase ConvTranspose where one invocation owns one output pixel and a block of that group's output channels: every gathered input sample feeds the whole block and the group's weight slab is staged once per workgroup. It serves grouped stride-two-or-more shapes whose per-group weights fit workgroup memory.
• nchw2d_grouped_stride_phase_blocked_bias — Grouped stride-phase ConvTranspose where one invocation owns one output pixel and a block of that group's output channels: every gathered input sample feeds the whole block and the group's weight slab is staged once per workgroup. It serves grouped stride-two-or-more shapes whose per-group weights fit workgroup memory.

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 — 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-1x1-gemm-tiled-reg.wgsl.jinja
• conv-1x1-gemm-tiled.wgsl.jinja
• conv-1x1-subgroup-matrix.wgsl.jinja
• conv-transpose-empty-input.wgsl.jinja
• conv-transpose1d-ncl.wgsl.jinja
• conv-transpose1d-phase-tiled.wgsl.jinja
• conv-transpose2d-col2im.wgsl.jinja
• conv-transpose2d-grouped-stride-phase-blocked.wgsl.jinja
• conv-transpose2d-grouped-stride-phase.wgsl.jinja
• conv-transpose2d-nchw.wgsl.jinja
• conv-transpose2d-phase-gemm-tiled.wgsl.jinja
• conv-transpose2d-weight-reorder.wgsl.jinja
• conv-transpose3d-col2im.wgsl.jinja
• conv-transpose3d-ncdhw.wgsl.jinja
• conv-transpose3d-weight-reorder.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.ConvTranspose", { version: 1 });
const { y } = await kernel({
  x: { data: xData, shape: [1, 1, 3] },
  w: { data: wData, shape: [1, 2, 2] },
});