| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.ConvTranspose |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 11 |
|
|
| ## Description |
|
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| 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`. |
|
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| See the [ONNX `ConvTranspose` spec](https://onnx.ai/onnx/operators/onnx__ConvTranspose.html) 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 batch size and `C` is the number of input channels. | required | |
| | `W` | `w` | `T` | — | — | Filter weight tensor of shape `(C x M/group x k1 x ... x kn)`, where `M` is the number of output feature maps. | required | |
| | `B` | `bias` | `T` | `1` | — | Optional 1-D 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 computed from the input size, kernel shape, strides, dilations, and padding. | required | |
|
|
| ## Attributes |
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| Attributes and default values (overridable per request): |
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| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `auto_pad` | `"NOTSET"` | Padding mode: `NOTSET` uses explicit pads; `SAME_UPPER` and `SAME_LOWER` make output spatial size equal input size times stride, with any odd extra padding added at the end or beginning respectively; `VALID` applies no padding. | |
| | `group` | `1` | Number of groups that input and output channels are divided into for grouped (depthwise) convolution. | |
| | `dilations` | — | Dilation factors for each spatial axis; defaults to one on every axis. | |
| | `kernel_shape` | — | Kernel dimensions for each spatial axis. When omitted, they are inferred from the spatial dimensions of `W`. | |
| | `output_padding` | — | Additional size on the high-index end of each output spatial axis; each value must be smaller than the corresponding stride or dilation. | |
| | `output_shape` | — | Requested output spatial dimensions. When present, it must match the declared spatial shape of `Y`. | |
| | `pads` | — | Padding at the beginning of every spatial axis followed by padding at the end of every spatial axis; defaults to zeros. | |
| | `strides` | — | Stride factors for each spatial axis; defaults to one on every axis. | |
|
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| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32`, `float16` | |
|
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| ## Device requirements |
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| 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, 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-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-transpose-empty-input.wgsl.jinja`](build/webgpu/conv-transpose-empty-input.wgsl.jinja) |
| - [`conv-transpose1d-ncl.wgsl.jinja`](build/webgpu/conv-transpose1d-ncl.wgsl.jinja) |
| - [`conv-transpose1d-phase-tiled.wgsl.jinja`](build/webgpu/conv-transpose1d-phase-tiled.wgsl.jinja) |
| - [`conv-transpose2d-col2im.wgsl.jinja`](build/webgpu/conv-transpose2d-col2im.wgsl.jinja) |
| - [`conv-transpose2d-grouped-stride-phase.wgsl.jinja`](build/webgpu/conv-transpose2d-grouped-stride-phase.wgsl.jinja) |
| - [`conv-transpose2d-nchw.wgsl.jinja`](build/webgpu/conv-transpose2d-nchw.wgsl.jinja) |
| - [`conv-transpose2d-phase-gemm-tiled.wgsl.jinja`](build/webgpu/conv-transpose2d-phase-gemm-tiled.wgsl.jinja) |
| - [`conv-transpose2d-weight-reorder.wgsl.jinja`](build/webgpu/conv-transpose2d-weight-reorder.wgsl.jinja) |
| - [`conv-transpose3d-col2im.wgsl.jinja`](build/webgpu/conv-transpose3d-col2im.wgsl.jinja) |
| - [`conv-transpose3d-ncdhw.wgsl.jinja`](build/webgpu/conv-transpose3d-ncdhw.wgsl.jinja) |
| - [`conv-transpose3d-weight-reorder.wgsl.jinja`](build/webgpu/conv-transpose3d-weight-reorder.wgsl.jinja) |
|
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| ## Use with `@huggingface/kernels` |
|
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| 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. |
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| The `version: 1` option selects the published kernel contract; it is independent of any operator opset, contrib `since_version`, or model version. |
|
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| 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.ConvTranspose", { version: 1 }); |
| const { y } = await kernel({ |
| x: { data: xData, shape: [1, 1, 3] }, |
| w: { data: wData, shape: [1, 2, 2] }, |
| }); |
| ``` |
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|
