| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.DepthToSpace |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 13 |
|
|
| ## Description |
|
|
| Rearranges data from the depth dimension into spatial blocks, expanding height and width by `blocksize` while reducing channels by `blocksize * blocksize`. The inverse of SpaceToDepth; supports `DCR` (depth-column-row) and `CRD` (column-row-depth) element orderings. |
|
|
| See the [ONNX `DepthToSpace` spec](https://onnx.ai/onnx/operators/onnx__DepthToSpace.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Bind key | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `input` | `input` | `T` | `4` | — | 4-D input tensor of shape `[N, C, H, W]`. | required | |
|
|
| ## Outputs |
|
|
| | Name | Bind key | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `output` | `output` | `T` | `4` | derived; see description | 4-D output tensor of shape `[N, C/(blocksize*blocksize), H*blocksize, W*blocksize]`. | required | |
|
|
| ## Attributes |
|
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| Attributes and default values (overridable per request): |
|
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| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `mode` | `"DCR"` | Element ordering within each block: `DCR`, the default depth-column-row order, or `CRD`, the column-row-depth order. | |
| | `blocksize` | — | Side length of the spatial blocks; input channels are divided by `blocksize * blocksize`. | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32`, `float16`, `int32`, `int16`, `int8`, `uint32`, `uint8`, `bool` | |
|
|
| ## 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 |
| - [`depth-to-space-nchw-coarsened4.wgsl.jinja`](build/webgpu/depth-to-space-nchw-coarsened4.wgsl.jinja) |
| - [`space-depth-permute.wgsl.jinja`](build/webgpu/space-depth-permute.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. |
|
|
| 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. |
|
|
| ```js |
| import { getKernel } from "@huggingface/kernels"; |
| |
| const kernel = await getKernel("webgpu-kernels/ai.onnx.DepthToSpace", { version: 1 }); |
| const { output } = await kernel({ input: { data: inputData, shape: [1, 8, 1, 1] } }, { |
| attrs: { blocksize: 2 }, |
| }); |
| ``` |
|
|
