| --- |
| library_name: kernels |
| license: apache-2.0 |
| tags: |
| - kernel |
| - webgpu |
| - wgsl |
| --- |
| # ai.onnx.Compress |
|
|
| `ai.onnx` · standard ONNX operator · ONNX opset ≥ 11 |
|
|
| ## Description |
|
|
| Selects slices from the input tensor along an axis where the corresponding `condition` element is true. If `axis` is omitted, the input is flattened and elements are selected by position. The condition may be shorter than the selected dimension; values beyond its length are discarded. The data-dependent output extent must equal the number of true entries in the inspected prefix. |
|
|
| See the [ONNX `Compress` spec](https://onnx.ai/onnx/operators/onnx__Compress.html) for the reference semantics. |
|
|
| ## Inputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `input` | `T` | — | — | Input tensor of rank `r >= 1` to select from. | required | |
| | `condition` | `C` | `1` | — | Rank-1 boolean mask indicating which slices or elements to select; may be shorter than the axis dimension, in which case trailing slices are discarded. | required | |
|
|
| ## Outputs |
|
|
| | Name | Logical dtype | Rank | Shape | Description | Presence | |
| | --- | --- | --- | --- | --- | --- | |
| | `output` | `T` | derived | — | Selected slices with rank `r` when `axis` is specified, or rank 1 when the input is flattened. The selected dimension equals the number of true values in the inspected condition prefix. | required | |
|
|
| ## Attributes |
|
|
| Attributes and default values (overridable per request): |
|
|
| | Attribute | Default | Description | |
| | --- | --- | --- | |
| | `axis` | — | Axis along which to select slices; if omitted the input is flattened before selection. Negative values index from the end; accepted range is `[-r, r-1]`. | |
|
|
| ## Type constraints |
|
|
| | Variable | Allowed dtypes | |
| | --- | --- | |
| | `T` | `float32`, `float16`, `uint32`, `int32`, `int16`, `uint8`, `int8`, `bool` | |
| | `C` | `bool` | |
|
|
| ## 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 cases |
| - [`compress-scatter.wgsl.jinja`](build/webgpu/compress-scatter.wgsl.jinja) |
| - [`compress.wgsl.jinja`](build/webgpu/compress.wgsl.jinja) |
| - [`scan-block-prefix-u32.wgsl.jinja`](build/webgpu/scan-block-prefix-u32.wgsl.jinja) |
| - [`scan-flags-block-exclusive.wgsl.jinja`](build/webgpu/scan-flags-block-exclusive.wgsl.jinja) |
|
|
| ## Use with `@huggingface/kernels` |
|
|
| ```sh |
| npm install --save-exact @huggingface/kernels@0.0.1-preview.3 |
| ``` |
|
|
| Outputs with inferable metadata are allocated automatically. Explicit `outputs` entries request optional results or provide metadata that cannot be inferred from the supplied inputs and attributes. |
|
|
| This example supplies explicit metadata for: |
|
|
| - `output` |
|
|
| 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. |
|
|
| A `bool` input takes 0/1 values in a `Uint8Array` together with `dtype: "bool"`, since no typed-array class implies it. |
|
|
| ```js |
| import { getKernel } from "@huggingface/kernels"; |
| |
| const kernel = await getKernel("webgpu-kernels/ai.onnx.Compress", { version: 1 }); |
| // Explicit destinations request optional results or supply metadata that cannot be inferred. |
| const { output } = await kernel({ |
| input: { data: inputData, shape: [3, 2] }, |
| condition: { data: conditionData, shape: [5], dtype: "bool" }, |
| }, { |
| outputs: { output: { shape: [2], dtype: "float32" } }, |
| }); |
| ``` |
|
|
