File size: 4,915 Bytes
a76bbb1 96d2c8a a76bbb1 96d2c8a a76bbb1 96d2c8a b94231d 96d2c8a b94231d 96d2c8a b94231d 96d2c8a b94231d 96d2c8a b94231d 96d2c8a 94c3f90 96d2c8a b94231d 96d2c8a 94c3f90 96d2c8a b94231d 96d2c8a b94231d 94c3f90 b94231d 96d2c8a b94231d 96d2c8a b94231d 96d2c8a b94231d 96d2c8a b94231d 96d2c8a 94c3f90 96d2c8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | ---
library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# ai.onnx.ReduceMin
`ai.onnx` · standard ONNX operator · ONNX opset ≥ 20
## Description
Computes the minimum of input tensor elements along the specified axes. The output rank matches the input when `keepdims` is 1; otherwise reduced dimensions are pruned. Reduction over an empty set yields positive infinity when the dtype supports it, or the dtype's maximum value otherwise. For Boolean inputs, `false` is less than `true`.
See the [ONNX `ReduceMin` spec](https://onnx.ai/onnx/operators/onnx__ReduceMin.html) for the reference semantics.
## Inputs
| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `x` | `data` | `T` | — | — | The input tensor to reduce. | required |
## Outputs
| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `y` | `reduced` | `T` | derived | — | The reduced output tensor containing minimum values. | required |
## Attributes
Default values (overridable per request):
| Attribute | Default | Description |
| --- | --- | --- |
| `axes` | `[]` | Values of the optional ONNX `axes` tensor input, supplied through this request attribute; an empty list follows `noop_with_empty_axes`. |
| `keepdims` | `1` | If 1, retains the reduced dimensions with size 1 in the output; if 0, those dimensions are pruned. |
| `noop_with_empty_axes` | `0` | When axes is empty: if 0 (default) reduces over all axes; if 1 the op acts as an identity (no-op reduction). |
## Type constraints
| Variable | Allowed dtypes |
| --- | --- |
| `T` | `float32`, `float16`, `int32`, `uint32`, `int8`, `uint8`, `bool` |
## 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.
- `subgroup_last_axis_vec4` — Reduces each contiguous last-axis row with subgroup collectives and vec4-packed reads.
- `subgroup_last_axis` — Reduces each contiguous last-axis row with subgroup collectives and scalar reads for an unaligned row width.
## Device requirements
Some implementation variants require `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 cases
- [`datamove-flat-copy-x4-tail.wgsl.jinja`](build/webgpu/datamove-flat-copy-x4-tail.wgsl.jinja)
- [`reduce-axis-split-reduce.wgsl.jinja`](build/webgpu/reduce-axis-split-reduce.wgsl.jinja)
- [`reduce-axis0-splitk-combine.wgsl.jinja`](build/webgpu/reduce-axis0-splitk-combine.wgsl.jinja)
- [`reduce-axis0-splitk-reduce.wgsl.jinja`](build/webgpu/reduce-axis0-splitk-reduce.wgsl.jinja)
- [`reduce-axis0-tilecols.wgsl.jinja`](build/webgpu/reduce-axis0-tilecols.wgsl.jinja)
- [`reduce-flat-partial.wgsl.jinja`](build/webgpu/reduce-flat-partial.wgsl.jinja)
- [`reduce-narrow-empty-identity.wgsl.jinja`](build/webgpu/reduce-narrow-empty-identity.wgsl.jinja)
- [`reduce-noop-empty-axes.wgsl.jinja`](build/webgpu/reduce-noop-empty-axes.wgsl.jinja)
- [`reduce-row-subgroup-rows.wgsl.jinja`](build/webgpu/reduce-row-subgroup-rows.wgsl.jinja)
- [`reduce-row-subgroup.wgsl.jinja`](build/webgpu/reduce-row-subgroup.wgsl.jinja)
- [`reduce-row-tree.wgsl.jinja`](build/webgpu/reduce-row-tree.wgsl.jinja)
- [`reduce-serial-axis.wgsl.jinja`](build/webgpu/reduce-serial-axis.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:
- `y`
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.ReduceMin", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { y } = await kernel({ x: { data: xData, shape: [3, 2, 2] } }, {
outputs: { y: { shape: [1, 1, 1], dtype: "float32" } },
});
```
|