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library_name: kernels
license: apache-2.0
tags:
- kernel
- webgpu
- wgsl
---
# com.microsoft.GatherBlockQuantized
`com.microsoft` · ONNX Runtime contrib operator · contrib since_version 1
## Description
Gathers rows from a block-wise quantized weight matrix and dequantizes them. This inference implementation supports the standard `gather_axis = 0`, `quantize_axis = 1` matrix subset with uint8 `data`, 4-bit packed or 8-bit values, rank-1 non-negative in-bounds int64 `indices` projected to uint32 WebGPU storage, and float32 scales/output. Higher-rank gathers, negative indices, int32 indices, int4/uint4 data, 2-bit data, float16/bfloat16 output, and non-default axes are not implemented.
See the [ONNX Runtime `GatherBlockQuantized` contrib-operator spec](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.GatherBlockQuantized) for the reference semantics.
## Inputs
| Name | Bind key | Logical dtype | WebGPU storage | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- | --- |
| `data` | `dataT` | `T1` | runtime-selected; narrow integers and bool use 32-bit slots | `2` | — | Constant uint8 weight matrix. With `bits = 4`, each byte stores two values low-nibble first; with `bits = 8`, each byte stores one value. | required |
| `indices` | `indicesT` | `Tind` | `uint32` | `1` | — | Non-negative logical int64 indices selecting rows from axis 0 of `data`. Every index must be less than the row count; values use checked uint32 WebGPU storage. | required |
| `scales` | `scalesT` | `T2` | same as logical dtype | `2` | — | Per-block dequantization scale factors of shape `(rows, ceil(output_columns / block_size))`. | required |
| `zero_points` | `zeroPointsT` | `T1` | runtime-selected; narrow integers and bool use 32-bit slots | `2` | — | Optional uint8 zero points. At 4 bits two zero points are packed per byte along the quantized axis, low-nibble first; at 8 bits the shape matches `scales`. If absent, uint8 data uses 2^(bits-1). | optional |
## Outputs
| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
| --- | --- | --- | --- | --- | --- | --- |
| `output` | `outputT` | `T2` | `2` | derived; see description | Dequantized floating-point output rows corresponding to the gathered indices. | required |
## Attributes
Default values (overridable per request):
| Attribute | Default | Description |
| --- | --- | --- |
| `bits` | `4` | Bits per quantized value. The schema default is 4; this implementation supports 4 or 8. |
| `block_size` | `128` | Number of values sharing a scale. Defaults to 128 and must be a power of two at least 16. |
| `gather_axis` | `0` | Axis from which values are gathered. This matrix implementation supports the standard default, axis 0. |
| `quantize_axis` | `1` | Axis split into quantization blocks. This matrix implementation supports the standard default, axis 1. |
## Type constraints
| Variable | Allowed dtypes |
| --- | --- |
| `T1` | `uint8` |
| `T2` | `float32` |
| `Tind` | `int64` |
## 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
- [`gather-block-quantized-q4-pair.wgsl.jinja`](build/webgpu/gather-block-quantized-q4-pair.wgsl.jinja)
- [`gather-block-quantized-q8-vec4.wgsl.jinja`](build/webgpu/gather-block-quantized-q8-vec4.wgsl.jinja)
## Use with `@huggingface/kernels`
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/com.microsoft.GatherBlockQuantized", { version: 1 });
const { outputT } = await kernel({
dataT: { data: dataTData, shape: [4, 8] },
indicesT: { data: indicesTData, shape: [2] },
scalesT: { data: scalesTData, shape: [4, 1] },
});
```
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