com.microsoft.MatMulNBits

com.microsoft · ONNX Runtime contrib operator · contrib since_version 1

Description

Matrix multiplication with B block-quantized along K and dequantized as (code - zero_point) * scale. Each power-of-two block_size group has a scale and optional zero point; optional bias is added afterward. Two-, four-, and eight-bit codes are packed low-first, and A may have rank 2 or 3. This package supports standard unpacked zero points with the same dtype as A. Deprecated g_idx, prepacked weights, and bfloat16 tensors are not implemented.

See the ONNX Runtime MatMulNBits contrib-operator spec for the reference semantics.

Inputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`A`	`aT`	`T1`	—	—	Float input matrix, not quantized. Rank 2 has shape `(M, K)` and rank 3 has shape `(batch, sequence, K)`; only the last axis is the reduction axis and the leading axes fold into the row count, so the ordinary activation needs no surrounding Reshape.	required
`B`	`bT`	`uint8`	`3`	—	Bit-packed uint8 weight matrix of shape `(N, k_blocks, blob_size)`, where `k_blocks = ceil(K / block_size)` and `blob_size = block_size * bits / 8`. Codes are packed low-first along K.	required
`scales`	`scalesT`	`T1`	`2`	—	Per-block dequantization scale factors of shape `(N, k_blocks)`, with the same dtype as `A`.	required
`zero_points`	`zeroPointsT`	`T3`	`2`	—	Standard unpacked per-block zero points with shape `(N, k_blocks)` and the same dtype as `A`. Omission uses `2^(bits - 1)`.	optional
`bias`	`biasT`	`T1`	`1`	—	Optional bias vector of shape `[N]` added to the output.	optional

Outputs

Name	Bind key	Logical dtype	Rank	Shape	Description	Presence
`Y`	`yT`	`T1`	same as `A`	derived; see description	Result of A multiplied by the dequantized weight matrix, with optional bias, same dtype and rank as A: the leading axes of A with a trailing N.	required

Attributes

Attributes and default values (overridable per request):

Attribute	Default	Description
`accuracy_level`	`0`	Minimum internal accuracy level: 0 (unset), 1 (float32), 2 (float16), 3 (bfloat16), or 4 (int8).
`bits`	`4`	Bit width used to quantize B; this package supports 2, 4, and 8.
`K`	—	Input feature dimension of the weight matrix.
`N`	—	Output feature dimension of the weight matrix.
`block_size`	—	Power-of-two quantization block size along K; it must be at least 16.

Type constraints

Variable	Allowed dtypes
`T1`	`float32`, `float16`
`T3`	`float32`, `float16`

Device requirements

Some implementation variants require subgroup-matrix and subgroups. These are route-specific capabilities, not package-wide requirements; availability also depends on the request shape and dtype.

Files

metadata.json — kernel metadata (id, digests, provenance)
manifest.json — the op contract (source of truth)
test.json — correctness cases
bench.json — benchmark + tuning cases
matmul-nbits-dp4a-quantize.wgsl.jinja
matmul-nbits-gemv-q4.wgsl.jinja
matmul-nbits-q4-dp4a-prefill.wgsl.jinja
matmul-nbits-q4-prefill-tile4x4.wgsl.jinja
matmul-nbits-q4-prefill-tiled-reg.wgsl.jinja
matmul-nbits-q4-prefill-tiled.wgsl.jinja
matmul-nbits-q4-sgmat.wgsl.jinja
matmul-nbits.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.

import { getKernel } from "@huggingface/kernels";

const kernel = await getKernel("webgpu-kernels/com.microsoft.MatMulNBits", { version: 1 });
const { yT } = await kernel({
  aT: { data: aTData, shape: [2, 17] },
  bT: { data: bTData, shape: [2, 2, 8] },
  scalesT: { data: scalesTData, shape: [2, 2] },
}, {
  attrs: { K: 17, N: 2, block_size: 16 },
});

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.