com.microsoft.QAttention

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

Description

Fused 8-bit self-attention: an exact int32 QKV projection of a uint8 activation by an int8 or uint8 weight, dequantized by input_scale * weight_scale and biased, followed by masked multi-head attention over the projected heads. Weight columns are laid out [Q | K | V], and per-column weight_scale / weight_zero_point are indexed in that column space. mask_index is additive with mask_filter_value; a unidirectional exclusion overwrites it. Only the uint8-activation, float32 form the reference CPU kernel supports is implemented; do_rotary and a shared past/present buffer are not.

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

Inputs

Name	Logical dtype	Rank	Shape	Description	Presence
`input`	`T1`	—	—	Quantized activations with shape `(batch, sequence, input_hidden_size)`. The input hidden size may exceed `hidden_size` when the projection was pruned.	required
`weight`	`T2`	—	—	Quantized projection weight with shape `(input_hidden_size, 3 * hidden_size)`; the column space is `[Q all heads \| K all heads \| V all heads]`.	required
`bias`	`T3`	—	—	Float bias with shape `(3 * hidden_size)`, added after dequantization in the same column space as `weight`.	required
`input_scale`	`T3`	—	—	Per-tensor activation scale; a scalar or a one-element tensor.	required
`weight_scale`	`T3`	—	—	Weight scale: a scalar for per-tensor quantization, or `(3 * hidden_size)` values indexed in the weight column space for per-column quantization.	required
`mask_index`	`T4`	—	—	Optional attention mask. Accepted spellings are `(batch)` end positions, `(2 * batch)` end positions followed by start positions, a raw `(batch, total_sequence_length)` key mask, and a raw `(batch, sequence, total_sequence_length)` mask. Every other spelling is rejected.	optional
`input_zero_point`	`T1`	—	—	Optional per-tensor activation zero point; a scalar or a one-element tensor. Absent means zero.	optional
`weight_zero_point`	`T2`	—	—	Optional weight zero point: a scalar, or `(3 * hidden_size)` values indexed in the weight column space. Absent means zero.	optional
`past`	`T3`	—	—	Optional cached keys and values with shape `(2, batch, num_heads, past_sequence_length, head_size)`; the cached tokens precede the projected ones.	optional

Outputs

Name	Logical dtype	Rank	Shape	Description	Presence
`output`	`T3`	`3`	derived	Attention output with shape `(batch, sequence, hidden_size)`.	required
`present`	`T3`	`5`	derived	Optional joined keys and values with shape `(2, batch, num_heads, past_sequence_length + sequence, head_size)`. It is required whenever `past` is supplied.	optional

Attributes

Attributes and default values (overridable per request):

Attribute	Default	Description
`do_rotary`	`0`	Rotary position embedding switch. Defaults to `0`, and only `0` is accepted: the reference CPU kernel ignores the attribute, so no reference definition of the rotation exists for this operator.
`mask_filter_value`	`-10000`	Value added to the score of a masked-out key. Defaults to `-10000.0`.
`num_heads`	—	Number of attention heads; `hidden_size` must be divisible by it. The request must supply it.
`past_present_share_buffer`	—	Declared for schema fidelity. Only `0` is accepted: `past` and `present` are distinct tensors with distinct bindings in every variant of this package, so there is no shared buffer for a non-zero value to name.
`scale`	—	Scale applied to the query-key product. When absent or `0`, `1 / sqrt(head_size)` is used.
`unidirectional`	`0`	When `1`, a token attends only to itself and earlier tokens. Defaults to `0`. A single-token query is never restricted, matching the reference kernel.

Type constraints

Variable	Allowed dtypes
`T1`	`uint8`
`T2`	`uint8`, `int8`
`T3`	`float32`
`T4`	`int32`

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.

sgmat_plain_mask_izp_wzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_plain_mask_izp_wzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_plain_mask_izp_nowzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_plain_mask_izp_nowzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_plain_mask_noizp_wzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_plain_mask_noizp_wzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_plain_mask_noizp_nowzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_plain_mask_noizp_nowzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_plain_nomask_izp_wzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_plain_nomask_izp_wzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_plain_nomask_izp_nowzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_plain_nomask_izp_nowzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_plain_nomask_noizp_wzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_plain_nomask_noizp_wzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_plain_nomask_noizp_nowzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_plain_nomask_noizp_nowzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_present_mask_izp_wzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_present_mask_izp_wzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_present_mask_izp_nowzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_present_mask_izp_nowzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_present_mask_noizp_wzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_present_mask_noizp_wzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_present_mask_noizp_nowzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_present_mask_noizp_nowzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_present_nomask_izp_wzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_present_nomask_izp_wzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_present_nomask_izp_nowzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_present_nomask_izp_nowzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_present_nomask_noizp_wzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_present_nomask_noizp_wzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_present_nomask_noizp_nowzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_present_nomask_noizp_nowzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_past_mask_izp_wzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_past_mask_izp_wzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_past_mask_izp_nowzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_past_mask_izp_nowzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_past_mask_noizp_wzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_past_mask_noizp_wzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_past_mask_noizp_nowzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_past_mask_noizp_nowzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_past_nomask_izp_wzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_past_nomask_izp_wzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_past_nomask_izp_nowzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_past_nomask_izp_nowzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_past_nomask_noizp_wzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_past_nomask_noizp_wzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
sgmat_past_nomask_noizp_nowzp — Projects QKV with exact integer products through bounded float32 matrix partials, then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.
packed_past_nomask_noizp_nowzp — Projects QKV with the packed 8-bit integer dot product (portable helpers where the language feature is absent), then dequantizes and scatters into the head-padded working buffers the flash scoring pass reads.

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, per-variant templates, provenance)
manifest.json — the op contract (source of truth)
test.json — correctness cases
bench.json — benchmark cases
qattention-attend.wgsl.jinja
qattention-kv-permute.wgsl.jinja
qattention-mask.wgsl.jinja
qattention-qkv-scatter.wgsl.jinja
quant-dp4a-matmul.wgsl.jinja
quant-exact-matrix.wgsl.jinja

Use with `@huggingface/kernels`

npm install --save-exact @huggingface/kernels@0.0.1-preview.3

Required output shapes and logical data types are inferred from the supplied inputs and attributes; result tensors are allocated automatically.

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.

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

const kernel = await getKernel("webgpu-kernels/com.microsoft.QAttention", { version: 1 });
const { output } = await kernel({
  input: { data: inputData, shape: [1, 2, 4] },
  weight: { data: weightData, shape: [4, 12] },
  bias: { data: biasData, shape: [12] },
  input_scale: { data: input_scaleData, shape: [1] },
  weight_scale: { data: weight_scaleData, shape: [1] },
}, {
  attrs: { num_heads: 2 },
});

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kernel

webgpu

wgsl

apache-2.0

WebGPU

Requires WebGPU support. See the compatibility table.