Hugging Face's logo

Kernels:
webgpu-kernels
/
com.microsoft.QMoE

Kernel card Files
xet
 Community

com.microsoft.QMoE

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

Description

Quantized mixture of experts over float32 activations and raw uint8-packed integer expert weights. The package implements 4- and 8-bit symmetric dequantization, ReLU, and interleaved SwiGLU for rank-2 or rank-3 input. Routing uses the softmax over the selected top-k logits, matching ONNX Runtime QMoE when the omitted router_weights input is absent. Biases, explicit zero points, FC3, sparse mixing, provider-prepacked layouts, and FP4/FP8 modes are not implemented.

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

Inputs

Name Bind key Logical dtype Rank Shape Description Presence
input inputT T — — Token activations of shape (num_tokens, hidden_size) or (batch_size, sequence_length, hidden_size). required
router_probs routerT T 2 — Routing logits of shape (num_tokens, num_experts), where num_tokens is the product of the leading input dimensions. required
fc1_experts_weights fc1T T1 3 — Raw packed FC1 weights of shape (num_experts, fusion_size * inter_size, hidden_size / pack_size), where fusion_size is 2 only for interleaved SwiGLU and pack_size is 2 for 4-bit or 1 for 8-bit weights. required
fc1_scales fc1ScalesT T2 — — Required FC1 dequantization scales: rank 2 (num_experts, fusion_size * inter_size) for column-wise quantization, or rank 3 with a trailing hidden_size / block_size axis. required
fc2_experts_weights fc2T T1 3 — Raw packed FC2 weights of shape (num_experts, hidden_size, inter_size / pack_size). required
fc2_scales fc2ScalesT T2 — — Required FC2 dequantization scales: rank 2 (num_experts, hidden_size) for column-wise quantization, or rank 3 with a trailing inter_size / block_size axis. required

Outputs

Name Bind key Logical dtype Rank Shape Description Presence
output outputT T same as input same as input Routed expert output with the same shape as input. required

Attributes

Attributes and default values (overridable per request):

Attribute Default Description
activation_alpha 1 Alpha used by SwiGLU; the exact standard default is 1.
activation_beta 0 Beta added to the SwiGLU linear branch; the exact standard default is 0.
activation_type "relu" Activation applied after FC1. This package supports relu and swiglu; the exact standard default is relu.
expert_weight_bits 4 Integer expert-weight bit width. This package supports 4 and 8; the exact standard default is 4.
k 1 Number of experts selected per token; the exact standard default is 1.
normalize_routing_weights 0 Accepted values are 0 and 1. With the separate router_weights input omitted, ONNX Runtime QMoE applies a softmax over the selected top-k logits for either value; the attribute only distinguishes the unsupported separate-weight path.
quant_type "int" Quantization family. This package supports only the exact standard default int.
swiglu_fusion 0 SwiGLU packing mode. ReLU uses the exact standard default 0; supported SwiGLU interleaves gate/up FC1 rows with value 1.
use_sparse_mixer 0 Whether to use sparse-mixer routing. The exact standard default and only supported value is 0.
weights_prepacked -1 Provider weight-layout selector. Values -1 and 0 both consume the public raw packed tensor layout; provider-specific prepacked value 1 is not portable and is rejected.
block_size — Optional quantization block size along the reduction dimension. Omission selects column-wise scaling; a supplied value must be a power of two of at least 16 and divide both hidden_size and inter_size.
swiglu_limit — Optional SwiGLU clamp limit. Omission means no finite clamp.

Type constraints

Variable Allowed dtypes
T float32
T1 uint8
T2 float32

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

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.QMoE", { version: 1 });
const { outputT } = await kernel({
  inputT: { data: inputTData, shape: [2, 2, 4] },
  routerT: { data: routerTData, shape: [4, 2] },
  fc1T: { data: fc1TData, shape: [2, 4, 2] },
  fc1ScalesT: { data: fc1ScalesTData, shape: [2, 4] },
  fc2T: { data: fc2TData, shape: [2, 4, 2] },
  fc2ScalesT: { data: fc2ScalesTData, shape: [2, 4] },
});
Downloads last month
-
kernel
webgpu
wgsl
apache-2.0
WebGPU

Requires WebGPU support. See the compatibility table.