auto-round-lib 0.13.3

Auto Round Kernel binary package

What is AutoRound Kernel?

AutoRound Kernel (ARK) is a low-bit acceleration library for Intel platform, providing high-performance kernels for LLM inference including weight-only quantization, flash attention (with SageAttention v1 support), and Mixture-of-Experts (MOE) computation.

The kernels are optimized for the following CPUs:

• Intel Xeon Scalable processor (formerly Sapphire Rapids, and Emerald Rapids)
• Intel Xeon 6 processors (formerly Sierra Forest and Granite Rapids)

The kernels are optimized for the following GPUs:

• Intel Arc B-Series Graphics and Intel Arc Pro B-Series Graphics (formerly Battlemage)

Key Features

AutoRound Kernel provides the following computational capabilities for LLM inference:

Weight-Only Quantization (WOQ) Linear

Weight dtype	Compute dtype	Scale dtype	Algorithm[1]
INT8	INT8[2] / BF16 / FP32	BF16 / FP32	sym / asym
INT4	INT8 / BF16 / FP32	BF16 / FP32	sym / asym
INT3	INT8 / BF16 / FP32	BF16 / FP32	sym / asym
INT2	INT8 / BF16 / FP32	BF16 / FP32	sym / asym
INT5	INT8 / BF16 / FP32	BF16 / FP32	sym / asym
INT6	INT8 / BF16 / FP32	BF16 / FP32	sym / asym
INT7	INT8 / BF16 / FP32	BF16 / FP32	sym / asym
INT1	INT8 / BF16 / FP32	BF16 / FP32	sym / asym
FP8 (E4M3, E5M2)	BF16 / FP32	FP32 / FP8 (E8M0)	NA
FP4 (E2M1)	BF16 / FP32	BF16 / FP32	NA

XPU Weight-Only Quantization

Weight dtype	Compute dtype	Scale dtype	Algorithm
INT8	INT8 / FP16	FP16	sym
INT4	INT8 / FP16	FP16	sym
INT2	INT8 / FP16	FP16	sym
FP8 (E4M3, E5M2)	FP16	FP16 / FP8 (E8M0)	NA

^[1]: Quantization algorithms for integer types: symmetric or asymmetric.
^[2]: Includes dynamic activation quantization; results are dequantized to floating-point formats.

Flash Attention (XPU only)

ARK provides multiple attention backends for prefill and decode, with SageAttention v1 support — offering INT8-quantized attention variants for higher throughput on Intel Arc GPUs. (SageAttention v2/v3 are under development.)

Backend	Description	Q/K/V dtype	Head dim	Features
sdpa	Standard flash attention (FP16/BF16)	FP16 / BF16	64, 96, 128, 192	Causal mask, additive mask, GQA
sagev1	SageAttention v1 with INT8 Q/K quantization	FP16 / BF16	64, 128	Block-wise INT8 QK, PV in half
sagev1_pvi8	SageAttention v1 with INT8 Q/K/V quantization	FP16 / BF16	64, 128	Block-wise INT8 QK + INT8 PV
sage	Low-level SageAttention with pre-quantized INT8 Q/K	INT8 (Q/K), FP16 (V)	64, 128	External Q/K scales
sage_pvi8	Low-level SageAttention with pre-quantized INT8 Q/K/V	INT8 (Q/K/V)	64, 128	External Q/K/V scales
sage_dynquant	SageAttention with fused dynamic INT8 quantization	FP16	64, 128	Auto-quantizes Q/K internally

All attention backends support both HND ([B, H, N, D]) and NHD ([B, N, H, D]) tensor layouts, as well as non-contiguous (sliced) input tensors.

MOE GEMM (XPU only)

Grouped GEMM for Mixture-of-Experts layers, supporting FP16/BF16 with variable token counts per expert.

Installation

1. Install via pip

pip install auto-round-lib

2. Install from Source

Requires a sourced oneAPI environment (2025.3+ recommended for SYCL TLA support).

# Source oneAPI environment
source /opt/intel/oneapi/setvars.sh

# Build and install
pip install . --no-build-isolation
# or
python setup.py bdist_wheel; pip install dist/*

The build system automatically detects the oneAPI version. SYCL TLA (Tensor Linear Algebra) kernels are enabled when oneAPI >= 2025.3.

Validated Hardware Environment

CPU based on Intel 64 architecture or compatible processors:

• Intel Xeon Scalable processor (Granite Rapids)

GPU built on Intel's Xe architecture:

• Intel Arc B-Series Graphics (Battlemage)

API Reference

QuantLinear (Weight-Only Quantization)

ARK exposes a unified weight-only linear interface through QuantLinear, QuantLinearGPTQ, QuantLinearAWQ, and QuantLinearFP8. Please refer to the QLinear for more integration details.

The expected lifecycle is: create the module, load quantized tensors from the checkpoint, call post_init() once to repack weights into the ARK-friendly layout, and then call forward() during inference.

Minimal usage:

from auto_round_kernel.qlinear import QuantLinear

qlinear = QuantLinear(
    bits=4,
    group_size=128,
    sym=True,
    in_features=in_features,
    out_features=out_features,
    bias=bias is not None,
    weight_dtype=weight_dtype,
)
# Load qweight, qzeros, scales, and bias from checkpoint.
qlinear.post_init()

# Run inference
y = qlinear(x)

Attention APIs

ark.sdpa — Standard Flash Attention

import auto_round_kernel as ark

output = ark.sdpa(
    query,
    key,
    value,
    attn_mask=None,
    dropout_p=0.0,
    is_causal=False,
    scale=None,
    tensor_layout="HND",  # "HND" or "NHD"
)

• Q/K/V dtype: FP16 or BF16
• Supported head dims: 64, 96, 128, 192
• Supports GQA (grouped query attention) via different Hq/Hkv

ark.sagev1 — SAGE v1 Attention

output = ark.sagev1(
    query,
    key,
    value,
    attn_mask=None,
    dropout_p=0.0,
    is_causal=False,
    scale=None,
    enable_gqa=False,
    quant_block_size=64,  # block size for INT8 QK quantization
    tensor_layout="HND",
)

• Q/K/V dtype: FP16 or BF16
• Supported head dims: 64, 128
• Internally quantizes Q/K to INT8 per block; PV computed in half precision
• Falls back to ark.sdpa when quant_block_size <= 0

ark.sagev1_pvi8 — SAGE v1 with INT8 PV

Same interface as sagev1 but also quantizes V to INT8 internally for higher throughput.

ark.sage_dynquant — SAGE with Fused Dynamic Quantization

output = ark.sage_dynquant(
    query,
    key,
    value,
    attn_mask=None,
    dropout_p=0.0,
    is_causal=False,
    scale=None,
    enable_gqa=False,
    quant_block_size=64,
)

• Takes FP16 Q/K/V, performs fused block-wise INT8 quantization of Q/K via SYCL kernel
• Supports quant_block_size: 1 (per-token), 32, 64, 128, 256
• Auto-pads sequence lengths for block alignment

ark.sageattn — SageAttention-Compatible Dispatcher

ARK provides a drop-in replacement for the sageattention.sageattn API, enabling seamless integration with existing SageAttention workflows:

import auto_round_kernel as ark

# Drop-in replacement for sageattention.sageattn
output = ark.sageattn(
    q,
    k,
    v,
    tensor_layout="HND",
    is_causal=False,
    sm_scale=None,
    return_lse=False,
    kernel="v1_pvhalf",  # or "v1_pvi8"
    **kwargs,
)

This mirrors the SageAttention interface for drop-in compatibility, allowing models using SageAttention to run on Intel Arc GPUs without code changes.

MOE GEMM

output = ark.moe_gemm(
    activations,  # [total_tokens, K] FP16/BF16
    weights,  # [num_experts, K, N] FP16/BF16
    num_tokens_per_expert,  # [num_experts] int32
    scales=None,  # optional [num_experts, N] FP16/BF16
)

Patching torch SDPA

ARK can globally replace torch.nn.functional.scaled_dot_product_attention for evaluation, including SageAttention backends:

import auto_round_kernel as ark

# Patch with standard SDPA backend
ark.patch_torch_sdpa(backend="sdpa")

# Patch with SageAttention v1 backend (INT8 QK, PV half)
ark.patch_torch_sdpa(backend="sagev1", quant_block_size=64)

# Patch with SageAttention v1 + INT8 PV backend
ark.patch_torch_sdpa(backend="sagev1_pvi8", quant_block_size=64)

# Restore original
ark.unpatch_torch_sdpa()

Or use the helper launcher for lm-eval:

cd /path/to/auto_round_extension/ark
PYTHONPATH=$PWD python tools/lm_eval_with_ark_sdpa.py \
  --model hf \
  --model_args pretrained=/path/to/model,trust_remote_code=True,dtype=bfloat16 \
  --tasks hellaswag,piqa,winogrande \
  --device xpu:0 \
  --batch_size 1

Low-Level Matrix Operations

# FP16/BF16 matrix multiply with bias
C = ark.matmul(A, B, bias)

# INT8 matrix multiply (s8s8s32)
C = ark.igemm_s8s8s32(A, B)

# Weight-only quantized GEMM with INT8 weights
C = ark.woqgemm_s8(A, B, scaleB, bias)

# General weight-only quantized GEMM
C = ark.woqgemm(A, B, bias, n, k, groupsize, compute_type, weight_type, scale_type, asym)

Testing

Unit tests are available in the test directory:

Test file	Description
test_weightonly.py	Weight-only quantized GEMM (CPU + XPU)
test_flash_attn.py	Flash attention (sdpa) correctness
test_sdpa.py	SDPA benchmark suite
test_sdpa_parity.py	SDPA parity with non-contiguous inputs and layouts
test_sage_dynquant.py	SageDynQuant block-wise benchmark
test_bench_bmg.py	BMG comparison benchmark
test_matmul.py	General matrix multiply
test_packq.py	Weight packing/unpacking
test_moe.py	MOE GEMM correctness

Notes

• The SDPA patch only routes calls to ARK on XPU when inputs match kernel constraints; otherwise it falls back to the original torch SDPA.
• Supported Q/K/V dtypes for attention are FP16 and BF16 (except SAGE variants which may use INT8 internally).
• dropout_p must be 0.0 for all ARK attention paths.
• Additive masks are supported when they can be normalized to [B, 1, Sq, Skv]; boolean masks fall back to torch.
• Non-contiguous (sliced) input tensors are supported for all attention backends.