v0.1.0

We’re excited to announce the first release of vllm-xpu-kernels!
This release includes migrated and reimplemented core kernels from IPEX. Please note that it is a pre-production release.

What's Changed

• setup build/lint system. add rms_norm as first op to verify functionality by @jikunshang in #1
• refactor files by ops in test folder by @jikunshang in #4
• readme update by @rogerxfeng8 in #5
• [CI] Enable CI by @jikunshang in #2
• use torch-xpu 2.8 link by @jikunshang in #6
• Add cache ops by @zufangzhu in #3
• Add rotary_embedding kernel by @Liangliang-Ma in #7
• Add silu_and_mul kernel by @Liangliang-Ma in #8
• add fp8 quantization kernels by @baodii in #12
• use function instead of lambda by @zufangzhu in #14
• silu_and_mul/rope use functor instead of lambda by @Liangliang-Ma in #15
• Add activation: gelu_fast/gelu_new/gelu_quick by @zhenwei-intel in #16
• feat(moe_sum): add moe_sum by @dbyoung18 in #17
• refactor the structure by @zufangzhu in #20
• use functor for rms_norm kernels by @jikunshang in #18
• add mul_and_silu/gelu_and_mul by @zhenwei-intel in #23
• swigluoai and mul by @mayuyuace in #19
• fix(build): Fix __assert_fail conflict between SYCL and PyTorch by @chaojun-zhang in #26
• feat(mla_cache): add concat_and_cache_mla & gather_cache by @dbyoung18 in #25
• add onednn w8a16 gemm by @zufangzhu in #24
• update onednn extension by @zufangzhu in #29
• update ci script by @jikunshang in #32
• add xpu op grouped topk by @mayuyuace in #27
• grouped topk from IPEX by @mayuyuace in #33
• feat(deepseek_rope): add deepseek_scaling_rope by @dbyoung18 in #34
• add ipex and model config for rmsmorm op by @wendyliu235 in #28
• feat(lora) Add lora bgmv_shrink & bgmv_expand kernels by @chaojun-zhang in #31
• reduce the kernel test coverage for simulator by @chaojun-zhang in #36
• Add chunk_prefill with cutlass backend by @YizhouZ in #38
• topk softmax by @mayuyuace in #39
• [CUTLASS][chunk_prefill] add prefetch and refine barrier scope by @YizhouZ in #44
• [UT][CHUNK_PREFILL]add ut mini scope for fmha by @YizhouZ in #46
• Grouped gemm cutlass by @Liangliang-Ma in #22
• [UT][GroupedGemm] Add grouped gemm pytest mini scope by @Liangliang-Ma in #47
• [FusedMoE] support TP by stream align with device by @Liangliang-Ma in #50
• [OneDNN] Zufang/onednn w4a16 int4 by @zufangzhu in #49
• [CHUNK_PREFILL] fix tiling shape by @YizhouZ in #51
• Reduction of the MINI profiler UT execution time by @chaojun-zhang in #52
• fix install build file lack by @jikunshang in #45
• add op moe_align_block_size & batched_moe_align_block_size by @mayuyuace in #54
• 【GPTQ】add stride setting by @zufangzhu in #57
• [CHUNK_PREFILL] enable sink and local attn by @YizhouZ in #58
• [FusedMoE] input reorder with sycl kernels by @Liangliang-Ma in #56
• Zufang/onednn fp8 gemm by @zufangzhu in #55
• [FusedMoE] Add bias for grouped gemm by @Liangliang-Ma in #62
• [GENERAL] add device index in vllmGetQueue by @YizhouZ in #61
• [Fix] use numel rather than dim, fix w8a8 acc by @zufangzhu in #63
• [Kernel benchmark] enable ipex path for reshape and cache by @DiweiSun in #41
• feat(moe_lora): Add MOE Lora Sum Kernel by @chaojun-zhang in #60
• [FusedMoE] fix an if-else in MoE with bias by @Liangliang-Ma in #65
• [FusedMoe] support fp16 grouped_gemm by @mayuyuace in #59
• [CHUNK_PREFILL] add policy 192 and check conditions by @YizhouZ in #68
• [FusedMoE] fix topk>1 acc issue and support different activation by @Liangliang-Ma in #66
• update clang format, for better indent by @jikunshang in #72
• [OneDNN] add primitive extension and cache locally by @zufangzhu in #73
• [UT] refine miniscope ut shape by @zufangzhu in #75
• [CI] install custom umd by @jikunshang in #77
• [FusedMoE] remove host experts token count to avoid blocking by @Liangliang-Ma in #78
• [CI]fix CI by @jikunshang in #79
• add MINI_PYTEST_PARAMS to test_moe_align_block_size by @mayuyuace in #81
• [CHUNK_PREFILL] kernel refactor using new api by @YizhouZ in #76
• [FusedMoE] refactor to new cutlass api by @Liangliang-Ma in #82
• upgrade torch-xpu 2.9 by @jikunshang in #70
• Support get xpu view from cpu tensor by @chaojun-zhang in #80
• [CHUNK_PREFILL] new api refactor phase 2 by @YizhouZ in #83
• [BUILD] refine CMake, enable AOT by @jikunshang in #89
• [cutlass] support fp8/int4/mxfp4 weights grouped gemm by @mayuyuace in #88
• [CHUNK_PREFILL] new api refactor phase3 by @YizhouZ in #90
• Fix lora accuracy and oom issues by @chaojun-zhang in #91
• [FusedMoE] tune bf16/fp16 grouped gemm grid shape and subgroup shape for decoding perf by @Liangliang-Ma in #96
• apply fused moe fp8/int4/fp4 by @mayuyuace in #98
• [OneDNN] Zufang/wint4aint8 by @zufangzhu in #93
• add contiguous inside rmsnorm kernel by @jikunshang in #95
• Add moe_gather for fused moe by @mayuyuace in #101
• Skip UVA tests for the mini pytest profiler. by @chaojun-zhang in #100
• fix bug of moe_gather by @mayuyuace in #102
• Refine topk softmax for different platform by @mayuyuace in #104
• [CI] clean up docker image in ci by @jikunshang in #99
• rename fused_moe to fused_moe_prelogue by @jikunshang in #105
• [CI]update docker file by @jikunshang in #106
• Reorg sycl-tla kernel code structure by @jikunshang in #103
• Skip fp8 quant large shape test for mini test profiler by @chaojun-zhang in #108
• change grouped gemm kernel fp8 scales to float by @mayuyuace in #110
• add force xe default kernel env var by @jikunshang in #111
• [CI]update pipeline by @jikunshang in #107
• add quantization into wheel package by @jikunshang in #112
• [OneDNN] add vllm own dnnl stream and engine by @zufangzhu in #113
• enable xpu_fused_moe ep by @mayuyuace in #114
• [CHUNK_PREFILL] add seq_k support by @YizhouZ in #115
• [build] Refactor sycl-tla kernel into dynamic library by @jikunshang in #116
• add paged decode split kernels for VLLM by @baodii in #123
• [FusedMoE] input reorder kernel support low precision inputs with scales by @Liangliang-Ma in #124
• support head_group_q more than 8 less than 16 by @baodii in #125
• Add weak_ref_tensor support for graph capture by @jikunshang in #121
• [OneDNN] gpu only build by @xinyu-intel in #120
• [build]split template build by @jikunshang in #126
• clean unused utils, add build version by @jikunshang in #122
• [lint]format cmake by @jikunshang in #127
• upgrade to torch 2.10 & oneapi 2025.3 by @jikunshang in #118

New Contributors

• @jikunshang made their first contribution in #1
• @rogerxfeng8 made their first contribution in #5
• @Liangliang-Ma made their first contribution in #7
• @baodii made their first contribution in #12
• @zhenwei-intel made their first contribution in #16
• @dbyoung18 made their first contribution in #17
• @mayuyuace made their first contribution in #19
• @chaojun-zhang made their first contribution in #26
• @wendyliu235 made their first contribution in #28
• @DiweiSun made their first contribution in #41
• @xinyu-intel made their first contribution in #120

Full Changelog: https://github.com/vllm-project/vllm-xpu-kernels/commits/v0.1.0