LM/vLLM: Difference between revisions
< LM
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source /opt/intel/oneapi/setvars.sh | source /opt/intel/oneapi/setvars.sh | ||
# 疑似 AI 誤導 | |||
# 3. 安裝 vLLM 0.30（XPU 版本） | # 3. 安裝 vLLM 0.30（XPU 版本） | ||
pip install vllm==0.30.0 | pip install vllm==0.30.0 | ||
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cd k8v4-xpu | cd k8v4-xpu | ||
# 疑似 AI 誤導 | |||
# 5. 編譯 KV Cache 核心庫（libxe2_kv.so） | # 5. 編譯 KV Cache 核心庫（libxe2_kv.so） | ||
# 專案包含 C/C++ 核心，需本機編譯 | # 專案包含 C/C++ 核心，需本機編譯 | ||
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== CCL Optimization == | == CCL Optimization == | ||
=== vLLM and oneccl mapping === | |||
{| class="wikitable" | |||
! vLLM || oneccl || native | |||
|- | |||
| 0.31.0 || 2022.1.2 || 2021.18.0 | |||
|- | |||
| 0.30.0 || 2022.0.0 || 2021.18.0 | |||
|} | |||
<syntaxhighlight lang="bash"> | |||
# pip | |||
sudo docker exec vllm-xpu-test pip show oneccl | |||
# native | |||
sudo docker exec vllm-xpu-test bash -lc ' | |||
grep -aoE "oneCCL[^[:cntrl:]]{0,100}|CCL[_ -]?version[^[:cntrl:]]{0,100}|202[0-9]\.[0-9]+(\.[0-9]+)?" \ | |||
/opt/venv/lib/libccl.so.2 | sort -u | head -n 50 | |||
' | |||
</syntaxhighlight> | |||
== TODO == | == TODO == | ||
Latest revision as of 09:49, 26 October 2026
compose
services:
vllm-xpu:
image: vllm/vllm-openai-xpu:v0.30.0
container_name: vllm-xpu-qwen38-exp
devices:
- /dev/dri:/dev/dri
volumes:
- ${HOME}/.cache/huggingface:/root/.cache/huggingface
environment:
- HF_HOME=/root/.cache/huggingface
- HF_TOKEN=${HF_TOKEN:-}
- no_proxy=localhost,127.0.0.1
- ZE_FLAT_DEVICE_HIERARCHY=COMPOSITE
- ZE_AFFINITY_MASK=0
- VLLM_WORKER_MULTIPROC_METHOD=spawn
- PYTORCH_ALLOC_CONF=expandable_segments:True
- VLLM_XPU_ENABLE_XPU_GRAPH=1
ports:
- "9000:9000"
entrypoint: ["vllm", "serve"]
command:
- "SergiioB/Qwen3.8-27B-GPTQ-Int4-sym-G128-MTP-BF16"
- "--served-model-name=qwen38"
- "--port=9000"
- "--host=0.0.0.0"
- "--quantization=gptq"
- "--dtype=float16"
- "--max-model-len=131072"
- "--gpu-memory-utilization=0.88"
- "--kv-cache-dtype=fp8"
- "--max-num-seqs=1"
- "--max-num-batched-tokens=8192"
- "--trust-remote-code"
- "--enable-auto-tool-choice"
- "--tool-call-parser=qwen3_xml"
- "--reasoning-parser=qwen3"
- "--limit-mm-per-prompt={\"image\":10}"
- "--speculative-config={\"method\":\"mtp\",\"num_speculative_tokens\":4}"
restart: no
Verify environments in container
| Command | Results in vllm v0.30.0 |
|---|---|
docker exec vllm-xpu-qwen38-prod bash -c \
"pip list | awk 'NR<3 || tolower(\$0) ~ /torch|intel|triton|vllm|oneccl|mkl|dpc|level/'"
|
Package Version Editable project location ---------------------------------------- --------------- ------------------------------------- dpcpp-cpp-rt 2026.0.0 intel-cmplr-lib-rt 2026.0.0 intel-cmplr-lib-ur 2026.0.0 intel-cmplr-lic-rt 2026.0.0 intel-opencl-rt 2026.0.0 intel-openmp 2026.0.0 intel-pti 0.17.0 intel-sycl-rt 2026.0.0 mkl 2026.0.0 oneccl 2022.0.0 oneccl-devel 2022.0.0 onemkl-license 2026.0.0 onemkl-sycl-blas 2026.0.0 onemkl-sycl-dft 2026.0.0 onemkl-sycl-lapack 2026.0.0 onemkl-sycl-rng 2026.0.0 onemkl-sycl-sparse 2026.0.0 open_clip_torch 2.32.0 torch 2.13.0+xpu torchaudio 2.11.0+xpu torchcodec 0.16.0+cpu torchvision 0.28.0+xpu triton 3.7.2+xpu triton-xpu 3.7.2 vllm 0.30.0+xpu vllm_test_utils 0.1 /workspace/vllm/tests/vllm_test_utils vllm-xpu-kernels 0.1.14.1 |
docker exec vllm-xpu-qwen38-prod bash -c 'pip show vllm-xpu-kernels'
|
Name: vllm-xpu-kernels Version: 0.1.14.1 Summary: A high-throughput and memory-efficient inference and serving engine for LLMs Home-page: https://github.com/vllm-project/vllm-xpu-kernels Author: vLLM Team Author-email: License-Expression: Apache-2.0 Location: /opt/venv/lib/python3.12/site-packages Requires: Required-by: vllm |
docker exec vllm-xpu-qwen38-prod bash -c 'lsb_release -a'
|
Distributor ID: Ubuntu Description: Ubuntu 24.04.5 LTS Release: 24.04 Codename: noble |
docker exec -it vllm-xpu-qwen38-prod bash
|
About CCL_SYCL_* variables
vllm-xpu-qwen38-prod | 2026:09:30-07:47:40: 118 |CCL_WARN| value of CCL_ATL_TRANSPORT changed to be ofi (default:mpi) vllm-xpu-qwen38-prod | 2026:09:30-07:47:40: 118 |CCL_WARN| value of CCL_SYCL_ALLREDUCE_SIMPLE_THRESHOLD changed to be 4294967296 (default:4194304) vllm-xpu-qwen38-prod | 2026:09:30-07:47:40: 118 |CCL_WARN| value of CCL_SYCL_REDUCE_SCATTER_SIMPLE_THRESHOLD changed to be 4294967296 (default:8388608) vllm-xpu-qwen38-prod | 2026:09:30-07:47:40: 118 |CCL_WARN| value of CCL_SYCL_ALLGATHERV_SIMPLE_THRESHOLD changed to be 4294967296 (default:1048576) vllm-xpu-qwen38-prod | 2026:09:30-07:47:40: 118 |CCL_WARN| value of CCL_SYCL_ALLTOALL_TMP_BUF changed to be 1 (default:0) vllm-xpu-qwen38-prod | 2026:09:30-07:47:40: 117 |CCL_WARN| value of CCL_ATL_TRANSPORT changed to be ofi (default:mpi) vllm-xpu-qwen38-prod | 2026:09:30-07:47:40: 117 |CCL_WARN| value of CCL_SYCL_ALLREDUCE_SIMPLE_THRESHOLD changed to be 4294967296 (default:4194304) vllm-xpu-qwen38-prod | 2026:09:30-07:47:40: 117 |CCL_WARN| value of CCL_SYCL_REDUCE_SCATTER_SIMPLE_THRESHOLD changed to be 4294967296 (default:8388608) vllm-xpu-qwen38-prod | 2026:09:30-07:47:40: 117 |CCL_WARN| value of CCL_SYCL_ALLGATHERV_SIMPLE_THRESHOLD changed to be 4294967296 (default:1048576) vllm-xpu-qwen38-prod | 2026:09:30-07:47:40: 117 |CCL_WARN| value of CCL_SYCL_ALLTOALL_TMP_BUF changed to be 1 (default:0) vllm-xpu-qwen38-prod | 2026:09:30-07:47:40: 118 |CCL_WARN| could not get local_idx/count from environment variables, trying to get them from ATL vllm-xpu-qwen38-prod | 2026:09:30-07:47:40: 117 |CCL_WARN| could not get local_idx/count from environment variables, trying to get them from ATL vllm-xpu-qwen38-prod | 2026:09:30-07:47:41: 223:[1] |CCL_WARN| no membind support for NUMA node 0, skip thread membind vllm-xpu-qwen38-prod | 2026:09:30-07:47:41: 224:[0] |CCL_WARN| no membind support for NUMA node 0, skip thread membind vllm-xpu-qwen38-prod | 2026:09:30-07:47:41: 118:[1] |CCL_WARN| topology recognition shows PCIe connection between devices. If this is not correct, you can disa ble topology recognition, with CCL_TOPO_FABRIC_VERTEX_CONNECTION_CHECK=0. This will assume XeLinks across devices vllm-xpu-qwen38-prod | 2026:09:30-07:47:41: 117:[0] |CCL_WARN| topology recognition shows PCIe connection between devices. If this is not correct, you can disa ble topology recognition, with CCL_TOPO_FABRIC_VERTEX_CONNECTION_CHECK=0. This will assume XeLinks across devices vllm-xpu-qwen38-prod | 2026:09:30-07:47:41: 118:[1] |CCL_WARN| pidfd is not supported, fallbacks to drmfd exchange mode vllm-xpu-qwen38-prod | 2026:09:30-07:47:41: 117:[0] |CCL_WARN| pidfd is not supported, fallbacks to drmfd exchange mode
k8v4
# 1. 建立並激活 Python 虛擬環境
python3 -m venv k8v4-env
source k8v4-env/bin/activate
# 2. 安裝 Intel oneAPI（若尚未安裝）
# 參考: https://www.intel.com/content/www/us/en/developer/tools/oneapi/index.html
source /opt/intel/oneapi/setvars.sh
# 疑似 AI 誤導
# 3. 安裝 vLLM 0.30（XPU 版本）
pip install vllm==0.30.0
# 4. 克隆 k8v4-xpu 專案
git clone https://github.com/Wrapzii/k8v4-xpu.git
cd k8v4-xpu
# 疑似 AI 誤導
# 5. 編譯 KV Cache 核心庫（libxe2_kv.so）
# 專案包含 C/C++ 核心，需本機編譯
pip install -e .
# 或手動編譯:
mkdir -p build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)
sudo make install
# 6. 設定環境變數
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
export VLLM_KV_CACHE_TYPE=k8v4 # 啟用 K8/V4 壓縮
# 7. 啟動 vLLM 服務（以 Swift-1.5 Qwen3.8-27B 模型為例）
vllm serve Wrapzii/Swift-1.5-Qwen3.8-27b-GPTQ-Int4-baked-v1-embed-int8 \
--tensor-parallel-size 1 \
--gpu-memory-utilization 0.90 \
--max-model-len 32768
CCL Optimization
vLLM and oneccl mapping
| vLLM | oneccl | native |
|---|---|---|
| 0.31.0 | 2022.1.2 | 2021.18.0 |
| 0.30.0 | 2022.0.0 | 2021.18.0 |
# pip
sudo docker exec vllm-xpu-test pip show oneccl
# native
sudo docker exec vllm-xpu-test bash -lc '
grep -aoE "oneCCL[^[:cntrl:]]{0,100}|CCL[_ -]?version[^[:cntrl:]]{0,100}|202[0-9]\.[0-9]+(\.[0-9]+)?" \
/opt/venv/lib/libccl.so.2 | sort -u | head -n 50
'
TODO
- Official Images for XPU
- Install using Python
- vllm serve arguments
- HF_* Environments
- ONEAPI_* SYCL_* Environments
- CCL_* Environments