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vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
Apache License 2.0
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[Installation]: vLLM Not Working on x86 CPUs from v0.6.1 Onwards #8460

Closed sanketkaleoss closed 2 months ago

sanketkaleoss commented 2 months ago

Your current environment

PyTorch version: 2.4.0+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 12.3.0-1ubuntu1~22.04) 12.3.0
Clang version: 17.0.2 (https://github.com/llvm/llvm-project b2417f51dbbd7435eb3aaf203de24de6754da50e)
CMake version: version 3.30.3
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jul 29 2024, 16:56:48) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-1023-aws-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             16
On-line CPU(s) list:                0-15
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz
CPU family:                         6
Model:                              106
Thread(s) per core:                 2
Core(s) per socket:                 8
Socket(s):                          1
Stepping:                           6
BogoMIPS:                           5799.92
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd ida arat avx512vbmi pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear flush_l1d arch_capabilities
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          384 KiB (8 instances)
L1i cache:                          256 KiB (8 instances)
L2 cache:                           10 MiB (8 instances)
L3 cache:                           54 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-15
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI Syscall hardening, KVM SW loop
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0+cpu
[pip3] torchvision==0.19.0+cpu
[pip3] transformers==4.44.2
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: N/A
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
Could not collect

How you are installing vllm

sudo apt-get update  -y
sudo apt-get install -y gcc-12 g++-12
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12

pip install --upgrade pip
pip install wheel packaging ninja setuptools>=49.4.0 numpy
pip install -v -r requirements-cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu

VLLM_TARGET_DEVICE=cpu python setup.py install

Before submitting a new issue...

DarkLight1337 commented 2 months ago

Can you show the error you've encountered during installation?

sanketkaleoss commented 2 months ago

Can you show the error you've encountered during installation? Screenshot 2024-09-12 165255 Sure, here it is.

dtrifiro commented 2 months ago

You're missing oneAPI Deep Neural Network Library (oneDNN) dependency, you can install it along with other required deps with:

apt-get install -y libnuma-dev libdnnl-dev opencl-dev
sanketkaleoss commented 2 months ago

You're missing oneAPI Deep Neural Network Library (oneDNN) dependency, you can install it along with other required deps with:

apt-get install -y libnuma-dev libdnnl-dev opencl-dev

Screenshot 2024-09-13 163044

Thanks for the reply. I installed as suggested. But I am facing this issue now.

Isotr0py commented 2 months ago

Perhaps the libdnnl-dev/oneDNN in apt-get is out of date for your linux distribution, you can install oneDNN from source:

git clone -b rls-v3.5 https://github.com/oneapi-src/oneDNN.git

cmake -B ./oneDNN/build -S ./oneDNN -G Ninja -DONEDNN_LIBRARY_TYPE=STATIC \ 
    -DONEDNN_BUILD_DOC=OFF \ 
    -DONEDNN_BUILD_EXAMPLES=OFF \ 
    -DONEDNN_BUILD_TESTS=OFF \ 
    -DONEDNN_BUILD_GRAPH=OFF \ 
    -DONEDNN_ENABLE_WORKLOAD=INFERENCE \ 
    -DONEDNN_ENABLE_PRIMITIVE=MATMUL
cmake --build ./oneDNN/build --target install --config Release