我想知道这个错误是否与传入的--device=cpu参数有关。
根据标志，您的CPU不支持AVX512,这是CPU推理的先决条件。

你好，@memduhcagridemir ,谢谢你的回复。确实有可能是这种情况。我在文档中看到，CPU只支持AVX512,而我只支持AVX2。这很有趣，因为我能够在我的机器上运行llama3(只是hf代码),尽管它有点慢(也使用ollama运行，它运行得相当快，4位)。作为附注，我本以为在查看这里的PR时会有一些消息打印出来：#3634 :

message(FATAL_ERROR "vLLM CPU backend requires AVX512 ISA support.")

🤔

另外，avx512可能不是所有模型的要求，请参见此链接：ollama/ollama#2205(评论)。

我认为我在这里遇到了同样的问题，我的盒子上有一个GPU,但它是一个旧的Nvidia Tesla DC GPU。如何查看我的CPU是否支持AVX512?

我也遇到了同样的问题。
这是我的环境：

Collecting environment information...
PyTorch version: 2.2.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.29.2
Libc version: glibc-2.31

Python version: 3.8.10 (default, Nov 22 2023, 10:22:35)  [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-173-generic-x86_64-with-glibc2.29
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090

Nvidia driver version: 550.54.14
cuDNN version: Could not collect
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
Byte Order:                         Little Endian
Address sizes:                      46 bits physical, 57 bits virtual
CPU(s):                             104
On-line CPU(s) list:                0-103
Thread(s) per core:                 2
Core(s) per socket:                 26
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              106
Model name:                         Intel(R) Xeon(R) Gold 5320 CPU @ 2.20GHz
Stepping:                           6
CPU MHz:                            2800.000
BogoMIPS:                           4400.00
Virtualization:                     VT-x
L1d cache:                          2.4 MiB
L1i cache:                          1.6 MiB
L2 cache:                           65 MiB
L3 cache:                           78 MiB
NUMA node0 CPU(s):                  0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102
NUMA node1 CPU(s):                  1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103
Vulnerability Gather data sampling: Mitigation; Microcode
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 vulnerable
Vulnerability Retbleed:             Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.24.4
[pip3] torch==2.2.1
[pip3] triton==2.2.0
[conda] Could not collect

显然，我的CPU支持avx512。但是当我运行

python3 -m vllm.entrypoints.openai.api_server \
--device cpu

和

curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
        "model": "facebook/opt-125m",
        "messages": [
            {"role": "system", "content": "You are an intelligent British female writer and translator who is good at writing science fiction using multiple languages. You won a Nobel price in literature five years ago."},
            {"role": "user", "content": "Please detailedly tell a story about an exciting aerospace expedition for a Chinese boy Lam and his German dog. They are sent to aerospace by mistake and strive to wait for rescue from motherland with no water and food supply for over a month. They are almost caught by aliens disguised as his mother. Moreover, please translate the above story to Chinese, German, French, Portuguese and Japanese respectively."}
        ], "temperature": 0
    }'

时，我得到了相同的错误

TypeError: __init__() got an unexpected keyword argument 'num_prefills'

我想问一下你是否解决了这个问题？

我将vllm==0.4.2更新为it,但仍然得到：

File "/home/gpu/srv/vllm/venv/lib64/python3.11/site-packages/vllm/engine/async_llm_engine.py", line 475, in engine_step
    request_outputs = await self.engine.step_async()
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/gpu/srv/vllm/venv/lib64/python3.11/site-packages/vllm/engine/async_llm_engine.py", line 221, in step_async
    output = await self.model_executor.execute_model_async(
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/gpu/srv/vllm/venv/lib64/python3.11/site-packages/vllm/executor/cpu_executor.py", line 101, in execute_model_async
    output = await make_async(self.driver_worker.execute_model
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib64/python3.11/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/gpu/srv/vllm/venv/lib64/python3.11/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/gpu/srv/vllm/venv/lib64/python3.11/site-packages/vllm/worker/cpu_worker.py", line 290, in execute_model
    output = self.model_runner.execute_model(seq_group_metadata_list,
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/gpu/srv/vllm/venv/lib64/python3.11/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/gpu/srv/vllm/venv/lib64/python3.11/site-packages/vllm/worker/cpu_model_runner.py", line 320, in execute_model
    ) = self.prepare_input_tensors(seq_group_metadata_list)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/gpu/srv/vllm/venv/lib64/python3.11/site-packages/vllm/worker/cpu_model_runner.py", line 270, in prepare_input_tensors
    ) = self._prepare_prompt(seq_group_metadata_list)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/gpu/srv/vllm/venv/lib64/python3.11/site-packages/vllm/worker/cpu_model_runner.py", line 152, in _prepare_prompt
    attn_metadata = self.attn_backend.make_metadata(
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/gpu/srv/vllm/venv/lib64/python3.11/site-packages/vllm/attention/backends/xformers.py", line 29, in make_metadata
    return XFormersMetadata(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: XFormersMetadata.__init__() got an unexpected keyword argument 'num_prefills'

(venv) [gpu@ava vllm]$ python collect_env.py
Collecting environment information...
PyTorch version: 2.3.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: CentOS Stream 8 (x86_64)
GCC version: (GCC) 8.5.0 20210514 (Red Hat 8.5.0-21)
Clang version: Could not collect
CMake version: version 3.29.2
Libc version: glibc-2.28

Python version: 3.11.7 (main, Jan 26 2024, 19:22:20) [GCC 8.5.0 20210514 (Red Hat 8.5.0-21)] (64-bit runtime)
Python platform: Linux-6.8.1-1.el8.elrepo.x86_64-x86_64-with-glibc2.28
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 550.54.15
cuDNN version: Could not collect
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
Byte Order:          Little Endian
CPU(s):              192
On-line CPU(s) list: 0-191
Thread(s) per core:  2
Core(s) per socket:  48
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
CPU family:          6
Model:               143
Model name:          Intel(R) Xeon(R) Platinum 8474C
Stepping:            8
CPU MHz:             786.324
BogoMIPS:            4200.00
Virtualization:      VT-x
L1d cache:           48K
L1i cache:           32K
L2 cache:            2048K
L3 cache:            99840K
NUMA node0 CPU(s):   0-47,96-143
NUMA node1 CPU(s):   48-95,144-191
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.0
[pip3] triton==2.3.0
[pip3] vllm-nccl-cu12==2.18.1.0.4.0
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.4.2
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      48-95,144-191   1               N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks