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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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[Performance]: Extremely low throughput #8847

Closed pminervini closed 2 weeks ago

pminervini commented 2 weeks ago

Proposal to improve performance

Hi all, I just started a vLLM server instance as follows:

python3 -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-8B-Instruct --enforce-eager --max-num-seqs 16

However, the throughput seems to be extremely low (0.8, 0.9 token/s):

INFO:     2.36.8.92:56164 - "POST /v1/chat/completions HTTP/1.1" 200 OK
INFO 09-26 11:35:23 engine.py:288] Added request chat-cc6296ad51b74d88bf17ba75459c06aa.
INFO 09-26 11:35:25 metrics.py:351] Avg prompt throughput: 2.3 tokens/s, Avg generation throughput: 0.2 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.1%, CPU KV cache usage: 0.0%.
INFO 09-26 11:35:31 metrics.py:351] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%.
INFO 09-26 11:35:37 metrics.py:351] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%.
INFO 09-26 11:35:43 metrics.py:351] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%.
INFO 09-26 11:35:49 metrics.py:351] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.3%, CPU KV cache usage: 0.0%.
INFO 09-26 11:35:55 metrics.py:351] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.3%, CPU KV cache usage: 0.0%.
INFO 09-26 11:36:01 metrics.py:351] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.3%, CPU KV cache usage: 0.0%.
INFO 09-26 11:36:08 metrics.py:351] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.5%, CPU KV cache usage: 0.0%.
INFO 09-26 11:36:14 metrics.py:351] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.5%, CPU KV cache usage: 0.0%.
INFO 09-26 11:36:20 metrics.py:351] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 0 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%.
INFO 09-26 11:36:20 logger.py:36] Received request chat-3a7f5f25941f491ebe029b3e6b9771b8: prompt: '<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHere is the query:\nHey how are you?\n\nCreate a concise, 3-5 word phrase with an emoji as a title for the previous query. Suitable Emojis for the summary can be used to enhance understanding but avoid quotation marks or special formatting. RESPOND ONLY WITH THE TITLE TEXT.\n\nExamples of titles:\n📉 Stock Market Trends\n🍪 Perfect Chocolate Chip Recipe\nEvolution of Music Streaming\nRemote Work Productivity Tips\nArtificial Intelligence in Healthcare\n🎮 Video Game Development Insights<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n', params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=[], stop_token_ids=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=50, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None), prompt_token_ids: [128000, 128006, 882, 128007, 271, 8586, 374, 279, 3319, 512, 19182, 1268, 527, 499, 1980, 4110, 264, 64694, 11, 220, 18, 12, 20, 3492, 17571, 449, 459, 43465, 439, 264, 2316, 369, 279, 3766, 3319, 13, 86346, 5867, 84528, 369, 279, 12399, 649, 387, 1511, 311, 18885, 8830, 719, 5766, 55331, 15785, 477, 3361, 37666, 13, 46577, 38539, 27785, 4874, 3247, 49673, 16139, 382, 41481, 315, 15671, 512, 9468, 241, 231, 12937, 8152, 50730, 198, 9468, 235, 103, 24118, 39520, 32013, 26371, 198, 35212, 3294, 315, 10948, 45910, 198, 25732, 5664, 5761, 1968, 26788, 198, 9470, 16895, 22107, 304, 39435, 198, 9468, 236, 106, 8519, 4140, 11050, 73137, 128009, 128006, 78191, 128007, 271], lora_request: None, prompt_adapter_request: None.
INFO 09-26 11:36:20 engine.py:288] Added request chat-3a7f5f25941f491ebe029b3e6b9771b8.

What could be the cause? The underlying GPU is a NVIDIA RTX 4000 -- I tried switching from bf16 to fp16 but the issue persists. Thanks!

Report of performance regression

No response

Misc discussion on performance

No response

Your current environment (if you think it is necessary)

Collecting environment information...
WARNING 09-26 11:41:07 _custom_ops.py:18] Failed to import from vllm._C with ModuleNotFoundError("No module named 'vllm._C'")
/home/pasquale/vllm/vllm/connections.py:8: RuntimeWarning: Failed to read commit hash:
No module named 'vllm._version'
  from vllm.version import __version__ as VLLM_VERSION

CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.1 LTS (x86_64)
GCC version: (Ubuntu 13.2.0-23ubuntu4) 13.2.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.39

Python version: 3.12.4 | packaged by Anaconda, Inc. | (main, Jun 18 2024, 15:12:24) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.8.0-41-generic-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: 12.0.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA RTX 4000 SFF Ada Generation
Nvidia driver version: 535.183.01
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
Address sizes:                        46 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               20
On-line CPU(s) list:                  0-19
Vendor ID:                            GenuineIntel
Model name:                           13th Gen Intel(R) Core(TM) i5-13500
CPU family:                           6
Model:                                191
Thread(s) per core:                   2
Core(s) per socket:                   14
Socket(s):                            1
Stepping:                             2
CPU(s) scaling MHz:                   59%
CPU max MHz:                          4800.0000
CPU min MHz:                          800.0000
BogoMIPS:                             4992.00
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 sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities
Virtualization:                       VT-x
L1d cache:                            544 KiB (14 instances)
L1i cache:                            704 KiB (14 instances)
L2 cache:                             11.5 MiB (8 instances)
L3 cache:                             24 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-19
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Mitigation; Clear Register File
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 BHI_DIS_S
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.68
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.45.0
[pip3] triton==3.0.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-cublas-cu12        12.1.3.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.1.105                 pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.1.0.70                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.0.2.54                pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.2.106               pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.4.5.107               pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.1.0.106               pypi_0    pypi
[conda] nvidia-ml-py              12.560.30                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.6.68                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.1.105                 pypi_0    pypi
[conda] pyzmq                     26.2.0                   pypi_0    pypi
[conda] torch                     2.4.0                    pypi_0    pypi
[conda] torchvision               0.19.0                   pypi_0    pypi
[conda] transformers              4.45.0                   pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi
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:
GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-19    0               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

Before submitting a new issue...

pminervini commented 2 weeks ago

It was a GPU issue :) from nvidia-smi -q:

    GPU Reset Status
        Reset Required                    : Yes
        Drain and Reset Recommended       : No

Now it's up to 20 tokens/s!