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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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[Usage]: Total generated tokens in benchmarking script #8769

Open double-vin opened 1 week ago

double-vin commented 1 week ago

Your current environment

Collecting environment information...
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.30.0
Libc version: glibc-2.35

Python version: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-4.18.0-372.9.1.el8.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A800 80GB PCIe
GPU 1: NVIDIA A800 80GB PCIe
GPU 2: NVIDIA A800 80GB PCIe
GPU 3: NVIDIA A800 80GB PCIe
GPU 4: NVIDIA A800 80GB PCIe
GPU 5: NVIDIA A800 80GB PCIe
GPU 6: NVIDIA A800 80GB PCIe
GPU 7: NVIDIA A800 80GB PCIe

Nvidia driver version: 535.54.03
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:                   43 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          128
On-line CPU(s) list:             0-127
Vendor ID:                       HygonGenuine
BIOS Vendor ID:                  Chengdu Hygon
Model name:                      Hygon C86 7385 32-core Processor
BIOS Model name:                 Hygon C86 7385 32-core Processor
CPU family:                      24
Model:                           2
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
Stepping:                        2
BogoMIPS:                        3999.97
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sme sev sev_es
Virtualization:                  AMD-V
L1d cache:                       2 MiB (64 instances)
L1i cache:                       4 MiB (64 instances)
L2 cache:                        32 MiB (64 instances)
L3 cache:                        128 MiB (16 instances)
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NUMA node0 CPU(s):               0-7,64-71
NUMA node1 CPU(s):               8-15,72-79
NUMA node2 CPU(s):               16-23,80-87
NUMA node3 CPU(s):               24-31,88-95
NUMA node4 CPU(s):               32-39,96-103
NUMA node5 CPU(s):               40-47,104-111
NUMA node6 CPU(s):               48-55,112-119
NUMA node7 CPU(s):               56-63,120-127
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          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; Retpolines, IBPB conditional, STIBP disabled, RSB filling
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==12.555.43
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.5.82
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] sentence-transformers==3.0.1
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.44.2
[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              12.555.43                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.5.82                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.1.105                 pypi_0    pypi
[conda] pyzmq                     26.2.0                   pypi_0    pypi
[conda] sentence-transformers     3.0.1                    pypi_0    pypi
[conda] torch                     2.4.0                    pypi_0    pypi
[conda] torchvision               0.19.0                   pypi_0    pypi
[conda] transformers              4.44.2                   pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.1.post2@9ba0817ff1eb514f51cc6de9cb8e16c98d6ee44f
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PIX     PXB     PXB     SYS     SYS     SYS     SYS     SYS     SYS     24-31,88-95     3               N/A
GPU1    PIX      X      PXB     PXB     SYS     SYS     SYS     SYS     SYS     SYS     24-31,88-95     3               N/A
GPU2    PXB     PXB      X      PXB     SYS     SYS     SYS     SYS     SYS     SYS     24-31,88-95     3               N/A
GPU3    PXB     PXB     PXB      X      SYS     SYS     SYS     SYS     SYS     SYS     24-31,88-95     3               N/A
GPU4    SYS     SYS     SYS     SYS      X      PIX     PXB     PXB     SYS     SYS     56-63,120-127   7               N/A
GPU5    SYS     SYS     SYS     SYS     PIX      X      PXB     PXB     SYS     SYS     56-63,120-127   7               N/A
GPU6    SYS     SYS     SYS     SYS     PXB     PXB      X      PXB     SYS     SYS     56-63,120-127   7               N/A
GPU7    SYS     SYS     SYS     SYS     PXB     PXB     PXB      X      SYS     SYS     56-63,120-127   7               N/A
NIC0    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS      X      PIX
NIC1    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX      X

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

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1

How would you like to use vllm

Example of command: python benchmark_serving.py --model /models/Llama-2-7b-chat-hf/ --dataset-name random --trust-remote-code --num-prompts 1 --radom-input-len 1024 --random-output-len 1024 Output: ============ Serving Benchmark Result ============ Successful requests: 1 Benchmark duration (s): 18.68 Total input tokens: 1024 Total generated tokens: 699 Request throughput (req/s): 0.05 Output token throughput (tok/s): 37.42 Total Token throughput (tok/s): 92.23 ---------------Time to First Token---------------- Mean TTFT (ms): 89.88 Median TTFT (ms): 89.88 P99 TTFT (ms): 89.88 -----Time per Output Token (excl. 1st token)------ Mean TPOT (ms): 26.63 Median TPOT (ms): 26.63 P99 TPOT (ms): 26.63 ---------------Inter-token Latency---------------- Mean ITL (ms): 26.63 Median ITL (ms): 26.57 P99 ITL (ms): 27.49

How does the Total generated tokens value here match with -- random output len 1024? I want to compare its performance with benchmark_throughput. py

Before submitting a new issue...

hmellor commented 1 week ago

This is because output_len maps to max_tokens in async_request_openai_completions from https://github.com/vllm-project/vllm/blob/main/benchmarks/backend_request_func.py, which makes it an upper bound rather than a guarantee.

If min_tokens or ignore_eos were added to the payload, then the model would generate exactly the number of tokens requested.

mgoin commented 1 week ago

@hmellor I wasn't aware of this thanks for highlighting - I think it would be best if we enforced the output_len to min_tokens as well to have clarity between deployments.

hmellor commented 1 week ago

Sounds good, it probably makes sense to do this for all the backends for consistency.

ywang96 commented 1 week ago

To clarify, back then when we worked on this serving benchmark, min_tokens wasn't (and still isn't) an option for multiple backends, and in reality downstream tasks are more likely to specify max_tokens in their payload (at least that's what I have observed in practice).

Perhaps we could open up the option to let user specify their sampling params but this does mean we need to keep track what's available for each backend internally, which adds some maintenance overhead.

hmellor commented 6 days ago

min_tokens wasn't (and still isn't) an option for multiple backends

Do they not support ignore_eos?

in reality downstream tasks are more likely to specify max_tokens in their payload

Agreed, but for benchmarking, does it not make more sense to generate the number of tokens specified by output_len? Either that or rename it to max_output_len to better represent what it actually does?

double-vin commented 5 days ago

When I use the qwen model, I still cannot control the Total generated tokens by adding min_tokens to the payload. Example of command: python benchmark_serving.py --model /models/Qwen1.5-7B-Chat --dataset-name random --trust-remote-code --num-prompts 1 --random-input-len 1024 --random-output-len 1024 Output: ============ Serving Benchmark Result ============ Successful requests: 1 Benchmark duration (s): 28.40 Total input tokens: 1024 Total generated tokens: 23

hmellor commented 4 days ago

Could you share exactly what change you made to add min_tokens to the payload? And could you try adding ignore_eos instead?

zonghuaxiansheng commented 2 days ago

When I use the qwen model, I still cannot control the Total generated tokens by adding min_tokens to the payload. Example of command: python benchmark_serving.py --model /models/Qwen1.5-7B-Chat --dataset-name random --trust-remote-code --num-prompts 1 --random-input-len 1024 --random-output-len 1024 Output: ============ Serving Benchmark Result ============ Successful requests: 1 Benchmark duration (s): 28.40 Total input tokens: 1024 Total generated tokens: 23

I also encountered the same issue. I found that the server did indeed generate --random-output-len number of output tokens, but decoding of some tokens resulted in empty characters, which leads to a smaller count of output tokens on the client.