[Usage]: Streaming response

Your current environment

PyTorch version: N/A
Is debug build: N/A
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04 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: 3.12.3 (main, Apr 10 2024, 05:33:47) [GCC 13.2.0] (64-bit runtime)
Python platform: Linux-6.8.0-1012-aws-x86_64-with-glibc2.39
Is CUDA available: N/A
CUDA runtime version: 12.0.140
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration:
GPU 0: Tesla T4
GPU 1: Tesla T4
GPU 2: Tesla T4
GPU 3: Tesla T4

Nvidia driver version: 555.42.06
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: N/A

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):                               48
On-line CPU(s) list:                  0-47
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz
CPU family:                           6
Model:                                85
Thread(s) per core:                   2
Core(s) per socket:                   24
Socket(s):                            1
Stepping:                             7
BogoMIPS:                             4999.98
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 arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch pti fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves ida arat pku ospke avx512_vnni
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            768 KiB (24 instances)
L1i cache:                            768 KiB (24 instances)
L2 cache:                             24 MiB (24 instances)
L3 cache:                             35.8 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-47
Vulnerability Gather data sampling:   Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit:          KVM: Mitigation: VMX unsupported
Vulnerability L1tf:                   Mitigation; PTE Inversion
Vulnerability Mds:                    Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown:               Mitigation; PTI
Vulnerability Mmio stale data:        Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Vulnerable
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Vulnerable
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Retpoline
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] No relevant packages
[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:
GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PHB     PHB     PHB     0-47    0               N/A
GPU1    PHB      X      PHB     PHB     0-47    0               N/A
GPU2    PHB     PHB      X      PHB     0-47    0               N/A
GPU3    PHB     PHB     PHB      X      0-47    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

How would you like to use vllm

I am working on a rag app where I use LLMs to analyze various documents. I'm looking to improve the ux by streaming responses in real time. a snippet of my code:

params = SamplingParams(temperature=TEMPERATURE, 
                        min_tokens=128, 
                        max_tokens=1024)
llm = LLM(MODEL_NAME, 
          tensor_parallel_size=4, 
          dtype="half", 
          gpu_memory_utilization=0.5, 
          max_model_len=27_000)

message = SYSTEM_PROMPT + "\n\n" + f"Question: {question}\n\nDocument: {document}"

response = llm.generate(message, params)

In its current form, generatemethod waits untiş the entire response is generated. I'd like to change this so that responses are streamed and displayed incrementally to the user, enhancing interactivity.

Does anyone have experience with implementing streaming for LLMs? Any guidance or examples would be appreciated!

vllm-project / vllm

[Usage]: Streaming response #6940

Your current environment

How would you like to use vllm