[Bug]: vllm will return empty string when request.stop is not null #11089
Description
Your current environment
OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k
Collecting environment information...
OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k
WARNING 12-11 15:59:05 _custom_ops.py:19] Failed to import from vllm._C with ModuleNotFoundError("No module named 'vllm._C'")
/root/vllm/vllm/connections.py:8: RuntimeWarning: Failed to read commit hash:
No module named 'vllm._version'
from vllm.version import version as VLLM_VERSION
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: Alibaba Cloud Linux release 3 (Soaring Falcon) (x86_64)
GCC version: (GCC) 10.2.1 20200825 (Alibaba 10.2.1-3.8 2.32)
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.32
Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.10.134-ruogui-af7e6c9e-x86_64-with-glibc2.32
Is CUDA available: True
CUDA runtime version: 12.5.40
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA H20
Nvidia driver version: 550.90.07
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
架构： x86_64
CPU 运行模式： 32-bit, 64-bit
字节序： Little Endian
CPU: 16
在线 CPU 列表： 0-15
每个核的线程数： 2
每个座的核数： 8
座： 1
NUMA 节点： 1
厂商 ID： GenuineIntel
CPU 系列： 6
型号： 143
型号名称： 06/8f
步进： 8
CPU MHz： 2600.000
BogoMIPS： 5200.00
超管理器厂商： KVM
虚拟化类型： 完全
L1d 缓存： 48K
L1i 缓存： 32K
L2 缓存： 2048K
L3 缓存： 99840K
NUMA 节点0 CPU： 0-15
标记： fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat clflush dts mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc bts rep_good nopl nonstop_tsc cpuid tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tdx_guest fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid bus_lock_detect cldemote movdiri movdir64b fsrm uintr md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Versions of relevant libraries:
[pip3] mypy-protobuf==3.6.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[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==12.535.161
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.77
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.45.2
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.0.0
[conda] numpy 1.26.4 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.4.5.8 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 12.535.161 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.6.77 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 4.45.2 pypi_0 pypi
[conda] transformers-stream-generator 0.0.5 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 (dev)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X 0-15 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
Model Input Dumps
No response
🐛 Describe the bug
when send a request with stop str, vllm will return several empty string at the beginning of inference with Stream=True.
when send a request without stop str, vllm will return non-empty string at the beginning of inference with Stream=True.
why?
I checked the code and found a problem. This is a function located in file vllm/vllm/sequence.py.
def get_output_text_to_return(self, buffer_length: int,
delta: bool) -> str:
"""If delta is True, only new text since the last call to
this method is returned"""
# We return the full output text if the sequence is finished.
truncate = buffer_length and not self.is_finished()
if not delta:
return self.output_text[:-buffer_length] if truncate else (
self.output_text)
length = len(self.output_text)
if truncate:
# if buffer_len > length, then length <0 after truncate
length -= buffer_length
last_offset = self._last_output_text_offset
if last_offset < length:
self._last_output_text_offset = length
return self.output_text[last_offset:length]
return ""
buffer_length is set:
# Number of characters to hold back for stop string evaluation
# until sequence is finished.
if self.stop and not self.include_stop_str_in_output:
self.output_text_buffer_length = max(len(s) for s in self.stop) - 1
When send a request with stop str, the variable buffer_length will greater than 0. So if buffer_length>length, an empty string will be returned instead of the newly generated token. This will seriously affect TTFT.
related pr:
https://github.com/vllm-project/vllm/pull/8335/files#diff-9d1cd5050a7ec1e588cae646f3f95ca134cffbd8b41d6655c6a14de70117b869 @njhill
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