[Bug]: VLLM config not set when using Flash Infer backend.

[timzsu]

Your current environment

The output of `python collect_env.py`

INFO 02-12 20:54:10 __init__.py:190] Automatically detected platform cuda.
Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
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 3.31.4
Libc version: glibc-2.35

Python version: 3.12.9 (main, Feb  5 2025, 08:49:00) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-125-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA H20
GPU 1: NVIDIA H20
GPU 2: NVIDIA H20
GPU 3: NVIDIA H20

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
Address sizes:                   52 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          176
On-line CPU(s) list:             0-175
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Platinum 8458P
CPU family:                      6
Model:                           143
Thread(s) per core:              2
Core(s) per socket:              44
Socket(s):                       2
Stepping:                        8
CPU max MHz:                     3800.0000
CPU min MHz:                     800.0000
BogoMIPS:                        5400.00
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 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 invpcid_single cdp_l2 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 avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear pconfig flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       4.1 MiB (88 instances)
L1i cache:                       2.8 MiB (88 instances)
L2 cache:                        176 MiB (88 instances)
L3 cache:                        165 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-43,88-131
NUMA node1 CPU(s):               44-87,132-175
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   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
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-ml-py==12.570.86
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pyzmq==26.2.1
[pip3] torch==2.5.1
[pip3] torchaudio==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.48.2
[pip3] triton==3.1.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.7.2
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
�[4mGPU0	GPU1	GPU2	GPU3	NIC0	NIC1	NIC2	NIC3	CPU Affinity	NUMA Affinity	GPU NUMA ID�[0m
GPU0	 X 	NV18	NV18	NV18	PIX	SYS	SYS	SYS	0-43,88-131	0		N/A
GPU1	NV18	 X 	NV18	NV18	SYS	SYS	SYS	SYS	0-43,88-131	0		N/A
GPU2	NV18	NV18	 X 	NV18	SYS	SYS	SYS	SYS	0-43,88-131	0		N/A
GPU3	NV18	NV18	NV18	 X 	SYS	SYS	SYS	SYS	44-87,132-175	1		N/A
NIC0	PIX	SYS	SYS	SYS	 X 	SYS	SYS	SYS				
NIC1	SYS	SYS	SYS	SYS	SYS	 X 	SYS	SYS				
NIC2	SYS	SYS	SYS	SYS	SYS	SYS	 X 	SYS				
NIC3	SYS	SYS	SYS	SYS	SYS	SYS	SYS	 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
  NIC2: mlx5_2
  NIC3: mlx5_bond_0

NCCL_VERSION=2.17.1-1
NVIDIA_PRODUCT_NAME=CUDA
VLLM_USAGE_SOURCE=production-docker-image
NVIDIA_CUDA_END_OF_LIFE=1
CUDA_VERSION=12.1.0
LD_LIBRARY_PATH=/usr/local/nvidia/lib64
NCCL_CUMEM_ENABLE=0
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

Bug: With flashinfer backend, a warning Current VLLM config is not set. is constantly raised during cuda graph capturing. This warning is not supposed to exist when we run the end-to-end LLM inference.

A Minimal Working Example

from vllm import LLM, SamplingParams
prompts = ["Hello, my name is",]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="facebook/opt-125m")
outputs = llm.generate(prompts, sampling_params)

Store the above code in mwe.py, then run

TORCH_CUDA_ARCH_LIST="9.0" VLLM_ATTENTION_BACKEND=FLASHINFER python mwe.py

Observed Result: During cuda graph capturing, WARNING 02-12 21:04:28 config.py:3432] Current VLLM config is not set. is repeatedly raised.

Bug Analysis and Proposed Fix

The bug is related to a recent flashinfer update (#11194). After this update, the flashinfer backend uses get_current_vllm_config() to fetch the vllm config stored as a global variable in vllm/config.py. However, the global variable is not set beforehand, which results in the failure of fetching this config when instantiating the flashinfer backend.

I propose to retrieve the vllm_config from the input instances instead of the global variable. Specifically, change https://github.com/vllm-project/vllm/blob/main/vllm/attention/backends/flashinfer.py#L187 and https://github.com/vllm-project/vllm/blob/main/vllm/attention/backends/flashinfer.py#L613 from

self.vllm_config = get_current_vllm_config()

to

self.vllm_config = self.runner.vllm_config

I tested the above modifications and found that the warning disappeared as expected. And I would like to create a PR for the fix.

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