[Bug]: Prefix not correctly propogated through Backend #27045
Description
Your current environment
The output of python collect_env.py
Collecting environment information...
==============================
System Info
==============================
OS : CentOS Stream 9 (x86_64)
GCC version : (GCC) 11.5.0 20240719 (Red Hat 11.5.0-11)
Clang version : 20.1.8 (CentOS 20.1.8-1.el9)
CMake version : version 4.1.0
Libc version : glibc-2.34
==============================
PyTorch Info
==============================
PyTorch version : 2.10.0a0+gitc8c5187
Is debug build : False
CUDA used to build PyTorch : 12.9
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.11 | packaged by Anaconda, Inc. | (main, Jun 5 2025, 13:09:17) [GCC 11.2.0] (64-bit runtime)
Python platform : Linux-6.9.0-0_fbk10_0_gc5fa564d33e3-x86_64-with-glibc2.34
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.9.86
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA H100
GPU 1: NVIDIA H100
GPU 2: NVIDIA H100
GPU 3: NVIDIA H100
GPU 4: NVIDIA H100
GPU 5: NVIDIA H100
GPU 6: NVIDIA H100
GPU 7: NVIDIA H100
Nvidia driver version : 535.183.06
cuDNN version : Probably one of the following:
/usr/lib64/libcudnn.so.8.9.2
/usr/lib64/libcudnn.so.9.9.0
/usr/lib64/libcudnn_adv.so.9.9.0
/usr/lib64/libcudnn_adv_infer.so.8.9.2
/usr/lib64/libcudnn_adv_train.so.8.9.2
/usr/lib64/libcudnn_cnn.so.9.9.0
/usr/lib64/libcudnn_cnn_infer.so.8.9.2
/usr/lib64/libcudnn_cnn_train.so.8.9.2
/usr/lib64/libcudnn_engines_precompiled.so.9.9.0
/usr/lib64/libcudnn_engines_runtime_compiled.so.9.9.0
/usr/lib64/libcudnn_graph.so.9.9.0
/usr/lib64/libcudnn_heuristic.so.9.9.0
/usr/lib64/libcudnn_ops.so.9.9.0
/usr/lib64/libcudnn_ops_infer.so.8.9.2
/usr/lib64/libcudnn_ops_train.so.8.9.2
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
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): 384
On-line CPU(s) list: 0-383
Vendor ID: AuthenticAMD
Model name: AMD EPYC 9654 96-Core Processor
CPU family: 25
Model: 17
Thread(s) per core: 2
Core(s) per socket: 96
Socket(s): 2
Stepping: 1
Frequency boost: enabled
CPU(s) scaling MHz: 84%
CPU max MHz: 3707.8120
CPU min MHz: 1500.0000
BogoMIPS: 4792.60
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 amd_lbr_v2 nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov succor smca fsrm flush_l1d debug_swap
Virtualization: AMD-V
L1d cache: 6 MiB (192 instances)
L1i cache: 6 MiB (192 instances)
L2 cache: 192 MiB (192 instances)
L3 cache: 768 MiB (24 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-95,192-287
NUMA node1 CPU(s): 96-191,288-383
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: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2: Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Not affected; BHI: Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flake8==7.3.0
[pip3] flake8-bugbear==24.12.12
[pip3] flake8-comprehensions==3.16.0
[pip3] flake8-executable==2.1.3
[pip3] flake8-logging-format==2024.24.12
[pip3] flake8-pyi==25.5.0
[pip3] flake8_simplify==0.22.0
[pip3] flashinfer-python==0.4.0
[pip3] mypy==1.16.0
[pip3] mypy_extensions==1.1.0
[pip3] numpy==2.1.0
[pip3] nvidia-cudnn-frontend==1.15.0
[pip3] nvidia-cutlass-dsl==4.2.1
[pip3] nvidia-ml-py==13.580.82
[pip3] optree==0.13.0
[pip3] pytorch-triton==3.5.0+git7416ffcb
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0a0+gitc8c5187
[pip3] torchvision==0.25.0a0+f5c6c2e
[pip3] transformers==4.57.0
[pip3] triton==3.4.0
[conda] flashinfer-python 0.4.0 pypi_0 pypi
[conda] mkl-include 2025.2.0 pypi_0 pypi
[conda] mkl-static 2025.2.0 pypi_0 pypi
[conda] numpy 2.1.0 pypi_0 pypi
[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi
[conda] nvidia-cutlass-dsl 4.2.1 pypi_0 pypi
[conda] nvidia-ml-py 13.580.82 pypi_0 pypi
[conda] optree 0.13.0 pypi_0 pypi
[conda] pytorch-triton 3.5.0+git7416ffcb pypi_0 pypi
[conda] pyzmq 27.1.0 pypi_0 pypi
[conda] torch 2.10.0a0+gitc8c5187 pypi_0 pypi
[conda] torchfix 0.4.0 pypi_0 pypi
[conda] torchvision 0.25.0a0+f5c6c2e dev_0 <develop>
[conda] transformers 4.57.0 pypi_0 pypi
[conda] triton 3.4.0 pypi_0 pypi
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.1.dev10334+g5bc26c438.d20251010 (git sha: 5bc26c438, date: 20251010)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 NODE NODE SYS SYS 0-95,192-287 0 N/A
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 PHB PHB SYS SYS 0-95,192-287 0 N/A
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 NODE NODE SYS SYS 0-95,192-287 0 N/A
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 NODE NODE SYS SYS 0-95,192-287 0 N/A
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS NODE NODE 96-191,288-383 1 N/A
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS NODE NODE 96-191,288-383 1 N/A
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS PHB PHB 96-191,288-383 1 N/A
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS NODE NODE 96-191,288-383 1 N/A
NIC0 NODE PHB NODE NODE SYS SYS SYS SYS X PIX SYS SYS
NIC1 NODE PHB NODE NODE SYS SYS SYS SYS PIX X SYS SYS
NIC2 SYS SYS SYS SYS NODE NODE PHB NODE SYS SYS X PIX
NIC3 SYS SYS SYS SYS NODE NODE PHB NODE 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
NIC2: mlx5_2
NIC3: mlx5_3
==============================
Environment Variables
==============================
CUDA_CACHE_PATH=/data/users/lucaskabela/.nv/ComputeCache
CUDA_VERSION=12.9
MAX_JOBS=382
CUDA_NVCC_EXECUTABLE=/usr/local/cuda-12.9/bin/nvcc
LD_LIBRARY_PATH=/usr/local/cuda-12.9/lib64:/home/lucaskabela/temp/OCR/resources/lm/video_asr_tools/tools/openfst/src
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_lucaskabela```
🐛 Describe the bug
While investigating an error with set_model_tag, I tried to use prefix around torch.compile to have modules written to different caching directories
However, prefix always is set to backbone in
vllm/vllm/compilation/backends.py
Line 520 in 7bb736d
This is because looking at callsites of VllmBackend, outside of deserialize_compile_artificats, prefix is not passed
We should modify the callsites in order to forward prefix so that when initializing a model from cold start we respect this setting
vllm/vllm/config/compilation.py
Line 664 in 7bb736d
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