[Bug]: ```--fully-sharded-loras``` doesn't work on V1

[shashwatj07]

Your current environment

The output of python collect_env.py

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 20.04.6 LTS (x86_64)
GCC version                  : (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version                : Could not collect
CMake version                : version 3.26.0
Libc version                 : glibc-2.31

==============================
       PyTorch Info
==============================
PyTorch version              : 2.7.0+cu126
Is debug build               : False
CUDA used to build PyTorch   : 12.6
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-5.15.0-1074-azure-x86_64-with-glibc2.31

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 11.7.99
CUDA_MODULE_LOADING set to   : LAZY
GPU models and configuration : 
GPU 0: NVIDIA A100-SXM4-40GB
GPU 1: NVIDIA A100-SXM4-40GB
GPU 2: NVIDIA A100-SXM4-40GB
GPU 3: NVIDIA A100-SXM4-40GB
GPU 4: NVIDIA A100-SXM4-40GB
GPU 5: NVIDIA A100-SXM4-40GB
GPU 6: NVIDIA A100-SXM4-40GB
GPU 7: NVIDIA A100-SXM4-40GB

Nvidia driver version        : 560.35.03
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.5.0
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
Byte Order:                           Little Endian
Address sizes:                        48 bits physical, 48 bits virtual
CPU(s):                               96
On-line CPU(s) list:                  0-95
Thread(s) per core:                   1
Core(s) per socket:                   48
Socket(s):                            2
NUMA node(s):                         4
Vendor ID:                            AuthenticAMD
CPU family:                           23
Model:                                49
Model name:                           AMD EPYC 7V12 64-Core Processor
Stepping:                             0
CPU MHz:                              2445.441
BogoMIPS:                             4890.88
Hypervisor vendor:                    Microsoft
Virtualization type:                  full
L1d cache:                            3 MiB
L1i cache:                            3 MiB
L2 cache:                             48 MiB
L3 cache:                             384 MiB
NUMA node0 CPU(s):                    0-23
NUMA node1 CPU(s):                    24-47
NUMA node2 CPU(s):                    48-71
NUMA node3 CPU(s):                    72-95
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:               Mitigation; untrained return thunk; SMT disabled
Vulnerability Spec rstack overflow:   Mitigation; safe RET, no microcode
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; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected
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 tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid

==============================
Versions of relevant libraries
==============================
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-cufile-cu12==1.11.1.6
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-ml-py==12.575.51
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvshmem-cu12==3.3.9
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pynvml==12.0.0
[pip3] pyzmq==27.0.0
[pip3] torch==2.7.0+cu126
[pip3] torchaudio==2.7.0+cu126
[pip3] torchvision==0.22.0+cu126
[pip3] transformers==4.53.1
[pip3] triton==3.3.0
[conda] numpy                     2.2.6                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.6.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.6.80                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.6.77                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.6.77                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.5.1.17                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.0.4                 pypi_0    pypi
[conda] nvidia-cufile-cu12        1.11.1.6                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.7.77                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.1.2                 pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.4.2                 pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.6.3                    pypi_0    pypi
[conda] nvidia-ml-py              12.575.51                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.26.2                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.6.85                  pypi_0    pypi
[conda] nvidia-nvshmem-cu12       3.3.9                    pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.6.77                  pypi_0    pypi
[conda] pynvml                    12.0.0                   pypi_0    pypi
[conda] pyzmq                     27.0.0                   pypi_0    pypi
[conda] torch                     2.7.0+cu126              pypi_0    pypi
[conda] torchaudio                2.7.0+cu126              pypi_0    pypi
[conda] torchvision               0.22.0+cu126             pypi_0    pypi
[conda] transformers              4.53.1                   pypi_0    pypi
[conda] triton                    3.3.0                    pypi_0    pypi

==============================
         vLLM Info
==============================
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:
  	�[4mGPU0	GPU1	GPU2	GPU3	GPU4	GPU5	GPU6	GPU7	NIC0	NIC1	NIC2	NIC3	NIC4	NIC5	NIC6	NIC7	CPU Affinity	NUMA Affinity	GPU NUMA ID�[0m
GPU0	 X 	NODE	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	24-47	1		N/A
GPU1	NODE	 X 	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	24-47	1		N/A
GPU2	SYS	SYS	 X 	NODE	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	SYS	SYS	SYS	SYS	0-23	0		N/A
GPU3	SYS	SYS	NODE	 X 	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	SYS	SYS	SYS	SYS	0-23	0		N/A
GPU4	SYS	SYS	SYS	SYS	 X 	NODE	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	SYS	SYS	72-95	3		N/A
GPU5	SYS	SYS	SYS	SYS	NODE	 X 	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	SYS	SYS	72-95	3		N/A
GPU6	SYS	SYS	SYS	SYS	SYS	SYS	 X 	NODE	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	48-71	2		N/A
GPU7	SYS	SYS	SYS	SYS	SYS	SYS	NODE	 X 	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	48-71	2		N/A
NIC0	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	 X 	NODE	SYS	SYS	SYS	SYS	SYS	SYS				
NIC1	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	NODE	 X 	SYS	SYS	SYS	SYS	SYS	SYS				
NIC2	SYS	SYS	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	 X 	NODE	SYS	SYS	SYS	SYS				
NIC3	SYS	SYS	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	NODE	 X 	SYS	SYS	SYS	SYS				
NIC4	SYS	SYS	SYS	SYS	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	 X 	NODE	SYS	SYS				
NIC5	SYS	SYS	SYS	SYS	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	NODE	 X 	SYS	SYS				
NIC6	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	 X 	NODE				
NIC7	SYS	SYS	SYS	SYS	SYS	SYS	NODE	NODE	SYS	SYS	SYS	SYS	SYS	SYS	NODE	 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_ib0
  NIC1: mlx5_ib1
  NIC2: mlx5_ib2
  NIC3: mlx5_ib3
  NIC4: mlx5_ib4
  NIC5: mlx5_ib5
  NIC6: mlx5_ib6
  NIC7: mlx5_ib7

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=all
NVIDIA_REQUIRE_CUDA=cuda>=11.7 brand=tesla,driver>=450,driver<451 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=510,driver<511 brand=unknown,driver>=510,driver<511 brand=nvidia,driver>=510,driver<511 brand=nvidiartx,driver>=510,driver<511 brand=quadro,driver>=510,driver<511 brand=quadrortx,driver>=510,driver<511 brand=titan,driver>=510,driver<511 brand=titanrtx,driver>=510,driver<511 brand=geforce,driver>=510,driver<511 brand=geforcertx,driver>=510,driver<511
NCCL_IB_PCI_RELAXED_ORDERING=1
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
NCCL_VERSION=2.13.4-1
NCCL_SOCKET_IFNAME=eth0
NCCL_DEBUG_SUBSYS=GRAPH,INIT,ENV
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NCCL_DEBUG=INFO
NVIDIA_PRODUCT_NAME=CUDA
PYTORCH_TYPE=stable
NVIDIA_CUDA_END_OF_LIFE=1
CUDA_DEVICE_ORDER=PCI_BUS_ID
CUDA_VERSION=11.7.0
NCCL_IB_TIMEOUT=22
LD_LIBRARY_PATH=/opt/nccl-rdma-sharp-plugins/lib:/opt/hpcx/ompi/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
NCCL_IB_DISABLE=0
OMP_NUM_THREADS=92
PYTORCH_BUILD_VERSION=1.13.1
VLLM_USE_V1=1
NCCL_TOPO_FILE=/opt/microsoft/ndv4-topo.xml
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

vllm serve meta-llama/Llama-3.1-70B -tp 8 --chat-template ./template.jinja --fully-sharded-loras --no-enable-prefix-caching --enable-lora --max-lora-rank 8 --lora-modules meta-llama/Llama-3.1-70B-lora-8-0=/tmp/adapters/meta-llama/Llama-3.1-70B/lora-8-0

Error Stack Trace

Before submitting a new issue...

• Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

[jeejeelee]

See: https://gist.github.com/shashwatj07/4a6b1dd00e80a8e0be6b35f223662c54#file-error-vllm-L2909

After learning the context, It looks like the problem is caused by the LoRA rank being too small, have you tried the larger rank, such as 64?

[shashwatj07]

See: https://gist.github.com/shashwatj07/4a6b1dd00e80a8e0be6b35f223662c54#file-error-vllm-L2909

After learning the context, It looks like the problem is caused by the LoRA rank being too small, have you tried the larger rank, such as 64?

I don't remember exactly. However, the same flag works with the same rank on vLLM V0. Let me know if you want me to check larger ranks on V1.

[jeejeelee]

Are you also using 0.9.2 for your V0 testing? I really appreciate it if you test with a larger --max-lora-rank

[shashwatj07]

Are you also using 0.9.2 for your V0 testing?

Yes

I really appreciate it if you test with a larger --max-lora-rank

I tried, still fails

[github-actions]

This issue has been automatically marked as stale because it has not had any activity within 90 days. It will be automatically closed if no further activity occurs within 30 days. Leave a comment if you feel this issue should remain open. Thank you!