[Bug]:AssertionError: assert self._connector_metadata is not None when using graph mode

[harrisonyhq]

Your current environment

The output of python collect_env.py

Your output of `python collect_env.py` here

Collecting environment information...

    System Info

==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version : Could not collect
CMake version : version 4.1.0
Libc version : glibc-2.35

==============================
PyTorch Info

PyTorch version : 2.8.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A

==============================
Python Environment

Python version : 3.12.11 (main, Jun 4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-5.15.0-107-generic-x86_64-with-glibc2.35

==============================
CUDA / GPU Info

Is CUDA available : True
CUDA runtime version : 12.8.93
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
GPU 4: NVIDIA H20
GPU 5: NVIDIA H20
GPU 6: NVIDIA H20
GPU 7: NVIDIA H20

Nvidia driver version : 535.183.01
cuDNN version : Could not collect
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: 46 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
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 intel_ppin 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 split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 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 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 Retbleed: Not affected
Vulnerability Spec rstack overflow: 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; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

==============================
Versions of relevant libraries

[pip3] flashinfer-python==0.3.1
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.14.1
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-ml-py==12.575.51
[pip3] nvidia-nccl-cu12==2.27.3
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pynvml==12.0.0
[pip3] pyzmq==27.1.0
[pip3] torch==2.8.0+cu128
[pip3] torchaudio==2.8.0+cu128
[pip3] torchvision==0.23.0+cu128
[pip3] transformers==4.57.0
[pip3] triton==3.4.0
[conda] Could not collect

==============================
vLLM Info

ROCM Version : Could not collect
vLLM Version : 0.11.0
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 PIX PIX NODE NODE SYS SYS SYS SYS 0-43,88-131 0 N/A
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 NODE NODE PIX PIX SYS SYS SYS SYS 0-43,88-131 0 N/A
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 NODE NODE NODE NODE SYS SYS SYS SYS 0-43,88-131 0 N/A
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 NODE NODE NODE NODE SYS SYS SYS SYS 0-43,88-131 0 N/A
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS SYS SYS PIX PIX NODE NODE 44-87,132-175 1 N/A
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS SYS SYS NODE NODE PIX PIX 44-87,132-175 1 N/A
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS SYS SYS NODE NODE NODE NODE 44-87,132-175 1 N/A
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS SYS SYS NODE NODE NODE NODE 44-87,132-175 1 N/A
NIC0 PIX NODE NODE NODE SYS SYS SYS SYS X PIX NODE NODE SYS SYS SYS SYS
NIC1 PIX NODE NODE NODE SYS SYS SYS SYS PIX X NODE NODE SYS SYS SYS SYS
NIC2 NODE PIX NODE NODE SYS SYS SYS SYS NODE NODE X PIX SYS SYS SYS SYS
NIC3 NODE PIX NODE NODE SYS SYS SYS SYS NODE NODE PIX X SYS SYS SYS SYS
NIC4 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS X PIX NODE NODE
NIC5 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS PIX X NODE NODE
NIC6 SYS SYS SYS SYS NODE PIX NODE NODE SYS SYS SYS SYS NODE NODE X PIX
NIC7 SYS SYS SYS SYS NODE PIX NODE NODE SYS SYS SYS SYS NODE NODE 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
NIC4: mlx5_4
NIC5: mlx5_5
NIC6: mlx5_6
NIC7: mlx5_7

==============================
Environment Variables

NVIDIA_VISIBLE_DEVICES=all
NVIDIA_REQUIRE_CUDA=cuda>=12.8 brand=unknown,driver>=470,driver<471 brand=grid,driver>=470,driver<471 brand=tesla,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=vapps,driver>=470,driver<471 brand=vpc,driver>=470,driver<471 brand=vcs,driver>=470,driver<471 brand=vws,driver>=470,driver<471 brand=cloudgaming,driver>=470,driver<471 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566
NCCL_VERSION=2.25.1-1
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NVIDIA_PRODUCT_NAME=CUDA
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.8.1
LD_LIBRARY_PATH=/usr/local/cuda/lib64
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

When we tried to use the SharedStorageConnector, according to the official document, we run examples/offline_inference/disaggregated-prefill-v1/run.sh,
If not using graph mode with enforce_eager = True, there is no problem; However, if we remove the enforce_eager = True, using the graph mode, there occurs an error as following:

2025-10-12 20:12:59,984 - INFO - autotuner.py:262 - flashinfer.jit: [Autotuner]: Autotuning process ends
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 67/67 [00:02<00:00, 31.70it/s]
Capturing CUDA graphs (decode, FULL):   0%|                                                                                                                                                                                                                                        | 0/67 [00:00<?, ?it/s]
[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/examples/offline_inference/disaggregated-prefill-v1/prefill_example.py", line 58, in <module>
[rank0]:     main()
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/examples/offline_inference/disaggregated-prefill-v1/prefill_example.py", line 24, in main
[rank0]:     llm = LLM(
[rank0]:           ^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/entrypoints/llm.py", line 297, in __init__
[rank0]:     self.llm_engine = LLMEngine.from_engine_args(
[rank0]:                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/v1/engine/llm_engine.py", line 177, in from_engine_args
[rank0]:     return cls(vllm_config=vllm_config,
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/v1/engine/llm_engine.py", line 114, in __init__
[rank0]:     self.engine_core = EngineCoreClient.make_client(
[rank0]:                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/v1/engine/core_client.py", line 82, in make_client
[rank0]:     return InprocClient(vllm_config, executor_class, log_stats)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/v1/engine/core_client.py", line 245, in __init__
[rank0]:     self.engine_core = EngineCore(*args, **kwargs)
[rank0]:                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/v1/engine/core.py", line 92, in __init__
[rank0]:     self._initialize_kv_caches(vllm_config)
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/v1/engine/core.py", line 207, in _initialize_kv_caches
[rank0]:     self.model_executor.initialize_from_config(kv_cache_configs)
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/v1/executor/abstract.py", line 75, in initialize_from_config
[rank0]:     self.collective_rpc("compile_or_warm_up_model")
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/executor/uniproc_executor.py", line 83, in collective_rpc
[rank0]:     return [run_method(self.driver_worker, method, args, kwargs)]
[rank0]:             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/utils/__init__.py", line 3122, in run_method
[rank0]:     return func(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/v1/worker/gpu_worker.py", line 344, in compile_or_warm_up_model
[rank0]:     cuda_graph_memory_bytes = self.model_runner.capture_model()
[rank0]:                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/v1/worker/gpu_model_runner.py", line 3463, in capture_model
[rank0]:     self._capture_cudagraphs(
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/v1/worker/gpu_model_runner.py", line 3523, in _capture_cudagraphs
[rank0]:     self._dummy_run(num_tokens,
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py", line 120, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/v1/worker/gpu_model_runner.py", line 3152, in _dummy_run
[rank0]:     outputs = self.model(
[rank0]:               ^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/compilation/cuda_graph.py", line 121, in __call__
[rank0]:     return self.runnable(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/model_executor/models/qwen2.py", line 505, in forward
[rank0]:     hidden_states = self.model(input_ids, positions, intermediate_tensors,
[rank0]:                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/compilation/decorators.py", line 317, in __call__
[rank0]:     model_output = self.forward(*args, **kwargs)
[rank0]:                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/model_executor/models/qwen2.py", line 341, in forward
[rank0]:     def forward(
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/_dynamo/eval_frame.py", line 375, in __call__
[rank0]:     return super().__call__(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/_dynamo/eval_frame.py", line 929, in _fn
[rank0]:     return fn(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/fx/graph_module.py", line 848, in call_wrapped
[rank0]:     return self._wrapped_call(self, *args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/fx/graph_module.py", line 424, in __call__
[rank0]:     raise e
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/fx/graph_module.py", line 411, in __call__
[rank0]:     return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "<eval_with_key>.58", line 212, in forward
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/fx/graph_module.py", line 848, in call_wrapped
[rank0]:     return self._wrapped_call(self, *args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/fx/graph_module.py", line 424, in __call__
[rank0]:     raise e
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/fx/graph_module.py", line 411, in __call__
[rank0]:     return super(self.cls, obj).__call__(*args, **kwargs)  # type: ignore[misc]
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1784, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "<eval_with_key>.2", line 5, in forward
[rank0]:   File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 1243, in __call__
[rank0]:     return self._op(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/attention/layer.py", line 624, in unified_attention_with_output
[rank0]:     maybe_save_kv_layer_to_connector(layer_name, kv_cache)
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/attention/layer.py", line 556, in maybe_save_kv_layer_to_connector
[rank0]:     connector.save_kv_layer(layer_name, kv_cache_layer,
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/distributed/kv_transfer/kv_connector/v1/shared_storage_connector.py", line 224, in save_kv_layer
[rank0]:     connector_metadata = self._get_connector_metadata()
[rank0]:                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/yinhanqiu1/workspace/vllm_110_adapt/vllm-110/vllm/vllm/distributed/kv_transfer/kv_connector/v1/base.py", line 132, in _get_connector_metadata
[rank0]:     assert self._connector_metadata is not None
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: AssertionError
[rank0]:[W1012 20:13:03.871606257 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
INFO 10-12 20:13:07 [__init__.py:216] Automatically detected platform cuda.
Error: output.txt file not found

Does any one knows what result in this error? How to fix it?

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[harrisonyhq]

I found that this error may have some relationship with CUDA Graph mode, after referred to the latest documents, I found the CUDA Graph mode now is updated and maybe need some extra config when running with kv cache connector. So I tried to add compilation_config in LLM args as following and it works:

llm = LLM(
        model="meta-llama/Llama-3.2-1B-Instruct",
        enforce_eager=True,
        gpu_memory_utilization=0.8,
        max_num_batched_tokens=64,
        max_num_seqs=16,
        kv_transfer_config=KVTransferConfig(
            kv_connector="SharedStorageConnector",
            kv_role="kv_both",
            kv_connector_extra_config={"shared_storage_path": "local_storage"},
        ),
        compilation_config = {"cudagraph_mode": "PIECEWISE"}
    )  # , max_model_len=2048, max_num_batched_tokens=2048)

Can anyone explain this to me if this is a properate correction?

[harrisonyhq]

@mgoin
Hello there, I tried to make the kv connector also compatible with FULL_AND_PIECEWISE CudagraphModes. I found that if CudagraphModes is FULL_AND_PIECEWISE, in _capture_cudagraphs, _dummy_run will conduct fully attention operations and in unified_attention_with_output, will try to save kv layer to connector, but in save_kv_layer in SharedStorageConnector, connector_metadata = self._get_connector_metadata() will fail due to not calling the maybe_get_kv_connector_output, in which we bind_connector_metadata.
I tried to solve this problem by following:
In /vllm/attention/layer.py：

# Add import of vllm.compilation.monitor at the beginning of the file

from vllm.compilation import monitor


def maybe_save_kv_layer_to_connector(
    layer_name: str,
    kv_cache_layer: List[torch.Tensor],
):
    if not has_kv_transfer_group() or not is_v1_kv_transfer_group():
        return

    # If capturing the cudagraph, skip saving
    # kv cache to connector

    if monitor.cudagraph_capturing_enabled:
        return
    connector = get_kv_transfer_group()

    forward_context: ForwardContext = get_forward_context()
    attn_metadata = forward_context.attn_metadata
    if attn_metadata is None:
        return
    assert isinstance(attn_metadata, dict)
    connector.save_kv_layer(layer_name, kv_cache_layer,
                            attn_metadata[layer_name])

In /vllm/compilation/monitor.py:

# line 42: set default cudagraph_capturing_enabled to false
cudagraph_capturing_enabled: bool = False

By this modification, we can use FULL_AND_PIECEWISE when using kv connector.
I wonder if this modification is identical to the original purpose of vLLM team? I would be grateful if you could explain this to me. Thanks a lot!

[hellosekai]

I have the same problem with running examples/offline_inference/disaggregated_prefill.py

[harrisonyhq]

I have the same problem with running examples/offline_inference/disaggregated_prefill.py

You can try my solution above, either add compilation_config = {"cudagraph_mode": "PIECEWISE"} to skip capture of full CUDA Graph for uniform decode or modify /vllm/attention/layer.py.

[QierLi]

Also impacted. Thanks for the sharing!

[harrisonyhq]

Also impacted. Thanks for the sharing!

Which one do you think is the better solution :)?
As FULL_AND_PIECEWISE is the default mode, maybe vLLM should support it without any special configuration?
Also I'm wondering if vLLM wants to capture the graph including kv connector in full mode? If so, add monitor.cudagraph_capturing_enabled in maybe_save_kv_layer_to_connector maybe is not a good solution. Is there any other better solutions?

[sdavidbd]

@harrisonyhq Thanks for reporting this!

The root cause appears to be that during full-graph capture, the KV connector APIs are invoked outside the usual connector context manager. As a result, the connector metadata is not set, breaking the invariant that KVConnectorMetadata must always be initialized before model execution.

When the capture process calls connector methods such as wait_for_layer_load() or save_kv_layer(), any access to the metadata triggers the assertion, since it remains unset in this path.

I don’t think the right fix is to simply bypass the KV connector path when capturing full graphs. Instead, we should enable a dedicated “dummy run” path for the KV connector, where it is initialized with a dummy metadata instance. This would preserve the metadata invariant while allowing the connector APIs to behave consistently across both normal and capture runs.

I’ll open a PR shortly with a proposed fix for this.