vllm - 💡(How to fix) Fix [Bug]: [LMCache] `update_state_after_alloc` passes wrong `cache_salt` to `free_lookup_locks`, leaking server read locks in multi-tenant deployments

vllm2026-05-31 07:10:39

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Fix / Workaround

============================== 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): 8 On-line CPU(s) list: 0-7 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Gold 6462C CPU family: 6 Model: 143 Thread(s) per core: 2 Core(s) per socket: 4 Socket(s): 1 Stepping: 8 CPU(s) scaling MHz: 100% CPU max MHz: 3900.0000 CPU min MHz: 800.0000 BogoMIPS: 6600.00 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ibrs_enhanced fsgsbase tsc_adjust 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 ida arat hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm uintr md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities Hypervisor vendor: KVM Virtualization type: full L1d cache: 192 KiB (4 instances) L1i cache: 128 KiB (4 instances) L2 cache: 8 MiB (4 instances) L3 cache: 60 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-7 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: Vulnerable Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Code Example

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 24.04.3 LTS (x86_64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version                : Could not collect
CMake version                : version 3.31.6
Libc version                 : glibc-2.39

==============================
       PyTorch Info
==============================
PyTorch version              : 2.10.0a0+b558c986e8.nv25.11
Is debug build               : False
CUDA used to build PyTorch   : 13.0
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.3 (main, Mar 23 2026, 19:04:32) [GCC 13.3.0] (64-bit runtime)
Python platform              : Linux-5.10.134-18.0.10.lifsea8.x86_64-x86_64-with-glibc2.39
    
==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 13.0.88
CUDA_MODULE_LOADING set to   : LAZY
GPU models and configuration : GPU 0: NVIDIA L20
Nvidia driver version        : 550.163.01
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.15.0
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
Address sizes:                      52 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             8
On-line CPU(s) list:                0-7
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6462C
CPU family:                         6
Model:                              143
Thread(s) per core:                 2
Core(s) per socket:                 4
Socket(s):                          1
Stepping:                           8
CPU(s) scaling MHz:                 100%
CPU max MHz:                        3900.0000
CPU min MHz:                        800.0000
BogoMIPS:                           6600.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ibrs_enhanced fsgsbase tsc_adjust 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 ida arat hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm uintr md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          192 KiB (4 instances)
L1i cache:                          128 KiB (4 instances)
L2 cache:                           8 MiB (4 instances)
L3 cache:                           60 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-7
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:    Vulnerable
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

==============================
Versions of relevant libraries
==============================
[pip3] mypy_extensions==1.1.0
[pip3] numpy==1.26.4
[pip3] nvidia-cudnn-frontend==1.15.0
[pip3] nvidia-dali-cuda130==1.52.0
[pip3] nvidia-ml-py==13.580.82
[pip3] nvidia-modelopt==0.37.0
[pip3] nvidia-nvcomp-cu13==5.0.0.6
[pip3] nvidia-nvimgcodec-cu13==0.6.1.37
[pip3] nvidia-nvjpeg==13.0.1.86
[pip3] nvidia-nvjpeg2k-cu13==0.9.0.43
[pip3] nvidia-nvtiff-cu13==0.6.0.78
[pip3] nvidia-resiliency-ext==0.4.1+cuda13
[pip3] onnx==1.18.0
[pip3] onnx-ir==0.1.12
[pip3] onnxscript==0.5.6
[pip3] optree==0.17.0
[pip3] pytorch-triton==3.5.0+gitde3506d2
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0a0+b558c986e8.nv25.11
[pip3] torch_tensorrt==2.10.0a0
[pip3] torchao==0.14.0+git
[pip3] torchdata==0.11.0
[pip3] torchprofile==0.0.4
[pip3] torchtitan==0.1.0
[pip3] torchvision==0.25.0a0+7a13ad0f
[pip3] triton_kernels==1.0.0+nv25.11
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.1.dev16489+g9efdddca2.d20260525 (git sha: 9efdddca2, date: 20260525)
vLLM Build Flags:
  CUDA Archs: 7.5 8.0 8.6 9.0 10.0 12.0+PTX; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-7     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

==============================
     Environment Variables
==============================
PYTORCH_BUILD_NUMBER=0
PYTORCH_HOME=/opt/pytorch/pytorch
CUDA_ARCH_LIST=7.5 8.0 8.6 9.0 10.0 12.0
LD_LIBRARY_PATH=/usr/local/cuda/compat/lib.real:/usr/local/lib/python3.12/dist-packages/torch/lib:/usr/local/lib/python3.12/dist-packages/torch_tensorrt/lib:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
TORCH_NCCL_USE_COMM_NONBLOCKING=0
NVIDIA_BUILD_ID=231036167
CUDA_VERSION=13.0.2.006
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE=1
CUBLAS_VERSION=13.1.0.3
TORCH_CUDA_ARCH_LIST=7.5 8.0 8.6 9.0 10.0 12.0+PTX
CUBLASMP_VERSION=0.6.0.84
CUDA_MODULE_LOADING=LAZY
NVIDIA_REQUIRE_CUDA=cuda>=9.0
PYTORCH_BUILD_VERSION=2.10.0a0+b558c98
NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
CUDA_COMPONENT_LIST=cccl crt nvrtc driver-dev culibos-dev cudart cudart-dev nvcc
NVIDIA_PYTORCH_VERSION=25.11
PYTORCH_VERSION=2.10.0a0+b558c98
CUDA_DRIVER_VERSION=580.95.05
NVIDIA_PRODUCT_NAME=PyTorch
CUDNN_VERSION=9.15.0.58
CUDNN_FRONTEND_VERSION=1.15.0
NVIDIA_REQUIRE_JETPACK_HOST_MOUNTS=
TORCHINDUCTOR_LOOP_ORDERING_AFTER_FUSION=0
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
NVIDIA_VISIBLE_DEVICES=0
NCCL_VERSION=2.28.8
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1

---

# update_state_after_alloc — missing cache_salt
self.scheduler_adapter.free_lookup_locks(
    token_ids=list(tracker.all_token_ids),
    start=0,
    end=free_end,
    request_id=request.request_id,
    # cache_salt missing — defaults to ""
)

---

from unittest.mock import MagicMock
from lmcache.integration.vllm.lmcache_mp_connector import LMCacheMPConnector, LMCacheMPRequestTracker

connector = object.__new__(LMCacheMPConnector)
connector.vllm_block_size = 16
connector.scheduler_adapter = MagicMock()

req = MagicMock()
req.request_id = "req-1"
req.all_token_ids = list(range(32))
req.block_hashes = []
req.cache_salt = "user-A"

tracker = LMCacheMPRequestTracker(req)
tracker.num_lmcache_hit_blocks = 2
tracker.num_vllm_hit_blocks = 2
connector.request_trackers = {"req-1": tracker}

blocks = MagicMock()
blocks.get_block_ids.return_value = ([0, 1],)
connector.update_state_after_alloc(req, blocks, num_external_tokens=0)

actual_salt = connector.scheduler_adapter.free_lookup_locks.call_args.kwargs.get("cache_salt", "<not passed>")
print(f"expected: 'user-A', got: {actual_salt!r}")  # got: '<not passed>'

---

self.scheduler_adapter.free_lookup_locks(
    token_ids=list(tracker.all_token_ids),
    start=0,
    end=free_end,
    request_id=request.request_id,
    cache_salt=tracker.cache_salt,   # +
)

RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 24.04.3 LTS (x86_64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version                : Could not collect
CMake version                : version 3.31.6
Libc version                 : glibc-2.39

==============================
       PyTorch Info
==============================
PyTorch version              : 2.10.0a0+b558c986e8.nv25.11
Is debug build               : False
CUDA used to build PyTorch   : 13.0
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.3 (main, Mar 23 2026, 19:04:32) [GCC 13.3.0] (64-bit runtime)
Python platform              : Linux-5.10.134-18.0.10.lifsea8.x86_64-x86_64-with-glibc2.39
    
==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 13.0.88
CUDA_MODULE_LOADING set to   : LAZY
GPU models and configuration : GPU 0: NVIDIA L20
Nvidia driver version        : 550.163.01
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.15.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.15.0
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
Address sizes:                      52 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             8
On-line CPU(s) list:                0-7
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6462C
CPU family:                         6
Model:                              143
Thread(s) per core:                 2
Core(s) per socket:                 4
Socket(s):                          1
Stepping:                           8
CPU(s) scaling MHz:                 100%
CPU max MHz:                        3900.0000
CPU min MHz:                        800.0000
BogoMIPS:                           6600.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ibrs_enhanced fsgsbase tsc_adjust 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 ida arat hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm uintr md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          192 KiB (4 instances)
L1i cache:                          128 KiB (4 instances)
L2 cache:                           8 MiB (4 instances)
L3 cache:                           60 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-7
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:    Vulnerable
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

==============================
Versions of relevant libraries
==============================
[pip3] mypy_extensions==1.1.0
[pip3] numpy==1.26.4
[pip3] nvidia-cudnn-frontend==1.15.0
[pip3] nvidia-dali-cuda130==1.52.0
[pip3] nvidia-ml-py==13.580.82
[pip3] nvidia-modelopt==0.37.0
[pip3] nvidia-nvcomp-cu13==5.0.0.6
[pip3] nvidia-nvimgcodec-cu13==0.6.1.37
[pip3] nvidia-nvjpeg==13.0.1.86
[pip3] nvidia-nvjpeg2k-cu13==0.9.0.43
[pip3] nvidia-nvtiff-cu13==0.6.0.78
[pip3] nvidia-resiliency-ext==0.4.1+cuda13
[pip3] onnx==1.18.0
[pip3] onnx-ir==0.1.12
[pip3] onnxscript==0.5.6
[pip3] optree==0.17.0
[pip3] pytorch-triton==3.5.0+gitde3506d2
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0a0+b558c986e8.nv25.11
[pip3] torch_tensorrt==2.10.0a0
[pip3] torchao==0.14.0+git
[pip3] torchdata==0.11.0
[pip3] torchprofile==0.0.4
[pip3] torchtitan==0.1.0
[pip3] torchvision==0.25.0a0+7a13ad0f
[pip3] triton_kernels==1.0.0+nv25.11
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.1.dev16489+g9efdddca2.d20260525 (git sha: 9efdddca2, date: 20260525)
vLLM Build Flags:
  CUDA Archs: 7.5 8.0 8.6 9.0 10.0 12.0+PTX; ROCm: Disabled; XPU: Disabled
GPU Topology:
        GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-7     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

==============================
     Environment Variables
==============================
PYTORCH_BUILD_NUMBER=0
PYTORCH_HOME=/opt/pytorch/pytorch
CUDA_ARCH_LIST=7.5 8.0 8.6 9.0 10.0 12.0
LD_LIBRARY_PATH=/usr/local/cuda/compat/lib.real:/usr/local/lib/python3.12/dist-packages/torch/lib:/usr/local/lib/python3.12/dist-packages/torch_tensorrt/lib:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
TORCH_NCCL_USE_COMM_NONBLOCKING=0
NVIDIA_BUILD_ID=231036167
CUDA_VERSION=13.0.2.006
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE=1
CUBLAS_VERSION=13.1.0.3
TORCH_CUDA_ARCH_LIST=7.5 8.0 8.6 9.0 10.0 12.0+PTX
CUBLASMP_VERSION=0.6.0.84
CUDA_MODULE_LOADING=LAZY
NVIDIA_REQUIRE_CUDA=cuda>=9.0
PYTORCH_BUILD_VERSION=2.10.0a0+b558c98
NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
CUDA_COMPONENT_LIST=cccl crt nvrtc driver-dev culibos-dev cudart cudart-dev nvcc
NVIDIA_PYTORCH_VERSION=25.11
PYTORCH_VERSION=2.10.0a0+b558c98
CUDA_DRIVER_VERSION=580.95.05
NVIDIA_PRODUCT_NAME=PyTorch
CUDNN_VERSION=9.15.0.58
CUDNN_FRONTEND_VERSION=1.15.0
NVIDIA_REQUIRE_JETPACK_HOST_MOUNTS=
TORCHINDUCTOR_LOOP_ORDERING_AFTER_FUSION=0
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
NVIDIA_VISIBLE_DEVICES=0
NCCL_VERSION=2.28.8
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1

</details>

🐛 Describe the bug

In vllm/distributed/kv_transfer/kv_connector/v1/lmcache_mp_connector.py: free_lookup_locks in update_state_after_alloc omits cache_salt, so it defaults to "". The lookup was submitted with cache_salt=<user value>, producing a mismatched ObjectKey — the server cannot find the lock to release, so matched KV chunks remain locked until TTL expiry and cannot be evicted.

# update_state_after_alloc — missing cache_salt
self.scheduler_adapter.free_lookup_locks(
    token_ids=list(tracker.all_token_ids),
    start=0,
    end=free_end,
    request_id=request.request_id,
    # cache_salt missing — defaults to ""
)

Trigger condition

Any request with cache_salt != "" where needs_retrieve() == False (vLLM already computed all LMCache-cached blocks).

Reproduction

from unittest.mock import MagicMock
from lmcache.integration.vllm.lmcache_mp_connector import LMCacheMPConnector, LMCacheMPRequestTracker

connector = object.__new__(LMCacheMPConnector)
connector.vllm_block_size = 16
connector.scheduler_adapter = MagicMock()

req = MagicMock()
req.request_id = "req-1"
req.all_token_ids = list(range(32))
req.block_hashes = []
req.cache_salt = "user-A"

tracker = LMCacheMPRequestTracker(req)
tracker.num_lmcache_hit_blocks = 2
tracker.num_vllm_hit_blocks = 2
connector.request_trackers = {"req-1": tracker}

blocks = MagicMock()
blocks.get_block_ids.return_value = ([0, 1],)
connector.update_state_after_alloc(req, blocks, num_external_tokens=0)

actual_salt = connector.scheduler_adapter.free_lookup_locks.call_args.kwargs.get("cache_salt", "<not passed>")
print(f"expected: 'user-A', got: {actual_salt!r}")  # got: '<not passed>'

Fix

self.scheduler_adapter.free_lookup_locks(
    token_ids=list(tracker.all_token_ids),
    start=0,
    end=free_end,
    request_id=request.request_id,
    cache_salt=tracker.cache_salt,   # +
)

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