vllm - 💡(How to fix) Fix [Bug]: qwen3.5 can not use --decode-context-parallel-size with --enable-prefix-caching [1 participants]

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vllm-project/vllm#40585Fetched 2026-04-23 07:24:12
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Error Message

(Worker_TP0_DCP0_EP0 pid=64160) INFO 04-22 11:54:08 [monitor.py:48] torch.compile and initial profiling/warmup run together took 23.82 s in total (Worker_TP0_DCP0_EP0 pid=64160) INFO 04-22 11:54:12 [worker.py:357] Available KV cache memory: 44.77 GiB (EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4). (EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1316] GPU KV cache size: 4,638,720 tokens (EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1321] Maximum concurrency for 262,144 tokens per request: 67.63x (EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4). (EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4). (EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4). (EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4). (EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4). (EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4). (EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4). Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|███████████████████████████████████████| 13/13 [00:09<00:00, 1.32it/s] (Worker_TP0_DCP0_EP0 pid=64160) INFO 04-22 11:54:24 [gpu_model_runner.py:5746] Graph capturing finished in 11 secs, took 0.10 GiB (EngineCore pid=64025) INFO 04-22 11:54:24 [core.py:281] init engine (profile, create kv cache, warmup model) took 44.60 seconds (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] EngineCore failed to start. (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] Traceback (most recent call last): (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 1073, in run_engine_core (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs) (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] File "/vllm-workspace/vllm/vllm/tracing/otel.py", line 178, in sync_wrapper (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] return func(*args, **kwargs) (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 839, in init (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] super().init( (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 141, in init (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] self.scheduler: SchedulerInterface = Scheduler( (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] ^^^^^^^^^^ (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] File "/vllm-workspace/vllm/vllm/v1/core/sched/async_scheduler.py", line 14, in init (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] super().init(*args, **kwargs) (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] File "/vllm-workspace/vllm/vllm/v1/core/sched/scheduler.py", line 225, in init (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] self.kv_cache_manager = KVCacheManager( (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] ^^^^^^^^^^^^^^^ (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] File "/vllm-workspace/vllm/vllm/v1/core/kv_cache_manager.py", line 131, in init (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] self.coordinator = get_kv_cache_coordinator( (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] ^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] File "/vllm-workspace/vllm/vllm/v1/core/kv_cache_coordinator.py", line 581, in get_kv_cache_coordinator (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] return HybridKVCacheCoordinator( (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] ^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] File "/vllm-workspace/vllm/vllm/v1/core/kv_cache_coordinator.py", line 402, in init (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] assert all( (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] ^^^^ (EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] AssertionError: block_size must be divisible by hash_block_size

Fix Action

Fix / Workaround

============================== 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): 180 On-line CPU(s) list: 0-179 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8457C CPU family: 6 Model: 143 Thread(s) per core: 2 Core(s) per socket: 45 Socket(s): 2 Stepping: 8 BogoMIPS: 5199.62 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 pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities Hypervisor vendor: KVM Virtualization type: full L1d cache: 4.2 MiB (90 instances) L1i cache: 2.8 MiB (90 instances) L2 cache: 180 MiB (90 instances) L3 cache: 195 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-89 NUMA node1 CPU(s): 90-179 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 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: Mitigation; TSX disabled

Code Example

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.5 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version                : 15.0.7
CMake version                : version 4.3.1
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.9.0+cpu
Is debug build               : False
CUDA used to build PyTorch   : None
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.11.14 (main, Feb 26 2026, 04:48:50) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-5.10.135.bsk.6-amd64-x86_64-with-glibc2.35
    

==============================
          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):                          180
On-line CPU(s) list:             0-179
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Platinum 8457C
CPU family:                      6
Model:                           143
Thread(s) per core:              2
Core(s) per socket:              45
Socket(s):                       2
Stepping:                        8
BogoMIPS:                        5199.62
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 pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       4.2 MiB (90 instances)
L1i cache:                       2.8 MiB (90 instances)
L2 cache:                        180 MiB (90 instances)
L3 cache:                        195 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-89
NUMA node1 CPU(s):               90-179
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 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:   Mitigation; TSX disabled

==============================
Versions of relevant libraries
==============================
[pip3] numpy==1.26.4
[pip3] pyzmq==27.1.0
[pip3] torch==2.9.0+cpu
[pip3] torch_npu==2.9.0.post1+gitdc51c2d
[pip3] torchaudio==2.9.0+cpu
[pip3] torchvision==0.24.0+cpu
[pip3] transformers==4.57.6
[pip3] triton-ascend==3.2.0.dev20260322
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.18.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
  Could not collect

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=none
TORCH_NCCL_TRACE_BUFFER_SIZE=100
NCCL_DEBUG=INFO
PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
TORCH_NCCL_ENABLE_TIMING=1
LD_LIBRARY_PATH=/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_1/lib:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_1/examples:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_1/tests/atbopstest:/usr/local/Ascend/cann-8.5.1/lib64:/usr/local/Ascend/cann-8.5.1/lib64/plugin/opskernel:/usr/local/Ascend/cann-8.5.1/lib64/plugin/nnengine:/usr/local/Ascend/cann-8.5.1/opp/built-in/op_impl/ai_core/tbe/op_tiling/lib/linux/x86_64:/usr/local/Ascend/cann-8.5.1/tools/aml/lib64:/usr/local/Ascend/cann-8.5.1/tools/aml/lib64/plugin:/usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64/common:/usr/local/Ascend/driver/lib64/driver:/opt/tiger/native_libhdfs/lib/native:/opt/tiger/jdk/jdk8u265-b01/jre/lib/amd64/server:/opt/tiger/yarn_deploy/hadoop_current/lib/native:/opt/tiger/yarn_deploy/hadoop_current/lib/native/ufs:/opt/tiger/yarn_deploy/hadoop/lib/native:/opt/tiger/yarn_deploy/hadoop_current/lib/native:/opt/tiger/yarn_deploy/hadoop_current/lzo/lib:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_1/lib:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_1/examples:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_1/tests/atbopstest:/usr/local/Ascend/ascend-toolkit/latest/tools/aml/lib64:/usr/local/Ascend/ascend-toolkit/latest/tools/aml/lib64/plugin:/usr/local/Ascend/ascend-toolkit/latest/lib64:/usr/local/Ascend/ascend-toolkit/latest/lib64/plugin/opskernel:/usr/local/Ascend/ascend-toolkit/latest/lib64/plugin/nnengine:/usr/local/Ascend/ascend-toolkit/latest/opp/built-in/op_impl/ai_core/tbe/op_tiling:/usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64/common/:/usr/local/Ascend/driver/lib64/driver/:/usr/local/python3.11.14/lib::/usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64/common:/usr/local/Ascend/driver/lib64/driver:/usr/local/Ascend/toolbox/latest/Ascend-DMI/lib64
OMP_NUM_THREADS=1
TORCH_DEVICE_BACKEND_AUTOLOAD=1
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1

---

vllm serve /dev/shm/Qwen3.5-35B-A3B --served-model-name default_model --enable-prompt-tokens-details --enable-mfu-metrics --max-num-batched-tokens=8K --max-num-seqs 128 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml --enable-prefix-caching --mamba-cache-mode align --enable-expert-parallel  --load-format runai_streamer --port 8000 --host :: --tensor-parallel-size 8 -dcp 4

---

(Worker_TP0_DCP0_EP0 pid=64160) INFO 04-22 11:54:08 [monitor.py:48] torch.compile and initial profiling/warmup run together took 23.82 s in total
(Worker_TP0_DCP0_EP0 pid=64160) INFO 04-22 11:54:12 [worker.py:357] Available KV cache memory: 44.77 GiB
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1316] GPU KV cache size: 4,638,720 tokens
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1321] Maximum concurrency for 262,144 tokens per request: 67.63x
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|███████████████████████████████████████| 13/13 [00:09<00:00,  1.32it/s]
(Worker_TP0_DCP0_EP0 pid=64160) INFO 04-22 11:54:24 [gpu_model_runner.py:5746] Graph capturing finished in 11 secs, took 0.10 GiB
(EngineCore pid=64025) INFO 04-22 11:54:24 [core.py:281] init engine (profile, create kv cache, warmup model) took 44.60 seconds
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] EngineCore failed to start.
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] Traceback (most recent call last):
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 1073, in run_engine_core
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     return func(*args, **kwargs)
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]            ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 839, in __init__
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     super().__init__(
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 141, in __init__
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     self.scheduler: SchedulerInterface = Scheduler(
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]                                          ^^^^^^^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/core/sched/async_scheduler.py", line 14, in __init__
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     super().__init__(*args, **kwargs)
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/core/sched/scheduler.py", line 225, in __init__
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     self.kv_cache_manager = KVCacheManager(
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]                             ^^^^^^^^^^^^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/core/kv_cache_manager.py", line 131, in __init__
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     self.coordinator = get_kv_cache_coordinator(
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]                        ^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/core/kv_cache_coordinator.py", line 581, in get_kv_cache_coordinator
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     return HybridKVCacheCoordinator(
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]            ^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/core/kv_cache_coordinator.py", line 402, in __init__
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     assert all(
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]            ^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] AssertionError: block_size must be divisible by hash_block_size
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 22.04.5 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version                : 15.0.7
CMake version                : version 4.3.1
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.9.0+cpu
Is debug build               : False
CUDA used to build PyTorch   : None
ROCM used to build PyTorch   : N/A
XPU used to build PyTorch    : N/A

==============================
      Python Environment
==============================
Python version               : 3.11.14 (main, Feb 26 2026, 04:48:50) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-5.10.135.bsk.6-amd64-x86_64-with-glibc2.35
    

==============================
          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):                          180
On-line CPU(s) list:             0-179
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Platinum 8457C
CPU family:                      6
Model:                           143
Thread(s) per core:              2
Core(s) per socket:              45
Socket(s):                       2
Stepping:                        8
BogoMIPS:                        5199.62
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 pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       4.2 MiB (90 instances)
L1i cache:                       2.8 MiB (90 instances)
L2 cache:                        180 MiB (90 instances)
L3 cache:                        195 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-89
NUMA node1 CPU(s):               90-179
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 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:   Mitigation; TSX disabled

==============================
Versions of relevant libraries
==============================
[pip3] numpy==1.26.4
[pip3] pyzmq==27.1.0
[pip3] torch==2.9.0+cpu
[pip3] torch_npu==2.9.0.post1+gitdc51c2d
[pip3] torchaudio==2.9.0+cpu
[pip3] torchvision==0.24.0+cpu
[pip3] transformers==4.57.6
[pip3] triton-ascend==3.2.0.dev20260322
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.18.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
  Could not collect

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=none
TORCH_NCCL_TRACE_BUFFER_SIZE=100
NCCL_DEBUG=INFO
PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
TORCH_NCCL_ENABLE_TIMING=1
LD_LIBRARY_PATH=/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_1/lib:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_1/examples:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_1/tests/atbopstest:/usr/local/Ascend/cann-8.5.1/lib64:/usr/local/Ascend/cann-8.5.1/lib64/plugin/opskernel:/usr/local/Ascend/cann-8.5.1/lib64/plugin/nnengine:/usr/local/Ascend/cann-8.5.1/opp/built-in/op_impl/ai_core/tbe/op_tiling/lib/linux/x86_64:/usr/local/Ascend/cann-8.5.1/tools/aml/lib64:/usr/local/Ascend/cann-8.5.1/tools/aml/lib64/plugin:/usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64/common:/usr/local/Ascend/driver/lib64/driver:/opt/tiger/native_libhdfs/lib/native:/opt/tiger/jdk/jdk8u265-b01/jre/lib/amd64/server:/opt/tiger/yarn_deploy/hadoop_current/lib/native:/opt/tiger/yarn_deploy/hadoop_current/lib/native/ufs:/opt/tiger/yarn_deploy/hadoop/lib/native:/opt/tiger/yarn_deploy/hadoop_current/lib/native:/opt/tiger/yarn_deploy/hadoop_current/lzo/lib:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_1/lib:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_1/examples:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_1/tests/atbopstest:/usr/local/Ascend/ascend-toolkit/latest/tools/aml/lib64:/usr/local/Ascend/ascend-toolkit/latest/tools/aml/lib64/plugin:/usr/local/Ascend/ascend-toolkit/latest/lib64:/usr/local/Ascend/ascend-toolkit/latest/lib64/plugin/opskernel:/usr/local/Ascend/ascend-toolkit/latest/lib64/plugin/nnengine:/usr/local/Ascend/ascend-toolkit/latest/opp/built-in/op_impl/ai_core/tbe/op_tiling:/usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64/common/:/usr/local/Ascend/driver/lib64/driver/:/usr/local/python3.11.14/lib::/usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64/common:/usr/local/Ascend/driver/lib64/driver:/usr/local/Ascend/toolbox/latest/Ascend-DMI/lib64
OMP_NUM_THREADS=1
TORCH_DEVICE_BACKEND_AUTOLOAD=1
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
</details>

🐛 Describe the bug

  • vllm-0.18.0
  • 8*910B
  • -tp 8 -dcp 4 with --enable-prefix-caching --mamba-cache-mode align
vllm serve /dev/shm/Qwen3.5-35B-A3B --served-model-name default_model --enable-prompt-tokens-details --enable-mfu-metrics --max-num-batched-tokens=8K --max-num-seqs 128 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml --enable-prefix-caching --mamba-cache-mode align --enable-expert-parallel  --load-format runai_streamer --port 8000 --host :: --tensor-parallel-size 8 -dcp 4

the error message

(Worker_TP0_DCP0_EP0 pid=64160) INFO 04-22 11:54:08 [monitor.py:48] torch.compile and initial profiling/warmup run together took 23.82 s in total
(Worker_TP0_DCP0_EP0 pid=64160) INFO 04-22 11:54:12 [worker.py:357] Available KV cache memory: 44.77 GiB
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1316] GPU KV cache size: 4,638,720 tokens
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1321] Maximum concurrency for 262,144 tokens per request: 67.63x
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
(EngineCore pid=64025) INFO 04-22 11:54:12 [kv_cache_utils.py:1308] Multiplying the GPU KV cache size by the cp_world_size 4 (pcp_world_size 1 * dcp_world_size 4).
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|███████████████████████████████████████| 13/13 [00:09<00:00,  1.32it/s]
(Worker_TP0_DCP0_EP0 pid=64160) INFO 04-22 11:54:24 [gpu_model_runner.py:5746] Graph capturing finished in 11 secs, took 0.10 GiB
(EngineCore pid=64025) INFO 04-22 11:54:24 [core.py:281] init engine (profile, create kv cache, warmup model) took 44.60 seconds
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] EngineCore failed to start.
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] Traceback (most recent call last):
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 1073, in run_engine_core
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs)
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/tracing/otel.py", line 178, in sync_wrapper
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     return func(*args, **kwargs)
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]            ^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 839, in __init__
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     super().__init__(
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/engine/core.py", line 141, in __init__
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     self.scheduler: SchedulerInterface = Scheduler(
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]                                          ^^^^^^^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/core/sched/async_scheduler.py", line 14, in __init__
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     super().__init__(*args, **kwargs)
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/core/sched/scheduler.py", line 225, in __init__
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     self.kv_cache_manager = KVCacheManager(
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]                             ^^^^^^^^^^^^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/core/kv_cache_manager.py", line 131, in __init__
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     self.coordinator = get_kv_cache_coordinator(
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]                        ^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/core/kv_cache_coordinator.py", line 581, in get_kv_cache_coordinator
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     return HybridKVCacheCoordinator(
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]            ^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]   File "/vllm-workspace/vllm/vllm/v1/core/kv_cache_coordinator.py", line 402, in __init__
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]     assert all(
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099]            ^^^^
(EngineCore pid=64025) ERROR 04-22 11:54:30 [core.py:1099] AssertionError: block_size must be divisible by hash_block_size

If i delete --enable-prefix-caching --mamba-cache-mode align, it can start successfully.

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.

extent analysis

TL;DR

The error is likely caused by an incompatible configuration of --enable-prefix-caching and --mamba-cache-mode align, and removing these options allows the engine to start successfully.

Guidance

  • The error message AssertionError: block_size must be divisible by hash_block_size suggests a configuration issue with the caching mechanism.
  • Removing the --enable-prefix-caching and --mamba-cache-mode align options allows the engine to start, indicating that these options may be the cause of the problem.
  • To fix the issue, try adjusting the caching configuration or removing these options to see if the engine starts successfully.
  • If the issue persists, further investigation into the caching mechanism and its configuration may be necessary.

Notes

  • The provided information does not specify the exact requirements for the block_size and hash_block_size parameters, so further research into the documentation or source code may be necessary to understand the constraints.
  • The error message suggests that the block_size must be divisible by hash_block_size, but the exact values of these parameters are not provided.

Recommendation

Apply workaround: Remove the --enable-prefix-caching and --mamba-cache-mode align options to allow the engine to start successfully, and then investigate the caching configuration to find a compatible setup.

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