[Bug]: In DeepSeek-V3.2 tokenizer mode, detokenization saturates the main thread, causing the server to hang #30343
Description
Your current environment
The output of python collect_env.py
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.35
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PyTorch Info
==============================
PyTorch version : 2.9.0+cu129
Is debug build : False
CUDA used to build PyTorch : 12.9
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-5.15.0-126-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.9.86
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA H200
GPU 1: NVIDIA H200
GPU 2: NVIDIA H200
GPU 3: NVIDIA H200
GPU 4: NVIDIA H200
GPU 5: NVIDIA H200
GPU 6: NVIDIA H200
GPU 7: NVIDIA H200
Nvidia driver version : 570.124.06
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): 192
On-line CPU(s) list: 0-63
Off-line CPU(s) list: 64-191
Vendor ID: GenuineIntel
Model name: INTEL(R) XEON(R) PLATINUM 8558
CPU family: 6
Model: 207
Thread(s) per core: 2
Core(s) per socket: 48
Socket(s): 2
Stepping: 2
Frequency boost: enabled
CPU max MHz: 2101.0000
CPU min MHz: 800.0000
BogoMIPS: 4200.00
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: 4.5 MiB (96 instances)
L1i cache: 3 MiB (96 instances)
L2 cache: 192 MiB (96 instances)
L3 cache: 520 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-47,96-143
NUMA node1 CPU(s): 48-95,144-191
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: 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; Retpolines; IBPB conditional; IBRS_FW; STIBP conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI Retpoline
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.5.3
[pip3] numpy==2.2.0
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.16.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.3.2
[pip3] nvidia-ml-py==13.580.82
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvshmem-cu12==3.3.20
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] pyzmq==27.1.0
[pip3] torch==2.9.0+cu129
[pip3] torchaudio==2.9.0+cu129
[pip3] torchvision==0.24.0+cu129
[pip3] transformers==4.57.3
[pip3] triton==3.5.0
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.11.2.dev575+gd698bb382 (git sha: d698bb382)
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 NIC8 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 NODE PIX NODE NODE NODE SYS SYS SYS SYS 0-47 0 N/A
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 NODE NODE PIX NODE NODE SYS SYS SYS SYS 0-47 0 N/A
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 NODE NODE NODE PIX NODE SYS SYS SYS SYS 0-47 0 N/A
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 NODE NODE NODE NODE PIX SYS SYS SYS SYS 0-47 0 N/A
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS SYS SYS SYS PIX NODE NODE NODE 48-63 1 N/A
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS SYS SYS SYS NODE PIX NODE NODE 48-63 1 N/A
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS SYS SYS SYS NODE NODE PIX NODE 48-63 1 N/A
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS SYS SYS SYS NODE NODE NODE PIX 48-63 1 N/A
NIC0 NODE NODE NODE NODE SYS SYS SYS SYS X NODE NODE NODE NODE SYS SYS SYS SYS
NIC1 PIX NODE NODE NODE SYS SYS SYS SYS NODE X NODE NODE NODE SYS SYS SYS SYS
NIC2 NODE PIX NODE NODE SYS SYS SYS SYS NODE NODE X NODE NODE SYS SYS SYS SYS
NIC3 NODE NODE PIX NODE SYS SYS SYS SYS NODE NODE NODE X NODE SYS SYS SYS SYS
NIC4 NODE NODE NODE PIX SYS SYS SYS SYS NODE NODE NODE NODE X SYS SYS SYS SYS
NIC5 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS SYS X NODE NODE NODE
NIC6 SYS SYS SYS SYS NODE PIX NODE NODE SYS SYS SYS SYS SYS NODE X NODE NODE
NIC7 SYS SYS SYS SYS NODE NODE PIX NODE SYS SYS SYS SYS SYS NODE NODE X NODE
NIC8 SYS SYS SYS SYS NODE NODE NODE PIX SYS SYS SYS SYS SYS NODE NODE 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_bond0
NIC1: mlx5_bond100
NIC2: mlx5_bond101
NIC3: mlx5_bond102
NIC4: mlx5_bond103
NIC5: mlx5_bond104
NIC6: mlx5_bond105
NIC7: mlx5_bond106
NIC8: mlx5_bond107
==============================
Environment Variables
==============================
NCCL_IB_BOND_MODE_BALANCE=1
NCCL_IB_TC=186
NCCL_IB_PCI_RELAXED_ORDERING=1
NCCL_NET_PLUGIN=stepfun
NCCL_SOCKET_IFNAME=bond0
NCCL_NVLS_ENABLE=0
NVIDIA_GDRCOPY=enabled
NCCL_IB_HCA==mlx5_bond100,mlx5_bond101,mlx5_bond102,mlx5_bond103,mlx5_bond104,mlx5_bond105,mlx5_bond106,mlx5_bond107
VLLM_USAGE_SOURCE=production-docker-image
NCCL_IB_GID_INDEX=3
CUDA_VERSION=12.9.1
NCCL_PXN_DISABLE=1
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
NCCL_IB_QPS_PER_CONNECTION=8
NCCL_IB_TIMEOUT=21
LD_LIBRARY_PATH=/kubebrain:/usr/local/cuda-12.9/compat:/usr/local/nvidia/lib64:/usr/local/lib/python3.12/dist-packages/nvidia/cuda_nvrtc/lib/:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64:/usr/local/nvidia/lib64
NCCL_IB_DISABLE=0
NCCL_IB_RETRY_CNT=7
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
🐛 Describe the bug
Summary
/health requests hang or take extremely long when serving DeepSeek-V3.2 in tokenizer-mode ; main thread is busy in detokenization.
When deployed with TP8, the vLLM API server process shows very high CPU usage; with DP8 + EP deployments, it’s even worse—after serving for a while, the API server process hangs and can no longer accept new HTTP requests, /health requests hang or take extremely long .
Repro Steps
Command:
vllm serve /path/to/DeepSeek-V3.2-Speciale --served-model-name dsv3p2-dp8 --max-model-len 163840 --max-num-seqs 256 --enable-chunked-prefill --data-parallel-size 8 --enable-expert-parallel --gpu-memory-utilization 0.9 --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3 --enable-auto-tool-choice --tokenizer-mode deepseek_v32
- Start server with the command above.
- Trigger generation in high load.
- Call
/healthwhile generation is active. - Observe
/healtheither never returns or takes a very long time.
Findings
-
py-spy dump --nativeshows main thread hot path:detokenize_incrementally→decode_next→__len__ (tokenizer)→get_added_vocab→added_tokens_decoderintokenizers.abi3.so. -
get_added_vocab()on the fast tokenizer rebuilds added token decoder each call; detokenizer callslen(tokenizer)per token, so this work repeats for every generated token.
Root Cause Hypothesis
detokenize_incrementally runs on every generated token and checks len(tokenizer), which invokes DeepseekV32Tokenizer.__len__.
143:161:vllm/tokenizers/detokenizer_utils.py
new_token_id = all_input_ids[-1]
# Hot path: executed once per generated token.
if 0 <= new_token_id < len(tokenizer): # calls tokenizer.__len__ each token
new_tokens = tokenizer.convert_ids_to_tokens(
[new_token_id], skip_special_tokens=skip_special_tokens
)
if isinstance(new_tokens, str):
new_tokens = [new_tokens]
else:
new_tokens = [""]
...
__len__ used to call get_added_vocab() on each invocation; for fast tokenizers this rebuilds/consults the native added_tokens_decoder (shows up as BTreeMap::insert), executed once per token on the GIL-holding main thread.
14:125:vllm/tokenizers/deepseekv32.py
class DeepseekV32Tokenizer(HfTokenizer):
def __init__(self, tokenizer: TokenizerLike):
self.tokenizer = tokenizer
def __len__(self) -> int:
# </think> is an added token in DeepseekV32 tokenizer
return self.vocab_size + len(self.get_added_vocab())
def get_added_vocab(self) -> dict[str, int]:
return self._added_vocab
Result: per-token detokenization triggers expensive native work, starving the event loop and causing /health and other HTTP endpoints to hang.
Proposed Fix
Cache added vocab at tokenizer init and reuse: In vllm/tokenizers/deepseekv32.py, prefetch self._added_vocab = tokenizer.get_added_vocab() and self._added_vocab_size = len(...) once. __len__ returns vocab_size + _added_vocab_size; get_added_vocab() returns the cached dict.
This removes per-token repeated get_added_vocab() calls.
I have already validated the fix locally and will submit the patch shortly.