[Bug]: 'FlashInferAllToAllManager' object has no attribute 'prepare_workspace' #27655
Description
Your current environment
The output of python collect_env.py
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 24.04.2 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 : 2.9.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.11.14 (main, Oct 21 2025, 18:31:21) [GCC 11.2.0] (64-bit runtime)
Python platform : Linux-5.10.134-19.103.al8.x86_64-x86_64-with-glibc2.39
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.9.86
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration :
GPU 0: NVIDIA B200
GPU 1: NVIDIA B200
GPU 2: NVIDIA B200
GPU 3: NVIDIA B200
GPU 4: NVIDIA B200
GPU 5: NVIDIA B200
GPU 6: NVIDIA B200
GPU 7: NVIDIA B200
Nvidia driver version : 570.172.08
cuDNN version : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.10.2
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): 288
On-line CPU(s) list: 0-287
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) 6960P
CPU family: 6
Model: 173
Thread(s) per core: 2
Core(s) per socket: 72
Socket(s): 2
Stepping: 1
CPU(s) scaling MHz: 99%
CPU max MHz: 3900.0000
CPU min MHz: 800.0000
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 monitor 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 cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm 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 hwp hwp_act_window hwp_epp hwp_pkg_req hfi 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 uintr md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 6.8 MiB (144 instances)
L1i cache: 9 MiB (144 instances)
L2 cache: 288 MiB (144 instances)
L3 cache: 864 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-71,144-215
NUMA node1 CPU(s): 72-143,216-287
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 / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.4.1
[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==1.15.0
[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-cutlass-dsl==4.2.1
[pip3] nvidia-ml-py==13.580.82
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.3.20
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pyzmq==27.1.0
[pip3] torch==2.9.0
[pip3] torchaudio==2.9.0
[pip3] torchvision==0.24.0
[pip3] transformers==4.57.1
[pip3] triton==3.5.0
[conda] flashinfer-python 0.4.1 pypi_0 pypi
[conda] numpy 2.2.6 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi
[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi
[conda] nvidia-cufile-cu12 1.13.1.3 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi
[conda] nvidia-cutlass-dsl 4.2.1 pypi_0 pypi
[conda] nvidia-ml-py 13.580.82 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.27.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi
[conda] nvidia-nvshmem-cu12 3.3.20 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi
[conda] pyzmq 27.1.0 pypi_0 pypi
[conda] torch 2.9.0 pypi_0 pypi
[conda] torchaudio 2.9.0 pypi_0 pypi
[conda] torchvision 0.24.0 pypi_0 pypi
[conda] transformers 4.57.1 pypi_0 pypi
[conda] triton 3.5.0 pypi_0 pypi
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.11.1rc3.dev47+g074475541.d20251027 (git sha: 074475541, date: 20251027)
vLLM Build Flags:
CUDA Archs: 7.5 8.0 8.6 9.0 10.0 12.0+PTX; 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 NODE NODE NODE SYS SYS SYS SYS 0-71,144-215 0 N/A
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 NODE PIX NODE NODE SYS SYS SYS SYS 0-71,144-215 0 N/A
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 NODE NODE PIX NODE SYS SYS SYS SYS 0-71,144-215 0 N/A
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 NODE NODE NODE PIX SYS SYS SYS SYS 0-71,144-215 0 N/A
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS SYS SYS PIX NODE NODE NODE 72-143,216-287 1 N/A
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS SYS SYS NODE PIX NODE NODE 72-143,216-287 1 N/A
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS SYS SYS NODE NODE PIX NODE 72-143,216-287 1 N/A
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS SYS SYS NODE NODE NODE PIX 72-143,216-287 1 N/A
NIC0 PIX NODE NODE NODE SYS SYS SYS SYS X NODE NODE NODE SYS SYS SYS SYS
NIC1 NODE PIX NODE NODE SYS SYS SYS SYS NODE X NODE NODE SYS SYS SYS SYS
NIC2 NODE NODE PIX NODE SYS SYS SYS SYS NODE NODE X NODE SYS SYS SYS SYS
NIC3 NODE NODE NODE PIX SYS SYS SYS SYS NODE NODE NODE X SYS SYS SYS SYS
NIC4 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS X NODE NODE NODE
NIC5 SYS SYS SYS SYS NODE PIX NODE NODE SYS SYS SYS SYS NODE X NODE NODE
NIC6 SYS SYS SYS SYS NODE NODE PIX NODE SYS SYS SYS SYS NODE NODE X NODE
NIC7 SYS SYS SYS SYS NODE NODE NODE PIX 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_bond_0
NIC1: mlx5_bond_1
NIC2: mlx5_bond_2
NIC3: mlx5_bond_3
NIC4: mlx5_bond_4
NIC5: mlx5_bond_5
NIC6: mlx5_bond_6
NIC7: mlx5_bond_7
==============================
Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=6,7,0,1,2,3,4,5
NCCL_IB_TC=16
CUBLAS_VERSION=12.9.1.4
NVIDIA_REQUIRE_CUDA=cuda>=11.0
CUDA_CACHE_PATH=/dev/shm/cuda
CUDA_CACHE_DISABLE=0
NCCL_NET_PLUGIN=none
TORCH_CUDA_ARCH_LIST=7.5 8.0 8.6 9.0 10.0 12.0+PTX
NCCL_VERSION=2.27.3
NCCL_SOCKET_IFNAME=bond1
NCCL_NVLS_ENABLE=0
NCCL_BUFFSIZE=4194304
NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
TORCH_NCCL_USE_COMM_NONBLOCKING=0
NCCL_DEBUG=WARN
CUDA_ARCH_LIST=7.5 8.0 8.6 9.0 10.0 12.0
NCCL_IB_HCA=mlx5_bond
NVIDIA_PRODUCT_NAME=PyTorch
NCCL_IB_GID_INDEX=3
CUDA_VERSION=12.9.1.010
PYTORCH_VERSION=2.8.0a0+5228986
PYTORCH_BUILD_NUMBER=0
CUBLASMP_VERSION=0.4.0.789
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
CUDNN_FRONTEND_VERSION=1.12.0
NCCL_IB_QPS_PER_CONNECTION=4
TORCH_NCCL_ASYNC_ERROR_HANDLING=1
NCCL_MIN_CTAS=4
NCCL_IB_TIMEOUT=22
CUDNN_VERSION=9.10.2.21
TORCHINDUCTOR_COMPILE_THREADS=1
NCCL_IB_SL=5
TORCH_NCCL_AVOID_RECORD_STREAMS=1
PYTORCH_HOME=/opt/pytorch/pytorch
LD_LIBRARY_PATH=/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:/usr/local/lib:/usr/local/lib64:/usr/lib/x86_64-linux-gnu:/usr/local/cuda/lib64:/opt/hpcx/ompi/lib
NVIDIA_BUILD_ID=177567386
CUDA_CACHE_MAXSIZE=1073741824
OMP_NUM_THREADS=1
CUDA_DRIVER_VERSION=575.57.08
PYTORCH_BUILD_VERSION=2.8.0a0+5228986
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
CUDA_MODULE_LOADING=LAZY
NVIDIA_REQUIRE_JETPACK_HOST_MOUNTS=
NVIDIA_PYTORCH_VERSION=25.06
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE=1
PYTORCH_NVML_BASED_CUDA_CHECK=1
🐛 Describe the bug
vLLM throws an error when using the flashinfer_all2allv all2all backend: AttributeError: 'FlashInferAllToAllManager' object has no attribute 'prepare_workspace'
my script:
#!/bin/bash
PORT=8235
NWORKERS=8
USE_DP=1
MAX_MODEL_LEN=40960
DO_NSYS=0
DP=1
TP=$NWORKERS
if (( USE_DP == 1 )); then
DP=$NWORKERS
TP=1
fi
MODEL_DIR=/root/workspace/models/Qwen3-235B-A22B-Instruct-2507-NVFP4
echo "MODEL_DIR: $MODEL_DIR"
env_vars=(
# For resolving bugs
"NCCL_SOCKET_IFNAME=eth0"
"GLOO_SOCKET_IFNAME=eth0"
"PYTORCH_CUDA_ALLOC_CONF=expandable_segments:False"
# For vLLM
"VLLM_USE_FLASHINFER_MOE_FP4=1"
"VLLM_FLASHINFER_MOE_BACKEND=throughput"
"VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB={\"2\":32,\"4\":32,\"8\":8}"
"VLLM_MOE_RANDOM_GATING=1"
)
for var in "${env_vars[@]}"; do
var_name="${var%%=*}"
var_value="${var#*=}"
echo -e "\t$var_name=$var_value"
done
CMD=( env )
for var in "${env_vars[@]}"; do
CMD+=( "$var" )
done
CMD+=(
vllm serve
$MODEL_DIR
--port "$PORT"
--gpu-memory-utilization 0.9
-dp $DP
-tp $TP
--enable-expert-parallel
--no-enable-prefix-caching
--enable-chunked-prefill
--all2all-backend flashinfer_all2allv
--max-num-seqs 1024
--kv-cache-dtype fp8
--async-scheduling
--max-num-batched-tokens 8192
--max-model-len $MAX_MODEL_LEN
--compilation-config "{\"pass_config\":{\"enable_fi_allreduce_fusion\":true,\"enable_attn_fusion\":true,\"enable_noop\":true},\"custom_ops\":[\"+quant_fp8\",\"+rms_norm\"],\"cudagraph_mode\":\"FULL_DECODE_ONLY\",\"splitting_ops\":[]}"
)
echo -e "\nExecuting command:"
printf " %s" "${CMD[@]}"
echo -e "\n"
"${CMD[@]}"And my output:
�[1;36m(EngineCore_DP7 pid=3825627)�[0;0m File "/root/workspace/vllm_nvfp4/vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py", line 238, in flashinfer_alltoall_dispatch
�[1;36m(EngineCore_DP7 pid=3825627)�[0;0m all2all_manager.prepare_workspace,
�[1;36m(EngineCore_DP7 pid=3825627)�[0;0m ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
�[1;36m(EngineCore_DP7 pid=3825627)�[0;0m AttributeError: 'FlashInferAllToAllManager' object has no attribute 'prepare_workspace'
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