[Bug]: No available block found in 60 second in shm #6614
Comments
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I think the root cause should be the engine iteration timeout error.
this is just a warning. |
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Please report this error under the tracking issue #5901 so we can get more data on how to fix it. |
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I also encountered this problem, I hope it can be solved.
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@youkaichao Please solve this problem as soon as possible. At present, it seems that the latest version of vLLM always has some distributed errors~ |
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Reopening so that it's not seen as being resolved by #5901 just yet. |
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@DarkLight1337 @youkaichao Is there any progress on this issue? What is the specific cause? I debugged and found that the stack is stuck at the CA stage. What is the specific cause? Thanks～ |
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What do you mean by CA? |
Custom Allreduce, stuck at |
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If you want a quick fix, you can directly disable custom allreduce by Debugging that part is quite difficult, and I cannot give any timeline for it. |
It can not always blamed on shm_broadcast. AsyncEngine start workers in advance, and shm_broadcast will not receive inputs forever if current step is stuck. |
Thanks, I ran into the same issue when I replaced AsyncEngine with LLM_Engine. |
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Seems like a large prompt can put the vllm server into a state where it just doesn't work anymore. Even small prompts stop working once this error (below) happens. I'm also seeing the "No available block found in 60 second" which is why I'm adding this on to this thread. python -m vllm.entrypoints.openai.api_server --model Qwen/Qwen2-72B-Instruct --tensor-parallel-size 8 --max-model-len 12591 --swap-space 1 --disable-custom-all-reduce I used this "--disable-custom-all-reduce" in hopes that it would be a work around (from comment above) but it did not stop the issue. |
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Same problem with |
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I meet the same error "No available block found in 60 second" in shm, so far is there any method to avoid this error? |
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@kkk-an |
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@kkk-an can you confirm that you’re using the latest version |
Hello, thanks for your kindly reply. The version I used is vllm==0.5.4, torch==2.4.0. Thank you! |
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I meet the same error "No available block found in 60 second" in shm,The version I used is vllm==0.5.3.post1+cu118, torch==2.3.1+cu118. |
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same error, with vllm-openai:v0.6.1.post2 docker image, when serving qwen2-vl-72B on 4*A40. |
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same warning, torch==2.3.1+cu121 stack info where the program stucks: ^C Exception in worker VllmWorkerProcess while processing method start_worker_execution_loop: , Traceback (most recent call last):
File "/opt/conda/lib/python3.10/site-packages/vllm/executor/multiproc_worker_utils.py", line 223, in _run_worker_process
output = executor(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/vllm/worker/worker_base.py", line 65, in start_worker_execution_loop
output = self.execute_model(execute_model_req=None)
File "/opt/conda/lib/python3.10/site-packages/vllm/worker/worker_base.py", line 250, in execute_model
broadcast_data = broadcast_tensor_dict(src=0)
File "/opt/conda/lib/python3.10/site-packages/vllm/distributed/communication_op.py", line 32, in broadcast_tensor_dict
return get_tp_group().broadcast_tensor_dict(tensor_dict, src)
File "/opt/conda/lib/python3.10/site-packages/vllm/distributed/parallel_state.py", line 529, in broadcast_tensor_dict
metadata_list = self.broadcast_object(None, src=src)
File "/opt/conda/lib/python3.10/site-packages/vllm/distributed/parallel_state.py", line 383, in broadcast_object
return self.mq_broadcaster.broadcast_object(obj)
File "/opt/conda/lib/python3.10/site-packages/vllm/distributed/device_communicators/shm_broadcast.py", line 461, in broadcast_object
return self.dequeue()
File "/opt/conda/lib/python3.10/site-packages/vllm/distributed/device_communicators/shm_broadcast.py", line 439, in dequeue
with self.acquire_read() as buf:
File "/opt/conda/lib/python3.10/contextlib.py", line 135, in __enter__
return next(self.gen)
File "/opt/conda/lib/python3.10/site-packages/vllm/distributed/device_communicators/shm_broadcast.py", line 386, in acquire_read
with self.buffer.get_metadata(self.current_idx) as metadata_buffer:
File "/opt/conda/lib/python3.10/contextlib.py", line 281, in helper
return _GeneratorContextManager(func, args, kwds)
File "/opt/conda/lib/python3.10/contextlib.py", line 103, in __init__
self.gen = func(*args, **kwds)
KeyboardInterrupt |
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For me, adding an interval between calling the generate function can significantly reduce the likelihood of this happening. I also found that when I request multiple GPUs in a large cluster to use vLLM, this issue seems to occur more frequently. However, when I am on a local machine (e.g., a local 4xA100 machine with 4 GPUs on a single motherboard), this issue has not occurred so far. The vLLM version is the same: 0.5.3.post1. |
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On a 8x Mi300X machine, I'm trying to start multiple instances of model (eg: llama3.1 70B tp2 x4 or tp4 x2). When I run vllm benchmark_serving.py, when the request rate is large (eg. 32 or inf). Then some of the instances will get this warning " No available block found in 60 second". In the meantime (not sure about the causality), the benchmark result will show quite a lot of requests failure (num_prompt = 1000, at request rate inf, only about 380 request is successful). |
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a workaround for people who suffer from this: you might use pipeline parallel, like changing the root cause is still unclear. it seems to be related with tp only, and sometimes it hangs inside pytorch's |
how to do this in running a api_server.py instance ? |
You can use |
Your current environment
Collecting environment information...
PyTorch version: 2.3.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.24.4
Libc version: glibc-2.35
Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.10.112-005.ali5000.alios7.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.3.107
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-SXM4-80GB
GPU 1: NVIDIA A100-SXM4-80GB
GPU 2: NVIDIA A100-SXM4-80GB
GPU 3: NVIDIA A100-SXM4-80GB
GPU 4: NVIDIA A100-SXM4-80GB
GPU 5: NVIDIA A100-SXM4-80GB
GPU 6: NVIDIA A100-SXM4-80GB
GPU 7: NVIDIA A100-SXM4-80GB
Nvidia driver version: 515.105.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
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): 128
On-line CPU(s) list: 0-127
Vendor ID: GenuineIntel
BIOS Vendor ID: Intel(R) Corporation
Model name: Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz
BIOS Model name: Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz
CPU family: 6
Model: 106
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 2
Stepping: 6
CPU max MHz: 3500.0000
CPU min MHz: 800.0000
BogoMIPS: 5800.00
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 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 invpcid_single intel_ppin 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 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 3 MiB (64 instances)
L1i cache: 2 MiB (64 instances)
L2 cache: 80 MiB (64 instances)
L3 cache: 96 MiB (2 instances)
NUMA node(s): 1
NUMA node0 CPU(s): 0-127
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: 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: Vulnerable: eIBRS with unprivileged eBPF
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] mypy==1.9.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.2
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] onnx==1.16.0
[pip3] onnx-graphsurgeon==0.3.27
[pip3] onnxruntime==1.16.3
[pip3] torch==2.3.0
[pip3] triton==2.3.0
[pip3] tritonclient==2.44.0
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.2
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 CPU Affinity NUMA Affinity
GPU0 X NV12 NV12 NV12 NV12 NV12 NV12 NV12 PXB SYS SYS SYS 0-127 N/A
GPU1 NV12 X NV12 NV12 NV12 NV12 NV12 NV12 PXB SYS SYS SYS 0-127 N/A
GPU2 NV12 NV12 X NV12 NV12 NV12 NV12 NV12 SYS PXB SYS SYS 0-127 N/A
GPU3 NV12 NV12 NV12 X NV12 NV12 NV12 NV12 SYS PXB SYS SYS 0-127 N/A
GPU4 NV12 NV12 NV12 NV12 X NV12 NV12 NV12 SYS SYS PXB SYS 0-127 N/A
GPU5 NV12 NV12 NV12 NV12 NV12 X NV12 NV12 SYS SYS PXB SYS 0-127 N/A
GPU6 NV12 NV12 NV12 NV12 NV12 NV12 X NV12 SYS SYS SYS PXB 0-127 N/A
GPU7 NV12 NV12 NV12 NV12 NV12 NV12 NV12 X SYS SYS SYS PXB 0-127 N/A
NIC0 PXB PXB SYS SYS SYS SYS SYS SYS X SYS SYS SYS
NIC1 SYS SYS PXB PXB SYS SYS SYS SYS SYS X SYS SYS
NIC2 SYS SYS SYS SYS PXB PXB SYS SYS SYS SYS X SYS
NIC3 SYS SYS SYS SYS SYS SYS PXB PXB SYS SYS SYS 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
🐛 Describe the bug
Now because it involves shm communication, are there any current requirements for the size of shm?
Currently, In v0.5.2, there will be a random timeout phenomenon. The specific reason is that shm
The shm size in the container is 90G. Is there a recommended shm size? Or is it due to other reasons?
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