[Bug]: resource_tracker unregister error with 2*3090 #5439
Description
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: Could not collect
CMake version: version 3.27.9
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-5.4.0-139-generic-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 GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090
GPU 4: NVIDIA GeForce RTX 3090
GPU 5: NVIDIA GeForce RTX 3090
GPU 6: NVIDIA GeForce RTX 3090
GPU 7: NVIDIA GeForce RTX 3090
Nvidia driver version: 535.129.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
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, 48 bits virtual
Byte Order: Little Endian
CPU(s): 96
On-line CPU(s) list: 0-95
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50GHz
CPU family: 6
Model: 85
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 2
Stepping: 7
Frequency boost: enabled
CPU max MHz: 2501.0000
CPU min MHz: 1000.0000
BogoMIPS: 5000.00
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 cdp_l3 invpcid_single intel_ppin 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 mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 1.5 MiB (48 instances)
L1i cache: 1.5 MiB (48 instances)
L2 cache: 48 MiB (48 instances)
L3 cache: 71.5 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-23,48-71
NUMA node1 CPU(s): 24-47,72-95
Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed: Mitigation; Enhanced IBRS
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, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; TSX disabled
Versions of relevant libraries:
[pip3] numpy==1.24.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] onnx==1.15.0rc2
[pip3] optree==0.10.0
[pip3] pytorch-quantization==2.1.2
[pip3] torch==2.3.0
[pip3] torch-tensorrt==2.2.0a0
[pip3] torchdata==0.7.0a0
[pip3] torchtext==0.17.0a0
[pip3] torchvision==0.17.0a0
[pip3] transformers==4.41.2
[pip3] triton==2.3.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.0
vLLM Build Flags:
CUDA Archs: 5.2 6.0 6.1 7.0 7.2 7.5 8.0 8.6 8.7 9.0+PTX; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X PIX PXB PXB SYS SYS SYS SYS 0-23,48-71 0 N/A
GPU1 PIX X PXB PXB SYS SYS SYS SYS 0-23,48-71 0 N/A
GPU2 PXB PXB X PXB SYS SYS SYS SYS 0-23,48-71 0 N/A
GPU3 PXB PXB PXB X SYS SYS SYS SYS 0-23,48-71 0 N/A
GPU4 SYS SYS SYS SYS X PIX PXB PXB 24-47,72-95 1 N/A
GPU5 SYS SYS SYS SYS PIX X PXB PXB 24-47,72-95 1 N/A
GPU6 SYS SYS SYS SYS PXB PXB X PXB 24-47,72-95 1 N/A
GPU7 SYS SYS SYS SYS PXB PXB PXB X 24-47,72-95 1 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
🐛 Describe the bug
export NCCL_P2P_DISABLE=1
root@k8s-master-45:/home/hxu/vllm-example# CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server --model /home/hxu/pretrain_models/Qwen1.5-4B/ --host 10.9.1.45 --port 9001 --max-model-len 4096 --tensor-parallel-size 2 --gpu-memory-utilization 0.5
INFO 06-12 04:58:55 api_server.py:177] vLLM API server version 0.5.0
INFO 06-12 04:58:55 api_server.py:178] args: Namespace(host='10.9.1.45', port=9001, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], model='/home/hxu/pretrain_models/Qwen1.5-4B/', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, download_dir=None, load_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=4096, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=2, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, gpu_memory_utilization=0.5, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=False, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, device='auto', image_input_type=None, image_token_id=None, image_input_shape=None, image_feature_size=None, image_processor=None, image_processor_revision=None, disable_image_processor=False, scheduler_delay_factor=0.0, enable_chunked_prefill=False, speculative_model=None, num_speculative_tokens=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, model_loader_extra_config=None, preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None)
2024-06-12 04:58:57,336 INFO worker.py:1753 -- Started a local Ray instance.
INFO 06-12 04:58:58 config.py:623] Defaulting to use mp for distributed inference
INFO 06-12 04:58:58 llm_engine.py:161] Initializing an LLM engine (v0.5.0) with config: model='/home/hxu/pretrain_models/Qwen1.5-4B/', speculative_config=None, tokenizer='/home/hxu/pretrain_models/Qwen1.5-4B/', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=/home/hxu/pretrain_models/Qwen1.5-4B/)
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
(VllmWorkerProcess pid=220882) INFO 06-12 04:59:04 multiproc_worker_utils.py:215] Worker ready; awaiting tasks
INFO 06-12 04:59:05 utils.py:623] Found nccl from library libnccl.so.2
(VllmWorkerProcess pid=220882) INFO 06-12 04:59:05 utils.py:623] Found nccl from library libnccl.so.2
INFO 06-12 04:59:05 pynccl.py:65] vLLM is using nccl==2.20.5
(VllmWorkerProcess pid=220882) INFO 06-12 04:59:05 pynccl.py:65] vLLM is using nccl==2.20.5
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/resource_tracker.py", line 209, in main
cache[rtype].remove(name)
KeyError: '/psm_feae849b'
(VllmWorkerProcess pid=220882) INFO 06-12 04:59:05 custom_all_reduce_utils.py:179] reading GPU P2P access cache from /root/.config/vllm/gpu_p2p_access_cache_for_0,1.json
INFO 06-12 04:59:05 custom_all_reduce_utils.py:179] reading GPU P2P access cache from /root/.config/vllm/gpu_p2p_access_cache_for_0,1.json
(VllmWorkerProcess pid=220882) WARNING 06-12 04:59:05 custom_all_reduce.py:179] Custom allreduce is disabled because your platform lacks GPU P2P capability or P2P test failed. To silence this warning, specify disable_custom_all_reduce=True explicitly.
WARNING 06-12 04:59:05 custom_all_reduce.py:179] Custom allreduce is disabled because your platform lacks GPU P2P capability or P2P test failed. To silence this warning, specify disable_custom_all_reduce=True explicitly.
(VllmWorkerProcess pid=220882) INFO 06-12 04:59:06 model_runner.py:159] Loading model weights took 3.6968 GB
INFO 06-12 04:59:07 model_runner.py:159] Loading model weights took 3.6968 GB
INFO 06-12 04:59:08 distributed_gpu_executor.py:56] # GPU blocks: 2122, # CPU blocks: 1310
(VllmWorkerProcess pid=220882) INFO 06-12 04:59:11 model_runner.py:878] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
(VllmWorkerProcess pid=220882) INFO 06-12 04:59:11 model_runner.py:882] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
INFO 06-12 04:59:11 model_runner.py:878] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
INFO 06-12 04:59:11 model_runner.py:882] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
INFO 06-12 04:59:26 model_runner.py:954] Graph capturing finished in 16 secs.
(VllmWorkerProcess pid=220882) INFO 06-12 04:59:26 model_runner.py:954] Graph capturing finished in 16 secs.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
INFO 06-12 04:59:27 serving_chat.py:92] Using default chat template:
INFO 06-12 04:59:27 serving_chat.py:92] {% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
INFO 06-12 04:59:27 serving_chat.py:92] You are a helpful assistant<|im_end|>
INFO 06-12 04:59:27 serving_chat.py:92] ' }}{% endif %}{{'<|im_start|>' + message['role'] + '
INFO 06-12 04:59:27 serving_chat.py:92] ' + message['content'] + '<|im_end|>' + '
INFO 06-12 04:59:27 serving_chat.py:92] '}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
INFO 06-12 04:59:27 serving_chat.py:92] ' }}{% endif %}
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
WARNING 06-12 04:59:27 serving_embedding.py:141] embedding_mode is False. Embedding API will not work.
INFO: Started server process [214994]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://10.9.1.45:9001 (Press CTRL+C to quit)i add some debug code in resouce_tracker.py
if cmd == 'REGISTER':
warnings.warn('type %s name %s' %(rtype, rtype))
cache[rtype].add(name)it shows UserWarning: type shared_memory name shared_memory
in addtions
df -lh | grep shm
shm 20G 320K 20G 1% /dev/shm