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vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
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[Bug]: Is vllm compatible with torchrun? #7939

Open HwwwwwwwH opened 3 weeks ago

HwwwwwwwH commented 3 weeks ago

Your current environment

The output of `python collect_env.py` ```text PyTorch version: 2.4.0+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 3.28.1 Libc version: glibc-2.35 Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.4.0-135-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 A100-SXM4-80GB GPU 1: NVIDIA A100-SXM4-80GB Nvidia driver version: 525.147.05 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, 57 bits virtual Byte Order: Little Endian CPU(s): 128 On-line CPU(s) list: 0-127 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8350C CPU @ 2.60GHz CPU family: 6 Model: 106 Thread(s) per core: 2 Core(s) per socket: 32 Socket(s): 2 Stepping: 6 Frequency boost: enabled CPU max MHz: 2601.0000 CPU min MHz: 800.0000 BogoMIPS: 5200.00 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 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 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid 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): 2 NUMA node0 CPU(s): 0-31,64-95 NUMA node1 CPU(s): 32-63,96-127 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Vulnerable Vulnerability Retbleed: Not affected Vulnerability Spec store bypass: Vulnerable Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers Vulnerability Spectre v2: Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Vulnerable Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] nvidia-cublas-cu12==12.1.3.1 [pip3] nvidia-cuda-cupti-cu12==12.1.105 [pip3] nvidia-cuda-nvrtc-cu12==12.1.105 [pip3] nvidia-cuda-runtime-cu12==12.1.105 [pip3] nvidia-cudnn-cu12==9.1.0.70 [pip3] nvidia-cufft-cu12==11.0.2.54 [pip3] nvidia-curand-cu12==10.3.2.106 [pip3] nvidia-cusolver-cu12==11.4.5.107 [pip3] nvidia-cusparse-cu12==12.1.0.106 [pip3] nvidia-ml-py==12.560.30 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] nvidia-nvjitlink-cu12==12.6.20 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pyzmq==26.2.0 [pip3] torch==2.4.0 [pip3] torchaudio==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.44.2 [pip3] triton==3.0.0 [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-cublas-cu12 12.1.3.1 pypi_0 pypi [conda] nvidia-cuda-cupti-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cuda-nvrtc-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cuda-runtime-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi [conda] nvidia-cufft-cu12 11.0.2.54 pypi_0 pypi [conda] nvidia-curand-cu12 10.3.2.106 pypi_0 pypi [conda] nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi [conda] nvidia-cusparse-cu12 12.1.0.106 pypi_0 pypi [conda] nvidia-ml-py 12.560.30 pypi_0 pypi [conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi [conda] nvidia-nvjitlink-cu12 12.6.20 pypi_0 pypi [conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi [conda] pyzmq 26.2.0 pypi_0 pypi [conda] torch 2.4.0 pypi_0 pypi [conda] torchaudio 2.4.0 pypi_0 pypi [conda] torchvision 0.19.0 pypi_0 pypi [conda] transformers 4.44.2 pypi_0 pypi [conda] triton 3.0.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.5.5@09c7792610ada9f88bbf87d32b472dd44bf23cc2 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 NIC8 CPU Affinity NUMA Affinity GPU0 X NV12 NODE NODE NODE PXB PXB SYS SYS SYS SYS 0-31,64-95 0 GPU1 NV12 X NODE NODE NODE PXB PXB SYS SYS SYS SYS 0-31,64-95 0 NIC0 NODE NODE X NODE NODE NODE NODE SYS SYS SYS SYS NIC1 NODE NODE NODE X PIX NODE NODE SYS SYS SYS SYS NIC2 NODE NODE NODE PIX X NODE NODE SYS SYS SYS SYS NIC3 PXB PXB NODE NODE NODE X PIX SYS SYS SYS SYS NIC4 PXB PXB NODE NODE NODE PIX X SYS SYS SYS SYS NIC5 SYS SYS SYS SYS SYS SYS SYS X PIX NODE NODE NIC6 SYS SYS SYS SYS SYS SYS SYS PIX X NODE NODE NIC7 SYS SYS SYS SYS SYS SYS SYS NODE NODE X PIX NIC8 SYS SYS SYS SYS SYS SYS SYS NODE NODE PIX 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_0 NIC1: mlx5_1 NIC2: mlx5_2 NIC3: mlx5_3 NIC4: mlx5_4 NIC5: mlx5_5 NIC6: mlx5_6 NIC7: mlx5_7 NIC8: mlx5_8 ```

🐛 Describe the bug

I want to do multi-processing offline inference with each model assigned to 2 GPUs. If there's no vllm, I can do it using torchrun with HF models. But when I try to start a vllm.LLM in my code, it hangs. I followed this page and set those environment variables. Here is the output

INFO 08-28 13:58:51 logger.py:151] Trace frame log is saved to /tmp/vllm/vllm-instance-992d96df3ed145f69a82cd0877a66b1f/VLLM_TRACE_FUNCTION_for_process_17519_thread_140693197510464_at_2024-08-28_13:58:51.235776.log
(VllmWorkerProcess pid=17651) WARNING 08-28 13:58:51 logger.py:147] VLLM_TRACE_FUNCTION is enabled. It will record every function executed by Python. This will slow down the code. It is suggested to be used for debugging hang or crashes only.
(VllmWorkerProcess pid=17651) INFO 08-28 13:58:51 logger.py:151] Trace frame log is saved to /tmp/vllm/vllm-instance-992d96df3ed145f69a82cd0877a66b1f/VLLM_TRACE_FUNCTION_for_process_17651_thread_140693197510464_at_2024-08-28_13:58:51.235989.log
(VllmWorkerProcess pid=17651) INFO 08-28 13:58:52 multiproc_worker_utils.py:215] Worker ready; awaiting tasks
DEBUG 08-28 13:58:52 parallel_state.py:845] world_size=2 rank=0 local_rank=0 distributed_init_method=tcp://127.0.0.1:47579 backend=nccl
(VllmWorkerProcess pid=17651) DEBUG 08-28 13:58:52 parallel_state.py:845] world_size=2 rank=1 local_rank=1 distributed_init_method=tcp://127.0.0.1:47579 backend=nccl

So I wanna ask is vllm compatible with torchrun? Or how can I achieve my goal?(Using ray or start multiple serving?)

Before submitting a new issue...

DarkLight1337 commented 3 weeks ago

@youkaichao can you take a look at this?

HwwwwwwwH commented 3 weeks ago

image I followed the tip above and it hung too. Here is the outputs:

W0828 15:53:31.816000 140318794401600 torch/distributed/run.py:779]
W0828 15:53:31.816000 140318794401600 torch/distributed/run.py:779] *****************************************
W0828 15:53:31.816000 140318794401600 torch/distributed/run.py:779] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W0828 15:53:31.816000 140318794401600 torch/distributed/run.py:779] *****************************************
Unrecognized keys in `rope_scaling` for 'rope_type'='dynamic': {'type'}
INFO 08-28 15:53:37 config.py:813] Defaulting to use mp for distributed inference
INFO 08-28 15:53:37 llm_engine.py:184] Initializing an LLM engine (v0.5.5) with config: model='/home/jeeves/InternVL2-40B', speculative_config=None, tokenizer='/home/jeeves/InternVL2-40B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=24576, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, pipeline_parallel_size=1, 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'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=/home/jeeves/InternVL2-40B, use_v2_block_manager=False, enable_prefix_caching=False)
Unrecognized keys in `rope_scaling` for 'rope_type'='dynamic': {'type'}
INFO 08-28 15:53:37 config.py:813] Defaulting to use mp for distributed inference
INFO 08-28 15:53:37 llm_engine.py:184] Initializing an LLM engine (v0.5.5) with config: model='/home/jeeves/InternVL2-40B', speculative_config=None, tokenizer='/home/jeeves/InternVL2-40B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=24576, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, pipeline_parallel_size=1, 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'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=/home/jeeves/InternVL2-40B, use_v2_block_manager=False, enable_prefix_caching=False)
INFO 08-28 15:53:37 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager
INFO 08-28 15:53:37 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager
(VllmWorkerProcess pid=18876) INFO 08-28 15:53:38 multiproc_worker_utils.py:215] Worker ready; awaiting tasks
(VllmWorkerProcess pid=18879) INFO 08-28 15:53:38 multiproc_worker_utils.py:215] Worker ready; awaiting tasks
youkaichao commented 3 weeks ago

torchrun is only used for launching the test script.

if you want multi-gpu inference, just check out https://docs.vllm.ai/en/stable/serving/distributed_serving.html . you don't need torchrun .