[Bug]: loading embedding model intfloat/e5-mistral-7b-instruct results in a bind error

Your current environment

The output of `python collect_env.py`

OS: Ubuntu 22.04.2 LTS (x86_64) GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 Clang version: Could not collect CMake version: version 3.22.1 Libc version: glibc-2.35 Python version: 3.11.7 (main, Dec 15 2023, 18:12:31) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-1065-nvidia-x86_64-with-glibc2.35 Is CUDA available: N/A CUDA runtime version: 12.5.82 CUDA_MODULE_LOADING set to: N/A GPU models and configuration: GPU 0: NVIDIA H100 80GB HBM3 GPU 1: NVIDIA H100 80GB HBM3 GPU 2: NVIDIA H100 80GB HBM3 GPU 3: NVIDIA H100 80GB HBM3 GPU 4: NVIDIA H100 80GB HBM3 GPU 5: NVIDIA H100 80GB HBM3 GPU 6: NVIDIA H100 80GB HBM3 GPU 7: NVIDIA H100 80GB HBM3 Nvidia driver version: 535.183.06 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: N/A CPU: 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): 224 On-line CPU(s) list: 0-223 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8480CL CPU family: 6 Model: 143 Thread(s) per core: 2 Core(s) per socket: 56 Socket(s): 2 Stepping: 7 CPU max MHz: 3800.0000 CPU min MHz: 800.0000 BogoMIPS: 4000.00 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 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 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 hwp hwp_act_window hwp_epp hwp_pkg_req 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 L1d cache: 5.3 MiB (112 instances) L1i cache: 3.5 MiB (112 instances) L2 cache: 224 MiB (112 instances) L3 cache: 210 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-55,112-167 NUMA node1 CPU(s): 56-111,168-223 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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] flake8==6.0.0 [pip3] mypy==1.8.0 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.26.4 [pip3] numpydoc==1.5.0 [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-nccl-cu12==2.20.5 [pip3] nvidia-nvjitlink-cu12==12.6.20 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pyzmq==25.1.2 [conda] _anaconda_depends 2024.02 py311_mkl_1 [conda] blas 1.0 mkl [conda] mkl 2023.1.0 h213fc3f_46344 [conda] mkl-service 2.4.0 py311h5eee18b_1 [conda] mkl_fft 1.3.8 py311h5eee18b_0 [conda] mkl_random 1.2.4 py311hdb19cb5_0 [conda] numpy 1.26.4 py311h08b1b3b_0 [conda] numpy-base 1.26.4 py311hf175353_0 [conda] numpydoc 1.5.0 py311h06a4308_0 [conda] pyzmq 25.1.2 py311h6a678d5_0 ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: N/A 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 NIC4 NIC5 NIC6 NIC7 NIC8 NIC9 NIC10 NIC11 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 PXB NODE NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS 0-55,112-167 0 N/A GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 NODE NODE NODE PXB NODE NODE SYS SYS SYS SYS SYS SYS 0-55,112-167 0 N/A GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 NODE NODE NODE NODE PXB NODE SYS SYS SYS SYS SYS SYS 0-55,112-167 0 N/A GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 NODE NODE NODE NODE NODE PXB SYS SYS SYS SYS SYS SYS 0-55,112-167 0 N/A GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS SYS SYS SYS SYS PXB NODE NODE NODE NODE NODE 56-111,168-223 1 N/A GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS SYS SYS SYS SYS NODE NODE NODE PXB NODE NODE 56-111,168-223 1 N/A GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS SYS SYS SYS SYS NODE NODE NODE NODE PXB NODE 56-111,168-223 1 N/A GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS SYS SYS SYS SYS NODE NODE NODE NODE NODE PXB 56-111,168-223 1 N/A NIC0 PXB NODE NODE NODE SYS SYS SYS SYS X NODE NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS NIC1 NODE NODE NODE NODE SYS SYS SYS SYS NODE X PIX NODE NODE NODE SYS SYS SYS SYS SYS SYS NIC2 NODE NODE NODE NODE SYS SYS SYS SYS NODE PIX X NODE NODE NODE SYS SYS SYS SYS SYS SYS NIC3 NODE PXB NODE NODE SYS SYS SYS SYS NODE NODE NODE X NODE NODE SYS SYS SYS SYS SYS SYS NIC4 NODE NODE PXB NODE SYS SYS SYS SYS NODE NODE NODE NODE X NODE SYS SYS SYS SYS SYS SYS NIC5 NODE NODE NODE PXB SYS SYS SYS SYS NODE NODE NODE NODE NODE X SYS SYS SYS SYS SYS SYS NIC6 SYS SYS SYS SYS PXB NODE NODE NODE SYS SYS SYS SYS SYS SYS X NODE NODE NODE NODE NODE NIC7 SYS SYS SYS SYS NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS NODE X PIX NODE NODE NODE NIC8 SYS SYS SYS SYS NODE NODE NODE NODE SYS SYS SYS SYS SYS SYS NODE PIX X NODE NODE NODE NIC9 SYS SYS SYS SYS NODE PXB NODE NODE SYS SYS SYS SYS SYS SYS NODE NODE NODE X NODE NODE NIC10 SYS SYS SYS SYS NODE NODE PXB NODE SYS SYS SYS SYS SYS SYS NODE NODE NODE NODE X NODE NIC11 SYS SYS SYS SYS NODE NODE NODE PXB SYS SYS SYS SYS SYS SYS NODE NODE 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_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 NIC9: mlx5_9 NIC10: mlx5_10 NIC11: mlx5_11

Model Input Dumps

vllm-eb_1 | INFO 09-19 08:56:24 api_server.py:520] vLLM API server version 0.6.1.post2 vllm-eb_1 | INFO 09-19 08:56:24 api_server.py:521] args: Namespace(host='0.0.0.0', port=8000, uvicorn_log_level='info', allow_credentials=False, allowed_origins=[''], allowed_methods=[''], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=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=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_auto_tool_choice=False, tool_call_parser=None, model='intfloat/e5-mistral-7b-instruct', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=True, download_dir='/models', load_format='auto', config_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=None, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=8, 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, cpu_offload_gb=0, gpu_memory_utilization=0.9, 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, limit_mm_per_prompt=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, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_model=None, speculative_model_quantization=None, num_speculative_tokens=None, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, override_neuron_config=None, disable_log_requests=False, max_log_len=None, disable_fastapi_docs=False) vllm-eb_1 | INFO 09-19 08:56:25 config.py:876] Defaulting to use mp for distributed inference vllm-eb_1 | INFO 09-19 08:56:25 llm_engine.py:223] Initializing an LLM engine (v0.6.1.post2) with config: model='intfloat/e5-mistral-7b-instruct', speculative_config=None, tokenizer='intfloat/e5-mistral-7b-instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=32768, download_dir='/models', load_format=LoadFormat.AUTO, tensor_parallel_size=8, 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=intfloat/e5-mistral-7b-instruct, use_v2_block_manager=False, num_scheduler_steps=1, enable_prefix_caching=False, use_async_output_proc=False) vllm-eb_1 | WARNING 09-19 08:56:26 multiproc_gpu_executor.py:53] Reducing Torch parallelism from 112 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed. vllm-eb_1 | INFO 09-19 08:56:26 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager vllm-eb_1 | INFO 09-19 08:56:26 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | INFO 09-19 08:56:26 selector.py:116] Using XFormers backend. vllm-eb_1 | /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | @torch.library.impl_abstract("xformers_flash::flash_fwd") vllm-eb_1 | /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | @torch.library.impl_abstract("xformers_flash::flash_bwd") vllm-eb_1 | (VllmWorkerProcess pid=139) INFO 09-19 08:56:27 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=139) INFO 09-19 08:56:27 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=141) INFO 09-19 08:56:27 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=141) INFO 09-19 08:56:27 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=139) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=139) @torch.library.impl_abstract("xformers_flash::flash_fwd") vllm-eb_1 | (VllmWorkerProcess pid=141) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=141) @torch.library.impl_abstract("xformers_flash::flash_fwd") vllm-eb_1 | (VllmWorkerProcess pid=139) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=139) @torch.library.impl_abstract("xformers_flash::flash_bwd") vllm-eb_1 | (VllmWorkerProcess pid=141) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=141) @torch.library.impl_abstract("xformers_flash::flash_bwd") vllm-eb_1 | (VllmWorkerProcess pid=140) INFO 09-19 08:56:28 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=140) INFO 09-19 08:56:28 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=142) INFO 09-19 08:56:28 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=142) INFO 09-19 08:56:28 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=140) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=140) @torch.library.impl_abstract("xformers_flash::flash_fwd") vllm-eb_1 | (VllmWorkerProcess pid=142) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=142) @torch.library.impl_abstract("xformers_flash::flash_fwd") vllm-eb_1 | (VllmWorkerProcess pid=143) INFO 09-19 08:56:28 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=143) INFO 09-19 08:56:28 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=144) INFO 09-19 08:56:28 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=144) INFO 09-19 08:56:28 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=145) INFO 09-19 08:56:28 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=145) INFO 09-19 08:56:28 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=143) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=143) @torch.library.impl_abstract("xformers_flash::flash_fwd") vllm-eb_1 | (VllmWorkerProcess pid=140) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=140) @torch.library.impl_abstract("xformers_flash::flash_bwd") vllm-eb_1 | (VllmWorkerProcess pid=144) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=144) @torch.library.impl_abstract("xformers_flash::flash_fwd") vllm-eb_1 | (VllmWorkerProcess pid=142) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=142) @torch.library.impl_abstract("xformers_flash::flash_bwd") vllm-eb_1 | (VllmWorkerProcess pid=145) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:211: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=145) @torch.library.impl_abstract("xformers_flash::flash_fwd") vllm-eb_1 | (VllmWorkerProcess pid=143) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=143) @torch.library.impl_abstract("xformers_flash::flash_bwd") vllm-eb_1 | (VllmWorkerProcess pid=144) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=144) @torch.library.impl_abstract("xformers_flash::flash_bwd") vllm-eb_1 | (VllmWorkerProcess pid=145) /usr/local/lib/python3.12/dist-packages/xformers/ops/fmha/flash.py:344: FutureWarning: torch.library.impl_abstract was renamed to torch.library.register_fake. Please use that instead; we will remove torch.library.impl_abstract in a future version of PyTorch. vllm-eb_1 | (VllmWorkerProcess pid=145) @torch.library.impl_abstract("xformers_flash::flash_bwd") vllm-eb_1 | (VllmWorkerProcess pid=141) INFO 09-19 08:56:38 multiproc_worker_utils.py:218] Worker ready; awaiting tasks vllm-eb_1 | (VllmWorkerProcess pid=142) INFO 09-19 08:56:38 multiproc_worker_utils.py:218] Worker ready; awaiting tasks vllm-eb_1 | (VllmWorkerProcess pid=140) INFO 09-19 08:56:38 multiproc_worker_utils.py:218] Worker ready; awaiting tasks vllm-eb_1 | (VllmWorkerProcess pid=143) INFO 09-19 08:56:38 multiproc_worker_utils.py:218] Worker ready; awaiting tasks vllm-eb_1 | (VllmWorkerProcess pid=144) INFO 09-19 08:56:38 multiproc_worker_utils.py:218] Worker ready; awaiting tasks vllm-eb_1 | (VllmWorkerProcess pid=139) INFO 09-19 08:56:38 multiproc_worker_utils.py:218] Worker ready; awaiting tasks vllm-eb_1 | (VllmWorkerProcess pid=145) INFO 09-19 08:56:38 multiproc_worker_utils.py:218] Worker ready; awaiting tasks vllm-eb_1 | (VllmWorkerProcess pid=140) INFO 09-19 08:56:40 utils.py:982] Found nccl from library libnccl.so.2 vllm-eb_1 | (VllmWorkerProcess pid=140) INFO 09-19 08:56:40 pynccl.py:63] vLLM is using nccl==2.20.5 vllm-eb_1 | (VllmWorkerProcess pid=141) INFO 09-19 08:56:40 utils.py:982] Found nccl from library libnccl.so.2 vllm-eb_1 | (VllmWorkerProcess pid=141) INFO 09-19 08:56:40 pynccl.py:63] vLLM is using nccl==2.20.5 vllm-eb_1 | INFO 09-19 08:56:40 utils.py:982] Found nccl from library libnccl.so.2 vllm-eb_1 | (VllmWorkerProcess pid=139) INFO 09-19 08:56:40 utils.py:982] Found nccl from library libnccl.so.2 vllm-eb_1 | (VllmWorkerProcess pid=142) INFO 09-19 08:56:40 utils.py:982] Found nccl from library libnccl.so.2 vllm-eb_1 | (VllmWorkerProcess pid=145) INFO 09-19 08:56:40 utils.py:982] Found nccl from library libnccl.so.2 vllm-eb_1 | INFO 09-19 08:56:40 pynccl.py:63] vLLM is using nccl==2.20.5 vllm-eb_1 | (VllmWorkerProcess pid=144) INFO 09-19 08:56:40 utils.py:982] Found nccl from library libnccl.so.2 vllm-eb_1 | (VllmWorkerProcess pid=142) INFO 09-19 08:56:40 pynccl.py:63] vLLM is using nccl==2.20.5 vllm-eb_1 | (VllmWorkerProcess pid=145) INFO 09-19 08:56:40 pynccl.py:63] vLLM is using nccl==2.20.5 vllm-eb_1 | (VllmWorkerProcess pid=139) INFO 09-19 08:56:40 pynccl.py:63] vLLM is using nccl==2.20.5 vllm-eb_1 | (VllmWorkerProcess pid=144) INFO 09-19 08:56:40 pynccl.py:63] vLLM is using nccl==2.20.5 vllm-eb_1 | (VllmWorkerProcess pid=143) INFO 09-19 08:56:40 utils.py:982] Found nccl from library libnccl.so.2 vllm-eb_1 | (VllmWorkerProcess pid=143) INFO 09-19 08:56:40 pynccl.py:63] vLLM is using nccl==2.20.5 vllm-eb_1 | INFO 09-19 08:56:53 custom_all_reduce_utils.py:204] generating GPU P2P access cache in /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json vllm-eb_1 | INFO 09-19 08:57:52 custom_all_reduce_utils.py:242] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json vllm-eb_1 | (VllmWorkerProcess pid=141) INFO 09-19 08:57:52 custom_all_reduce_utils.py:242] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json vllm-eb_1 | (VllmWorkerProcess pid=140) INFO 09-19 08:57:52 custom_all_reduce_utils.py:242] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json vllm-eb_1 | (VllmWorkerProcess pid=143) INFO 09-19 08:57:52 custom_all_reduce_utils.py:242] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json vllm-eb_1 | (VllmWorkerProcess pid=145) INFO 09-19 08:57:52 custom_all_reduce_utils.py:242] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json vllm-eb_1 | (VllmWorkerProcess pid=139) INFO 09-19 08:57:52 custom_all_reduce_utils.py:242] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json vllm-eb_1 | (VllmWorkerProcess pid=142) INFO 09-19 08:57:52 custom_all_reduce_utils.py:242] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json vllm-eb_1 | (VllmWorkerProcess pid=144) INFO 09-19 08:57:52 custom_all_reduce_utils.py:242] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json vllm-eb_1 | INFO 09-19 08:57:52 shm_broadcast.py:238] vLLM message queue communication handle: Handle(connect_ip='127.0.0.1', local_reader_ranks=[1, 2, 3, 4, 5, 6, 7], buffer=<vllm.distributed.device_communicators.shm_broadcast.ShmRingBuffer object at 0x14b8d63ccf80>, local_subscribe_port=59627, remote_subscribe_port=None) vllm-eb_1 | INFO 09-19 08:57:52 model_runner.py:1014] Starting to load model intfloat/e5-mistral-7b-instruct... vllm-eb_1 | (VllmWorkerProcess pid=140) INFO 09-19 08:57:52 model_runner.py:1014] Starting to load model intfloat/e5-mistral-7b-instruct... vllm-eb_1 | (VllmWorkerProcess pid=139) INFO 09-19 08:57:52 model_runner.py:1014] Starting to load model intfloat/e5-mistral-7b-instruct... vllm-eb_1 | (VllmWorkerProcess pid=142) INFO 09-19 08:57:52 model_runner.py:1014] Starting to load model intfloat/e5-mistral-7b-instruct... vllm-eb_1 | (VllmWorkerProcess pid=144) INFO 09-19 08:57:52 model_runner.py:1014] Starting to load model intfloat/e5-mistral-7b-instruct... vllm-eb_1 | (VllmWorkerProcess pid=143) INFO 09-19 08:57:52 model_runner.py:1014] Starting to load model intfloat/e5-mistral-7b-instruct... vllm-eb_1 | (VllmWorkerProcess pid=145) INFO 09-19 08:57:52 model_runner.py:1014] Starting to load model intfloat/e5-mistral-7b-instruct... vllm-eb_1 | (VllmWorkerProcess pid=141) INFO 09-19 08:57:52 model_runner.py:1014] Starting to load model intfloat/e5-mistral-7b-instruct... vllm-eb_1 | INFO 09-19 08:57:52 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | INFO 09-19 08:57:52 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=140) INFO 09-19 08:57:52 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=140) INFO 09-19 08:57:52 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=141) INFO 09-19 08:57:52 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=141) INFO 09-19 08:57:52 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=145) INFO 09-19 08:57:52 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=145) INFO 09-19 08:57:52 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=144) INFO 09-19 08:57:52 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=144) INFO 09-19 08:57:52 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=139) INFO 09-19 08:57:52 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=139) INFO 09-19 08:57:52 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=143) INFO 09-19 08:57:52 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=143) INFO 09-19 08:57:52 selector.py:116] Using XFormers backend. vllm-eb_1 | (VllmWorkerProcess pid=142) INFO 09-19 08:57:52 selector.py:240] Cannot use FlashAttention-2 backend due to sliding window. vllm-eb_1 | (VllmWorkerProcess pid=142) INFO 09-19 08:57:52 selector.py:116] Using XFormers backend. vllm-eb_1 | INFO 09-19 08:57:52 weight_utils.py:242] Using model weights format ['.safetensors'] Loading safetensors checkpoint shards: 0% Completed | 0/2 [00:00<?, ?it/s] vllm-eb_1 | (VllmWorkerProcess pid=141) INFO 09-19 08:57:52 weight_utils.py:242] Using model weights format ['.safetensors'] vllm-eb_1 | (VllmWorkerProcess pid=145) INFO 09-19 08:57:52 weight_utils.py:242] Using model weights format ['.safetensors'] vllm-eb_1 | (VllmWorkerProcess pid=143) INFO 09-19 08:57:52 weight_utils.py:242] Using model weights format ['.safetensors'] vllm-eb_1 | (VllmWorkerProcess pid=142) INFO 09-19 08:57:52 weight_utils.py:242] Using model weights format ['.safetensors'] vllm-eb_1 | (VllmWorkerProcess pid=139) INFO 09-19 08:57:52 weight_utils.py:242] Using model weights format ['.safetensors'] vllm-eb_1 | (VllmWorkerProcess pid=144) INFO 09-19 08:57:52 weight_utils.py:242] Using model weights format ['.safetensors'] vllm-eb_1 | (VllmWorkerProcess pid=140) INFO 09-19 08:57:52 weight_utils.py:242] Using model weights format ['.safetensors'] Loading safetensors checkpoint shards: 50% Completed | 1/2 [00:00<00:00, 3.16it/s] Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:00<00:00, 2.22it/s] Loading safetensors checkpoint shards: 100% Completed | 2/2 [00:00<00:00, 2.32it/s] vllm-eb_1 | vllm-eb_1 | INFO 09-19 08:57:53 model_runner.py:1025] Loading model weights took 1.6646 GB vllm-eb_1 | (VllmWorkerProcess pid=141) INFO 09-19 08:57:54 model_runner.py:1025] Loading model weights took 1.6646 GB vllm-eb_1 | (VllmWorkerProcess pid=145) INFO 09-19 08:57:54 model_runner.py:1025] Loading model weights took 1.6646 GB vllm-eb_1 | (VllmWorkerProcess pid=143) INFO 09-19 08:57:54 model_runner.py:1025] Loading model weights took 1.6646 GB vllm-eb_1 | (VllmWorkerProcess pid=142) INFO 09-19 08:57:54 model_runner.py:1025] Loading model weights took 1.6646 GB vllm-eb_1 | (VllmWorkerProcess pid=140) INFO 09-19 08:57:54 model_runner.py:1025] Loading model weights took 1.6646 GB vllm-eb_1 | (VllmWorkerProcess pid=139) INFO 09-19 08:57:54 model_runner.py:1025] Loading model weights took 1.6646 GB vllm-eb_1 | (VllmWorkerProcess pid=144) INFO 09-19 08:57:54 model_runner.py:1025] Loading model weights took 1.6646 GB vllm-eb_1 | INFO 09-19 08:57:55 serving_embedding.py:192] Activating the server engine with embedding enabled. vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:19] Available routes are: vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:27] Route: /openapi.json, Methods: HEAD, GET vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:27] Route: /docs, Methods: HEAD, GET vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:27] Route: /docs/oauth2-redirect, Methods: HEAD, GET vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:27] Route: /redoc, Methods: HEAD, GET vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:27] Route: /health, Methods: GET vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:27] Route: /tokenize, Methods: POST vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:27] Route: /detokenize, Methods: POST vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:27] Route: /v1/models, Methods: GET vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:27] Route: /version, Methods: GET vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:27] Route: /v1/chat/completions, Methods: POST vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:27] Route: /v1/completions, Methods: POST vllm-eb_1 | INFO 09-19 08:57:55 launcher.py:27] Route: /v1/embeddings, Methods: POST vllm-eb_1 | INFO: Started server process [1] vllm-eb_1 | INFO: Waiting for application startup. vllm-eb_1 | INFO: Application startup complete. vllm-eb_1 | ERROR: [Errno 98] error while attempting to bind on address ('0.0.0.0', 8000): address already in use vllm-eb_1 | INFO: Waiting for application shutdown. vllm-eb_1 | INFO: Application shutdown complete. vllm-eb_1 | (VllmWorkerProcess pid=144) INFO 09-19 08:57:55 multiproc_worker_utils.py:244] Worker exiting vllm-eb_1 | (VllmWorkerProcess pid=141) INFO 09-19 08:57:55 multiproc_worker_utils.py:244] Worker exiting vllm-eb_1 | (VllmWorkerProcess pid=139) INFO 09-19 08:57:55 multiproc_worker_utils.py:244] Worker exiting vllm-eb_1 | (VllmWorkerProcess pid=140) INFO 09-19 08:57:55 multiproc_worker_utils.py:244] Worker exiting vllm-eb_1 | (VllmWorkerProcess pid=145) INFO 09-19 08:57:55 multiproc_worker_utils.py:244] Worker exiting vllm-eb_1 | (VllmWorkerProcess pid=142) INFO 09-19 08:57:55 multiproc_worker_utils.py:244] Worker exiting vllm-eb_1 | (VllmWorkerProcess pid=143) INFO 09-19 08:57:55 multiproc_worker_utils.py:244] Worker exiting vllm-eb_1 | ERROR 09-19 08:57:55 multiproc_worker_utils.py:120] Worker VllmWorkerProcess pid 143 died, exit code: 1 vllm-eb_1 | INFO 09-19 08:57:55 multiproc_worker_utils.py:124] Killing local vLLM worker processes vllm-eb_1 | [rank0]:[W919 08:57:57.219824437 CudaIPCTypes.cpp:16] Producer process has been terminated before all shared CUDA tensors released. See Note [Sharing CUDA tensors] vllm-eb_1 | /usr/lib/python3.12/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked shared_memory objects to clean up at shutdown vllm-eb_1 | warnings.warn('resource_tracker: There appear to be %d '

🐛 Describe the bug

When building vllm-eb using this docker container:

version: '3.8'

services:
  vllm-eb:
    volumes:
      - ./models:/models
    networks:
      - vllm
    image: deployment/vllm:llama
    build:
      context: ./vllm
      target: vllm-openai
      args:
        max_jobs: 8
        nvcc_threads: 4
        RUN_WHEEL_CHECK: "false"
    runtime: nvidia
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              capabilities: [ gpu ]
    ulimits:
      memlock: -1
      stack: -1
    ports:
      - "3010:8000"
    ipc: host
    command:
      - "--model"
      - "intfloat/e5-mistral-7b-instruct"
      - "--gpu-memory-utilization"
      - ".90"
      - "--tensor-parallel-size"
      - "8"
      - "--trust-remote-code"
      - "--download-dir"
      - "/models"
      - "--host"
      - "0.0.0.0"
networks:
  vllm:

I get the following error when composing up:

vllm-eb_1 | ERROR: [Errno 98] error while attempting to bind on address ('0.0.0.0', 8000): address already in use

I tried reproducing with other LLM models to see if this was just a embedding model issue and they were able to boot up properly.

Thanks for looking into this issue!

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vllm-project / vllm