Closed warlockedward closed 2 weeks ago
DarkLight1337
commented
2 weeks ago The stack trace indicates that you have OOM issue. Please decrease max_model_len and/or max_num_seqs so the model can fit inside your GPUs. You can also increase tp to split the model across more GPUs.
warlockedward
commented
2 weeks ago The stack trace indicates that you have OOM issue. Please decrease
max_model_lenand/ormax_num_seqsso the model can fit inside your GPUs. You can also increasetpto split the model across more GPUs.
I tried modifying max_model_len it does work, but in the original v0.6.2 version I kept the existing parameters running with no task exceptions, I'm not sure what changed with this update to v0.6.3 to cause this problem
DarkLight1337
commented
2 weeks ago The stack trace indicates that you have OOM issue. Please decrease
max_model_lenand/ormax_num_seqsso the model can fit inside your GPUs. You can also increasetpto split the model across more GPUs.I tried modifying max_model_len it does work, but in the original v0.6.2 version I kept the existing parameters running with no task exceptions, I'm not sure what changed with this update to v0.6.3 to cause this problem
The previous version didn't allow multi-image input. I see that you've set --limit-mm-per-prompt image=2,video=1 which will cause the model to allocate additional memory, now that multi-image is supported. (By the way, video input isn't supported for this model)
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 22.04.4 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.22.1 Libc version: glibc-2.35 Python version: 3.11.10 (main, Oct 3 2024, 07:29:13) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-113-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.5.82 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: Tesla V100-SXM2-32GB GPU 1: Tesla V100-SXM2-32GB GPU 2: Tesla V100-SXM2-32GB GPU 3: Tesla V100-SXM2-32GB GPU 4: Tesla V100-SXM2-32GB GPU 5: Tesla V100-SXM2-32GB GPU 6: Tesla V100-SXM2-32GB GPU 7: Tesla V100-SXM2-32GB Nvidia driver version: 555.42.06 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.so.9.0.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.0.0 /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.so.9.0.0 /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_engines_precompiled.so.9.0.0 /usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.0.0 /usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.0.0 /usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.0.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.0.0 /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) Gold 6271C CPU @ 2.60GHz CPU family: 6 Model: 85 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 2 Stepping: 7 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 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 hwp_epp 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: 66 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-23,48-71 NUMA node1 CPU(s): 24-47,72-95 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled 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 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 IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI Syscall hardening, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; TSX disabled 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==12.6.77 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pyzmq==26.2.0 [pip3] torch==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.45.2 [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 12.6.77 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] torchvision 0.19.0 pypi_0 pypi [conda] transformers 4.45.2 pypi_0 pypi [conda] triton 3.0.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.6.4.dev3+gf0fe4fe8 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 GPU NUMA ID GPU0 X NV1 NV2 NV1 SYS SYS SYS NV2 NODE NODE SYS SYS 0-23,48-71 0 N/A GPU1 NV1 X NV1 NV2 SYS SYS NV2 SYS NODE NODE SYS SYS 0-23,48-71 0 N/A GPU2 NV2 NV1 X NV2 SYS NV1 SYS SYS PIX PIX SYS SYS 0-23,48-71 0 N/A GPU3 NV1 NV2 NV2 X NV1 SYS SYS SYS PIX PIX SYS SYS 0-23,48-71 0 N/A GPU4 SYS SYS SYS NV1 X NV2 NV2 NV1 SYS SYS PIX PIX 24-47,72-95 1 N/A GPU5 SYS SYS NV1 SYS NV2 X NV1 NV2 SYS SYS PIX PIX 24-47,72-95 1 N/A GPU6 SYS NV2 SYS SYS NV2 NV1 X NV1 SYS SYS NODE NODE 24-47,72-95 1 N/A GPU7 NV2 SYS SYS SYS NV1 NV2 NV1 X SYS SYS NODE NODE 24-47,72-95 1 N/A NIC0 NODE NODE PIX PIX SYS SYS SYS SYS X PIX SYS SYS NIC1 NODE NODE PIX PIX SYS SYS SYS SYS PIX X SYS SYS NIC2 SYS SYS SYS SYS PIX PIX NODE NODE SYS SYS X PIX NIC3 SYS SYS SYS SYS PIX PIX NODE NODE SYS SYS 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 ```Model Input Dumps
INFO 10-15 02:46:25 api_server.py:166] Multiprocessing frontend to use ipc:///tmp/e45c9a02-fb52-4618-b461-49904a0cac09 for IPC Path. INFO 10-15 02:46:25 api_server.py:179] Started engine process with PID 1016869 WARNING 10-15 02:46:25 config.py:1674] Casting torch.bfloat16 to torch.float16. /model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/connections.py:8: RuntimeWarning: Failed to read commit hash: No module named 'vllm._version' from vllm.version import version as VLLM_VERSION WARNING 10-15 02:46:29 config.py:1674] Casting torch.bfloat16 to torch.float16. INFO 10-15 02:46:30 config.py:887] Defaulting to use mp for distributed inference WARNING 10-15 02:46:30 config.py:380] To see benefits of async output processing, enable CUDA graph. Since, enforce-eager is enabled, async output processor cannot be used INFO 10-15 02:46:34 config.py:887] Defaulting to use mp for distributed inference WARNING 10-15 02:46:34 config.py:380] To see benefits of async output processing, enable CUDA graph. Since, enforce-eager is enabled, async output processor cannot be used INFO 10-15 02:46:34 llm_engine.py:237] Initializing an LLM engine (vdev) with config: model='/model/models/Llama-3.2-11B-Vision-Instruct', speculative_config=None, tokenizer='/model/models/Llama-3.2-11B-Vision-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=False, dtype=torch.float16, max_seq_len=32768, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=2, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, 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=18, served_model_name=Llama-3.2-11B-Vision-Instruct, use_v2_block_manager=True, num_scheduler_steps=1, chunked_prefill_enabled=False multi_step_stream_outputs=True, enable_prefix_caching=False, use_async_output_proc=False, use_cached_outputs=True, mm_processor_kwargs=None) WARNING 10-15 02:46:34 multiproc_gpu_executor.py:53] Reducing Torch parallelism from 48 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed. INFO 10-15 02:46:34 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager INFO 10-15 02:46:35 enc_dec_model_runner.py:141] EncoderDecoderModelRunner requires XFormers backend; overriding backend auto-selection and forcing XFormers. INFO 10-15 02:46:35 selector.py:115] Using XFormers backend. /model/anaconda3/envs/llm/lib/python3.11/site-packages/xformers/ops/fmha/flash.py:211: FutureWarning:
torch.library.impl_abstractwas renamed totorch.library.register_fake. Please use that instead; we will removetorch.library.impl_abstractin a future version of PyTorch. @torch.library.impl_abstract("xformers_flash::flash_fwd") /model/anaconda3/envs/llm/lib/python3.11/site-packages/xformers/ops/fmha/flash.py:344: FutureWarning:torch.library.impl_abstractwas renamed totorch.library.register_fake. Please use that instead; we will removetorch.library.impl_abstractin a future version of PyTorch. @torch.library.impl_abstract("xformers_flash::flash_bwd") /model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/connections.py:8: RuntimeWarning: Failed to read commit hash: No module named 'vllm._version' from vllm.version import version as VLLM_VERSION (VllmWorkerProcess pid=1017165) INFO 10-15 02:46:38 enc_dec_model_runner.py:141] EncoderDecoderModelRunner requires XFormers backend; overriding backend auto-selection and forcing XFormers. (VllmWorkerProcess pid=1017165) INFO 10-15 02:46:38 selector.py:115] Using XFormers backend. (VllmWorkerProcess pid=1017165) /model/anaconda3/envs/llm/lib/python3.11/site-packages/xformers/ops/fmha/flash.py:211: FutureWarning:torch.library.impl_abstractwas renamed totorch.library.register_fake. Please use that instead; we will removetorch.library.impl_abstractin a future version of PyTorch. (VllmWorkerProcess pid=1017165) @torch.library.impl_abstract("xformers_flash::flash_fwd") (VllmWorkerProcess pid=1017165) /model/anaconda3/envs/llm/lib/python3.11/site-packages/xformers/ops/fmha/flash.py:344: FutureWarning:torch.library.impl_abstractwas renamed totorch.library.register_fake. Please use that instead; we will removetorch.library.impl_abstractin a future version of PyTorch. (VllmWorkerProcess pid=1017165) @torch.library.impl_abstract("xformers_flash::flash_bwd") (VllmWorkerProcess pid=1017165) INFO 10-15 02:46:39 multiproc_worker_utils.py:216] Worker ready; awaiting tasks INFO 10-15 02:46:39 utils.py:1008] Found nccl from library libnccl.so.2 (VllmWorkerProcess pid=1017165) INFO 10-15 02:46:39 utils.py:1008] Found nccl from library libnccl.so.2 INFO 10-15 02:46:39 pynccl.py:63] vLLM is using nccl==2.20.5 (VllmWorkerProcess pid=1017165) INFO 10-15 02:46:39 pynccl.py:63] vLLM is using nccl==2.20.5 (VllmWorkerProcess pid=1017165) INFO 10-15 02:46:39 custom_all_reduce_utils.py:242] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_4,5.json INFO 10-15 02:46:39 custom_all_reduce_utils.py:242] reading GPU P2P access cache from /root/.cache/vllm/gpu_p2p_access_cache_for_4,5.json INFO 10-15 02:46:39 shm_broadcast.py:241] vLLM message queue communication handle: Handle(connect_ip='127.0.0.1', local_reader_ranks=[1], buffer=<vllm.distributed.device_communicators.shm_broadcast.ShmRingBuffer object at 0x7f54e3b09110>, local_subscribe_port=45621, remote_subscribe_port=None) INFO 10-15 02:46:39 model_runner.py:1060] Starting to load model /model/models/Llama-3.2-11B-Vision-Instruct... (VllmWorkerProcess pid=1017165) INFO 10-15 02:46:39 model_runner.py:1060] Starting to load model /model/models/Llama-3.2-11B-Vision-Instruct... INFO 10-15 02:46:39 selector.py:115] Using XFormers backend. (VllmWorkerProcess pid=1017165) INFO 10-15 02:46:39 selector.py:115] Using XFormers backend. Loading safetensors checkpoint shards: 0% Completed | 0/5 [00:00<?, ?it/s] Loading safetensors checkpoint shards: 20% Completed | 1/5 [00:02<00:11, 2.97s/it] Loading safetensors checkpoint shards: 40% Completed | 2/5 [00:06<00:09, 3.06s/it] Loading safetensors checkpoint shards: 60% Completed | 3/5 [00:07<00:04, 2.18s/it] Loading safetensors checkpoint shards: 80% Completed | 4/5 [00:10<00:02, 2.46s/it] Loading safetensors checkpoint shards: 100% Completed | 5/5 [00:13<00:00, 2.66s/it] Loading safetensors checkpoint shards: 100% Completed | 5/5 [00:13<00:00, 2.63s/it]INFO 10-15 02:46:53 model_runner.py:1071] Loading model weights took 10.0714 GB (VllmWorkerProcess pid=1017165) INFO 10-15 02:46:53 model_runner.py:1071] Loading model weights took 10.0714 GB INFO 10-15 02:46:53 enc_dec_model_runner.py:301] Starting profile run for multi-modal models. (VllmWorkerProcess pid=1017165) INFO 10-15 02:46:53 enc_dec_model_runner.py:301] Starting profile run for multi-modal models. Process SpawnProcess-1: ERROR 10-15 02:47:20 multiproc_worker_utils.py:117] Worker VllmWorkerProcess pid 1017165 died, exit code: -15 INFO 10-15 02:47:20 multiproc_worker_utils.py:121] Killing local vLLM worker processes Traceback (most recent call last): File "/model/anaconda3/envs/llm/lib/python3.11/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/model/anaconda3/envs/llm/lib/python3.11/multiprocessing/process.py", line 108, in run self._target(*self._args, self._kwargs) File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/engine/multiprocessing/engine.py", line 392, in run_mp_engine engine = MQLLMEngine.from_engine_args(engine_args=engine_args, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/engine/multiprocessing/engine.py", line 141, in from_engine_args return cls( ^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/engine/multiprocessing/engine.py", line 78, in init self.engine = LLMEngine(args, ^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/engine/llm_engine.py", line 349, in init self._initialize_kv_caches() File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/engine/llm_engine.py", line 484, in _initialize_kv_caches self.model_executor.determine_num_available_blocks()) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/executor/distributed_gpu_executor.py", line 39, in determine_num_available_blocks num_blocks = self._run_workers("determine_num_available_blocks", ) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/executor/multiproc_gpu_executor.py", line 192, in _run_workers driver_worker_output = driver_worker_method(args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context return func(*args, kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/worker/worker.py", line 223, in determine_num_available_blocks self.model_runner.profile_run() File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context return func(*args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/worker/enc_dec_model_runner.py", line 359, in profile_run self.execute_model(model_input, kv_caches, intermediate_tensors) File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context return func(args, kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/worker/enc_dec_model_runner.py", line 203, in execute_model hidden_or_intermediate_states = model_executable( ^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl return self._call_impl(*args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl return forward_call(*args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/model_executor/models/mllama.py", line 1084, in forward cross_attention_states = self.vision_model(pixel_values, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl return self._call_impl(args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl return forward_call(*args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/model_executor/models/mllama.py", line 508, in forward patch_embeds = self.patch_embedding( ^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl return self._call_impl(*args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl return forward_call(args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/model_executor/models/mllama.py", line 227, in forward x = self._unfold(x) ^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl return self._call_impl(*args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl return forward_call(args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/nn/modules/fold.py", line 298, in forward return F.unfold(input, self.kernel_size, self.dilation, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/torch/nn/functional.py", line 4853, in unfold return torch._C._nn.im2col(input, _pair(kernel_size), _pair(dilation), _pair(padding), _pair(stride)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.59 GiB. GPU 0 has a total capacity of 31.73 GiB of which 3.11 GiB is free. Including non-PyTorch memory, this process has 28.62 GiB memory in use. Of the allocated memory 28.12 GiB is allocated by PyTorch, and 80.04 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [rank0]:[W1015 02:47:22.322553616 CudaIPCTypes.cpp:16] Producer process has been terminated before all shared CUDA tensors released. See Note [Sharing CUDA tensors] Traceback (most recent call last): File "/model/anaconda3/envs/llm/bin/vllm", line 8, in
sys.exit(main())
^^^^^^
File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/scripts.py", line 195, in main
args.dispatch_function(args)
File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/scripts.py", line 41, in serve
uvloop.run(run_server(args))
File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/uvloop/init.py", line 105, in run
return runner.run(wrapper())
^^^^^^^^^^^^^^^^^^^^^
File "/model/anaconda3/envs/llm/lib/python3.11/asyncio/runners.py", line 118, in run
return self._loop.run_until_complete(task)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/uvloop/init.py", line 61, in wrapper
return await main
^^^^^^^^^^
File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 552, in run_server
async with build_async_engine_client(args) as engine_client:
File "/model/anaconda3/envs/llm/lib/python3.11/contextlib.py", line 210, in aenter
return await anext(self.gen)
^^^^^^^^^^^^^^^^^^^^^
File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 107, in build_async_engine_client
async with build_async_engine_client_from_engine_args(
File "/model/anaconda3/envs/llm/lib/python3.11/contextlib.py", line 210, in aenter
return await anext(self.gen)
^^^^^^^^^^^^^^^^^^^^^
File "/model/anaconda3/envs/llm/lib/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 194, in build_async_engine_client_from_engine_args
raise RuntimeError(
RuntimeError: Engine process failed to start
(llm) root@ubuntu:/model# /model/anaconda3/envs/llm/lib/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '
/model/anaconda3/envs/llm/lib/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked shared_memory objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '
🐛 Describe the bug
CUDA_VISIBLE_DEVICES=4,5 vllm serve /model/models/Llama-3.2-11B-Vision-Instruct --port xxxxxxx --api-key sk-xxxxxxxxxxxxxxxxxxxxxxxx -tp 2 --served-model-name Llama-3.2-11B-Vision-Instruct --dtype float16 --enforce-eager --limit-mm-per-prompt image=2,video=1 --seed 18 --enable-auto-tool-choice --tool-call-parser llama3_json --chat-template /model/vllm/examples/tool_chat_template_llama3.2_json.jinja --max-model-len 32768
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