Open khayamgondal opened 1 week ago
DarkLight1337
commented
1 week ago Just a sanity check, are you running other processes that use that GPU at the same time as vLLM? You can try to reduce the memory usage by setting --max-model-len and/or --max-num-seqs. On the other hand, --gpu-memory-utilization should be set to a higher value to let vLLM use more GPU.
khayamgondal
commented
1 week ago Thanks, I am not running other processes.
I am still seeing OOM even with specifying max-model-len=32
vllm serve /models/LLM/Llama-3.2-11B-Vision --disable-log-requests --gpu-memory-utilization 0.9 --max-model-len 32
OOM errors are thrown after INFO 10-24 18:10:17 enc_dec_model_runner.py:301] Starting profile run for multi-modal models.
With max-num-seqs I see a different error (below)
vllm serve /models/LLM/Llama-3.2-11B-Vision --disable-log-requests --gpu-memory-utilization 0.9 --max-model-len 512 --max-num-seqs 128
INFO 10-24 18:12:26 model_runner.py:1067] Loading model weights took 19.8557 GB
INFO 10-24 18:12:26 enc_dec_model_runner.py:301] Starting profile run for multi-modal models.
INFO 10-24 18:12:39 gpu_executor.py:122] # GPU blocks: 2252, # CPU blocks: 1638
INFO 10-24 18:12:39 gpu_executor.py:126] Maximum concurrency for 512 tokens per request: 70.38x
INFO 10-24 18:12:41 model_runner.py:1395] 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 10-24 18:12:41 model_runner.py:1399] 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.
Process SpawnProcess-1:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 1795, in capture
output_hidden_or_intermediate_states = self.model(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1552, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1561, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/mllama.py", line 1233, in forward
skip_cross_attention = max(attn_metadata.encoder_seq_lens) == 0
RuntimeError: CUDA error: operation not permitted when stream is capturing
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.10/dist-packages/vllm/engine/multiprocessing/engine.py", line 390, in run_mp_engine
engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
File "/usr/local/lib/python3.10/dist-packages/vllm/engine/multiprocessing/engine.py", line 139, in from_engine_args
return cls(
File "/usr/local/lib/python3.10/dist-packages/vllm/engine/multiprocessing/engine.py", line 78, in __init__
self.engine = LLMEngine(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 348, in __init__
self._initialize_kv_caches()
File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 496, in _initialize_kv_caches
self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks)
File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 129, in initialize_cache
self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 271, in initialize_cache
self._warm_up_model()
File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 287, in _warm_up_model
self.model_runner.capture_model(self.gpu_cache)
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 1515, in capture_model
graph_runner.capture(**capture_inputs)
File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 1794, in capture
with torch.cuda.graph(self._graph, pool=memory_pool, stream=stream):
File "/usr/local/lib/python3.10/dist-packages/torch/cuda/graphs.py", line 184, in __exit__
self.cuda_graph.capture_end()
File "/usr/local/lib/python3.10/dist-packages/torch/cuda/graphs.py", line 82, in capture_end
super().capture_end()
RuntimeError: CUDA error: operation failed due to a previous error during capture
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Traceback (most recent call last):
File "/usr/local/bin/vllm", line 8, in <module>
sys.exit(main())
File "/usr/local/lib/python3.10/dist-packages/vllm/scripts.py", line 195, in main
args.dispatch_function(args)
File "/usr/local/lib/python3.10/dist-packages/vllm/scripts.py", line 41, in serve
uvloop.run(run_server(args))
File "/usr/local/lib/python3.10/dist-packages/uvloop/__init__.py", line 82, in run
return loop.run_until_complete(wrapper())
File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
File "/usr/local/lib/python3.10/dist-packages/uvloop/__init__.py", line 61, in wrapper
return await main
File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 552, in run_server
async with build_async_engine_client(args) as engine_client:
File "/usr/lib/python3.10/contextlib.py", line 199, in __aenter__
return await anext(self.gen)
File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 107, in build_async_engine_client
async with build_async_engine_client_from_engine_args(
File "/usr/lib/python3.10/contextlib.py", line 199, in __aenter__
return await anext(self.gen)
File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 194, in build_async_engine_client_from_engine_args
raise RuntimeError(
RuntimeError: Engine process failed to startEven with specifying cpu-offload-gb I see OOM
Looks like OOM happens after Starting profile run for multi-modal models.
To my surprise I can load NVLM using
vllm serve /models/LLM/NVLM-D-72B/ --disable-log-requests --gpu-memory-utilization 0.7 --cpu-offload-gb 100 --max-model-len 512
DarkLight1337
commented
1 week ago Can you try the settings shown in the example script?
Your current environment
The output of `python collect_env.py`
``` Collecting environment information... PyTorch version: 2.4.0a0+3bcc3cddb5.nv24.07 Is debug build: False CUDA used to build PyTorch: 12.5 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.4 LTS (aarch64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.30.0 Libc version: glibc-2.35 Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-6.5.0-1024-nvidia-64k-aarch64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GH200 480GB Nvidia driver version: 550.107.02 cuDNN version: Probably one of the following: /usr/lib/aarch64-linux-gnu/libcudnn.so.9.2.1 /usr/lib/aarch64-linux-gnu/libcudnn_adv.so.9.2.1 /usr/lib/aarch64-linux-gnu/libcudnn_cnn.so.9.2.1 /usr/lib/aarch64-linux-gnu/libcudnn_engines_precompiled.so.9.2.1 /usr/lib/aarch64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.2.1 /usr/lib/aarch64-linux-gnu/libcudnn_graph.so.9.2.1 /usr/lib/aarch64-linux-gnu/libcudnn_heuristic.so.9.2.1 /usr/lib/aarch64-linux-gnu/libcudnn_ops.so.9.2.1 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: aarch64 CPU op-mode(s): 64-bit Byte Order: Little Endian CPU(s): 72 On-line CPU(s) list: 0-71 Vendor ID: ARM Model name: Neoverse-V2 Model: 0 Thread(s) per core: 1 Core(s) per socket: 72 Socket(s): 1 Stepping: r0p0 Frequency boost: disabled CPU max MHz: 3456.0000 CPU min MHz: 81.0000 BogoMIPS: 2000.00 Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti L1d cache: 4.5 MiB (72 instances) L1i cache: 4.5 MiB (72 instances) L2 cache: 72 MiB (72 instances) L3 cache: 114 MiB (1 instance) NUMA node(s): 9 NUMA node0 CPU(s): 0-71 NUMA node1 CPU(s): NUMA node2 CPU(s): NUMA node3 CPU(s): NUMA node4 CPU(s): NUMA node5 CPU(s): NUMA node6 CPU(s): NUMA node7 CPU(s): NUMA node8 CPU(s): 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 Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; __user pointer sanitization Vulnerability Spectre v2: Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] jupyterlab_nvidia_nsight==0.6.0 [pip3] numpy==1.26.4 [pip3] nvidia-cudnn-frontend==1.5.1 [pip3] nvidia-dali-cuda120==1.39.0 [pip3] nvidia-ml-py==12.560.30 [pip3] nvidia-modelopt==0.13.0 [pip3] nvidia-nvimgcodec-cu12==0.2.0.7 [pip3] nvidia-pyindex==1.0.9 [pip3] onnx==1.16.0 [pip3] optree==0.12.1 [pip3] pytorch-triton==3.0.0+989adb9a2 [pip3] pyzmq==26.0.3 [pip3] torch==2.4.0a0+3bcc3cddb5.nv24.7 [pip3] torch-tensorrt==2.5.0a0 [pip3] torchvision==0.19.0a0 [pip3] transformers==4.45.2 [pip3] triton==3.0.0+git692143cd [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.6.3.post2.dev23+g440130a9 vLLM Build Flags: CUDA Archs: 9.0+PTX; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 NIC0 NIC1 NIC2 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X SYS SYS SYS 0-71 0 1 NIC0 SYS X SYS SYS NIC1 SYS SYS X PIX NIC2 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 ```Model Input Dumps
No response
🐛 Describe the bug
vllm serve fails with OOM error.
I am running server like
vllm serve /models/LLM/Llama-3.2-11B-Vision --disable-log-requests --gpu-memory-utilization 0.2I also tried different values for gpu-memory-utilizationVLLM version == 0.6.3Before submitting a new issue...