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vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
Apache License 2.0
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[Feature]: Support custom `max_mm_tokens` #9169

Open SepehrV opened 3 days ago

SepehrV commented 3 days ago

Your current environment

Collecting environment information... 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: Debian GNU/Linux 11 (bullseye) (x86_64) GCC version: (Debian 10.2.1-6) 10.2.1 20210110 Clang version: Could not collect CMake version: version 3.18.4 Libc version: glibc-2.31

Python version: 3.9.2 (default, Feb 28 2021, 17:03:44) [GCC 10.2.1 20210110] (64-bit runtime) Python platform: Linux-5.4.210.bsk.6-amd64-x86_64-with-glibc2.31 Is CUDA available: True CUDA runtime version: 12.1.105 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: 535.161.08 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0 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 Byte Order: Little Endian Address sizes: 46 bits physical, 57 bits virtual CPU(s): 128 On-line CPU(s) list: 0-127 Thread(s) per core: 2 Core(s) per socket: 32 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 106 Model name: Intel(R) Xeon(R) Platinum 8336C CPU @ 2.30GHz Stepping: 6 CPU MHz: 3000.000 CPU max MHz: 3500.0000 CPU min MHz: 800.0000 BogoMIPS: 4600.00 Virtualization: VT-x L1d cache: 3 MiB L1i cache: 2 MiB L2 cache: 80 MiB L3 cache: 108 MiB 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: Mitigation; Clear CPU buffers; SMT vulnerable 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: Not affected 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 hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries: [pip3] byted-torch==2.1.0.post2 [pip3] byted-torch-monitor==0.0.1 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.26.2 [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] torchaudio==2.1.0+cu121 [pip3] torchvision==0.19.0 [pip3] transformers==4.45.0.dev0 [pip3] transformers-stream-generator==0.0.5 [pip3] triton==3.0.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.6.1.post2@4dfdf4319676c3dca72cdfba20470ac76d0cadf4 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 NIC0 NIC1 NIC2 NIC3 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV12 SYS SYS NODE PXB 32-63,96-127 1 N/A GPU1 NV12 X SYS SYS NODE PXB 32-63,96-127 1 N/A NIC0 SYS SYS X NODE SYS SYS NIC1 SYS SYS NODE X SYS SYS NIC2 NODE NODE SYS SYS X NODE NIC3 PXB PXB SYS SYS 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

How would you like to use vllm

currently, total number of images that can be passed to vllm engine for a multi-modal model is limited to max_seq_length // max_mm_tokens

https://github.com/vllm-project/vllm/blob/2a131965a8144d571a4a211a44d1fc32e202ae10/vllm/worker/model_runner.py#L1240

however, max_mm_tokens is quite large for qwen2-vl models (8575). This means at max sequence length of 32k, vllm would only allow 3 images to be passed to the model.

in reality however, the size of images are way smaller than what was used to calculate max_mm_tokens.

currently there is no workaround to indicate this in vllm launch (AFAIK).

any suggestion how I can circumvent this limitation?

Before submitting a new issue...

DarkLight1337 commented 3 days ago

This is a tricky issue as vLLM cannot recover from OOM. Overriding max_mm_tokens to a smaller value enables users to crash vLLM by sending images with specific resolutions (a vulnerability that you'll have to manage on your end, e.g. by resizing images before passing them into vLLM). If you're still ok with this, we can add custom max_mm_tokens as an advanced CLI option.

SepehrV commented 2 days ago

@DarkLight1337 got it. custom max_mm_tokens through cli would be a great option to have for now as a work around.

longer term though, is it possible to truncate visual tokens to the model to ensure OOM doesn't happen? Or is there any strong reason that VLLM has to set the limit on the number of input images (rather than final token count)?

DarkLight1337 commented 2 days ago

Or is there any strong reason that VLLM has to set the limit on the number of input images (rather than final token count)?

This is because images have to additionally go through the vision encoder of the model, making image tokens more expensive than text tokens. So, the worst case scenario of memory usage is when all of the input tokens are made up of image tokens. Without this setting, we don't know how many images the user can input in advance and thus cannot pre-allocate the model memory (which is needed to determine how much remaining memory is available for KV cache etc.)