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A high-throughput and memory-efficient inference and serving engine for LLMs
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[Bug]: RAM OOM Error Loading 480GB MoE Model Despite Fix in PR #1395 #4786

Open hxer7963 opened 6 months ago

hxer7963 commented 6 months ago

Your current environment

PyTorch version: 2.1.2+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: 14.0.0-1ubuntu1.1 CMake version: version 3.26.2 Libc version: glibc-2.35

Python version: 3.9.18 (main, Sep 11 2023, 13:41:44) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.4.119-19-0009.11-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 A800-SXM4-80GB GPU 1: NVIDIA A800-SXM4-80GB GPU 2: NVIDIA A800-SXM4-80GB GPU 3: NVIDIA A800-SXM4-80GB GPU 4: NVIDIA A800-SXM4-80GB GPU 5: NVIDIA A800-SXM4-80GB GPU 6: NVIDIA A800-SXM4-80GB GPU 7: NVIDIA A800-SXM4-80GB

Nvidia driver version: 470.182.03 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: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 232 On-line CPU(s) list: 0-231 Vendor ID: AuthenticAMD Model name: AMD EPYC 7K83 64-Core Processor CPU family: 25 Model: 1 Thread(s) per core: 2 Core(s) per socket: 58 Socket(s): 2 Stepping: 1 BogoMIPS: 4890.80 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr wbnoinvd arat umip pku ospke vaes vpclmulqdq rdpid fsrm Hypervisor vendor: KVM Virtualization type: full L1d cache: 3.6 MiB (116 instances) L1i cache: 3.6 MiB (116 instances) L2 cache: 58 MiB (116 instances) L3 cache: 512 MiB (16 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-115 NUMA node1 CPU(s): 116-231 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: 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; Full AMD retpoline, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] mypy==0.991 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.26.4 [pip3] nvidia-nccl-cu12==2.18.1 [pip3] torch==2.1.2 [pip3] triton==2.1.0 [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-nccl-cu12 2.18.1 pypi_0 pypi [conda] torch 2.1.2 pypi_0 pypi [conda] triton 2.1.0 pypi_0 pypiROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.4.0 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU0 X NV8 NV8 NV8 NV8 NV8 NV8 NV8 0-115 0 GPU1 NV8 X NV8 NV8 NV8 NV8 NV8 NV8 0-115 0 GPU2 NV8 NV8 X NV8 NV8 NV8 NV8 NV8 0-115 0 GPU3 NV8 NV8 NV8 X NV8 NV8 NV8 NV8 0-115 0 GPU4 NV8 NV8 NV8 NV8 X NV8 NV8 NV8 116-231 1 GPU5 NV8 NV8 NV8 NV8 NV8 X NV8 NV8 116-231 1 GPU6 NV8 NV8 NV8 NV8 NV8 NV8 X NV8 116-231 1 GPU7 NV8 NV8 NV8 NV8 NV8 NV8 NV8 X 116-231 1

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

🐛 Describe the bug

I run the following command on a server with 8 * A800 (80GB) GPUs and 1.8TB of memory:

python -m vllm.entrypoints.openai.api_server --model /mnt/llm_dataset/models/xverse_model/moe/moe24b-v112-276b/  --dtype auto --api-key xxx
 --trust-remote-code -tp 8 --port 8001

I noticed that vLLM initially loads the model into the 8 A800 GPUs, with each GPU consuming approximately 64GB of memory on average, without pre-allocating memory. Subsequently, I observed that the DRAM memory keeps increasing until an Out-of-Memory (OOM) error occurs. Please refer to the screenshot below for details.

p0 p1 p2

Then, I learned that I can reduce the risk of RAM OOM by setting the --max-parallel-loading-workers parameter. However, when I set --max-parallel-loading-workers 1, I encountered the following error:

NotImplementedError: max_concurrent_workers is not supported yet.

2588

I noticed that PR #1395 has fixed the RAM OOM issue, but why am I still encountering OOM errors when loading a 480GB MoE model with 8tp and 1.8TB of memory?

mgoin commented 6 months ago

Hi @hxer7963, if my math is correct I do not think you can fit an unquantized 480GB model on 8x80GB GPUs. 8 80 = 640GB available memory and the model weights at BF16 would take up 2 480 = 960GB, so that is very much over the memory available. Maybe if you had an 8bit or 4bit quantization with GPTQ, you could fit the model.

Also, I would recommend trying to upgrade vLLM as you are on 0.4.0 per vLLM Version: 0.4.0

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