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A high-throughput and memory-efficient inference and serving engine for LLMs
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[Bug]: Met a error when deploying an AWQ model on H20. #8141

Open medwang1 opened 2 weeks ago

medwang1 commented 2 weeks ago

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 20.04.6 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.31 Python version: 3.10.14 (main, May 6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.4.119-0009.32-x86_64-with-glibc2.31 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA H20 GPU 1: NVIDIA H20 GPU 2: NVIDIA H20 GPU 3: NVIDIA H20 GPU 4: NVIDIA H20 GPU 5: NVIDIA H20 GPU 6: NVIDIA H20 GPU 7: NVIDIA H20 Nvidia driver version: 535.161.07 cuDNN version: Probably one of the following: /usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn.so.8 /usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8 /usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_adv_train.so.8 /usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8 /usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8 /usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8 /usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_ops_train.so.8 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: 52 bits physical, 57 bits virtual CPU(s): 384 On-line CPU(s) list: 0-383 Thread(s) per core: 2 Core(s) per socket: 96 Socket(s): 2 NUMA node(s): 2 Vendor ID: AuthenticAMD CPU family: 25 Model: 17 Model name: AMD EPYC 9K84 96-Core Processor Stepping: 1 Frequency boost: enabled CPU MHz: 3699.753 CPU max MHz: 2600.0000 CPU min MHz: 1500.0000 BogoMIPS: 5199.96 Virtualization: AMD-V L1d cache: 6 MiB L1i cache: 6 MiB L2 cache: 192 MiB L3 cache: 768 MiB NUMA node0 CPU(s): 0-95,192-287 NUMA node1 CPU(s): 96-191,288-383 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Spec store bypass: Vulnerable Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers Vulnerability Spectre v2: Vulnerable, IBPB: disabled, STIBP: disabled 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 mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d 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==12.6.68 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pyzmq==26.2.0 [pip3] torch==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.44.2 [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 12.6.68 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 4.44.2 pypi_0 pypi [conda] triton 3.0.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.5.5@09c7792610ada9f88bbf87d32b472dd44bf23cc2 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 NIC7CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 PIX NODE NODE NODE SYS SYS SYSSYS 0-95,192-287 0 N/A GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 NODE PIX PHB NODE SYS SYS SYSSYS 0-95,192-287 0 N/A GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 NODE PHB PIX NODE SYS SYS SYSSYS 0-95,192-287 0 N/A GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 NODE NODE NODE PIX SYS SYS SYSSYS 0-95,192-287 0 N/A GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS SYS SYS PIX NODE NODENODE 96-191,288-383 1 N/A GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS SYS SYS NODE PIX NODENODE 96-191,288-383 1 N/A GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS SYS SYS NODE NODE PIXPHB 96-191,288-383 1 N/A GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS SYS SYS NODE NODE PHBPIX 96-191,288-383 1 N/A NIC0 PIX NODE NODE NODE SYS SYS SYS SYS X NODE NODE NODE SYS SYS SYSSYS NIC1 NODE PIX PHB NODE SYS SYS SYS SYS NODE X PHB NODE SYS SYS SYSSYS NIC2 NODE PHB PIX NODE SYS SYS SYS SYS NODE PHB X NODE SYS SYS SYSSYS NIC3 NODE NODE NODE PIX SYS SYS SYS SYS NODE NODE NODE X SYS SYS SYSSYS NIC4 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS X NODE NODENODE NIC5 SYS SYS SYS SYS NODE PIX NODE NODE SYS SYS SYS SYS NODE X NODENODE NIC6 SYS SYS SYS SYS NODE NODE PIX PHB SYS SYS SYS SYS NODE NODE X PHB NIC7 SYS SYS SYS SYS NODE NODE PHB PIX SYS SYS SYS SYS NODE NODE PHB 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_bond_0 NIC1: mlx5_bond_1 NIC2: mlx5_bond_2 NIC3: mlx5_bond_3 NIC4: mlx5_bond_4 NIC5: mlx5_bond_5 NIC6: mlx5_bond_6 NIC7: mlx5_bond_7 ```

Describe the bug

I want to deploy a AWQ model on H20. But I met some errors when I curl it. I use export VLLM_LOGGING_LEVEL=DEBUG to show traceback, but it seems that useless info is printed. The phenomenon is that the gpu memory is released to 0. I think there is a coredump.

Reproduction

CUDA_VISIBLE_DEVICES=0 lmdeploy serve api_server /data/model/Yi-1.5-34B-Chat-AWQ --server-port 8081 --model-name yi --cache-max-entry-count 0.7 --tp 1 --session-len 4096 --enable-prefix-caching --model-format awq --log-level DEBUG

Yi-1.5-34B-Chat-AWQ from: https://huggingface.co/modelscope/Yi-1.5-34B-Chat-AWQ.

curl --request POST \
  --url http://127.0.0.1:8081/v1/chat/completions \
  --header 'content-type: application/json' \
  --data '{
    "model": "yi",
    "temperature": 0.7,
    "top_p": 0.8,
    "messages": [
        {
            "role": "user",
            "content": "你是谁"
        }
    ],
    "max_tokens": 1024,
    "repetition_penalty": 1.0
}'

Error traceback

INFO:     Uvicorn running on http://0.0.0.0:8082 (Press CTRL+C to quit)
INFO 09-04 11:39:56 metrics.py:351] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%.
INFO 09-04 11:40:06 metrics.py:351] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%.
INFO 09-04 11:40:10 logger.py:36] Received request chat-b85cadf8667a43d5b4aa34cfe4b30a8a: prompt: '<|im_start|>user\n你是谁<|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=0.8, top_k=-1, min_p=0.0, seed=None, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=[], stop_token_ids=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None), prompt_token_ids: [6, 2942, 144, 59568, 13961, 60638, 7, 144, 6, 14135, 144], lora_request: None, prompt_adapter_request: None.
INFO 09-04 11:40:10 async_llm_engine.py:208] Added request chat-b85cadf8667a43d5b4aa34cfe4b30a8a.
DEBUG 09-04 11:40:10 async_llm_engine.py:899] Waiting for new requests...
DEBUG 09-04 11:40:10 async_llm_engine.py:913] Got new requests!

then no info is printed, the gpu memory is released to 0.

nvidia-smi info

Wed Sep  4 11:28:31 2024       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.161.07             Driver Version: 535.161.07   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA H20                     On  | 00000000:03:00.0 Off |                    0 |
| N/A   28C    P0              71W / 500W |      0MiB / 97871MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   1  NVIDIA H20                     On  | 00000000:16:00.0 Off |                    0 |
| N/A   28C    P0              72W / 500W |      0MiB / 97871MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   2  NVIDIA H20                     On  | 00000000:1C:00.0 Off |                    0 |
| N/A   27C    P0              72W / 500W |      0MiB / 97871MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   3  NVIDIA H20                     On  | 00000000:2E:00.0 Off |                    0 |
| N/A   26C    P0              71W / 500W |      0MiB / 97871MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   4  NVIDIA H20                     On  | 00000000:84:00.0 Off |                    0 |
| N/A   28C    P0              72W / 500W |      0MiB / 97871MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   5  NVIDIA H20                     On  | 00000000:9C:00.0 Off |                    0 |
| N/A   28C    P0              71W / 500W |      0MiB / 97871MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   6  NVIDIA H20                     On  | 00000000:B6:00.0 Off |                    0 |
| N/A   27C    P0              70W / 500W |      0MiB / 97871MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+
|   7  NVIDIA H20                     On  | 00000000:BB:00.0 Off |                    0 |
| N/A   27C    P0              70W / 500W |      0MiB / 97871MiB |      0%      Default |
|                                         |                      |             Disabled |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+
medwang1 commented 2 weeks ago

I see this: https://giter.club/vllm-project/vllm/issues/4392

I met the same bug recently on H20. My torch version is: 2.3.1+cu121
You need to check your conda list | grep cublas first. Ensure there is only one cublas and that the version is greater than 12.3, then run pip install nvidia-cublas-cu12==12.4.5.8.
It works for me.

from vllm.

I try it. The problem is solved. But I don't know why.