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A high-throughput and memory-efficient inference and serving engine for LLMs
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[Bug]: An EXTREMELY WEIRD bug when I import evaluate before vllm #9678

Open cafeii opened 1 month ago

cafeii commented 1 month ago

Your current environment

The output of `python collect_env.py` 2024-10-25 10:53:08.913038: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2024-10-25 10:53:08.930326: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-10-25 10:53:08.951819: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-10-25 10:53:08.958215: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2024-10-25 10:53:08.973693: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-10-25 10:53:10.049917: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT 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 22.04.3 LTS (x86_64) 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: 3.11.7 | packaged by conda-forge | (main, Dec 15 2023, 08:38:37) [GCC 12.3.0] (64-bit runtime) Python platform: Linux-6.8.0-45-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.4.131 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA RTX 6000 Ada Generation GPU 1: NVIDIA RTX 6000 Ada Generation GPU 2: NVIDIA RTX 6000 Ada Generation GPU 3: NVIDIA RTX 6000 Ada Generation Nvidia driver version: 550.107.02 cuDNN version: Probably one of the following: /usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn.so.8 /usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8 /usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_adv_train.so.8 /usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8 /usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8 /usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8 /usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_ops_train.so.8 /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: 架构： x86_64 CPU 运行模式： 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual 字节序： Little Endian CPU: 80 在线 CPU 列表： 0-79 厂商 ID： GenuineIntel 型号名称： Intel(R) Xeon(R) Gold 5218R CPU @ 2.10GHz CPU 系列： 6 型号： 85 每个核的线程数： 2 每个座的核数： 20 座： 2 步进： 7 CPU 最大 MHz： 4000.0000 CPU 最小 MHz： 800.0000 BogoMIPS： 4200.00 标记： 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 cdp_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow 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 vnmi pku ospke avx512_vnni md_clear flush_l1d arch_capabilities 虚拟化： VT-x L1d 缓存： 1.3 MiB (40 instances) L1i 缓存： 1.3 MiB (40 instances) L2 缓存： 40 MiB (40 instances) L3 缓存： 55 MiB (2 instances) NUMA 节点： 2 NUMA 节点0 CPU： 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78 NUMA 节点1 CPU： 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79 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 Reg file data sampling: Not affected Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; TSX disabled Versions of relevant libraries: [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==12.550.52 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] nvidia-nvjitlink-cu12==12.5.82 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] onnxruntime==1.16.3 [pip3] optree==0.11.0 [pip3] pynvml==11.5.0 [pip3] pyzmq==25.1.2 [pip3] sentence-transformers==3.0.0 [pip3] torch==2.4.0 [pip3] torchaudio==2.1.2 [pip3] torchsde==0.2.6 [pip3] torchvision==0.19.0 [pip3] transformers==4.45.2 [pip3] transformers-stream-generator==0.0.4 [pip3] triton==3.0.0 [conda] blas 1.0 mkl defaults [conda] cuda-cudart 12.1.105 0 nvidia [conda] cuda-cupti 12.1.105 0 nvidia [conda] cuda-libraries 12.1.0 0 nvidia [conda] cuda-nvcc 12.1.105 0 nvidia [conda] cuda-nvrtc 12.1.105 0 nvidia [conda] cuda-nvtx 12.1.105 0 nvidia [conda] cuda-opencl 12.3.101 0 nvidia [conda] cuda-runtime 12.1.0 0 nvidia [conda] faiss-gpu 1.8.0 py3.11_h4c7d538_0_cuda12.1.1 pytorch [conda] ffmpeg 4.3 hf484d3e_0 pytorch [conda] libcublas 12.1.0.26 0 nvidia [conda] libcufft 11.0.2.4 0 nvidia [conda] libcufile 1.8.1.2 0 nvidia [conda] libcurand 10.3.4.101 0 nvidia [conda] libcusolver 11.4.4.55 0 nvidia [conda] libcusparse 12.0.2.55 0 nvidia [conda] libfaiss 1.8.0 h046e95b_0_cuda12.1.1 pytorch [conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch [conda] libnpp 12.0.2.50 0 nvidia [conda] libnvjitlink 12.1.105 0 nvidia [conda] libnvjpeg 12.1.1.14 0 nvidia [conda] mkl 2023.1.0 h213fc3f_46344 defaults [conda] mkl-service 2.4.0 py311h5eee18b_1 defaults [conda] mkl_fft 1.3.8 py311h5eee18b_0 defaults [conda] mkl_random 1.2.4 py311hdb19cb5_0 defaults [conda] numpy 1.26.2 py311h08b1b3b_0 defaults [conda] numpy-base 1.26.2 py311hf175353_0 defaults [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-nccl-cu12 2.20.5 pypi_0 pypi [conda] nvidia-nvjitlink-cu12 12.5.82 pypi_0 pypi [conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi [conda] optree 0.11.0 pypi_0 pypi [conda] pynvml 11.5.0 pypi_0 pypi [conda] pytorch-cuda 12.1 ha16c6d3_5 pytorch [conda] pytorch-mutex 1.0 cuda pytorch [conda] pyzmq 25.1.2 py311h34ded2d_0 conda-forge [conda] sentence-transformers 3.0.0 pypi_0 pypi [conda] torch 2.4.0 pypi_0 pypi [conda] torchaudio 2.1.2 py311_cu121 pytorch [conda] torchsde 0.2.6 pypi_0 pypi [conda] torchvision 0.19.0 pypi_0 pypi [conda] transformers 4.45.2 pypi_0 pypi [conda] transformers-stream-generator 0.0.4 pypi_0 pypi [conda] triton 3.0.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.6.3.post1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 GPU2 GPU3 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NODE SYS SYS 0,2,4,6,8,10 0 N/A GPU1 NODE X SYS SYS 0,2,4,6,8,10 0 N/A GPU2 SYS SYS X NODE 1,3,5,7,9,11 1 N/A GPU3 SYS SYS NODE X 1,3,5,7,9,11 1 N/A 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

Model Input Dumps

No response

🐛 Describe the bug

evaluate == 0.4.3
vllm == 0.6.3.post1

If I import evaluate before vllm:

import evaluate
import vllm
vllm.LLM('my_local_path_of_mistral_7b_base')

A bug will be raised:

2024-10-25 10:55:49.480431: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-10-25 10:55:49.497640: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-10-25 10:55:49.518802: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-10-25 10:55:49.525222: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-10-25 10:55:49.540380: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-10-25 10:55:50.763501: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
ERROR 10-25 10:55:54 registry.py:267] Error in inspecting model architecture 'MistralForCausalLM'
ERROR 10-25 10:55:54 registry.py:267] Traceback (most recent call last):
ERROR 10-25 10:55:54 registry.py:267]   File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/model_executor/models/registry.py", line 428, in _run_in_subprocess
ERROR 10-25 10:55:54 registry.py:267]     returned.check_returncode()
ERROR 10-25 10:55:54 registry.py:267]   File "/home/lzc/.conda/envs/workspace/lib/python3.11/subprocess.py", line 502, in check_returncode
ERROR 10-25 10:55:54 registry.py:267]     raise CalledProcessError(self.returncode, self.args, self.stdout,
ERROR 10-25 10:55:54 registry.py:267] subprocess.CalledProcessError: Command '['/home/lzc/.conda/envs/workspace/bin/python', '-m', 'vllm.model_executor.models.registry']' returned non-zero exit status 1.
ERROR 10-25 10:55:54 registry.py:267] 
ERROR 10-25 10:55:54 registry.py:267] The above exception was the direct cause of the following exception:
ERROR 10-25 10:55:54 registry.py:267] 
ERROR 10-25 10:55:54 registry.py:267] Traceback (most recent call last):
ERROR 10-25 10:55:54 registry.py:267]   File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/model_executor/models/registry.py", line 265, in _try_inspect_model_cls
ERROR 10-25 10:55:54 registry.py:267]     return model.inspect_model_cls()
ERROR 10-25 10:55:54 registry.py:267]            ^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 10-25 10:55:54 registry.py:267]   File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/model_executor/models/registry.py", line 227, in inspect_model_cls
ERROR 10-25 10:55:54 registry.py:267]     return _run_in_subprocess(
ERROR 10-25 10:55:54 registry.py:267]            ^^^^^^^^^^^^^^^^^^^
ERROR 10-25 10:55:54 registry.py:267]   File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/model_executor/models/registry.py", line 431, in _run_in_subprocess
ERROR 10-25 10:55:54 registry.py:267]     raise RuntimeError(f"Error raised in subprocess:\n"
ERROR 10-25 10:55:54 registry.py:267] RuntimeError: Error raised in subprocess:
ERROR 10-25 10:55:54 registry.py:267] Error: mkl-service + Intel(R) MKL: MKL_THREADING_LAYER=INTEL is incompatible with libgomp-a34b3233.so.1 library.
ERROR 10-25 10:55:54 registry.py:267]   Try to import numpy first or set the threading layer accordingly. Set MKL_SERVICE_FORCE_INTEL to force it.
ERROR 10-25 10:55:54 registry.py:267] 
Traceback (most recent call last):
  File "/home/lzc/workspace/LESS/test.py", line 3, in <module>
    model = vllm.LLM(
            ^^^^^^^^^
  File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/entrypoints/llm.py", line 177, in __init__
    self.llm_engine = LLMEngine.from_engine_args(
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/engine/llm_engine.py", line 570, in from_engine_args
    engine_config = engine_args.create_engine_config()
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/engine/arg_utils.py", line 903, in create_engine_config
    model_config = self.create_model_config()
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/engine/arg_utils.py", line 839, in create_model_config
    return ModelConfig(
           ^^^^^^^^^^^^
  File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/config.py", line 200, in __init__
    self.multimodal_config = self._init_multimodal_config(
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/config.py", line 219, in _init_multimodal_config
    if ModelRegistry.is_multimodal_model(architectures):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/model_executor/models/registry.py", line 386, in is_multimodal_model
    return self.inspect_model_cls(architectures).supports_multimodal
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/model_executor/models/registry.py", line 355, in inspect_model_cls
    return self._raise_for_unsupported(architectures)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/vllm/model_executor/models/registry.py", line 317, in _raise_for_unsupported
    raise ValueError(
ValueError: Model architectures ['MistralForCausalLM'] are not supported for now. Supported architectures: ['AquilaModel', 'AquilaForCausalLM', 'ArcticForCausalLM', 'BaiChuanForCausalLM', 'BaichuanForCausalLM', 'BloomForCausalLM', 'CohereForCausalLM', 'DbrxForCausalLM', 'DeciLMForCausalLM', 'DeepseekForCausalLM', 'DeepseekV2ForCausalLM', 'ExaoneForCausalLM', 'FalconForCausalLM', 'GemmaForCausalLM', 'Gemma2ForCausalLM', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTJForCausalLM', 'GPTNeoXForCausalLM', 'GraniteForCausalLM', 'GraniteMoeForCausalLM', 'InternLMForCausalLM', 'InternLM2ForCausalLM', 'JAISLMHeadModel', 'JambaForCausalLM', 'LlamaForCausalLM', 'LLaMAForCausalLM', 'MambaForCausalLM', 'MistralForCausalLM', 'MixtralForCausalLM', 'QuantMixtralForCausalLM', 'MptForCausalLM', 'MPTForCausalLM', 'MiniCPMForCausalLM', 'MiniCPM3ForCausalLM', 'NemotronForCausalLM', 'OlmoForCausalLM', 'OlmoeForCausalLM', 'OPTForCausalLM', 'OrionForCausalLM', 'PersimmonForCausalLM', 'PhiForCausalLM', 'Phi3ForCausalLM', 'Phi3SmallForCausalLM', 'PhiMoEForCausalLM', 'Qwen2ForCausalLM', 'Qwen2MoeForCausalLM', 'RWForCausalLM', 'StableLMEpochForCausalLM', 'StableLmForCausalLM', 'Starcoder2ForCausalLM', 'SolarForCausalLM', 'XverseForCausalLM', 'BartModel', 'BartForConditionalGeneration', 'Gemma2Model', 'MistralModel', 'Qwen2ForRewardModel', 'Phi3VForCausalLM', 'Blip2ForConditionalGeneration', 'ChameleonForConditionalGeneration', 'ChatGLMModel', 'ChatGLMForConditionalGeneration', 'FuyuForCausalLM', 'InternVLChatModel', 'LlavaForConditionalGeneration', 'LlavaNextForConditionalGeneration', 'LlavaNextVideoForConditionalGeneration', 'LlavaOnevisionForConditionalGeneration', 'MiniCPMV', 'MolmoForCausalLM', 'NVLM_D', 'PaliGemmaForConditionalGeneration', 'PixtralForConditionalGeneration', 'QWenLMHeadModel', 'Qwen2VLForConditionalGeneration', 'UltravoxModel', 'MllamaForConditionalGeneration', 'EAGLEModel', 'MedusaModel', 'MLPSpeculatorPreTrainedModel']

But it would be ok if I import vllm first:

import vllm
import evaluate
vllm.LLM('my_local_path_of_mistral_7b_base')

The result is:

2024-10-25 10:58:41.972433: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-10-25 10:58:41.989092: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-10-25 10:58:42.009503: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-10-25 10:58:42.015482: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-10-25 10:58:42.030683: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-10-25 10:58:43.098735: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
INFO 10-25 10:58:54 llm_engine.py:237] Initializing an LLM engine (v0.6.3.post1) with config: model='/home/lzc/workspace/LESS/out/mistral-7b-less-p0.05-lora-tydiqa/full', speculative_config=None, tokenizer='/home/lzc/workspace/LESS/out/mistral-7b-less-p0.05-lora-tydiqa/full', 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.bfloat16, max_seq_len=32768, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, 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=0, served_model_name=/home/lzc/workspace/LESS/out/mistral-7b-less-p0.05-lora-tydiqa/full, num_scheduler_steps=1, chunked_prefill_enabled=False multi_step_stream_outputs=True, enable_prefix_caching=False, use_async_output_proc=True, use_cached_outputs=False, mm_processor_kwargs=None)
INFO 10-25 10:58:54 selector.py:247] Cannot use FlashAttention-2 backend due to sliding window.
INFO 10-25 10:58:54 selector.py:115] Using XFormers backend.
/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/xformers/ops/fmha/flash.py:211: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
  @torch.library.impl_abstract("xformers_flash::flash_fwd")
/home/lzc/.conda/envs/workspace/lib/python3.11/site-packages/xformers/ops/fmha/flash.py:344: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
  @torch.library.impl_abstract("xformers_flash::flash_bwd")
INFO 10-25 10:58:55 model_runner.py:1056] Starting to load model /home/lzc/workspace/LESS/out/mistral-7b-less-p0.05-lora-tydiqa/full...
INFO 10-25 10:58:55 selector.py:247] Cannot use FlashAttention-2 backend due to sliding window.
INFO 10-25 10:58:55 selector.py:115] Using XFormers backend.
Loading safetensors checkpoint shards:   0% Completed | 0/3 [00:00<?, ?it/s]
Loading safetensors checkpoint shards:  33% Completed | 1/3 [00:01<00:02,  1.31s/it]
Loading safetensors checkpoint shards:  67% Completed | 2/3 [00:02<00:01,  1.38s/it]
Loading safetensors checkpoint shards: 100% Completed | 3/3 [00:04<00:00,  1.34s/it]
Loading safetensors checkpoint shards: 100% Completed | 3/3 [00:04<00:00,  1.35s/it]

INFO 10-25 10:59:00 model_runner.py:1067] Loading model weights took 13.4966 GB
INFO 10-25 10:59:03 gpu_executor.py:122] # GPU blocks: 12871, # CPU blocks: 2048
INFO 10-25 10:59:03 gpu_executor.py:126] Maximum concurrency for 32768 tokens per request: 6.28x
INFO 10-25 10:59:06 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 10-25 10:59:06 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.

I just cannot find out why

Before submitting a new issue...

DarkLight1337 commented 1 month ago 
ERROR 10-25 10:55:54 registry.py:267] RuntimeError: Error raised in subprocess:
ERROR 10-25 10:55:54 registry.py:267] Error: mkl-service + Intel(R) MKL: MKL_THREADING_LAYER=INTEL is incompatible with libgomp-a34b3233.so.1 library.
ERROR 10-25 10:55:54 registry.py:267]   Try to import numpy first or set the threading layer accordingly. Set MKL_SERVICE_FORCE_INTEL to force it.

You should update your numpy version.