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[Bug]: How to run microsoft/llava-med-v1.5-mistral-7b by vllm #11449

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jianghuyihei opened this issue Dec 24, 2024 · 10 comments
Closed
1 task done

[Bug]: How to run microsoft/llava-med-v1.5-mistral-7b by vllm #11449

jianghuyihei opened this issue Dec 24, 2024 · 10 comments
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bug Something isn't working

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@jianghuyihei
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Your current environment

The output of `python collect_env.py` 
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.10.0 (default, Mar  3 2022, 09:58:08) [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.10.112-005.ali5000.alios7.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A100-SXM4-80GB
Nvidia driver version: 535.129.03
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
Address sizes:                   46 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          128
On-line CPU(s) list:             0-91,96-123
Off-line CPU(s) list:            92-95,124-127
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz
CPU family:                      6
Model:                           106
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
Stepping:                        6
CPU max MHz:                     3500.0000
CPU min MHz:                     800.0000
BogoMIPS:                        5800.00
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 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm 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 split_lock_detect 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 fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       3 MiB (64 instances)
L1i cache:                       2 MiB (64 instances)
L2 cache:                        80 MiB (64 instances)
L3 cache:                        96 MiB (2 instances)
NUMA node(s):                    2
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 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

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pytorch_revgrad==0.2.0
[pip3] pyzmq==26.2.0
[pip3] torch==2.5.1
[pip3] torchao==0.7.0
[pip3] torchvision==0.20.1
[pip3] transformers==4.47.1
[pip3] triton==3.1.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-cublas-cu12        12.4.5.8                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.4.127                 pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.4.127                 pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.4.127                 pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.1.0.70                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.2.1.3                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.5.147               pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.6.1.9                 pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.3.1.170               pypi_0    pypi
[conda] nvidia-ml-py              12.560.30                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.21.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.4.127                 pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.4.127                 pypi_0    pypi
[conda] pytorch-revgrad           0.2.0                    pypi_0    pypi
[conda] pyzmq                     26.2.0                   pypi_0    pypi
[conda] torch                     2.5.1                    pypi_0    pypi
[conda] torchao                   0.7.0                    pypi_0    pypi
[conda] torchvision               0.20.1                   pypi_0    pypi
[conda] transformers              4.47.1                   pypi_0    pypi
[conda] triton                    3.1.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.4.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    NIC6    NIC7    NIC8    NIC9    NIC10   NIC11   NIC12   NIC13   NIC14   NIC15   CPU Affinity NUMA Affinity   GPU NUMA ID
GPU0     X      PXB     PXB     PXB     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PXB     SYS 0-31,64-91       0               N/A
NIC0    PXB      X      PIX     PIX     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX     SYS
NIC1    PXB     PIX      X      PIX     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX     SYS
NIC2    PXB     PIX     PIX      X      SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX     SYS
NIC3    SYS     SYS     SYS     SYS      X      PIX     PIX     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX
NIC4    SYS     SYS     SYS     SYS     PIX      X      PIX     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX
NIC5    SYS     SYS     SYS     SYS     PIX     PIX      X      SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX
NIC6    SYS     SYS     SYS     SYS     SYS     SYS     SYS      X      PIX     PIX     SYS     SYS     SYS     PIX     SYS     SYS     SYS
NIC7    SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX      X      PIX     SYS     SYS     SYS     PIX     SYS     SYS     SYS
NIC8    SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX     PIX      X      SYS     SYS     SYS     PIX     SYS     SYS     SYS
NIC9    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS      X      PIX     PIX     SYS     PIX     SYS     SYS
NIC10   SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX      X      PIX     SYS     PIX     SYS     SYS
NIC11   SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX     PIX      X      SYS     PIX     SYS     SYS
NIC12   SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX     PIX     PIX     SYS     SYS     SYS      X      SYS     SYS     SYS
NIC13   SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PIX     PIX     PIX     SYS      X      SYS     SYS
NIC14   PXB     PIX     PIX     PIX     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS      X      SYS
NIC15   SYS     SYS     SYS     SYS     PIX     PIX     PIX     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS      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
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8
  NIC9: mlx5_9
  NIC10: mlx5_10
  NIC11: mlx5_11
  NIC12: mlx5_bond_0
  NIC13: mlx5_bond_1
  NIC14: mlx5_bond_2
  NIC15: mlx5_bond_3

NVIDIA_VISIBLE_DEVICES=3
NVIDIA_REQUIRE_CUDA=
NCCL_MIN_NCHANNELS=2
NCCL_VERSION=2
NVIDIA_DRIVER_CAPABILITIES=all
NCCL_DEBUG=INFO
NVIDIA_PRODUCT_NAME=CUDA
NCCL_NSOCKS_PERTHREAD=1
CUDA_VERSION=11.8.0
NCCL_MAX_NCHANNELS=2
NVIDIA_DISABLE_REQUIRE=1
NCCL_ASYNC_ERROR_HANDLING=1
NCCL_SOCKET_NTHREADS=2
LD_LIBRARY_PATH=/home/pai/envs/medical/lib/python3.10/site-packages/cv2/../../lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/lib/x86_64-linux-gnu:/lib/x86_64-linux-gnu:/home/pai/lib:/home/pai/jre/lib/amd64/server:/home/pai/jre/lib/amd64/server
NCCL_LAUNCH_MODE=PARALLEL
CUDA_MODULE_LOADING=LAZY

Model Input Dumps

No response

🐛 Describe the bug

from vllm import LLM, SamplingParams
from transformers import AutoConfig, AutoModelForCausalLM, \
                         MistralConfig, MistralModel, MistralForCausalLM

from utils import LlavaMistralConfig,LlavaMistralConfig, LlavaMistralForCausalLM

AutoConfig.register("llava_mistral", LlavaMistralConfig)
AutoModelForCausalLM.register(LlavaMistralConfig, LlavaMistralForCausalLM)
self.llm = LLM(
            model= "microsoft/llava-med-v1.5-mistral-7b",
        )

Error：

ValueError: Model architectures ['LlavaMistralForCausalLM'] are not supported for now. Supported architectures: dict_keys(['AquilaModel', 'AquilaForCausalLM', 'ArcticForCausalLM', 'BaiChuanForCausalLM', 'BaichuanForCausalLM', 'BloomForCausalLM', 'CohereForCausalLM', 'DbrxForCausalLM', 'DeciLMForCausalLM', 'DeepseekForCausalLM', 'DeepseekV2ForCausalLM', 'ExaoneForCausalLM', 'FalconForCausalLM', 'GemmaForCausalLM', 'Gemma2ForCausalLM', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTJForCausalLM', 'GPTNeoXForCausalLM', 'GraniteForCausalLM', 'GraniteMoeForCausalLM', 'InternLMForCausalLM', 'InternLM2ForCausalLM', 'InternLM2VEForCausalLM', 'JAISLMHeadModel', 'JambaForCausalLM', 'LlamaForCausalLM', 'LLaMAForCausalLM', 'MambaForCausalLM', 'FalconMambaForCausalLM', 'MiniCPMForCausalLM', 'MiniCPM3ForCausalLM', 'MistralForCausalLM', 'MixtralForCausalLM', 'QuantMixtralForCausalLM', 'MptForCausalLM', 'MPTForCausalLM', 'NemotronForCausalLM', 'OlmoForCausalLM', 'OlmoeForCausalLM', 'OPTForCausalLM', 'OrionForCausalLM', 'PersimmonForCausalLM', 'PhiForCausalLM', 'Phi3ForCausalLM', 'Phi3SmallForCausalLM', 'PhiMoEForCausalLM', 'Qwen2ForCausalLM', 'Qwen2MoeForCausalLM', 'RWForCausalLM', 'StableLMEpochForCausalLM', 'StableLmForCausalLM', 'Starcoder2ForCausalLM', 'SolarForCausalLM', 'XverseForCausalLM', 'BartModel', 'BartForConditionalGeneration', 'Florence2ForConditionalGeneration', 'BertModel', 'RobertaModel', 'XLMRobertaModel', 'Gemma2Model', 'LlamaModel', 'MistralModel', 'Qwen2Model', 'Qwen2ForRewardModel', 'Qwen2ForSequenceClassification', 'LlavaNextForConditionalGeneration', 'Phi3VForCausalLM', 'Qwen2VLForConditionalGeneration', 'Blip2ForConditionalGeneration', 'ChameleonForConditionalGeneration', 'ChatGLMModel', 'ChatGLMForConditionalGeneration', 'FuyuForCausalLM', 'H2OVLChatModel', 'InternVLChatModel', 'Idefics3ForConditionalGeneration', 'LlavaForConditionalGeneration', 'LlavaNextVideoForConditionalGeneration', 'LlavaOnevisionForConditionalGeneration', 'MiniCPMV', 'MolmoForCausalLM', 'NVLM_D', 'PaliGemmaForConditionalGeneration', 'PixtralForConditionalGeneration', 'QWenLMHeadModel', 'Qwen2AudioForConditionalGeneration', 'UltravoxModel', 'MllamaForConditionalGeneration', 'EAGLEModel', 'MedusaModel', 'MLPSpeculatorPreTrainedModel'])

How to register the class 'LlavaMistralForCausalLM' to adapt it to VLLM

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@jianghuyihei jianghuyihei added the bug Something isn't working label Dec 24, 2024
@DarkLight1337
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Please see #7984 (comment)

@jianghuyihei
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#7984 (comment)
I have tried it before, but got an error

python convert_llava_weights_to_hf.py --text_model_id mistralai/Mistral-7B-Instruct-v0.2 --vision_model_id openai/clip-vit-large-patch14-336 --output_hub_path models/LLava_Med --old_state_dict_id microsoft/llava-med-v1.5-mistral-7b

Error:
Entry Not Found for url: https://hf-mirror.com/microsoft/llava-med-v1.5-mistral-7b/resolve/main/model_state_dict.bin.

@DarkLight1337
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It seems that you're not accessing HuggingFace directly. Can you try downloading the models first, then reference them by their local filepaths?

@Isotr0py
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Seems that the conversion script doesn't work with this llava variant model currently, because it uses safetensors instead of model_state_dict.bin to store weights...

@jianghuyihei
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It seems that you're not accessing HuggingFace directly. Can you try downloading the models first, then reference them by their local filepaths?

The files I downloaded are:
config.json model-00002-of-00004.safetensors model.safetensors.index.json tokenizer.model
generation_config.json model-00003-of-00004.safetensors special_tokens_map.json
model-00001-of-00004.safetensors model-00004-of-00004.safetensors tokenizer_config.json

There are no model_state_dict.bin

@DarkLight1337 DarkLight1337 mentioned this issue Dec 24, 2024
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@jianghuyihei
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jianghuyihei commented Dec 24, 2024 •
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It seems that you're not accessing HuggingFace directly. Can you try downloading the models first, then reference them by their local filepaths?

I have solve the promblem by:

    if "Qwen" in text_model_id:
        state_dict = load_original_state_dict(old_state_dict_id)
    else:
        # microsoft/llava-med-v1.5-mistral-7b
        # state_dict_path = hf_hub_download(old_state_dict_id, "model_state_dict.bin")
        load_model = LlavaMistralForCausalLM.from_pretrained(old_state_dict_id)
        state_dict = load_model.state_dict()
        # state_dict = torch.load(state_dict_path, map_location="cpu")
        

    state_dict = convert_state_dict_to_hf(state_dict)
    model.load_state_dict(state_dict, strict=True, assign=True)

but still error:
RuntimeError: Error(s) in loading state_dict for LlavaMistralForCausalLM:
Some weights of the model checkpoint at microsoft/llava-med-v1.5-mistral-7b were not used when initializing LlavaMistralForCausalLM: ['model.vision_tower.vision_tower.vision_model.embeddings.class_embedding', ...

Missing key(s) in state_dict: "model.embed_tokens.weight", "model.layers.0.self_attn.q_proj.weight", "model.layers.0.self_attn.k_proj.weight", "model.layers.0.self_attn.v_proj.weight", "model.layers.0.self_attn.o_proj.weight",...

@Isotr0py
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@jianghuyihei You can try this modified script: https://github.com/Isotr0py/transformers/blob/fix-llava-convert/src/transformers/models/llava/convert_llava_weights_to_hf.py

@jianghuyihei
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@jianghuyihei You can try this modified script: https://github.com/Isotr0py/transformers/blob/fix-llava-convert/src/transformers/models/llava/convert_llava_weights_to_hf.py

Thanks for your help, I also found this function and it is running successfully.

@DarkLight1337
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Thanks @Isotr0py ! Perhaps we can try to add this script to transformers repo?

@Isotr0py
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Sure! Just open a PR to add this to transformers as well: huggingface/transformers#35406

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