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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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[Usage]: MistralModel architecture not supported #9736

Closed dequeueing closed 2 weeks ago

dequeueing commented 2 weeks 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: Rocky Linux release 8.6 (Green Obsidian) (x86_64)
GCC version: (GCC) 12.2.0
Clang version: Could not collect
CMake version: version 3.20.2
Libc version: glibc-2.28

Python version: 3.10.15 (main, Oct  3 2024, 07:27:34) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-4.18.0-372.32.1.el8_6.x86_64-x86_64-with-glibc2.28
Is CUDA available: False
CUDA runtime version: 12.1.66
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect
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
CPU(s):              32
On-line CPU(s) list: 0-31
Thread(s) per core:  1
Core(s) per socket:  16
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
CPU family:          6
Model:               106
Model name:          Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
Stepping:            6
CPU MHz:             3400.000
CPU max MHz:         3400.0000
CPU min MHz:         800.0000
BogoMIPS:            4800.00
Virtualization:      VT-x
L1d cache:           48K
L1i cache:           32K
L2 cache:            1280K
L3 cache:            24576K
NUMA node0 CPU(s):   0-15
NUMA node1 CPU(s):   16-31
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 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 split_lock_detect wbnoinvd dtherm ida arat pln pts hwp_epp avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities

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==12.6.77
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.46.0
[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     12.6.77                  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              4.46.0                   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:
Could not collect

How would you like to use vllm

I want to run a model "mistralai/Mistral-7B-Instruct-v0.3". Here is how I download the model:


# Download model and tokenizer
model = AutoModel.from_pretrained(MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)

# Save model and tokenizer
tokenizer.save_pretrained(model_save_path)
model.save_pretrained(model_save_path)

print(f"Model and tokenizer saved to {model_save_path}")

The inference python script:

from transformers import AutoModel, AutoTokenizer
from vllm import LLM, SamplingParams
from config import model_save_path

if __name__ == "__main__":
    # model configuration
    sampling_params = SamplingParams(temperature=0.8, top_k=50, top_p=0.95)
    llm = LLM(model=model_save_path)

    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]

    outputs = llm.generate(prompts, sampling_params)

    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

And my config file:

{
  "_name_or_path": "mistralai/Mistral-7B-Instruct-v0.3",
  "architectures": [
    "MistralModel"
  ],
  "attention_dropout": 0.0,
  "bos_token_id": 1,
  "eos_token_id": 2,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 32768,
  "model_type": "mistral",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "rms_norm_eps": 1e-05,
  "rope_theta": 1000000.0,
  "sliding_window": null,
  "tie_word_embeddings": false,
  "torch_dtype": "float32",
  "transformers_version": "4.46.0",
  "use_cache": true,
  "vocab_size": 32768
}

The result I got:

/work/cse-wangtj/.conda/envs/myenv/lib/python3.10/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")
/work/cse-wangtj/.conda/envs/myenv/lib/python3.10/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")

Loading safetensors checkpoint shards:   0% Completed | 0/6 [00:00<?, ?it/s]

Loading safetensors checkpoint shards:  17% Completed | 1/6 [00:06<00:33,  6.75s/it]

Loading safetensors checkpoint shards:  33% Completed | 2/6 [00:13<00:27,  6.76s/it]

Loading safetensors checkpoint shards:  50% Completed | 3/6 [00:20<00:20,  6.86s/it]

Loading safetensors checkpoint shards:  67% Completed | 4/6 [00:27<00:13,  6.94s/it]

Loading safetensors checkpoint shards:  83% Completed | 5/6 [00:32<00:06,  6.32s/it]

Loading safetensors checkpoint shards: 100% Completed | 6/6 [00:39<00:00,  6.56s/it]

Loading safetensors checkpoint shards: 100% Completed | 6/6 [00:39<00:00,  6.64s/it]

[rank0]: Traceback (most recent call last):
[rank0]:   File "/work/cse-wangtj/test/vllm/offline.py", line 18, in <module>
[rank0]:     outputs = llm.generate(prompts, sampling_params)
[rank0]:   File "/work/cse-wangtj/.conda/envs/myenv/lib/python3.10/site-packages/vllm/utils.py", line 1063, in inner
[rank0]:     return fn(*args, **kwargs)
[rank0]:   File "/work/cse-wangtj/.conda/envs/myenv/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 320, in generate
[rank0]:     raise ValueError(
[rank0]: ValueError: LLM.generate() is only supported for (conditional) generation models (XForCausalLM, XForConditionalGeneration).

Why vllm does not support MistralModel?

Before submitting a new issue...

Isotr0py commented 2 weeks ago

That's because you are using AutoModel instead of AutoModelForCausalLM to load and save your model checkpoint from remote.

You can use this code to output the checkpoint:

# Download model and tokenizer
model = AutoModelForCausalLM.from_pretrained(MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)

# Save model and tokenizer
tokenizer.save_pretrained(model_save_path)
model.save_pretrained(model_save_path)

print(f"Model and tokenizer saved to {model_save_path}")

BTW, I recommend you to use huggingface-cli download to download the model repo instead of load/save from checkpoint, because loading model to memory is unnecessary for downloading:

huggingface-cli download mistralai/Mistral-7B-Instruct-v0.3 --local-dir <Your_model_save_path>
dequeueing commented 2 weeks ago

Great, that solves the problem. Thank you!