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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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[Bug]: Failed to run Llava with tensor parallelism. #3882

Closed Isotr0py closed 5 months ago

Isotr0py commented 5 months ago

Your current environment

Collecting environment information...
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 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: version 3.29.0
Libc version: glibc-2.31

Python version: 3.10.13 | packaged by conda-forge | (main, Dec 23 2023, 15:36:39) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-5.15.133+-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: Tesla T4
GPU 1: Tesla T4

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
Byte Order:                         Little Endian
Address sizes:                      46 bits physical, 48 bits virtual
CPU(s):                             4
On-line CPU(s) list:                0-3
Thread(s) per core:                 2
Core(s) per socket:                 2
Socket(s):                          1
NUMA node(s):                       1
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              85
Model name:                         Intel(R) Xeon(R) CPU @ 2.00GHz
Stepping:                           3
CPU MHz:                            2000.130
BogoMIPS:                           4000.26
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          64 KiB
L1i cache:                          64 KiB
L2 cache:                           2 MiB
L3 cache:                           38.5 MiB
NUMA node0 CPU(s):                  0-3
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Mitigation; PTE Inversion
Vulnerability Mds:                  Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Meltdown:             Mitigation; PTI
Vulnerability Mmio stale data:      Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed:             Mitigation; IBRS
Vulnerability Spec rstack overflow: 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; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; Clear CPU buffers; SMT Host state unknown
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities

Versions of relevant libraries:
[pip3] flake8==7.0.0
[pip3] msgpack-numpy==0.4.8
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] onnx==1.15.0
[pip3] pytorch-ignite==0.4.13
[pip3] pytorch-lightning==2.2.1
[pip3] torch==2.1.2+cu121
[pip3] torchaudio==2.1.2
[pip3] torchdata==0.7.1
[pip3] torchinfo==1.8.0
[pip3] torchmetrics==1.3.2
[pip3] torchtext==0.16.2
[pip3] torchvision==0.16.2
[pip3] triton==2.1.0
[conda] magma-cuda121             2.6.1                         1    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] msgpack-numpy             0.4.8                    pypi_0    pypi
[conda] numpy                     1.26.4          py310hb13e2d6_0    conda-forge
[conda] pytorch-ignite            0.4.13                   pypi_0    pypi
[conda] pytorch-lightning         2.2.1                    pypi_0    pypi
[conda] torch                     2.1.2+cu121              pypi_0    pypi
[conda] torchaudio                2.1.2                    pypi_0    pypi
[conda] torchdata                 0.7.1                    pypi_0    pypi
[conda] torchinfo                 1.8.0                    pypi_0    pypi
[conda] torchmetrics              1.3.2                    pypi_0    pypi
[conda] torchtext                 0.16.2                   pypi_0    pypi
[conda] torchvision               0.16.2                   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.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PHB     0-3     0               N/A
GPU1    PHB      X      0-3     0               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

🐛 Describe the bug

Failed to run examples/llava_example.py with tensor_parallel_size > 1 (tensor_parallel_size = 1 works well):

import argparse
import os
import subprocess

import torch

from vllm import LLM
from vllm.sequence import MultiModalData

# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.

def run_llava_pixel_values():
    llm = LLM(
        model="llava-hf/llava-1.5-7b-hf",
        tensor_parallel_size=2,
        image_input_type="pixel_values",
        image_token_id=32000,
        image_input_shape="1,3,336,336",
        image_feature_size=576,
    )

    prompt = "<image>" * 576 + (
        "\nUSER: What is the content of this image?\nASSISTANT:")

    # This should be provided by another online or offline component.
    images = torch.load("images/stop_sign_pixel_values.pt")

    outputs = llm.generate(prompt,
                           multi_modal_data=MultiModalData(
                               type=MultiModalData.Type.IMAGE, data=images))
    for o in outputs:
        generated_text = o.outputs[0].text
        print(generated_text)

# The rest is same to `examples/llava_example.py`

Error:

Traceback (most recent call last):
  File "/kaggle/working/vllm/examples/llava_example.py", line 92, in <module>
    main(args)
  File "/kaggle/working/vllm/examples/llava_example.py", line 63, in main
    run_llava_pixel_values()
  File "/kaggle/working/vllm/examples/llava_example.py", line 14, in run_llava_pixel_values
    llm = LLM(
  File "/opt/conda/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 112, in __init__
    self.llm_engine = LLMEngine.from_engine_args(
  File "/opt/conda/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 196, in from_engine_args
    engine = cls(
  File "/opt/conda/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 110, in __init__
    self.model_executor = executor_class(model_config, cache_config,
  File "/opt/conda/lib/python3.10/site-packages/vllm/executor/ray_gpu_executor.py", line 62, in __init__
    self._init_workers_ray(placement_group)
  File "/opt/conda/lib/python3.10/site-packages/vllm/executor/ray_gpu_executor.py", line 192, in _init_workers_ray
    self._run_workers(
  File "/opt/conda/lib/python3.10/site-packages/vllm/executor/ray_gpu_executor.py", line 340, in _run_workers
    ray_worker_outputs = ray.get(ray_worker_outputs)
  File "/opt/conda/lib/python3.10/site-packages/ray/_private/auto_init_hook.py", line 21, in auto_init_wrapper
    return fn(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/ray/_private/client_mode_hook.py", line 103, in wrapper
    return func(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/ray/_private/worker.py", line 2667, in get
    values, debugger_breakpoint = worker.get_objects(object_refs, timeout=timeout)
  File "/opt/conda/lib/python3.10/site-packages/ray/_private/worker.py", line 864, in get_objects
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AssertionError): ray::RayWorkerVllm.execute_method() (pid=1960, ip=172.19.2.2, actor_id=8c8fd794c14dae433cbd283401000000, repr=<vllm.engine.ray_utils.RayWorkerVllm object at 0x7f96f04da4a0>)
  File "/opt/conda/lib/python3.10/site-packages/vllm/engine/ray_utils.py", line 45, in execute_method
    raise e
  File "/opt/conda/lib/python3.10/site-packages/vllm/engine/ray_utils.py", line 37, in execute_method
    return executor(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/vllm/worker/worker.py", line 107, in load_model
    self.model_runner.load_model()
  File "/opt/conda/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 95, in load_model
    self.model = get_model(
  File "/opt/conda/lib/python3.10/site-packages/vllm/model_executor/model_loader.py", line 93, in get_model
    model = model_class(model_config.hf_config,
  File "/opt/conda/lib/python3.10/site-packages/vllm/model_executor/models/llava.py", line 71, in __init__
    assert self.vision_language_config, (
AssertionError: Provide `image_input_type` and other vision related configurations through LLM entrypoint or engine arguments.
Isotr0py commented 5 months ago

Well, it seems that vision_language_config is not passed to the worker in RayGPUExecutor.