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[Bug]: mismatch between multimodal tokens and placeholders for Llava-Next (4 GPUs) #8421

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sayakpaul opened this issue Sep 12, 2024 · 13 comments
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@sayakpaul
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Your current environment

The output of `python collect_env.py`
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: 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 3.27.7
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-5.15.0-1049-aws-x86_64-with-glibc2.31
Is CUDA available: False
CUDA runtime version: 12.1.105
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
Address sizes:                      48 bits physical, 48 bits virtual
CPU(s):                             64
On-line CPU(s) list:                0-63
Thread(s) per core:                 2
Core(s) per socket:                 32
Socket(s):                          1
NUMA node(s):                       2
Vendor ID:                          AuthenticAMD
CPU family:                         25
Model:                              1
Model name:                         AMD EPYC 7R13 Processor
Stepping:                           1
CPU MHz:                            2649.998
BogoMIPS:                           5299.99
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          1 MiB
L1i cache:                          1 MiB
L2 cache:                           16 MiB
L3 cache:                           128 MiB
NUMA node0 CPU(s):                  0-15,32-47
NUMA node1 CPU(s):                  16-31,48-63
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Mitigation; safe RET
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; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
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 tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid

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] onnx==1.16.2
[pip3] onnxruntime==1.19.2
[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: 0.6.1@5a60699c452c0b9b8086a978d8572c257c2c3cc4
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
Could not collect

Running my original script through SLURM so that is why the output above doesn't have any GPUs. I am on 4 H100s.

Model Input Dumps

No response

🐛 Describe the bug

Similar to #7996, I am running into when using Llava-NexT:

[rank0]: ValueError: Attempted to assign 2340 + 2144 + 1850 + 2160 + 2832 + 2438 + 2340 + 2830 + 2536 + 1948 = 23418 multimodal tokens to 23516 placeholders

All the code is here:
https://github.com/sayakpaul/simple-image-recaptioning

This is why I launch it:

# full CC3M training set
python main.py \
    --data_path="pipe:curl -s -f -L https://huggingface.co/datasets/pixparse/cc3m-wds/resolve/main/cc3m-train-{0000..0575}.tar" --batch_size=48

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@DarkLight1337
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Can you post the images in the batch that is causing the error?

@DarkLight1337 DarkLight1337 changed the title [Bug]: mismatch between multimodel tokens and placeholders for Llava-Next (4 GPUs) [Bug]: mismatch between multimodal tokens and placeholders for Llava-Next (4 GPUs) Sep 12, 2024
@sayakpaul
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Strangely, even if I wrap the infer() call here under a try-except to serialize the faulty images, it won't do it and just error out with original error message.

Is this expected?

@DarkLight1337
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I think if there is an internal failure inside the model, the whole vLLM engine needs to be started anew. You can try to narrow down the batch number that causes the error and post the corresponding images.

@sayakpaul
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Hmm quite strangely, it doesn't happen when using a single GPU. Does that sound similar?

@DarkLight1337
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Hmm quite strangely, it doesn't happen when using a single GPU. Does that sound similar?

I haven't heard of such issues resulting from using multiple GPUs.

Another thing you can try is to increase max_model_len.

@sayakpaul
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It is already at 32k.

@DarkLight1337
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It would greatly help debugging if you could identify which batch is consistently causing this error.

@sayakpaul
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Yeah I am trying. I am unable to find any outputs I am capturing with print() in my logs. Do I have to configure any special logging primitives?

@DarkLight1337
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DarkLight1337 commented Sep 15, 2024

A few things you can try:

  • Set the environment variable according to here and set enforce_eager=True to disable cuda graph.
  • Catch the ValueError inside the model itself and inspect the pixel_values.

@DarkLight1337
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Can you check whether #8496 fixes the issue for you?

@sayakpaul
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Thanks, will try when I get a moment.

@sayakpaul
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Seems to be working.

@sayakpaul
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sayakpaul commented Sep 19, 2024

Will close this issue as #8496 seems to be working beautifully. I wanted to ask a silly question hence not opening a new issue.

I have the following simple script:

Code
import os
import queue
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
import fire

from data_processing import initialize_dataloader
from model import load_vllm_engine, infer
from utils import save_results


def main(
    data_path: str,
    batch_size: int = 48,
    dataloader_num_workers: int = 8,
    output_dir: str = "sample_outputs",
    max_tokens: int = 120,
    detect_watermarks: bool = False,
):
    vllm_engine, sampling_params = load_vllm_engine(max_tokens=max_tokens)

    dataloader = initialize_dataloader(
        data_path=data_path,
        batch_size=batch_size,
        dataloader_num_workers=dataloader_num_workers,
        output_dir=output_dir,
        detect_watermarks=detect_watermarks,
    )

    output_queue = queue.Queue()
    save_thread = ThreadPoolExecutor(max_workers=dataloader_num_workers)
    os.makedirs(output_dir, exist_ok=True)
    save_future = save_thread.submit(save_results, output_queue, output_dir)

    try:
        print("Starting the generation process.")
        for batch in tqdm(dataloader):
            batch["sampling_params"] = sampling_params
            try:
                outputs = infer(vllm_engine, batch)
                if outputs is not None:
                    original_captions = batch["original_captions"]
                    img_bytes = batch["img_bytes"]
                    img_hashes = batch["img_hashes"]
                    output_queue.put((original_captions, outputs, img_bytes, img_hashes))
            except:
                continue
    finally:
        output_queue.put(None)
        save_thread.shutdown(wait=True)

    save_future.result()
    print("All processes completed. Captions generation and saving done.")


if __name__ == "__main__":
    fire.Fire(main)

Once it finishes execution on multiple GPUs successfully, I get:

All processes completed. Captions generation and saving done.
ERROR 09-19 02:37:04 multiproc_worker_utils.py:120] Worker VllmWorkerProcess pid 404188 died, exit code: -15
INFO 09-19 02:37:04 multiproc_worker_utils.py:123] Killing local vLLM worker processes
[rank0]:[W919 02:37:09.827306477 CudaIPCTypes.cpp:16] Producer process has been terminated before all shared CUDA tensors released. See Note [Sharing CUDA tensors]
/fsx/sayak/vllm/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 1 leaked shared_memory objects to clean up at shutdown

Warnings could likely be ignored but I see an "ERROR". Should vllm engine be closed in a different way?

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