[Bug]: LLM.beam_search Doesn't Pass Multimodal Data

[alex-jw-brooks]

Your current environment

The output of `python collect_env.py`

PyTorch version: 2.6.0+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Red Hat Enterprise Linux 9.4 (Plow) (x86_64)
GCC version: (GCC) 11.4.1 20231218 (Red Hat 11.4.1-4)
Clang version: 17.0.6 (Red Hat, Inc. 17.0.6-5.el9)
CMake version: version 3.26.5
Libc version: glibc-2.34

Python version: 3.9.18 (main, Dec 4 2024, 00:00:00) [GCC 11.4.1 20231218 (Red Hat 11.4.1-3)] (64-bit runtime)
Python platform: Linux-5.14.0-427.61.1.el9_4.x86_64-x86_64-with-glibc2.34
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA L40S
GPU 1: NVIDIA L40S

Nvidia driver version: 570.124.06
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
Address sizes: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 48
On-line CPU(s) list: 0-47
Vendor ID: GenuineIntel
Model name: Intel Xeon Processor (SapphireRapids)
CPU family: 6
Model: 143
Thread(s) per core: 2
Core(s) per socket: 12
Socket(s): 2
Stepping: 4
BogoMIPS: 4200.00
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 cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities
Virtualization: VT-x
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 1.5 MiB (48 instances)
L1i cache: 1.5 MiB (48 instances)
L2 cache: 96 MiB (24 instances)
L3 cache: 32 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-23
NUMA node1 CPU(s): 24-47
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: Unknown: No mitigations
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
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: Not affected

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[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-cusparselt-cu12==0.6.2
[pip3] nvidia-ml-py==12.570.86
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] onnx==1.17.0
[pip3] onnxconverter-common==1.13.0
[pip3] pynvml==12.0.0
[pip3] pyzmq==26.3.0
[pip3] tf2onnx==1.16.1
[pip3] torch==2.6.0
[pip3] torchaudio==2.6.0
[pip3] torchvision==0.21.0
[pip3] transformers==4.51.0.dev0
[pip3] triton==3.2.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: N/A (dev)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X PIX 0-47 0-1 N/A
GPU1 PIX X 0-47 0-1 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

VLLM_ATTENTION_BACKEND=XFORMERS
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

Currently, we don't pass multimodal data through LLM.beam_search, and it is dropped silently when initializing the beams - this PR fixed it for the async version in the EngineClient but it would be nice to make a similar fix in LLM.beam_search unless there is a reason not to.

You can reproduce this with something like the following:

import vllm
from vllm import LLM
from vllm.sampling_params import BeamSearchParams, SamplingParams
from vllm.assets.audio import AudioAsset

model_name = "Qwen/Qwen2-Audio-7B-Instruct"

wav = AudioAsset("mary_had_lamb").audio_and_sample_rate

question = "What is recited in the audio?"
audio_in_prompt = "Audio 1: <|audio_bos|><|AUDIO|><|audio_eos|>\n"
prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
            "<|im_start|>user\n"
            f"{audio_in_prompt}{question}<|im_end|>\n"
            "<|im_start|>assistant\n")

inputs = [
    {
        "prompt": prompt,
        "multi_modal_data": {"audio": wav},
    },
]


llm = LLM(
    model=model_name,
    max_model_len=4096,
    max_num_seqs=5,
    limit_mm_per_prompt={"audio": 1}, 
)

outputs = llm.beam_search(inputs, BeamSearchParams(beam_width=1, max_tokens=50, temperature=0))
for output in outputs:
    generated_text = output.sequences[0].text
    print(f"Generated text from beam search (single beam 0 temp): {generated_text!r}")

outputs = llm.generate(inputs, SamplingParams(max_tokens=50, temperature=0))
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"From greedy decoding (generate): {generated_text!r}")

You'll find the beam search output doesn't pass the audio (i.e., output identical to if you don't pass the mm data and call generate).

....
Generated text from beam search (single beam 0 temp): "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>\nWhat is recited in the audio?<|im_end|>\n<|im_start|>assistant\nThe speech in the audio is in Mandarin saying '日本政府已经调整于七月四日'.<|im_end|>"

...

From greedy decoding (generate): "The recited content in the audio is: 'First words I spoke in the original cornograph A little piece of practical poetry Mary had a little lamb Its fleece was white as snow And everywhere that Mary went The lamb was sure to go.'"

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