[Feature] support multimodal inputs with multiple requests

examples/offline_inference/qwen2_5_omni/README.md

@@ -32,3 +32,11 @@ Then run the command below.
 ```bash
 bash run_single_prompt.sh
 ```
+
+### FAQ
+
+If you encounter error about backend of librosa, try to install ffmpeg with command below.
+```
+sudo apt update
+sudo apt install ffmpeg
+```

examples/offline_inference/qwen2_5_omni/end2end.py

@@ -1,166 +1,145 @@
-import argparse
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+This example shows how to use vLLM-omni for running offline inference
+with the correct prompt format on Qwen2.5-Omni
+"""
+
 import os
-import os as _os_env_toggle
-import random
+from typing import NamedTuple
 
-import numpy as np
 import soundfile as sf
-import torch
-from utils import make_omni_prompt
+from vllm.assets.audio import AudioAsset
+from vllm.assets.image import ImageAsset
+from vllm.assets.video import VideoAsset
+from vllm.multimodal.image import convert_image_mode
 from vllm.sampling_params import SamplingParams
+from vllm.utils import FlexibleArgumentParser
 
-from vllm_omni.entrypoints.omni_llm import OmniLLM
-
-_os_env_toggle.environ["VLLM_USE_V1"] = "1"
+from vllm_omni import OmniLLM
 
 SEED = 42
-# Set all random seeds
-random.seed(SEED)
-np.random.seed(SEED)
-torch.manual_seed(SEED)
-torch.cuda.manual_seed(SEED)
-torch.cuda.manual_seed_all(SEED)
 
-# Make PyTorch deterministic
-torch.backends.cudnn.deterministic = True
-torch.backends.cudnn.benchmark = False
 
-# Set environment variables for deterministic behavior
-os.environ["PYTHONHASHSEED"] = str(SEED)
-os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
+class QueryResult(NamedTuple):
+    inputs: dict
+    limit_mm_per_prompt: dict[str, int]
 
 
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--model",
-        required=True,
-        help="Path to merged model directory (will be created if downloading).",
-    )
-    parser.add_argument("--thinker-model", type=str, default=None)
-    parser.add_argument("--talker-model", type=str, default=None)
-    parser.add_argument("--code2wav-model", type=str, default=None)
-    parser.add_argument(
-        "--hf-hub-id",
-        default="Qwen/Qwen2.5-Omni-7B",
-        help="Hugging Face repo id to download if needed.",
-    )
-    parser.add_argument("--hf-revision", default=None, help="Optional HF revision (branch/tag/commit).")
-    parser.add_argument("--prompts", nargs="+", default=None, help="Input text prompts.")
-    parser.add_argument("--voice-type", default="default", help="Voice type, e.g., m02, f030, default.")
-    parser.add_argument(
-        "--code2wav-dir",
-        default=None,
-        help="Path to code2wav folder (contains spk_dict.pt).",
-    )
-    parser.add_argument("--dit-ckpt", default=None, help="Path to DiT checkpoint file (e.g., dit.pt).")
-    parser.add_argument("--bigvgan-ckpt", default=None, help="Path to BigVGAN checkpoint file.")
-    parser.add_argument("--dtype", default="bfloat16", choices=["float16", "bfloat16", "float32"])
-    parser.add_argument("--max-model-len", type=int, default=32768)
-    parser.add_argument(
-        "--init-sleep-seconds",
-        type=int,
-        default=20,
-        help="Sleep seconds after starting each stage process to allow initialization (default: 20)",
-    )
+# NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
+# lower-end GPUs.
+# Unless specified, these settings have been tested to work on a single L4.
 
-    parser.add_argument("--thinker-only", action="store_true")
-    parser.add_argument("--text-only", action="store_true")
-    parser.add_argument("--do-wave", action="store_true")
-    parser.add_argument(
-        "--prompt_type",
-        choices=[
-            "text",
-            "audio",
-            "audio-long",
-            "audio-long-chunks",
-            "audio-long-expand-chunks",
-            "image",
-            "video",
-            "video-frames",
-            "audio-in-video",
-            "audio-in-video-v2",
-            "audio-multi-round",
-            "badcase-vl",
-            "badcase-text",
-            "badcase-image-early-stop",
-            "badcase-two-audios",
-            "badcase-two-videos",
-            "badcase-multi-round",
-            "badcase-voice-type",
-            "badcase-voice-type-v2",
-            "badcase-audio-tower-1",
-            "badcase-audio-only",
-        ],
-        default="text",
-    )
-    parser.add_argument("--use-torchvision", action="store_true")
-    parser.add_argument("--tokenize", action="store_true")
-    parser.add_argument(
-        "--output-wav",
-        default="output.wav",
-        help="[Deprecated] Output wav directory (use --output-dir).",
+default_system = (
+    "You are Qwen, a virtual human developed by the Qwen Team, Alibaba "
+    "Group, capable of perceiving auditory and visual inputs, as well as "
+    "generating text and speech."
+)
+
+
+def get_text_query(question: str = None) -> QueryResult:
+    if question is None:
+        question = "Explain the system architecture for a scalable audio generation pipeline. Answer in 15 words."
+    prompt = (
+        f"<|im_start|>system\n{default_system}<|im_end|>\n"
+        "<|im_start|>user\n"
+        f"{question}<|im_end|>\n"
+        f"<|im_start|>assistant\n"
     )
-    parser.add_argument(
-        "--output-dir",
-        default="outputs",
-        help="Output directory to save text and wav files together.",
+    return QueryResult(
+        inputs={
+            "prompt": prompt,
+        },
+        limit_mm_per_prompt={},
     )
-    parser.add_argument(
-        "--thinker-hidden-states-dir",
-        default="thinker_hidden_states",
-        help="Path to thinker hidden states directory.",
+
+
+def get_mixed_modalities_query() -> QueryResult:
+    question = "What is recited in the audio? What is the content of this image? Why is this video funny?"
+    prompt = (
+        f"<|im_start|>system\n{default_system}<|im_end|>\n"
+        "<|im_start|>user\n<|audio_bos|><|AUDIO|><|audio_eos|>"
+        "<|vision_bos|><|IMAGE|><|vision_eos|>"
+        "<|vision_bos|><|VIDEO|><|vision_eos|>"
+        f"{question}<|im_end|>\n"
+        f"<|im_start|>assistant\n"
     )
-    parser.add_argument(
-        "--batch-timeout",
-        type=int,
-        default=5,
-        help="Timeout for batching in seconds (default: 5)",
+    return QueryResult(
+        inputs={
+            "prompt": prompt,
+            "multi_modal_data": {
+                "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
+                "image": convert_image_mode(ImageAsset("cherry_blossom").pil_image, "RGB"),
+                "video": VideoAsset(name="baby_reading", num_frames=16).np_ndarrays,
+            },
+        },
+        limit_mm_per_prompt={"audio": 1, "image": 1, "video": 1},
     )
-    parser.add_argument(
-        "--init-timeout",
-        type=int,
-        default=300,
-        help="Timeout for initializing stages in seconds (default: 300)",
+
+
+def get_use_audio_in_video_query() -> QueryResult:
+    question = "Describe the content of the video, then convert what the baby say into text."
+    prompt = (
+        f"<|im_start|>system\n{default_system}<|im_end|>\n"
+        "<|im_start|>user\n<|vision_bos|><|VIDEO|><|vision_eos|>"
+        f"{question}<|im_end|>\n"
+        f"<|im_start|>assistant\n"
     )
-    parser.add_argument(
-        "--shm-threshold-bytes",
-        type=int,
-        default=65536,
-        help="Threshold for using shared memory in bytes (default: 65536)",
+    asset = VideoAsset(name="baby_reading", num_frames=16)
+    audio = asset.get_audio(sampling_rate=16000)
+
+    return QueryResult(
+        inputs={
+            "prompt": prompt,
+            "multi_modal_data": {
+                "video": asset.np_ndarrays,
+                "audio": audio,
+            },
+            "mm_processor_kwargs": {
+                "use_audio_in_video": True,
+            },
+        },
+        limit_mm_per_prompt={"audio": 1, "video": 1},
     )
-    parser.add_argument(
-        "--enable-stats",
-        action="store_true",
-        default=False,
-        help="Enable writing detailed statistics (default: disabled)",
+
+
+def get_multi_audios_query() -> QueryResult:
+    question = "Are these two audio clips the same?"
+    prompt = (
+        f"<|im_start|>system\n{default_system}<|im_end|>\n"
+        "<|im_start|>user\n<|audio_bos|><|AUDIO|><|audio_eos|>"
+        "<|audio_bos|><|AUDIO|><|audio_eos|>"
+        f"{question}<|im_end|>\n"
+        f"<|im_start|>assistant\n"
     )
-    parser.add_argument(
-        "--txt-prompts",
-        type=str,
-        default=None,
-        help="Path to a .txt file with one prompt per line (preferred).",
+    return QueryResult(
+        inputs={
+            "prompt": prompt,
+            "multi_modal_data": {
+                "audio": [
+                    AudioAsset("winning_call").audio_and_sample_rate,
+                    AudioAsset("mary_had_lamb").audio_and_sample_rate,
+                ],
+            },
+        },
+        limit_mm_per_prompt={
+            "audio": 2,
+        },
     )
-    args = parser.parse_args()
-    return args
 
 
-def main():
-    args = parse_args()
-    model_name = args.model
-    try:
-        # Preferred: load from txt file (one prompt per line)
-        if getattr(args, "txt_prompts", None) and args.prompt_type == "text":
-            with open(args.txt_prompts, encoding="utf-8") as f:
-                lines = [ln.strip() for ln in f.readlines()]
-            args.prompts = [ln for ln in lines if ln != ""]
-            print(f"[Info] Loaded {len(args.prompts)} prompts from {args.txt_prompts}")
-    except Exception as e:
-        print(f"[Error] Failed to load prompts: {e}")
-        raise
-
-    if args.prompts is None:
-        raise ValueError("No prompts provided. Use --prompts ... or --txt-prompts <file.txt> (with --prompt_type text)")
+query_map = {
+    "mixed_modalities": get_mixed_modalities_query,
+    "use_audio_in_video": get_use_audio_in_video_query,
+    "multi_audios": get_multi_audios_query,
+    "text": get_text_query,
+}
+
+
+def main(args):
+    model_name = "Qwen/Qwen2.5-Omni-7B"
+    query_result = query_map[args.query_type]()
+
     omni_llm = OmniLLM(
         model=model_name,
         log_stats=args.enable_stats,
@@ -205,8 +184,16 @@ def main():
         code2wav_sampling_params,
     ]
 
-    prompt = [make_omni_prompt(args, prompt) for prompt in args.prompts]
-    omni_outputs = omni_llm.generate(prompt, sampling_params_list)
+    if args.txt_prompts is None:
+        prompts = [query_result.inputs for _ in range(args.num_prompts)]
+    else:
+        assert args.query_type == "text", "txt-prompts is only supported for text query type"
+        with open(args.txt_prompts, encoding="utf-8") as f:
+            lines = [ln.strip() for ln in f.readlines()]
+            prompts = [get_text_query(ln).inputs for ln in lines if ln != ""]
+            print(f"[Info] Loaded {len(prompts)} prompts from {args.txt_prompts}")
+
+    omni_outputs = omni_llm.generate(prompts, sampling_params_list)
 
     # Determine output directory: prefer --output-dir; fallback to --output-wav
     output_dir = args.output_dir if getattr(args, "output_dir", None) else args.output_wav
@@ -217,7 +204,7 @@ def main():
                 request_id = int(output.request_id)
                 text_output = output.outputs[0].text
                 # Save aligned text file per request
-                prompt_text = args.prompts[request_id]
+                prompt_text = prompts[request_id]["prompt"]
                 out_txt = os.path.join(output_dir, f"{request_id:05d}.txt")
                 lines = []
                 lines.append("Prompt:\n")
@@ -239,5 +226,67 @@ def main():
                 print(f"Request ID: {request_id}, Saved audio to {output_wav}")
 
 
+def parse_args():
+    parser = FlexibleArgumentParser(description="Demo on using vLLM for offline inference with audio language models")
+    parser.add_argument(
+        "--query-type",
+        "-q",
+        type=str,
+        default="mixed_modalities",
+        choices=query_map.keys(),
+        help="Query type.",
+    )
+    parser.add_argument(
+        "--enable-stats",
+        action="store_true",
+        default=False,
+        help="Enable writing detailed statistics (default: disabled)",
+    )
+    parser.add_argument(
+        "--init-sleep-seconds",
+        type=int,
+        default=20,
+        help="Sleep seconds after starting each stage process to allow initialization (default: 20)",
+    )
+    parser.add_argument(
+        "--batch-timeout",
+        type=int,
+        default=5,
+        help="Timeout for batching in seconds (default: 5)",
+    )
+    parser.add_argument(
+        "--init-timeout",
+        type=int,
+        default=300,
+        help="Timeout for initializing stages in seconds (default: 300)",
+    )
+    parser.add_argument(
+        "--shm-threshold-bytes",
+        type=int,
+        default=65536,
+        help="Threshold for using shared memory in bytes (default: 65536)",
+    )
+    parser.add_argument(
+        "--output-wav",
+        default="output_audio",
+        help="[Deprecated] Output wav directory (use --output-dir).",
+    )
+    parser.add_argument(
+        "--num-prompts",
+        type=int,
+        default=1,
+        help="Number of prompts to generate.",
+    )
+    parser.add_argument(
+        "--txt-prompts",
+        type=str,
+        default=None,
+        help="Path to a .txt file with one prompt per line (preferred).",
+    )
+
+    return parser.parse_args()
+
+
 if __name__ == "__main__":
-    main()
+    args = parse_args()
+    main(args)