[Doc] Add qwen2-audio eager mode tutorial (#1371)

### What this PR does / why we need it?
Add qwen2-audio eager mode tutorial.


Signed-off-by: shen-shanshan <467638484@qq.com>

examples/offline_inference_audio_language.py

@@ -26,74 +26,51 @@ on HuggingFace model repository.
 
 from vllm import LLM, SamplingParams
 from vllm.assets.audio import AudioAsset
-from vllm.utils import FlexibleArgumentParser
 
 audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
 question_per_audio_count = {
-    0: "What is 1+1?",
     1: "What is recited in the audio?",
     2: "What sport and what nursery rhyme are referenced?"
 }
 
-# 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.
-
-
-# Qwen2-Audio
-def run_qwen2_audio(question: str, audio_count: int):
-    model_name = "Qwen/Qwen2-Audio-7B-Instruct"
-
-    llm = LLM(model=model_name,
-              max_model_len=4096,
-              max_num_seqs=5,
-              limit_mm_per_prompt={"audio": audio_count})
 
+def prepare_inputs(audio_count: int):
     audio_in_prompt = "".join([
-        f"Audio {idx+1}: "
-        f"<|audio_bos|><|AUDIO|><|audio_eos|>\n" for idx in range(audio_count)
+        f"Audio {idx+1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n"
+        for idx in range(audio_count)
     ])
-
+    question = question_per_audio_count[audio_count]
     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")
-    stop_token_ids = None
-    return llm, prompt, stop_token_ids
+
+    mm_data = {
+        "audio":
+        [asset.audio_and_sample_rate for asset in audio_assets[:audio_count]]
+    }
+
+    # Merge text prompt and audio data into inputs
+    inputs = {"prompt": prompt, "multi_modal_data": mm_data}
+    return inputs
 
 
-model_example_map = {"qwen2_audio": run_qwen2_audio}
+def main(audio_count: int):
+    # 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.
+    # `limit_mm_per_prompt`: the max num items for each modality per prompt.
+    llm = LLM(model="Qwen/Qwen2-Audio-7B-Instruct",
+              max_model_len=4096,
+              max_num_seqs=5,
+              limit_mm_per_prompt={"audio": audio_count},
+              enforce_eager=True)
 
+    inputs = prepare_inputs(audio_count)
 
-def main(args):
-    model = args.model_type
-    if model not in model_example_map:
-        raise ValueError(f"Model type {model} is not supported.")
-
-    audio_count = args.num_audios
-    llm, prompt, stop_token_ids = model_example_map[model](
-        question_per_audio_count[audio_count], audio_count)
-
-    # We set temperature to 0.2 so that outputs can be different
-    # even when all prompts are identical when running batch inference.
     sampling_params = SamplingParams(temperature=0.2,
                                      max_tokens=64,
-                                     stop_token_ids=stop_token_ids)
-
-    mm_data = {}
-    if audio_count > 0:
-        mm_data = {
-            "audio": [
-                asset.audio_and_sample_rate
-                for asset in audio_assets[:audio_count]
-            ]
-        }
-
-    assert args.num_prompts > 0
-    inputs = {"prompt": prompt, "multi_modal_data": mm_data}
-    if args.num_prompts > 1:
-        # Batch inference
-        inputs = [inputs] * args.num_prompts  # type: ignore
+                                     stop_token_ids=None)
 
     outputs = llm.generate(inputs, sampling_params=sampling_params)
 
@@ -103,24 +80,5 @@ def main(args):
 
 
 if __name__ == "__main__":
-    parser = FlexibleArgumentParser(
-        description='Demo on using vLLM for offline inference with '
-        'audio language models')
-    parser.add_argument('--model-type',
-                        '-m',
-                        type=str,
-                        default="qwen2_audio",
-                        choices=model_example_map.keys(),
-                        help='Huggingface "model_type".')
-    parser.add_argument('--num-prompts',
-                        type=int,
-                        default=1,
-                        help='Number of prompts to run.')
-    parser.add_argument("--num-audios",
-                        type=int,
-                        default=1,
-                        choices=[0, 1, 2],
-                        help="Number of audio items per prompt.")
-
-    args = parser.parse_args()
-    main(args)
+    audio_count = 2
+    main(audio_count)