[Misc] Add `--seed` option to offline multi-modal examples (#14934)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

.buildkite/test-pipeline.yaml

@@ -226,10 +226,13 @@ steps:
     - python3 offline_inference/basic/chat.py
     - python3 offline_inference/prefix_caching.py
     - python3 offline_inference/llm_engine_example.py
-    - python3 offline_inference/vision_language.py
-    - python3 offline_inference/vision_language_multi_image.py
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_embedding.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
     - VLLM_USE_V1=0 python3 other/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 other/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
     - python3 offline_inference/encoder_decoder.py
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
     - python3 offline_inference/basic/classify.py
     - python3 offline_inference/basic/embed.py
     - python3 offline_inference/basic/score.py

examples/offline_inference/audio_language.py

@@ -7,11 +7,13 @@ For most models, the prompt format should follow corresponding examples
 on HuggingFace model repository.
 """
 import os
+from dataclasses import asdict
+from typing import NamedTuple, Optional
 
 from huggingface_hub import snapshot_download
 from transformers import AutoTokenizer
 
-from vllm import LLM, SamplingParams
+from vllm import LLM, EngineArgs, SamplingParams
 from vllm.assets.audio import AudioAsset
 from vllm.lora.request import LoRARequest
 from vllm.utils import FlexibleArgumentParser
@@ -23,21 +25,31 @@ question_per_audio_count = {
     2: "What sport and what nursery rhyme are referenced?"
 }
 
+
+class ModelRequestData(NamedTuple):
+    engine_args: EngineArgs
+    prompt: str
+    stop_token_ids: Optional[list[int]] = None
+    lora_requests: Optional[list[LoRARequest]] = None
+
+
 # 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.
 
 
 # MiniCPM-O
-def run_minicpmo(question: str, audio_count: int):
+def run_minicpmo(question: str, audio_count: int) -> ModelRequestData:
     model_name = "openbmb/MiniCPM-o-2_6"
     tokenizer = AutoTokenizer.from_pretrained(model_name,
                                               trust_remote_code=True)
-    llm = LLM(model=model_name,
-              trust_remote_code=True,
-              max_model_len=4096,
-              max_num_seqs=5,
-              limit_mm_per_prompt={"audio": audio_count})
+    engine_args = EngineArgs(
+        model=model_name,
+        trust_remote_code=True,
+        max_model_len=4096,
+        max_num_seqs=5,
+        limit_mm_per_prompt={"audio": audio_count},
+    )
 
     stop_tokens = ['<|im_end|>', '<|endoftext|>']
     stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
@@ -52,11 +64,16 @@ def run_minicpmo(question: str, audio_count: int):
                                            tokenize=False,
                                            add_generation_prompt=True,
                                            chat_template=audio_chat_template)
-    return llm, prompt, stop_token_ids
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        stop_token_ids=stop_token_ids,
+    )
 
 
 # Phi-4-multimodal-instruct
-def run_phi4mm(questions: str, audio_count: int):
+def run_phi4mm(question: str, audio_count: int) -> ModelRequestData:
     """
     Phi-4-multimodal-instruct supports both image and audio inputs. Here, we
     show how to process audio inputs.
@@ -67,9 +84,9 @@ def run_phi4mm(questions: str, audio_count: int):
     speech_lora_path = os.path.join(model_path, "speech-lora")
     placeholders = "".join([f"<|audio_{i+1}|>" for i in range(audio_count)])
 
-    prompts = f"<|user|>{placeholders}{questions}<|end|><|assistant|>"
+    prompts = f"<|user|>{placeholders}{question}<|end|><|assistant|>"
 
-    llm = LLM(
+    engine_args = EngineArgs(
         model=model_path,
         trust_remote_code=True,
         max_model_len=4096,
@@ -79,24 +96,24 @@ def run_phi4mm(questions: str, audio_count: int):
         lora_extra_vocab_size=0,
         limit_mm_per_prompt={"audio": audio_count},
     )
-    lora_request = LoRARequest("speech", 1, speech_lora_path)
-    # To maintain code compatibility in this script, we add LoRA here.
-    llm.llm_engine.add_lora(lora_request=lora_request)
-    # You can also add LoRA using:
-    # llm.generate(prompts, lora_request=lora_request,...)
 
-    stop_token_ids = None
-    return llm, prompts, stop_token_ids
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompts,
+        lora_requests=[LoRARequest("speech", 1, speech_lora_path)],
+    )
 
 
 # Qwen2-Audio
-def run_qwen2_audio(question: str, audio_count: int):
+def run_qwen2_audio(question: str, audio_count: int) -> ModelRequestData:
     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})
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=5,
+        limit_mm_per_prompt={"audio": audio_count},
+    )
 
     audio_in_prompt = "".join([
         f"Audio {idx+1}: "
@@ -107,12 +124,15 @@ def run_qwen2_audio(question: str, audio_count: int):
               "<|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
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+    )
 
 
 # Ultravox 0.5-1B
-def run_ultravox(question: str, audio_count: int):
+def run_ultravox(question: str, audio_count: int) -> ModelRequestData:
     model_name = "fixie-ai/ultravox-v0_5-llama-3_2-1b"
 
     tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -124,29 +144,39 @@ def run_ultravox(question: str, audio_count: int):
                                            tokenize=False,
                                            add_generation_prompt=True)
 
-    llm = LLM(model=model_name,
-              max_model_len=4096,
-              max_num_seqs=5,
-              trust_remote_code=True,
-              limit_mm_per_prompt={"audio": audio_count})
-    stop_token_ids = None
-    return llm, prompt, stop_token_ids
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=5,
+        trust_remote_code=True,
+        limit_mm_per_prompt={"audio": audio_count},
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+    )
 
 
 # Whisper
-def run_whisper(question: str, audio_count: int):
+def run_whisper(question: str, audio_count: int) -> ModelRequestData:
     assert audio_count == 1, (
         "Whisper only support single audio input per prompt")
     model_name = "openai/whisper-large-v3-turbo"
 
     prompt = "<|startoftranscript|>"
 
-    llm = LLM(model=model_name,
-              max_model_len=448,
-              max_num_seqs=5,
-              limit_mm_per_prompt={"audio": audio_count})
-    stop_token_ids = None
-    return llm, prompt, stop_token_ids
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=448,
+        max_num_seqs=5,
+        limit_mm_per_prompt={"audio": audio_count},
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+    )
 
 
 model_example_map = {
@@ -164,14 +194,24 @@ def main(args):
         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)
+    req_data = model_example_map[model](question_per_audio_count[audio_count],
+                                        audio_count)
+
+    engine_args = asdict(req_data.engine_args) | {"seed": args.seed}
+    llm = LLM(**engine_args)
+
+    # To maintain code compatibility in this script, we add LoRA here.
+    # You can also add LoRA using:
+    # llm.generate(prompts, lora_request=lora_request,...)
+    if req_data.lora_requests:
+        for lora_request in req_data.lora_requests:
+            llm.llm_engine.add_lora(lora_request=lora_request)
 
     # 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)
+                                     stop_token_ids=req_data.stop_token_ids)
 
     mm_data = {}
     if audio_count > 0:
@@ -183,7 +223,7 @@ def main(args):
         }
 
     assert args.num_prompts > 0
-    inputs = {"prompt": prompt, "multi_modal_data": mm_data}
+    inputs = {"prompt": req_data.prompt, "multi_modal_data": mm_data}
     if args.num_prompts > 1:
         # Batch inference
         inputs = [inputs] * args.num_prompts
@@ -214,6 +254,10 @@ if __name__ == "__main__":
                         default=1,
                         choices=[0, 1, 2],
                         help="Number of audio items per prompt.")
+    parser.add_argument("--seed",
+                        type=int,
+                        default=None,
+                        help="Set the seed when initializing `vllm.LLM`.")
 
     args = parser.parse_args()
     main(args)

examples/offline_inference/encoder_decoder_multimodal.py

@@ -4,16 +4,23 @@ This example shows how to use vLLM for running offline inference with
 the explicit/implicit prompt format on enc-dec LMMs for text generation.
 """
 import time
+from collections.abc import Sequence
+from dataclasses import asdict
+from typing import NamedTuple
 
-from vllm import LLM, SamplingParams
+from vllm import LLM, EngineArgs, PromptType, SamplingParams
 from vllm.assets.audio import AudioAsset
 from vllm.assets.image import ImageAsset
 from vllm.utils import FlexibleArgumentParser
 
 
+class ModelRequestData(NamedTuple):
+    engine_args: EngineArgs
+    prompts: Sequence[PromptType]
+
+
 def run_florence2():
-    # Create a Florence-2 encoder/decoder model instance
-    llm = LLM(
+    engine_args = EngineArgs(
         model="microsoft/Florence-2-large",
         tokenizer="facebook/bart-large",
         max_num_seqs=8,
@@ -39,12 +46,15 @@ def run_florence2():
             "decoder_prompt": "",
         },
     ]
-    return llm, prompts
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
 
 
 def run_mllama():
-    # Create a Mllama encoder/decoder model instance
-    llm = LLM(
+    engine_args = EngineArgs(
         model="meta-llama/Llama-3.2-11B-Vision-Instruct",
         max_model_len=4096,
         max_num_seqs=2,
@@ -69,12 +79,15 @@ def run_mllama():
             "decoder_prompt": "<|image|><|begin_of_text|>Please describe the image.",   # noqa: E501
         },
     ]
-    return llm, prompts
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
 
 
 def run_whisper():
-    # Create a Whisper encoder/decoder model instance
-    llm = LLM(
+    engine_args = EngineArgs(
         model="openai/whisper-large-v3-turbo",
         max_model_len=448,
         max_num_seqs=16,
@@ -99,7 +112,11 @@ def run_whisper():
             "decoder_prompt": "<|startoftranscript|>",
         }
     ]
-    return llm, prompts
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
 
 
 model_example_map = {
@@ -114,7 +131,12 @@ def main(args):
     if model not in model_example_map:
         raise ValueError(f"Model type {model} is not supported.")
 
-    llm, prompts = model_example_map[model]()
+    req_data = model_example_map[model]()
+
+    engine_args = asdict(req_data.engine_args) | {"seed": args.seed}
+    llm = LLM(**engine_args)
+
+    prompts = req_data.prompts
 
     # Create a sampling params object.
     sampling_params = SamplingParams(
@@ -153,6 +175,10 @@ if __name__ == "__main__":
                         default="mllama",
                         choices=model_example_map.keys(),
                         help='Huggingface "model_type".')
+    parser.add_argument("--seed",
+                        type=int,
+                        default=None,
+                        help="Set the seed when initializing `vllm.LLM`.")
 
     args = parser.parse_args()
     main(args)

examples/offline_inference/vision_language_embedding.py

@@ -7,11 +7,12 @@ For most models, the prompt format should follow corresponding examples
 on HuggingFace model repository.
 """
 from argparse import Namespace
+from dataclasses import asdict
 from typing import Literal, NamedTuple, Optional, TypedDict, Union, get_args
 
 from PIL.Image import Image
 
-from vllm import LLM
+from vllm import LLM, EngineArgs
 from vllm.multimodal.utils import fetch_image
 from vllm.utils import FlexibleArgumentParser
 
@@ -37,12 +38,12 @@ Query = Union[TextQuery, ImageQuery, TextImageQuery]
 
 
 class ModelRequestData(NamedTuple):
-    llm: LLM
+    engine_args: EngineArgs
     prompt: str
     image: Optional[Image]
 
 
-def run_e5_v(query: Query):
+def run_e5_v(query: Query) -> ModelRequestData:
     llama3_template = '<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n'  # noqa: E501
 
     if query["modality"] == "text":
@@ -58,20 +59,20 @@ def run_e5_v(query: Query):
         modality = query['modality']
         raise ValueError(f"Unsupported query modality: '{modality}'")
 
-    llm = LLM(
+    engine_args = EngineArgs(
         model="royokong/e5-v",
         task="embed",
         max_model_len=4096,
     )
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
         image=image,
     )
 
 
-def run_vlm2vec(query: Query):
+def run_vlm2vec(query: Query) -> ModelRequestData:
     if query["modality"] == "text":
         text = query["text"]
         prompt = f"Find me an everyday image that matches the given caption: {text}"  # noqa: E501
@@ -87,7 +88,7 @@ def run_vlm2vec(query: Query):
         modality = query['modality']
         raise ValueError(f"Unsupported query modality: '{modality}'")
 
-    llm = LLM(
+    engine_args = EngineArgs(
         model="TIGER-Lab/VLM2Vec-Full",
         task="embed",
         trust_remote_code=True,
@@ -95,7 +96,7 @@ def run_vlm2vec(query: Query):
     )
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
         image=image,
     )
@@ -126,15 +127,18 @@ def get_query(modality: QueryModality):
     raise ValueError(msg)
 
 
-def run_encode(model: str, modality: QueryModality):
+def run_encode(model: str, modality: QueryModality, seed: Optional[int]):
     query = get_query(modality)
     req_data = model_example_map[model](query)
 
+    engine_args = asdict(req_data.engine_args) | {"seed": seed}
+    llm = LLM(**engine_args)
+
     mm_data = {}
     if req_data.image is not None:
         mm_data["image"] = req_data.image
 
-    outputs = req_data.llm.embed({
+    outputs = llm.embed({
         "prompt": req_data.prompt,
         "multi_modal_data": mm_data,
     })
@@ -144,7 +148,7 @@ def run_encode(model: str, modality: QueryModality):
 
 
 def main(args: Namespace):
-    run_encode(args.model_name, args.modality)
+    run_encode(args.model_name, args.modality, args.seed)
 
 
 model_example_map = {
@@ -167,5 +171,10 @@ if __name__ == "__main__":
                         default="image",
                         choices=get_args(QueryModality),
                         help='Modality of the input.')
+    parser.add_argument("--seed",
+                        type=int,
+                        default=None,
+                        help="Set the seed when initializing `vllm.LLM`.")
+
     args = parser.parse_args()
     main(args)

examples/offline_inference/vision_language_multi_image.py

@@ -6,13 +6,14 @@ using the chat template defined by the model.
 """
 import os
 from argparse import Namespace
+from dataclasses import asdict
 from typing import NamedTuple, Optional
 
 from huggingface_hub import snapshot_download
 from PIL.Image import Image
 from transformers import AutoProcessor, AutoTokenizer
 
-from vllm import LLM, SamplingParams
+from vllm import LLM, EngineArgs, SamplingParams
 from vllm.lora.request import LoRARequest
 from vllm.multimodal.utils import fetch_image
 from vllm.utils import FlexibleArgumentParser
@@ -25,11 +26,12 @@ IMAGE_URLS = [
 
 
 class ModelRequestData(NamedTuple):
-    llm: LLM
+    engine_args: EngineArgs
     prompt: str
-    stop_token_ids: Optional[list[int]]
     image_data: list[Image]
-    chat_template: Optional[str]
+    stop_token_ids: Optional[list[int]] = None
+    chat_template: Optional[str] = None
+    lora_requests: Optional[list[LoRARequest]] = None
 
 
 # NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
@@ -37,53 +39,55 @@ class ModelRequestData(NamedTuple):
 # Unless specified, these settings have been tested to work on a single L4.
 
 
-def load_aria(question, image_urls: list[str]) -> ModelRequestData:
+def load_aria(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "rhymes-ai/Aria"
-    llm = LLM(model=model_name,
-              tokenizer_mode="slow",
-              trust_remote_code=True,
-              dtype="bfloat16",
-              limit_mm_per_prompt={"image": len(image_urls)})
+    engine_args = EngineArgs(
+        model=model_name,
+        tokenizer_mode="slow",
+        trust_remote_code=True,
+        dtype="bfloat16",
+        limit_mm_per_prompt={"image": len(image_urls)},
+    )
     placeholders = "<fim_prefix><|img|><fim_suffix>\n" * len(image_urls)
     prompt = (f"<|im_start|>user\n{placeholders}{question}<|im_end|>\n"
               "<|im_start|>assistant\n")
     stop_token_ids = [93532, 93653, 944, 93421, 1019, 93653, 93519]
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
         stop_token_ids=stop_token_ids,
         image_data=[fetch_image(url) for url in image_urls],
-        chat_template=None,
     )
 
 
-def load_deepseek_vl2(question: str, image_urls: list[str]):
+def load_deepseek_vl2(question: str,
+                      image_urls: list[str]) -> ModelRequestData:
     model_name = "deepseek-ai/deepseek-vl2-tiny"
 
-    llm = LLM(model=model_name,
-              max_model_len=4096,
-              max_num_seqs=2,
-              hf_overrides={"architectures": ["DeepseekVLV2ForCausalLM"]},
-              limit_mm_per_prompt={"image": len(image_urls)})
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=2,
+        hf_overrides={"architectures": ["DeepseekVLV2ForCausalLM"]},
+        limit_mm_per_prompt={"image": len(image_urls)},
+    )
 
     placeholder = "".join(f"image_{i}:<image>\n"
                           for i, _ in enumerate(image_urls, start=1))
     prompt = f"<|User|>: {placeholder}{question}\n\n<|Assistant|>:"
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
-        stop_token_ids=None,
         image_data=[fetch_image(url) for url in image_urls],
-        chat_template=None,
     )
 
 
-def load_gemma3(question, image_urls: list[str]) -> ModelRequestData:
+def load_gemma3(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "google/gemma-3-4b-it"
 
-    llm = LLM(
+    engine_args = EngineArgs(
         model=model_name,
         max_model_len=8192,
         max_num_seqs=2,
@@ -112,18 +116,16 @@ def load_gemma3(question, image_urls: list[str]) -> ModelRequestData:
                                            add_generation_prompt=True)
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
-        stop_token_ids=None,
         image_data=[fetch_image(url) for url in image_urls],
-        chat_template=None,
     )
 
 
 def load_h2ovl(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "h2oai/h2ovl-mississippi-800m"
 
-    llm = LLM(
+    engine_args = EngineArgs(
         model=model_name,
         trust_remote_code=True,
         max_model_len=8192,
@@ -146,19 +148,18 @@ def load_h2ovl(question: str, image_urls: list[str]) -> ModelRequestData:
     stop_token_ids = [tokenizer.eos_token_id]
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
         stop_token_ids=stop_token_ids,
         image_data=[fetch_image(url) for url in image_urls],
-        chat_template=None,
     )
 
 
-def load_idefics3(question, image_urls: list[str]) -> ModelRequestData:
+def load_idefics3(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "HuggingFaceM4/Idefics3-8B-Llama3"
 
     # The configuration below has been confirmed to launch on a single L40 GPU.
-    llm = LLM(
+    engine_args = EngineArgs(
         model=model_name,
         max_model_len=8192,
         max_num_seqs=16,
@@ -177,18 +178,16 @@ def load_idefics3(question, image_urls: list[str]) -> ModelRequestData:
                              for i, _ in enumerate(image_urls, start=1))
     prompt = f"<|begin_of_text|>User:{placeholders}\n{question}<end_of_utterance>\nAssistant:"  # noqa: E501
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
-        stop_token_ids=None,
         image_data=[fetch_image(url) for url in image_urls],
-        chat_template=None,
     )
 
 
 def load_internvl(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "OpenGVLab/InternVL2-2B"
 
-    llm = LLM(
+    engine_args = EngineArgs(
         model=model_name,
         trust_remote_code=True,
         max_model_len=4096,
@@ -214,19 +213,18 @@ def load_internvl(question: str, image_urls: list[str]) -> ModelRequestData:
     stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
         stop_token_ids=stop_token_ids,
         image_data=[fetch_image(url) for url in image_urls],
-        chat_template=None,
     )
 
 
-def load_mllama(question, image_urls: list[str]) -> ModelRequestData:
+def load_mllama(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct"
 
     # The configuration below has been confirmed to launch on a single L40 GPU.
-    llm = LLM(
+    engine_args = EngineArgs(
         model=model_name,
         max_model_len=4096,
         max_num_seqs=16,
@@ -236,19 +234,17 @@ def load_mllama(question, image_urls: list[str]) -> ModelRequestData:
     placeholders = "<|image|>" * len(image_urls)
     prompt = f"{placeholders}<|begin_of_text|>{question}"
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
-        stop_token_ids=None,
         image_data=[fetch_image(url) for url in image_urls],
-        chat_template=None,
     )
 
 
-def load_nvlm_d(question: str, image_urls: list[str]):
+def load_nvlm_d(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "nvidia/NVLM-D-72B"
 
     # Adjust this as necessary to fit in GPU
-    llm = LLM(
+    engine_args = EngineArgs(
         model=model_name,
         trust_remote_code=True,
         max_model_len=8192,
@@ -266,14 +262,11 @@ def load_nvlm_d(question: str, image_urls: list[str]):
     prompt = tokenizer.apply_chat_template(messages,
                                            tokenize=False,
                                            add_generation_prompt=True)
-    stop_token_ids = None
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
-        stop_token_ids=stop_token_ids,
         image_data=[fetch_image(url) for url in image_urls],
-        chat_template=None,
     )
 
 
@@ -281,7 +274,7 @@ def load_pixtral_hf(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "mistral-community/pixtral-12b"
 
     # Adjust this as necessary to fit in GPU
-    llm = LLM(
+    engine_args = EngineArgs(
         model=model_name,
         max_model_len=8192,
         max_num_seqs=2,
@@ -291,14 +284,11 @@ def load_pixtral_hf(question: str, image_urls: list[str]) -> ModelRequestData:
 
     placeholders = "[IMG]" * len(image_urls)
     prompt = f"<s>[INST]{question}\n{placeholders}[/INST]"
-    stop_token_ids = None
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
-        stop_token_ids=stop_token_ids,
         image_data=[fetch_image(url) for url in image_urls],
-        chat_template=None,
     )
 
 
@@ -315,7 +305,7 @@ def load_phi3v(question: str, image_urls: list[str]) -> ModelRequestData:
     #
     # https://huggingface.co/microsoft/Phi-3.5-vision-instruct#loading-the-model-locally
     # https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/main/processing_phi3_v.py#L194
-    llm = LLM(
+    engine_args = EngineArgs(
         model="microsoft/Phi-3.5-vision-instruct",
         trust_remote_code=True,
         max_model_len=4096,
@@ -326,14 +316,11 @@ def load_phi3v(question: str, image_urls: list[str]) -> ModelRequestData:
     placeholders = "\n".join(f"<|image_{i}|>"
                              for i, _ in enumerate(image_urls, start=1))
     prompt = f"<|user|>\n{placeholders}\n{question}<|end|>\n<|assistant|>\n"
-    stop_token_ids = None
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
-        stop_token_ids=stop_token_ids,
         image_data=[fetch_image(url) for url in image_urls],
-        chat_template=None,
     )
 
 
@@ -347,7 +334,7 @@ def load_phi4mm(question: str, image_urls: list[str]) -> ModelRequestData:
     # Since the vision-lora and speech-lora co-exist with the base model,
     # we have to manually specify the path of the lora weights.
     vision_lora_path = os.path.join(model_path, "vision-lora")
-    llm = LLM(
+    engine_args = EngineArgs(
         model=model_path,
         trust_remote_code=True,
         max_model_len=10000,
@@ -357,30 +344,23 @@ def load_phi4mm(question: str, image_urls: list[str]) -> ModelRequestData:
         max_lora_rank=320,
         lora_extra_vocab_size=0,
     )
-    lora_request = LoRARequest("vision", 1, vision_lora_path)
-    # To maintain code compatibility in this script, we add LoRA here.
-    llm.llm_engine.add_lora(lora_request=lora_request)
-    # You can also add LoRA using:
-    # llm.generate(prompts, lora_request=lora_request,...)
 
     placeholders = "".join(f"<|image_{i}|>"
                            for i, _ in enumerate(image_urls, start=1))
     prompt = f"<|user|>{placeholders}{question}<|end|><|assistant|>"
-    stop_token_ids = None
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
-        stop_token_ids=stop_token_ids,
         image_data=[fetch_image(url) for url in image_urls],
-        chat_template=None,
+        lora_requests=[LoRARequest("vision", 1, vision_lora_path)],
     )
 
 
 def load_qwen_vl_chat(question: str,
                       image_urls: list[str]) -> ModelRequestData:
     model_name = "Qwen/Qwen-VL-Chat"
-    llm = LLM(
+    engine_args = EngineArgs(
         model=model_name,
         trust_remote_code=True,
         max_model_len=1024,
@@ -411,7 +391,7 @@ def load_qwen_vl_chat(question: str,
     stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
         stop_token_ids=stop_token_ids,
         image_data=[fetch_image(url) for url in image_urls],
@@ -419,7 +399,7 @@ def load_qwen_vl_chat(question: str,
     )
 
 
-def load_qwen2_vl(question, image_urls: list[str]) -> ModelRequestData:
+def load_qwen2_vl(question: str, image_urls: list[str]) -> ModelRequestData:
     try:
         from qwen_vl_utils import process_vision_info
     except ModuleNotFoundError:
@@ -431,7 +411,7 @@ def load_qwen2_vl(question, image_urls: list[str]) -> ModelRequestData:
     model_name = "Qwen/Qwen2-VL-7B-Instruct"
 
     # Tested on L40
-    llm = LLM(
+    engine_args = EngineArgs(
         model=model_name,
         max_model_len=32768 if process_vision_info is None else 4096,
         max_num_seqs=5,
@@ -460,23 +440,19 @@ def load_qwen2_vl(question, image_urls: list[str]) -> ModelRequestData:
                                            tokenize=False,
                                            add_generation_prompt=True)
 
-    stop_token_ids = None
-
     if process_vision_info is None:
         image_data = [fetch_image(url) for url in image_urls]
     else:
         image_data, _ = process_vision_info(messages)
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
-        stop_token_ids=stop_token_ids,
         image_data=image_data,
-        chat_template=None,
     )
 
 
-def load_qwen2_5_vl(question, image_urls: list[str]) -> ModelRequestData:
+def load_qwen2_5_vl(question: str, image_urls: list[str]) -> ModelRequestData:
     try:
         from qwen_vl_utils import process_vision_info
     except ModuleNotFoundError:
@@ -487,7 +463,7 @@ def load_qwen2_5_vl(question, image_urls: list[str]) -> ModelRequestData:
 
     model_name = "Qwen/Qwen2.5-VL-3B-Instruct"
 
-    llm = LLM(
+    engine_args = EngineArgs(
         model=model_name,
         max_model_len=32768 if process_vision_info is None else 4096,
         max_num_seqs=5,
@@ -516,8 +492,6 @@ def load_qwen2_5_vl(question, image_urls: list[str]) -> ModelRequestData:
                                            tokenize=False,
                                            add_generation_prompt=True)
 
-    stop_token_ids = None
-
     if process_vision_info is None:
         image_data = [fetch_image(url) for url in image_urls]
     else:
@@ -525,11 +499,9 @@ def load_qwen2_5_vl(question, image_urls: list[str]) -> ModelRequestData:
                                             return_video_kwargs=False)
 
     return ModelRequestData(
-        llm=llm,
+        engine_args=engine_args,
         prompt=prompt,
-        stop_token_ids=stop_token_ids,
         image_data=image_data,
-        chat_template=None,
     )
 
 
@@ -551,14 +523,25 @@ model_example_map = {
 }
 
 
-def run_generate(model, question: str, image_urls: list[str]):
+def run_generate(model, question: str, image_urls: list[str],
+                 seed: Optional[int]):
     req_data = model_example_map[model](question, image_urls)
 
+    engine_args = asdict(req_data.engine_args) | {"seed": args.seed}
+    llm = LLM(**engine_args)
+
+    # To maintain code compatibility in this script, we add LoRA here.
+    # You can also add LoRA using:
+    # llm.generate(prompts, lora_request=lora_request,...)
+    if req_data.lora_requests:
+        for lora_request in req_data.lora_requests:
+            llm.llm_engine.add_lora(lora_request=lora_request)
+
     sampling_params = SamplingParams(temperature=0.0,
                                      max_tokens=128,
                                      stop_token_ids=req_data.stop_token_ids)
 
-    outputs = req_data.llm.generate(
+    outputs = llm.generate(
         {
             "prompt": req_data.prompt,
             "multi_modal_data": {
@@ -572,13 +555,24 @@ def run_generate(model, question: str, image_urls: list[str]):
         print(generated_text)
 
 
-def run_chat(model: str, question: str, image_urls: list[str]):
+def run_chat(model: str, question: str, image_urls: list[str],
+             seed: Optional[int]):
     req_data = model_example_map[model](question, image_urls)
 
+    engine_args = asdict(req_data.engine_args) | {"seed": seed}
+    llm = LLM(**engine_args)
+
+    # To maintain code compatibility in this script, we add LoRA here.
+    # You can also add LoRA using:
+    # llm.generate(prompts, lora_request=lora_request,...)
+    if req_data.lora_requests:
+        for lora_request in req_data.lora_requests:
+            llm.llm_engine.add_lora(lora_request=lora_request)
+
     sampling_params = SamplingParams(temperature=0.0,
                                      max_tokens=128,
                                      stop_token_ids=req_data.stop_token_ids)
-    outputs = req_data.llm.chat(
+    outputs = llm.chat(
         [{
             "role":
             "user",
@@ -607,11 +601,12 @@ def run_chat(model: str, question: str, image_urls: list[str]):
 def main(args: Namespace):
     model = args.model_type
     method = args.method
+    seed = args.seed
 
     if method == "generate":
-        run_generate(model, QUESTION, IMAGE_URLS)
+        run_generate(model, QUESTION, IMAGE_URLS, seed)
     elif method == "chat":
-        run_chat(model, QUESTION, IMAGE_URLS)
+        run_chat(model, QUESTION, IMAGE_URLS, seed)
     else:
         raise ValueError(f"Invalid method: {method}")
 
@@ -632,6 +627,10 @@ if __name__ == "__main__":
                         default="generate",
                         choices=["generate", "chat"],
                         help="The method to run in `vllm.LLM`.")
+    parser.add_argument("--seed",
+                        type=int,
+                        default=None,
+                        help="Set the seed when initializing `vllm.LLM`.")
 
     args = parser.parse_args()
     main(args)