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[V1] Enable multi-input by default (#15799)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2025-04-12 16:52:39 +08:00
committed by GitHub
parent f069f3ea74
commit d9fc8cd9da
21 changed files with 214 additions and 105 deletions
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79
examples/offline_inference/vision_language.py
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@@ -45,7 +45,7 @@ def run_aria(questions: list[str], modality: str) -> ModelRequestData:
max_model_len=4096,
max_num_seqs=2,
dtype="bfloat16",
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
prompts = [(f"<|im_start|>user\n<fim_prefix><|img|><fim_suffix>{question}"
@@ -71,7 +71,7 @@ def run_aya_vision(questions: list[str], modality: str) -> ModelRequestData:
max_model_len=2048,
max_num_seqs=2,
mm_processor_kwargs={"crop_to_patches": True},
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
prompts = [
f"<|START_OF_TURN_TOKEN|><|USER_TOKEN|><image>{question}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
@@ -92,7 +92,7 @@ def run_blip2(questions: list[str], modality: str) -> ModelRequestData:
prompts = [f"Question: {question} Answer:" for question in questions]
engine_args = EngineArgs(
model="Salesforce/blip2-opt-6.7b",
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
return ModelRequestData(
@@ -110,7 +110,7 @@ def run_chameleon(questions: list[str], modality: str) -> ModelRequestData:
model="facebook/chameleon-7b",
max_model_len=4096,
max_num_seqs=2,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
return ModelRequestData(
@@ -129,8 +129,8 @@ def run_deepseek_vl2(questions: list[str], modality: str) -> ModelRequestData:
model=model_name,
max_model_len=4096,
max_num_seqs=2,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
hf_overrides={"architectures": ["DeepseekVLV2ForCausalLM"]},
limit_mm_per_prompt={"image": 1},
)
prompts = [
@@ -155,7 +155,7 @@ def run_florence2(questions: list[str], modality: str) -> ModelRequestData:
max_num_seqs=2,
trust_remote_code=True,
dtype="bfloat16",
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
prompts = ["<MORE_DETAILED_CAPTION>" for _ in questions]
@@ -175,7 +175,7 @@ def run_fuyu(questions: list[str], modality: str) -> ModelRequestData:
model="adept/fuyu-8b",
max_model_len=2048,
max_num_seqs=2,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
return ModelRequestData(
@@ -194,7 +194,7 @@ def run_gemma3(questions: list[str], modality: str) -> ModelRequestData:
max_model_len=2048,
max_num_seqs=2,
mm_processor_kwargs={"do_pan_and_scan": True},
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
prompts = [("<bos><start_of_turn>user\n"
@@ -219,7 +219,7 @@ def run_glm4v(questions: list[str], modality: str) -> ModelRequestData:
trust_remote_code=True,
enforce_eager=True,
hf_overrides={"architectures": ["GLM4VForCausalLM"]},
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
prompts = [
@@ -246,7 +246,7 @@ def run_h2ovl(questions: list[str], modality: str) -> ModelRequestData:
model=model_name,
trust_remote_code=True,
max_model_len=8192,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
tokenizer = AutoTokenizer.from_pretrained(model_name,
@@ -287,7 +287,7 @@ def run_idefics3(questions: list[str], modality: str) -> ModelRequestData:
"longest_edge": 3 * 364
},
},
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
prompts = [(
f"<|begin_of_text|>User:<image>{question}<end_of_utterance>\nAssistant:"
@@ -314,7 +314,7 @@ def run_smolvlm(questions: list[str], modality: str) -> ModelRequestData:
"longest_edge": 384
},
},
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
prompts = [
(f"<|im_start|>User:<image>{question}<end_of_utterance>\nAssistant:")
@@ -337,7 +337,7 @@ def run_internvl(questions: list[str], modality: str) -> ModelRequestData:
model=model_name,
trust_remote_code=True,
max_model_len=4096,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
tokenizer = AutoTokenizer.from_pretrained(model_name,
@@ -375,7 +375,7 @@ def run_llava(questions: list[str], modality: str) -> ModelRequestData:
engine_args = EngineArgs(
model="llava-hf/llava-1.5-7b-hf",
max_model_len=4096,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
return ModelRequestData(
@@ -392,7 +392,7 @@ def run_llava_next(questions: list[str], modality: str) -> ModelRequestData:
engine_args = EngineArgs(
model="llava-hf/llava-v1.6-mistral-7b-hf",
max_model_len=8192,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
return ModelRequestData(
@@ -414,7 +414,7 @@ def run_llava_next_video(questions: list[str],
model="llava-hf/LLaVA-NeXT-Video-7B-hf",
max_model_len=8192,
max_num_seqs=2,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
return ModelRequestData(
@@ -442,7 +442,7 @@ def run_llava_onevision(questions: list[str],
engine_args = EngineArgs(
model="llava-hf/llava-onevision-qwen2-7b-ov-hf",
max_model_len=16384,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
return ModelRequestData(
@@ -465,7 +465,7 @@ def run_mantis(questions: list[str], modality: str) -> ModelRequestData:
model="TIGER-Lab/Mantis-8B-siglip-llama3",
max_model_len=4096,
hf_overrides={"architectures": ["MantisForConditionalGeneration"]},
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
stop_token_ids = [128009]
@@ -506,7 +506,7 @@ def run_minicpmv_base(questions: list[str], modality: str, model_name):
max_model_len=4096,
max_num_seqs=2,
trust_remote_code=True,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
# NOTE The stop_token_ids are different for various versions of MiniCPM-V
# 2.0
@@ -561,7 +561,7 @@ def run_mistral3(questions: list[str], modality: str) -> ModelRequestData:
max_model_len=8192,
max_num_seqs=2,
tensor_parallel_size=2,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
prompts = [f"<s>[INST]{question}\n[IMG][/INST]" for question in questions]
@@ -587,7 +587,7 @@ def run_mllama(questions: list[str], modality: str) -> ModelRequestData:
model=model_name,
max_model_len=8192,
max_num_seqs=2,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -611,7 +611,7 @@ def run_mllama(questions: list[str], modality: str) -> ModelRequestData:
)
def run_llama4(questions: list[str], modality: str):
def run_llama4(questions: list[str], modality: str) -> ModelRequestData:
assert modality == "image"
model_name = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
@@ -621,8 +621,8 @@ def run_llama4(questions: list[str], modality: str):
max_model_len=8192,
max_num_seqs=4,
tensor_parallel_size=8,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
gpu_memory_utilization=0.4,
limit_mm_per_prompt={"image": 1},
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -657,7 +657,7 @@ def run_molmo(questions: list[str], modality: str) -> ModelRequestData:
model=model_name,
trust_remote_code=True,
dtype="bfloat16",
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
prompts = [
@@ -683,7 +683,7 @@ def run_nvlm_d(questions: list[str], modality: str) -> ModelRequestData:
trust_remote_code=True,
max_model_len=4096,
tensor_parallel_size=4,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
tokenizer = AutoTokenizer.from_pretrained(model_name,
@@ -710,7 +710,8 @@ def run_paligemma(questions: list[str], modality: str) -> ModelRequestData:
prompts = ["caption en" for _ in questions]
engine_args = EngineArgs(
model="google/paligemma-3b-mix-224",
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache)
limit_mm_per_prompt={"image": 1},
)
return ModelRequestData(
engine_args=engine_args,
@@ -726,7 +727,8 @@ def run_paligemma2(questions: list[str], modality: str) -> ModelRequestData:
prompts = ["caption en" for _ in questions]
engine_args = EngineArgs(
model="google/paligemma2-3b-ft-docci-448",
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache)
limit_mm_per_prompt={"image": 1},
)
return ModelRequestData(
engine_args=engine_args,
@@ -762,7 +764,7 @@ def run_phi3v(questions: list[str], modality: str) -> ModelRequestData:
max_num_seqs=2,
# Note - mm_processor_kwargs can also be passed to generate/chat calls
mm_processor_kwargs={"num_crops": 16},
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
return ModelRequestData(
@@ -793,6 +795,7 @@ def run_phi4mm(questions: list[str], modality: str) -> ModelRequestData:
max_num_seqs=2,
enable_lora=True,
max_lora_rank=320,
limit_mm_per_prompt={"image": 1},
)
return ModelRequestData(
@@ -813,7 +816,7 @@ def run_pixtral_hf(questions: list[str], modality: str) -> ModelRequestData:
model=model_name,
max_model_len=6144,
max_num_seqs=2,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
prompts = [f"<s>[INST]{question}\n[IMG][/INST]" for question in questions]
@@ -834,7 +837,7 @@ def run_qwen_vl(questions: list[str], modality: str) -> ModelRequestData:
max_model_len=1024,
max_num_seqs=2,
hf_overrides={"architectures": ["QwenVLForConditionalGeneration"]},
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
prompts = [f"{question}Picture 1: <img></img>\n" for question in questions]
@@ -859,7 +862,7 @@ def run_qwen2_vl(questions: list[str], modality: str) -> ModelRequestData:
"min_pixels": 28 * 28,
"max_pixels": 1280 * 28 * 28,
},
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
if modality == "image":
@@ -894,7 +897,7 @@ def run_qwen2_5_vl(questions: list[str], modality: str) -> ModelRequestData:
"max_pixels": 1280 * 28 * 28,
"fps": 1,
},
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
if modality == "image":
@@ -925,7 +928,7 @@ def run_skyworkr1v(questions: list[str], modality: str) -> ModelRequestData:
model=model_name,
trust_remote_code=True,
max_model_len=4096,
disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
limit_mm_per_prompt={"image": 1},
)
tokenizer = AutoTokenizer.from_pretrained(model_name,
@@ -1082,7 +1085,15 @@ def main(args):
req_data = model_example_map[model](questions, modality)
engine_args = asdict(req_data.engine_args) | {"seed": args.seed}
# Disable other modalities to save memory
default_limits = {"image": 0, "video": 0, "audio": 0}
req_data.engine_args.limit_mm_per_prompt = default_limits | dict(
req_data.engine_args.limit_mm_per_prompt or {})
engine_args = asdict(req_data.engine_args) | {
"seed": args.seed,
"disable_mm_preprocessor_cache": args.disable_mm_preprocessor_cache,
}
llm = LLM(**engine_args)
# To maintain code compatibility in this script, we add LoRA here.