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

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

docs/source/models/supported_models.md

@@ -759,7 +759,7 @@ On the other hand, modalities separated by `/` are mutually exclusive.
 See [this page](#multimodal-inputs) on how to pass multi-modal inputs to the model.
 
 :::{important}
-To enable multiple multi-modal items per text prompt, you have to set `limit_mm_per_prompt` (offline inference)
+**To enable multiple multi-modal items per text prompt in vLLM V0**, you have to set `limit_mm_per_prompt` (offline inference)
 or `--limit-mm-per-prompt` (online serving). For example, to enable passing up to 4 images per text prompt:
 
 Offline inference:
@@ -777,6 +777,8 @@ Online serving:
 vllm serve Qwen/Qwen2-VL-7B-Instruct --limit-mm-per-prompt image=4
 ```
 
+**This is no longer required if you are using vLLM V1.**
+
 :::
 
 :::{note}

docs/source/serving/offline_inference.md

@@ -110,6 +110,30 @@ If you run out of CPU RAM, try the following options:
 - (Multi-modal models only) you can set the size of multi-modal input cache using `VLLM_MM_INPUT_CACHE_GIB` environment variable (default 4 GiB).
 - (CPU backend only) you can set the size of KV cache using `VLLM_CPU_KVCACHE_SPACE` environment variable (default 4 GiB).
 
+#### Disable unused modalities
+
+You can disable unused modalities (except for text) by setting its limit to zero.
+
+For example, if your application only accepts image input, there is no need to allocate any memory for videos.
+
+```python
+from vllm import LLM
+
+# Accept images but not videos
+llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct",
+          limit_mm_per_prompt={"video": 0})
+```
+
+You can even run a multi-modal model for text-only inference:
+
+```python
+from vllm import LLM
+
+# Don't accept images. Just text.
+llm = LLM(model="google/gemma-3-27b-it",
+          limit_mm_per_prompt={"image": 0})
+```
+
 ### Performance optimization and tuning
 
 You can potentially improve the performance of vLLM by finetuning various options.

examples/offline_inference/audio_language.py

@@ -196,6 +196,11 @@ def main(args):
     req_data = model_example_map[model](question_per_audio_count[audio_count],
                                         audio_count)
 
+    # 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}
     llm = LLM(**engine_args)
 

examples/offline_inference/encoder_decoder_multimodal.py

@@ -133,6 +133,11 @@ def main(args):
 
     req_data = model_example_map[model]()
 
+    # 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}
     llm = LLM(**engine_args)
 

examples/offline_inference/vision_language.py

@@ -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.

examples/offline_inference/vision_language_embedding.py

@@ -63,6 +63,7 @@ def run_e5_v(query: Query) -> ModelRequestData:
         model="royokong/e5-v",
         task="embed",
         max_model_len=4096,
+        limit_mm_per_prompt={"image": 1},
     )
 
     return ModelRequestData(
@@ -93,6 +94,7 @@ def run_vlm2vec(query: Query) -> ModelRequestData:
         task="embed",
         trust_remote_code=True,
         mm_processor_kwargs={"num_crops": 4},
+        limit_mm_per_prompt={"image": 1},
     )
 
     return ModelRequestData(
@@ -131,6 +133,11 @@ def run_encode(model: str, modality: QueryModality, seed: Optional[int]):
     query = get_query(modality)
     req_data = model_example_map[model](query)
 
+    # 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": seed}
     llm = LLM(**engine_args)
 

examples/offline_inference/vision_language_multi_image.py

@@ -687,6 +687,11 @@ def run_chat(model: str, question: str, image_urls: list[str],
              seed: Optional[int]):
     req_data = model_example_map[model](question, image_urls)
 
+    # 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": seed}
     llm = LLM(**engine_args)
 

tests/entrypoints/openai/test_audio.py

@@ -12,7 +12,9 @@ from ...utils import RemoteOpenAIServer
 MODEL_NAME = "fixie-ai/ultravox-v0_5-llama-3_2-1b"
 TEST_AUDIO_URLS = [
     AudioAsset("winning_call").url,
+    AudioAsset("mary_had_lamb").url,
 ]
+MAXIMUM_AUDIOS = 2
 
 
 @pytest.fixture(scope="module")
@@ -24,6 +26,8 @@ def server():
         "5",
         "--enforce-eager",
         "--trust-remote-code",
+        "--limit-mm-per-prompt",
+        f"audio={MAXIMUM_AUDIOS}",
     ]
 
     with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
@@ -46,7 +50,7 @@ def base64_encoded_audio() -> dict[str, str]:
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.parametrize("audio_url", TEST_AUDIO_URLS)
+@pytest.mark.parametrize("audio_url", [TEST_AUDIO_URLS[0]])
 async def test_single_chat_session_audio(client: openai.AsyncOpenAI,
                                          model_name: str, audio_url: str):
     messages = [{
@@ -100,7 +104,7 @@ async def test_single_chat_session_audio(client: openai.AsyncOpenAI,
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.parametrize("audio_url", TEST_AUDIO_URLS)
+@pytest.mark.parametrize("audio_url", [TEST_AUDIO_URLS[0]])
 async def test_single_chat_session_audio_base64encoded(
         client: openai.AsyncOpenAI, model_name: str, audio_url: str,
         base64_encoded_audio: dict[str, str]):
@@ -158,7 +162,7 @@ async def test_single_chat_session_audio_base64encoded(
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.parametrize("audio_url", TEST_AUDIO_URLS)
+@pytest.mark.parametrize("audio_url", [TEST_AUDIO_URLS[0]])
 async def test_single_chat_session_input_audio(
         client: openai.AsyncOpenAI, model_name: str, audio_url: str,
         base64_encoded_audio: dict[str, str]):
@@ -330,28 +334,21 @@ async def test_chat_streaming_input_audio(client: openai.AsyncOpenAI,
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-@pytest.mark.parametrize("audio_url", TEST_AUDIO_URLS)
+@pytest.mark.parametrize(
+    "audio_urls", [TEST_AUDIO_URLS, TEST_AUDIO_URLS + [TEST_AUDIO_URLS[0]]])
 async def test_multi_audio_input(client: openai.AsyncOpenAI, model_name: str,
-                                 audio_url: str,
+                                 audio_urls: list[str]):
-                                 base64_encoded_audio: dict[str, str]):
 
     messages = [{
         "role":
         "user",
         "content": [
-            {
+            *({
                 "type": "audio_url",
                 "audio_url": {
                     "url": audio_url
                 }
-            },
+            } for audio_url in audio_urls),
-            {
-                "type": "input_audio",
-                "input_audio": {
-                    "data": base64_encoded_audio[audio_url],
-                    "format": "wav"
-                }
-            },
             {
                 "type": "text",
                 "text": "What's happening in this audio?"
@@ -359,20 +356,30 @@ async def test_multi_audio_input(client: openai.AsyncOpenAI, model_name: str,
         ],
     }]
 
-    with pytest.raises(openai.BadRequestError):  # test multi-audio input
+    if len(audio_urls) > MAXIMUM_AUDIOS:
-        await client.chat.completions.create(
+        with pytest.raises(openai.BadRequestError):  # test multi-audio input
+            await client.chat.completions.create(
+                model=model_name,
+                messages=messages,
+                max_completion_tokens=10,
+                temperature=0.0,
+            )
+
+        # the server should still work afterwards
+        completion = await client.completions.create(
+            model=model_name,
+            prompt=[0, 0, 0, 0, 0],
+            max_tokens=5,
+            temperature=0.0,
+        )
+        completion = completion.choices[0].text
+        assert completion is not None and len(completion) >= 0
+    else:
+        chat_completion = await client.chat.completions.create(
             model=model_name,
             messages=messages,
             max_completion_tokens=10,
             temperature=0.0,
         )
-
+        message = chat_completion.choices[0].message
-    # the server should still work afterwards
+        assert message.content is not None and len(message.content) >= 0
-    completion = await client.completions.create(
-        model=model_name,
-        prompt=[0, 0, 0, 0, 0],
-        max_tokens=5,
-        temperature=0.0,
-    )
-    completion = completion.choices[0].text
-    assert completion is not None and len(completion) >= 0

tests/models/decoder_only/vision_language/vlm_utils/core.py

@@ -51,6 +51,10 @@ def run_test(
     model_info.check_available_online(on_fail="skip")
     model_info.check_transformers_version(on_fail="skip")
 
+    # Disable other modalities to save memory
+    default_limits = {"image": 0, "video": 0, "audio": 0}
+    limit_mm_per_prompt = default_limits | limit_mm_per_prompt
+
     vllm_outputs_per_mm = []
     hf_outputs_per_mm = []
 

tests/models/test_oot_registration.py

@@ -90,6 +90,7 @@ def test_oot_registration_multimodal(
                   max_model_len=4096,
                   enforce_eager=True,
                   limit_mm_per_prompt={"image": 1})
+
         first_token = llm.get_tokenizer().decode(0)
         outputs = llm.generate(prompts, sampling_params)
 

tests/multimodal/test_processing.py

@@ -972,10 +972,13 @@ def test_limit_mm_per_prompt_dummy(model_id, limit, num_supported, is_valid):
     if is_valid:
         exc_ctx = nullcontext()
     else:
-        exc_ctx = pytest.raises(ValueError, match="this model only supports")
+        exc_ctx = pytest.raises(ValueError, match="The model only supports")
 
     with exc_ctx:
-        profiler.get_decoder_dummy_data(model_config.max_model_len)
+        profiler.get_decoder_dummy_data(
+            model_config.max_model_len,
+            mm_counts=limit_mm_per_prompt,
+        )
 
 
 @pytest.mark.parametrize("model_id", ["llava-hf/llava-v1.6-mistral-7b-hf"])

vllm/config.py

@@ -2667,14 +2667,20 @@ class MultiModalConfig:
                                usedforsecurity=False).hexdigest()
         return hash_str
 
+    def get_default_limit_per_prompt(self) -> int:
+        """
+        Return the default number of input items allowed per prompt
+        for any modality if not specified by the user.
+        """
+        return 999 if envs.VLLM_USE_V1 else 1
+
     def get_limit_per_prompt(self, modality: str) -> int:
         """
         Get the maximum number of input items allowed per prompt
         for the given modality.
-
-        If not set by the user, this defaults to `1`.
         """
-        return self.limit_per_prompt.get(modality, 1)
+        default = self.get_default_limit_per_prompt()
+        return self.limit_per_prompt.get(modality, default)
 
     # TODO: Add configs to init vision tower or not.
 

vllm/engine/arg_utils.py

@@ -671,13 +671,13 @@ class EngineArgs:
             type=nullable_kvs,
             default=EngineArgs.limit_mm_per_prompt,
             # The default value is given in
-            # MultiModalConfig.get_limit_per_prompt
+            # MultiModalConfig.get_default_limit_per_prompt
             help=('For each multimodal plugin, limit how many '
                   'input instances to allow for each prompt. '
                   'Expects a comma-separated list of items, '
                   'e.g.: `image=16,video=2` allows a maximum of 16 '
-                  'images and 2 videos per prompt. Defaults to 1 for '
+                  'images and 2 videos per prompt. Defaults to '
-                  'each modality.'))
+                  '1 (V0) or 999 (V1) for each modality.'))
         parser.add_argument(
             '--mm-processor-kwargs',
             default=None,

vllm/entrypoints/chat_utils.py

@@ -35,7 +35,7 @@ from typing_extensions import Required, TypeAlias, TypedDict
 
 from vllm.config import ModelConfig
 from vllm.logger import init_logger
-from vllm.multimodal import MultiModalDataDict
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalDataDict
 from vllm.multimodal.utils import MediaConnector
 from vllm.transformers_utils.processor import cached_get_processor
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
@@ -452,8 +452,6 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
 
         self._model_config = model_config
         self._tokenizer = tokenizer
-        self._allowed_items = (model_config.multimodal_config.limit_per_prompt
-                               if model_config.multimodal_config else {})
 
         self._items_by_modality = defaultdict[str, list[_T]](list)
 
@@ -465,6 +463,10 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
     def allowed_local_media_path(self):
         return self._model_config.allowed_local_media_path
 
+    @property
+    def mm_registry(self):
+        return MULTIMODAL_REGISTRY
+
     @staticmethod
     @cache
     def _cached_token_str(tokenizer: AnyTokenizer, token_index: int) -> str:
@@ -540,12 +542,29 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
         Add a multi-modal item to the current prompt and returns the
         placeholder string to use, if any.
         """
-        allowed_count = self._allowed_items.get(modality, 1)
+        mm_registry = self.mm_registry
+        model_config = self.model_config
+
+        input_modality = modality.replace("_embeds", "")
+
+        if mm_registry.has_processor(model_config):
+            mm_processor = mm_registry.create_processor(model_config)
+            allowed_counts = mm_processor.info.get_allowed_mm_limits()
+            allowed_count = allowed_counts.get(input_modality, 0)
+        else:
+            mm_config = model_config.multimodal_config
+            if mm_config is None:
+                msg = "This model does not support multi-modal inputs"
+                raise ValueError(msg)
+
+            allowed_count = mm_config.get_limit_per_prompt(input_modality)
+
         current_count = len(self._items_by_modality[modality]) + 1
         if current_count > allowed_count:
             raise ValueError(
                 f"At most {allowed_count} {modality}(s) may be provided in "
-                "one request.")
+                "one request. You can set `--limit-mm-per-prompt` to "
+                "increase this limit if the model supports it.")
 
         self._items_by_modality[modality].append(item)
 

vllm/model_executor/models/minicpmo.py

@@ -126,7 +126,7 @@ class MiniCPMOMultiModalDataParser(MiniCPMVMultiModalDataParser):
     def _parse_audio_data(
         self,
         data: Union[dict[str, torch.Tensor], ModalityData[AudioItem]],
-    ) -> ModalityDataItems[Any, Any]:
+    ) -> Optional[ModalityDataItems[Any, Any]]:
         if isinstance(data, dict):
             return MiniCPMOAudioEmbeddingItems(
                 data,

vllm/model_executor/models/minicpmv.py

@@ -290,7 +290,7 @@ class MiniCPMVMultiModalDataParser(MultiModalDataParser):
     def _parse_image_data(
         self,
         data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
-    ) -> ModalityDataItems[Any, Any]:
+    ) -> Optional[ModalityDataItems[Any, Any]]:
         if isinstance(data, dict):
             return MiniCPMVImageEmbeddingItems(
                 data,
@@ -302,7 +302,7 @@ class MiniCPMVMultiModalDataParser(MultiModalDataParser):
     def _parse_video_data(
         self,
         data: Union[dict[str, torch.Tensor], ModalityData[VideoItem]],
-    ) -> ModalityDataItems[Any, Any]:
+    ) -> Optional[ModalityDataItems[Any, Any]]:
         if isinstance(data, dict):
             return MiniCPMVVideoEmbeddingItems(
                 data,

vllm/model_executor/models/qwen2_vl.py

@@ -720,7 +720,7 @@ class Qwen2VLMultiModalDataParser(MultiModalDataParser):
     def _parse_image_data(
         self,
         data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
-    ) -> ModalityDataItems[Any, Any]:
+    ) -> Optional[ModalityDataItems[Any, Any]]:
         if isinstance(data, dict):
             return DictEmbeddingItems(
                 data,
@@ -734,7 +734,7 @@ class Qwen2VLMultiModalDataParser(MultiModalDataParser):
     def _parse_video_data(
         self,
         data: Union[dict[str, torch.Tensor], ModalityData[VideoItem]],
-    ) -> ModalityDataItems[Any, Any]:
+    ) -> Optional[ModalityDataItems[Any, Any]]:
         if isinstance(data, dict):
             return DictEmbeddingItems(
                 data,

vllm/multimodal/processing.py

@@ -1034,6 +1034,20 @@ class BaseProcessingInfo:
         """
         raise NotImplementedError
 
+    def get_allowed_mm_limits(self) -> Mapping[str, int]:
+        """Return the maximum allowed number of items for each modality."""
+        supported_mm_limits = self.get_supported_mm_limits()
+        mm_config = self.ctx.get_mm_config()
+
+        allowed_limits = dict[str, int]()
+        for modality, supported_limit in supported_mm_limits.items():
+            user_limit = mm_config.get_limit_per_prompt(modality)
+
+            allowed_limits[modality] = (user_limit if supported_limit is None
+                                        else min(user_limit, supported_limit))
+
+        return allowed_limits
+
 
 _I = TypeVar("_I", bound=BaseProcessingInfo)
 
@@ -1087,14 +1101,24 @@ class BaseMultiModalProcessor(ABC, Generic[_I]):
         before passing them to :meth:`_get_hf_mm_data`.
         """
         mm_items = self.data_parser.parse_mm_data(mm_data)
-        mm_config = self.info.ctx.get_mm_config()
+        supported_mm_limits = self.info.get_supported_mm_limits()
+        allowed_mm_limits = self.info.get_allowed_mm_limits()
 
         for modality, items in mm_items.items():
-            limit = mm_config.get_limit_per_prompt(modality)
+            supported_limit = supported_mm_limits.get(modality, 0)
-            if len(items) > limit:
+            allowed_limit = allowed_mm_limits.get(modality, 0)
+            num_items = len(items)
+
+            if supported_limit is not None and num_items > supported_limit:
                 raise ValueError(
-                    f"You set {modality}={limit} (or defaulted to 1) in "
+                    f"The model only supports at most {supported_limit} "
-                    f"`--limit-mm-per-prompt`, but passed {len(items)} "
+                    f"{modality} items, but you passed {num_items} "
+                    f"{modality} items in the same prompt.")
+
+            if num_items > allowed_limit:
+                raise ValueError(
+                    f"You set or defaulted to {modality}={allowed_limit} "
+                    f"in --limit-mm-per-prompt`, but passed {num_items} "
                     f"{modality} items in the same prompt.")
 
         return mm_items

vllm/multimodal/profiling.py

@@ -162,23 +162,7 @@ class MultiModalProfiler(Generic[_I]):
         return self.processor.dummy_inputs
 
     def get_mm_limits(self) -> Mapping[str, int]:
-        mm_config = self.processing_info.ctx.get_mm_config()
+        return self.processing_info.get_allowed_mm_limits()
-        supported_mm_limits = self.processing_info.get_supported_mm_limits()
-
-        mm_limits = {
-            modality: mm_config.get_limit_per_prompt(modality)
-            for modality in supported_mm_limits
-        }
-
-        for modality, supported_limit in supported_mm_limits.items():
-            limit = mm_limits[modality]
-            if supported_limit is not None and supported_limit < limit:
-                raise ValueError(
-                    f"You set {modality}={limit} (or defaulted to 1) in "
-                    f"`--limit-mm-per-prompt`, but this model only supports "
-                    f"at most {supported_limit} {modality} items.")
-
-        return mm_limits
 
     def _get_dummy_mm_inputs(
         self,

vllm/multimodal/registry.py

@@ -265,8 +265,10 @@ class MultiModalRegistry:
 
             return profiler.get_mm_max_tokens(
                 seq_len,
-                {modality: 1
+                {
-                 for modality in mm_limits},
+                    modality: 1
+                    for modality, limit in mm_limits.items() if limit > 0
+                },
             )
 
         return {

vllm/multimodal/utils.py

@@ -264,7 +264,7 @@ fetch_video = global_media_connector.fetch_video
 
 def encode_audio_base64(
     audio: np.ndarray,
-    sampling_rate: int,
+    sampling_rate: float,
 ) -> str:
     """Encode audio as base64."""
     audio_io = AudioMediaIO()