[V1][VLM] V1 support for selected single-image models.

docs/source/models/supported_models.md

@@ -570,28 +570,28 @@ See [this page](#generative-models) for more information on how to use generativ
   - `rhymes-ai/Aria`
   -
   - ✅︎
-  -
+  - ✅︎
 * - `Blip2ForConditionalGeneration`
   - BLIP-2
   - T + I<sup>E</sup>
   - `Salesforce/blip2-opt-2.7b`, `Salesforce/blip2-opt-6.7b`, etc.
   -
   - ✅︎
-  -
+  - ✅︎
 * - `ChameleonForConditionalGeneration`
   - Chameleon
   - T + I
   - `facebook/chameleon-7b` etc.
   -
   - ✅︎
-  -
+  - ✅︎
 * - `FuyuForCausalLM`
   - Fuyu
   - T + I
   - `adept/fuyu-8b` etc.
   -
   - ✅︎
-  -
+  - ✅︎
 * - `ChatGLMModel`
   - GLM-4V
   - T + I
@@ -633,7 +633,7 @@ See [this page](#generative-models) for more information on how to use generativ
   - `llava-hf/llava-v1.6-mistral-7b-hf`, `llava-hf/llava-v1.6-vicuna-7b-hf`, etc.
   -
   - ✅︎
-  -
+  - ✅︎
 * - `LlavaNextVideoForConditionalGeneration`
   - LLaVA-NeXT-Video
   - T + V

examples/offline_inference_vision_language.py

@@ -24,10 +24,13 @@ def run_aria(question: str, modality: str):
     assert modality == "image"
     model_name = "rhymes-ai/Aria"
 
+    # NOTE: Need L40 (or equivalent) to avoid OOM
     llm = LLM(model=model_name,
               tokenizer_mode="slow",
-              trust_remote_code=True,
               dtype="bfloat16",
+              max_model_len=4096,
+              max_num_seqs=2,
+              trust_remote_code=True,
               disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache)
 
     prompt = (f"<|im_start|>user\n<fim_prefix><|img|><fim_suffix>\n{question}"
@@ -57,6 +60,7 @@ def run_chameleon(question: str, modality: str):
     prompt = f"{question}<image>"
     llm = LLM(model="facebook/chameleon-7b",
               max_model_len=4096,
+              max_num_seqs=2,
               disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache)
     stop_token_ids = None
     return llm, prompt, stop_token_ids
@@ -257,7 +261,7 @@ def run_minicpmv(question: str, modality: str):
     # 2.5
     # model_name = "openbmb/MiniCPM-Llama3-V-2_5"
 
-    #2.6
+    # 2.6
     model_name = "openbmb/MiniCPM-V-2_6"
     tokenizer = AutoTokenizer.from_pretrained(model_name,
                                               trust_remote_code=True)
@@ -430,9 +434,11 @@ def run_pixtral_hf(question: str, modality: str):
 
     model_name = "mistral-community/pixtral-12b"
 
+    # NOTE: Need L40 (or equivalent) to avoid OOM
     llm = LLM(
         model=model_name,
         max_model_len=8192,
+        max_num_seqs=2,
         disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
     )
 

tests/models/decoder_only/vision_language/test_models.py

@@ -140,10 +140,7 @@
     "aria": VLMTestInfo(
         models=["rhymes-ai/Aria"],
         tokenizer_mode="slow",
-        test_type=(
-            VLMTestType.IMAGE,
-            VLMTestType.MULTI_IMAGE,
-        ),
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
         dtype="bfloat16",
         prompt_formatter=lambda img_prompt: f"<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n ", # noqa: E501
         img_idx_to_prompt=lambda idx: "<fim_prefix><|img|><fim_suffix>\n",
@@ -179,6 +176,7 @@
         test_type=VLMTestType.IMAGE,
         prompt_formatter=lambda img_prompt: f"USER: {img_prompt}\nASSISTANT:",
         max_model_len=4096,
+        max_num_seqs=2,
         auto_cls=AutoModelForVision2Seq,
         postprocess_inputs=model_utils.cast_dtype_post_processor(
             "pixel_values"
@@ -201,7 +199,6 @@
         vllm_output_post_proc=model_utils.fuyu_vllm_to_hf_output,
         num_logprobs=10,
         image_size_factors=[(), (0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
-        marks=[large_gpu_mark(min_gb=48)],
     ),
     "glm4": VLMTestInfo(
         models=["THUDM/glm-4v-9b"],

tests/multimodal/test_processing.py

@@ -528,7 +528,7 @@ def _rand_audio(
 
 def _test_processing_cache_correctness(
     model_id: str,
-    modalities: set[str],
+    modalities: dict[str, bool],
     hit_rate: float,
     num_batches: int,
     simplify_rate: float,
@@ -583,9 +583,8 @@ def _test_processing_cache_correctness(
         partial(_rand_audio, rng, min_len=256, max_len=512, sr=16000),
     }
     input_max_count = {
-        "image": 3,
-        "video": 3,
-        "audio": 3,
+        modality: 3 if supports_multi else 1
+        for modality, supports_multi in modalities.items()
     }
 
     for batch_idx in range(num_batches):
@@ -624,20 +623,24 @@ def _test_processing_cache_correctness(
 
 # yapf: disable
 @pytest.mark.parametrize(("model_id", "modalities"), [
-    ("llava-hf/llava-1.5-7b-hf", {"image"}),
-    ("TIGER-Lab/Mantis-8B-siglip-llama3", {"image"}),
-    ("mistral-community/pixtral-12b", {"image"}),
-    ("Qwen/Qwen2-VL-2B-Instruct", {"image", "video"}),
-    ("Qwen/Qwen2-Audio-7B-Instruct", {"audio"}),
-    ("fixie-ai/ultravox-v0_3", {"audio"}),
+    ("rhymes-ai/Aria", {"image": True}),
+    ("Salesforce/blip2-opt-2.7b", {"image": False}),
+    ("facebook/chameleon-7b", {"image": True}),
+    ("adept/fuyu-8b", {"image": False}),
+    ("llava-hf/llava-1.5-7b-hf", {"image": True}),
+    ("TIGER-Lab/Mantis-8B-siglip-llama3", {"image": True}),
+    ("mistral-community/pixtral-12b", {"image": True}),
+    ("Qwen/Qwen2-VL-2B-Instruct", {"image": True, "video": True}),
+    ("Qwen/Qwen2-Audio-7B-Instruct", {"audio": True}),
+    ("fixie-ai/ultravox-v0_3", {"audio": True}),
 ])
 @pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
 @pytest.mark.parametrize("num_batches", [32])
 @pytest.mark.parametrize("simplify_rate", [1.0])
 # yapf: enable
 def test_processing_cache_correctness(
     model_id: str,
-    modalities: set[str],
+    modalities: dict[str, bool],
     hit_rate: float,
     num_batches: int,
     simplify_rate: float,
@@ -653,15 +656,15 @@ def test_processing_cache_correctness(
 
 # yapf: disable
 @pytest.mark.parametrize(("model_id", "modalities"), [
-    ("microsoft/Phi-3-vision-128k-instruct", {"image"}),
+    ("microsoft/Phi-3-vision-128k-instruct", {"image": True}),
 ])
 @pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
 @pytest.mark.parametrize("num_batches", [32])
 @pytest.mark.parametrize("simplify_rate", [1.0])
 # yapf: enable
 def test_processing_cache_correctness_phi3v(
     model_id: str,
-    modalities: set[str],
+    modalities: dict[str, bool],
     hit_rate: float,
     num_batches: int,
     simplify_rate: float,