[VLM] Merged multi-modal processor and V1 support for Qwen-VL (vllm-p…

…roject#12504)

Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: Isotr0py <[email protected]>

docs/source/models/supported_models.md

@@ -745,7 +745,7 @@ See [this page](#generative-models) for more information on how to use generativ
   - `Qwen/Qwen-VL`, `Qwen/Qwen-VL-Chat`, etc.
   - ✅︎
   - ✅︎
-  -
+  - ✅︎
 * - `Qwen2AudioForConditionalGeneration`
   - Qwen2-Audio
   - T + A<sup>+</sup>

tests/models/multimodal/processing/test_common.py

@@ -16,7 +16,6 @@
 
 def _test_processing_correctness(
     model_id: str,
-    modalities: dict[str, bool],
     hit_rate: float,
     num_batches: int,
     simplify_rate: float,
@@ -25,11 +24,6 @@ def _test_processing_correctness(
     model_info.check_available_online(on_fail="skip")
     model_info.check_transformers_version(on_fail="skip")
 
-    limit_mm_per_prompt = {
-        modality: 3 if supports_multi else 1
-        for modality, supports_multi in modalities.items()
-    }
-
     model_config = ModelConfig(
         model_id,
         task="auto",
@@ -40,18 +34,29 @@ def _test_processing_correctness(
         dtype="float16",
         revision=None,
         hf_overrides=model_info.hf_overrides,
-        limit_mm_per_prompt=limit_mm_per_prompt,
     )
 
     model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)
     factories = MULTIMODAL_REGISTRY._processor_factories[model_cls]
     ctx = InputProcessingContext(
         model_config,
-        tokenizer=cached_get_tokenizer(model_config.tokenizer),
+        tokenizer=cached_get_tokenizer(
+            model_config.tokenizer,
+            trust_remote_code=model_info.trust_remote_code,
+        ),
     )
     # Ensure that it can fit all of the data
     cache = ProcessingCache(capacity=1 << 30)
 
+    processing_info = factories.info(ctx)
+    supported_mm_limits = processing_info.get_supported_mm_limits()
+    limit_mm_per_prompt = {
+        modality: 3 if limit is None else limit
+        for modality, limit in supported_mm_limits.items()
+    }
+
+    model_config.get_multimodal_config().limit_per_prompt = limit_mm_per_prompt
+
     baseline_processor = factories.build_processor(ctx, cache=None)
     cached_processor = factories.build_processor(ctx, cache=cache)
     dummy_inputs = baseline_processor.dummy_inputs
@@ -82,8 +87,8 @@ def _test_processing_correctness(
         mm_data = {
             k:
             [(input_to_hit[k] if rng.rand() < hit_rate else input_factory[k]())
-             for _ in range(rng.randint(limit_mm_per_prompt[k]))]
-            for k in modalities
+             for _ in range(rng.randint(limit))]
+            for k, limit in limit_mm_per_prompt.items()
         }
 
         mm_counts = {k: len(vs) for k, vs in mm_data.items()}
@@ -135,53 +140,49 @@ def _test_processing_correctness(
 
 # yapf: disable
 # True if the model supports multiple data items of the modality per request
-@pytest.mark.parametrize(("model_id", "modalities"), [
-    ("rhymes-ai/Aria", {"image": True}),
-    ("Salesforce/blip2-opt-2.7b", {"image": False}),
-    ("facebook/chameleon-7b", {"image": False}),
-    ("deepseek-ai/deepseek-vl2-tiny", {"image": True}),
-    ("adept/fuyu-8b", {"image": False}),
-    ("llava-hf/llava-1.5-7b-hf", {"image": True}),
-    ("llava-hf/llava-v1.6-mistral-7b-hf", {"image": True}),
-    ("llava-hf/LLaVA-NeXT-Video-7B-hf", {"video": False}),
-    ("llava-hf/llava-onevision-qwen2-0.5b-ov-hf", {"image": True, "video": True}),  # noqa: E501
-    ("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("model_id", [
+    "rhymes-ai/Aria",
+    "Salesforce/blip2-opt-2.7b",
+    "facebook/chameleon-7b",
+    "deepseek-ai/deepseek-vl2-tiny",
+    "adept/fuyu-8b",
+    "llava-hf/llava-1.5-7b-hf",
+    "llava-hf/llava-v1.6-mistral-7b-hf",
+    "llava-hf/LLaVA-NeXT-Video-7B-hf",
+    "llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
+    "TIGER-Lab/Mantis-8B-siglip-llama3",
+    "mistral-community/pixtral-12b",
+    "Qwen/Qwen-VL-Chat",
+    "Qwen/Qwen2-VL-2B-Instruct",
+    "Qwen/Qwen2-Audio-7B-Instruct",
+    "fixie-ai/ultravox-v0_3",
 ])
 @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_correctness(
     model_id: str,
-    modalities: dict[str, bool],
     hit_rate: float,
     num_batches: int,
     simplify_rate: float,
 ):
     _test_processing_correctness(
         model_id,
-        modalities,
         hit_rate=hit_rate,
         num_batches=num_batches,
         simplify_rate=simplify_rate,
     )
 
 
 # yapf: disable
-@pytest.mark.parametrize(("model_id", "modalities"), [
-    ("microsoft/Phi-3-vision-128k-instruct", {"image": True}),
-])
+@pytest.mark.parametrize("model_id", ["microsoft/Phi-3-vision-128k-instruct"])
 @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_correctness_phi3v(
     model_id: str,
-    modalities: dict[str, bool],
     hit_rate: float,
     num_batches: int,
     simplify_rate: float,
@@ -195,7 +196,6 @@ def test_processing_correctness_phi3v(
 
     _test_processing_correctness(
         model_id,
-        modalities,
         hit_rate=hit_rate,
         num_batches=num_batches,
         simplify_rate=simplify_rate,