[3/N] Support and implement merged input processor for LLaVA model (#10676)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: Roger Wang <ywang@roblox.com>

tests/multimodal/test_mapper.py

@@ -2,7 +2,7 @@ from contextlib import nullcontext
 
 import numpy as np
 import pytest
-from transformers import CLIPImageProcessor, LlavaNextImageProcessor
+from transformers import LlavaNextImageProcessor
 
 from vllm.config import ModelConfig
 from vllm.multimodal import MultiModalRegistry
@@ -14,49 +14,6 @@ def mm_registry():
     return MultiModalRegistry()
 
 
-@pytest.mark.parametrize("dtype", ["half", "float"])
-@pytest.mark.parametrize("size_factor", [0.25, 0.5, 1.0])
-def test_clip_image_processor(image_assets, mm_registry, dtype, size_factor):
-    MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
-
-    hf_processor = CLIPImageProcessor.from_pretrained(MODEL_NAME)
-    assert isinstance(hf_processor, CLIPImageProcessor)
-
-    model_config = ModelConfig(
-        model=MODEL_NAME,
-        task="auto",
-        tokenizer=MODEL_NAME,
-        tokenizer_mode="auto",
-        trust_remote_code=False,
-        seed=0,
-        dtype=dtype,
-        revision=None,
-        limit_mm_per_prompt={"image": 1},
-    )
-
-    mm_registry.init_mm_limits_per_prompt(model_config)
-
-    for asset in image_assets:
-        image = rescale_image_size(asset.pil_image, size_factor)
-
-        hf_result = hf_processor.preprocess(
-            image,
-            return_tensors="pt",
-        )
-        vllm_result = mm_registry.map_input(
-            model_config,
-            {"image": image},
-        )
-
-        assert hf_result.keys() == vllm_result.keys()
-        for key, hf_tensor in hf_result.items():
-            hf_arr: np.ndarray = hf_tensor.numpy()
-            vllm_arr: np.ndarray = vllm_result[key].numpy()
-
-            assert hf_arr.shape == vllm_arr.shape, f"Failed for key={key}"
-            assert np.allclose(hf_arr, vllm_arr), f"Failed for key={key}"
-
-
 @pytest.mark.parametrize("dtype", ["half", "float"])
 @pytest.mark.parametrize("size_factor", [0.25, 0.5, 1.0])
 def test_llava_next_image_processor(image_assets, mm_registry, dtype,
@@ -107,7 +64,7 @@ def test_llava_next_image_processor(image_assets, mm_registry, dtype,
      (2, 1, False), (2, 2, True)],
 )
 def test_mm_limits(image_assets, mm_registry, num_images, limit, is_valid):
-    MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
+    MODEL_NAME = "llava-hf/llava-v1.6-mistral-7b-hf"
 
     model_config = ModelConfig(
         model=MODEL_NAME,
@@ -138,7 +95,7 @@ def test_mm_limits(image_assets, mm_registry, num_images, limit, is_valid):
 # NOTE: We don't test zero images since the HF processor doesn't support it
 @pytest.mark.parametrize("num_images", [1, 2])
 def test_image_mapper_multi(image_assets, mm_registry, num_images):
-    MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
+    MODEL_NAME = "llava-hf/llava-v1.6-mistral-7b-hf"
 
     model_config = ModelConfig(
         model=MODEL_NAME,