[VLM] Merged multi-modal processor for LLaVA-NeXT

tests/multimodal/test_processing.py

@@ -1,5 +1,7 @@
+from contextlib import nullcontext
 from functools import partial
 from typing import cast
+from unittest.mock import MagicMock
 
 import numpy as np
 import pytest
@@ -526,6 +528,100 @@ def _rand_audio(
     return rng.rand(audio_len), sr
 
 
+@pytest.mark.parametrize("model_id", ["llava-hf/llava-v1.6-mistral-7b-hf"])
+@pytest.mark.parametrize(
+    ("limit", "num_supported", "is_valid"),
+    [(0, 0, True), (0, 1, True), (1, 0, False), (1, 1, True), (1, 2, True),
+     (2, 1, False), (2, 2, True)],
+)
+def test_limit_mm_per_prompt_dummy(model_id, limit, num_supported, is_valid):
+    limit_mm_per_prompt = {"image": limit}
+
+    model_config = ModelConfig(
+        model=model_id,
+        task="auto",
+        tokenizer=model_id,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        seed=0,
+        dtype="half",
+        revision=None,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+    )
+    model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)
+
+    processor_factory = MULTIMODAL_REGISTRY._processor_factories[model_cls]
+    ctx = InputProcessingContext(
+        model_config,
+        tokenizer=cached_get_tokenizer(model_config.tokenizer),
+    )
+
+    processor = processor_factory(ctx, cache=None)
+
+    mock_supported_mm_limits = MagicMock(return_value={"image": num_supported})
+    processor.get_supported_mm_limits = mock_supported_mm_limits
+
+    if is_valid:
+        exc_ctx = nullcontext()
+    else:
+        exc_ctx = pytest.raises(ValueError, match="this model only supports")
+
+    with exc_ctx:
+        processor._get_and_validate_dummy_mm_counts()
+
+
+@pytest.mark.parametrize("model_id", ["llava-hf/llava-v1.6-mistral-7b-hf"])
+@pytest.mark.parametrize(
+    ("num_images", "limit", "is_valid"),
+    [(0, 0, True), (0, 1, True), (1, 0, False), (1, 1, True), (1, 2, True),
+     (2, 1, False), (2, 2, True)],
+)
+def test_limit_mm_per_prompt_apply(model_id, num_images, limit, is_valid):
+    limit_mm_per_prompt = {"image": limit}
+
+    model_config = ModelConfig(
+        model=model_id,
+        task="auto",
+        tokenizer=model_id,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        seed=0,
+        dtype="half",
+        revision=None,
+        limit_mm_per_prompt=limit_mm_per_prompt,
+    )
+    model_cls = MULTIMODAL_REGISTRY._get_model_cls(model_config)
+
+    processor_factory = MULTIMODAL_REGISTRY._processor_factories[model_cls]
+    ctx = InputProcessingContext(
+        model_config,
+        tokenizer=cached_get_tokenizer(model_config.tokenizer),
+    )
+
+    processor = processor_factory(ctx, cache=None)
+
+    rng = np.random.RandomState(0)
+    image = _rand_img(rng, min_wh=128, max_wh=256)
+    if num_images == 0:
+        mm_data = {}
+    elif num_images == 1:
+        mm_data = {"image": image}
+    else:
+        mm_data = {"image": [image] * num_images}
+
+    if is_valid:
+        exc_ctx = nullcontext()
+    else:
+        exc_ctx = pytest.raises(ValueError, match=f"passed {num_images} image")
+
+    with exc_ctx:
+        processor.apply(
+            "<image>" * num_images,
+            mm_data=mm_data,
+            hf_processor_mm_kwargs={},
+        )
+
+
 def _test_processing_cache_correctness(
     model_id: str,
     modalities: dict[str, bool],
@@ -631,6 +727,7 @@ def _test_processing_cache_correctness(
     ("facebook/chameleon-7b", {"image": False}),
     ("adept/fuyu-8b", {"image": False}),
     ("llava-hf/llava-1.5-7b-hf", {"image": True}),
+    ("llava-hf/llava-v1.6-mistral-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}),

tests/plugins/vllm_add_dummy_model/vllm_add_dummy_model/my_llava.py

@@ -3,13 +3,11 @@
 import torch
 
 from vllm.model_executor.models.llava import (LlavaForConditionalGeneration,
-                                              LlavaMultiModalProcessor,
-                                              get_max_llava_image_tokens)
+                                              LlavaMultiModalProcessor)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 
 
-@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_image_tokens)
 @MULTIMODAL_REGISTRY.register_processor(LlavaMultiModalProcessor)
 class MyLlava(LlavaForConditionalGeneration):
 

vllm/model_executor/models/clip.py

@@ -24,6 +24,8 @@
                                    resolve_visual_encoder_outputs)
 from vllm.sequence import SequenceData
 
+from .vision import VisionEncoderInfo
+
 
 def get_clip_patch_grid_length(*, image_size: int, patch_size: int) -> int:
     assert image_size % patch_size == 0
@@ -149,6 +151,29 @@ def input_processor_for_clip(
                         multi_modal_placeholders={"image": ranges})
 
 
+class CLIPEncoderInfo(VisionEncoderInfo[CLIPVisionConfig]):
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        return get_clip_image_feature_size(self.vision_config)
+
+    def get_max_image_tokens(self) -> int:
+        return get_max_clip_image_tokens(self.vision_config)
+
+    def get_num_patches(self) -> int:
+        return get_clip_patch_grid_length(
+            image_size=self.vision_config.image_size,
+            patch_size=self.vision_config.patch_size,
+        )
+
+    def get_image_size(self) -> int:
+        return self.vision_config.image_size
+
+
 # Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/clip/modeling_clip.py#L164 # noqa
 class CLIPVisionEmbeddings(nn.Module):
 

vllm/model_executor/models/fuyu.py

@@ -76,7 +76,7 @@ def _get_image_target_size(self) -> ImageSize:
         return ImageSize(width=target_size["width"],
                          height=target_size["height"])
 
-    def _get_image_grid_size(
+    def _get_image_feature_grid_size(
         self,
         *,
         image_width: int,
@@ -99,7 +99,7 @@ def _get_image_grid_size(
     def get_mm_max_tokens_per_item(self) -> Mapping[str, int]:
         target_width, target_height = self._get_image_target_size()
 
-        max_ncols, max_nrows = self._get_image_grid_size(
+        max_ncols, max_nrows = self._get_image_feature_grid_size(
             image_width=target_width,
             image_height=target_height,
         )
@@ -172,7 +172,7 @@ def get_replacement_fuyu(item_idx: int):
             images = mm_items.get_items("image", ImageProcessorItems)
             image_size = images.get_image_size(item_idx)
 
-            ncols, nrows = self._get_image_grid_size(
+            ncols, nrows = self._get_image_feature_grid_size(
                 image_width=image_size.width,
                 image_height=image_size.height,
             )