[Model] Compute Llava Next Max Tokens / Dummy Data From Gridpoints

tests/models/decoder_only/vision_language/test_llava_next.py

@@ -3,12 +3,13 @@
 import pytest
 from transformers import AutoConfig, AutoModelForVision2Seq, AutoTokenizer
 
+from vllm.inputs import InputContext
 from vllm.multimodal.utils import rescale_image_size
 from vllm.sequence import SampleLogprobs
 
 from ....conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner,
                           _ImageAssets)
-from ...utils import check_logprobs_close
+from ...utils import build_model_context, check_logprobs_close
 
 _LIMIT_IMAGE_PER_PROMPT = 4
 
@@ -22,6 +23,19 @@
 models = ["llava-hf/llava-v1.6-mistral-7b-hf"]
 
 
+@pytest.fixture()
+def get_max_llava_next_image_tokens():
+    from vllm.model_executor.models.llava_next import (
+        get_max_llava_next_image_tokens)
+    return get_max_llava_next_image_tokens
+
+
+@pytest.fixture()
+def dummy_data_for_llava_next():
+    from vllm.model_executor.models.llava_next import dummy_data_for_llava_next
+    return dummy_data_for_llava_next
+
+
 def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
                                          Optional[SampleLogprobs]],
                       model: str):
@@ -281,3 +295,52 @@ def test_models_multiple_image_inputs(hf_runner, vllm_runner, image_assets,
         num_logprobs=num_logprobs,
         tensor_parallel_size=1,
     )
+
+
+@pytest.mark.parametrize("gridpoints,expected_max_tokens", [
+    ([[336, 336]], 1176),
+    ([[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]], 2928),
+])
+def test_get_max_llava_next_image_tokens(gridpoints, expected_max_tokens,
+                                         get_max_llava_next_image_tokens):
+    ctx = build_model_context(model_name="llava-hf/llava-v1.6-mistral-7b-hf")
+
+    # Update the config image_grid_pinpoints
+    # and calculate the resulting max tokens
+    ctx.model_config.hf_config.image_grid_pinpoints = gridpoints
+
+    actual_max_tokens = get_max_llava_next_image_tokens(
+        InputContext(ctx.model_config))
+
+    assert expected_max_tokens == actual_max_tokens
+
+
+@pytest.mark.parametrize(
+    "gridpoints,expected_size",
+    [
+        # One point; it has to be the largest
+        ([[336, 336]], (336, 336)),
+        # Default for most llava next models; the 2x2 tile is the largest
+        ([[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]],
+         (672, 672)),
+        # If two rectangular gridpoints are the same, the more vertical
+        # one has the higher feature count due to newline features
+        ([[336, 672], [672, 336]], (672, 336))
+    ])
+def test_dummy_data_for_llava_next_feature_size(dummy_data_for_llava_next,
+                                                gridpoints, expected_size):
+    ctx = build_model_context(model_name="llava-hf/llava-v1.6-mistral-7b-hf")
+
+    # Update the config image_grid_pinpoints
+    ctx.model_config.hf_config.image_grid_pinpoints = gridpoints
+    seq_len = 5000  # bigger than the max feature size for any image
+
+    seq_data, mm_data = dummy_data_for_llava_next(
+        ctx,
+        seq_len=seq_len,
+        mm_counts={"image": 1},
+    )
+
+    # The dummy data dims should match the gridpoint with the biggest feat size
+    assert mm_data["image"].size == expected_size
+    assert len(seq_data.get_token_ids()) >= seq_len

vllm/model_executor/models/llava_next.py

@@ -33,9 +33,6 @@
 from .utils import (AutoWeightsLoader, embed_multimodal, flatten_bn,
                     init_vllm_registered_model)
 
-# Result in the max possible feature size (2x2 grid of 336x336px tiles)
-MAX_IMAGE_FEATURE_SIZE_HEIGHT = MAX_IMAGE_FEATURE_SIZE_WIDTH = 448
-
 
 class LlavaNextImagePixelInputs(TypedDict):
     type: Literal["pixel_values"]
@@ -149,11 +146,28 @@ def get_llava_next_image_feature_size(
 
 
 def get_max_llava_next_image_tokens(ctx: InputContext):
-    return get_llava_next_image_feature_size(
-        ctx.get_hf_config(LlavaNextConfig),
-        input_height=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
-        input_width=MAX_IMAGE_FEATURE_SIZE_WIDTH,
-    )
+    """Compute the max feature size for all possible image grid pinpoints."""
+    return _get_pinpoint_with_largest_features(ctx)[0]
+
+
+def _get_pinpoint_with_largest_features(
+        ctx: InputContext) -> Tuple[int, Tuple[int, int]]:
+    """Get the grid pinpoint with the largest features & its feature size."""
+    hf_config = ctx.get_hf_config(LlavaNextConfig)
+    largest_feature_size = 0
+    largest_feature_pinpoint = None
+    for (height, width) in hf_config.image_grid_pinpoints:
+        feat_size = get_llava_next_image_feature_size(
+            hf_config,
+            input_height=height,
+            input_width=width,
+        )
+        if feat_size > largest_feature_size:
+            largest_feature_size = feat_size
+            largest_feature_pinpoint = (height, width)
+    if not largest_feature_size or largest_feature_pinpoint is None:
+        raise ValueError("Cannot have a largest feature size of 0!")
+    return largest_feature_size, largest_feature_pinpoint
 
 
 def dummy_data_for_llava_next(ctx: InputContext, seq_len: int,
@@ -162,7 +176,8 @@ def dummy_data_for_llava_next(ctx: InputContext, seq_len: int,
     vision_config = hf_config.vision_config
     num_images = mm_counts["image"]
 
-    image_feature_size = get_max_llava_next_image_tokens(ctx)
+    image_feature_size, pinpoint = _get_pinpoint_with_largest_features(ctx)
+    max_feat_height, max_feat_width = pinpoint
 
     if isinstance(vision_config, CLIPVisionConfig):
         seq_data = dummy_seq_data_for_clip(
@@ -176,8 +191,8 @@ def dummy_data_for_llava_next(ctx: InputContext, seq_len: int,
         mm_data = dummy_image_for_clip(
             vision_config,
             num_images,
-            image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH,
-            image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
+            image_width_override=max_feat_width,
+            image_height_override=max_feat_height,
         )
 
         return seq_data, mm_data
@@ -193,8 +208,8 @@ def dummy_data_for_llava_next(ctx: InputContext, seq_len: int,
         mm_data = dummy_image_for_siglip(
             vision_config,
             num_images,
-            image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH,
-            image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
+            image_width_override=max_feat_width,
+            image_height_override=max_feat_height,
         )
 
         return seq_data, mm_data