[Bugfix] Fix precision error in LLaVA-NeXT (#11735)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

tests/models/decoder_only/vision_language/processing/test_llava_next.py

@@ -15,10 +15,9 @@ def processor_for_llava_next():
     return LlavaNextMultiModalProcessor
 
 
-# FIXME: image_size [(198, 176), (176, 198)]
 @pytest.mark.parametrize("model_id", ["llava-hf/llava-v1.6-mistral-7b-hf"])
 @pytest.mark.parametrize("image_size", [(1669, 2560), (2560, 1669), (183, 488),
-                                        (488, 183)])
+                                        (488, 183), (198, 176), (176, 198)])
 @pytest.mark.parametrize("num_imgs", [1, 2])
 def test_processor_prompt_replacements(
     processor_for_llava_next,

vllm/model_executor/models/llava_next.py

@@ -2,6 +2,7 @@ from functools import cached_property
 from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple,
                     TypedDict, Union)
 
+import numpy as np
 import torch
 import torch.nn as nn
 from transformers import BatchFeature, LlavaNextConfig, LlavaNextProcessor
@@ -139,16 +140,21 @@ class LlavaNextMultiModalProcessor(LlavaMultiModalProcessor):
         current_height = npatches * num_patch_height
         current_width = npatches * num_patch_width
 
-        original_aspect_ratio = original_width / original_height
-        current_aspect_ratio = current_width / current_height
+        # NOTE: HF resizes based on float32
+        original_aspect_ratio = np.array(original_width / original_height,
+                                         dtype=np.float32)
+        current_aspect_ratio = np.array(current_width / current_height,
+                                        dtype=np.float32)
 
         if original_aspect_ratio > current_aspect_ratio:
-            scale_factor = current_width / original_width
+            scale_factor = np.array(current_width / original_width,
+                                    dtype=np.float32)
             new_height = int(original_height * scale_factor)
             padding = (current_height - new_height) // 2
             current_height -= 2 * padding
         else:
-            scale_factor = current_height / original_height
+            scale_factor = np.array(current_height / original_height,
+                                    dtype=np.float32)
             new_width = int(original_width * scale_factor)
             padding = (current_width - new_width) // 2
             current_width -= 2 * padding

vllm/model_executor/models/llava_onevision.py

@@ -3,6 +3,7 @@ from functools import cached_property
 from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple,
                     TypedDict, Union)
 
+import numpy as np
 import torch
 import torch.nn as nn
 from transformers import (BatchFeature, LlavaOnevisionConfig,
@@ -127,18 +128,24 @@ class LlavaOnevisionMultiModalProcessor(LlavaNextMultiModalProcessor):
         current_height = npatches * num_patch_height
         current_width = npatches * num_patch_width
 
-        original_aspect_ratio = original_width / original_height
-        current_aspect_ratio = current_width / current_height
+        # NOTE: HF resizes based on float32
+        original_aspect_ratio = np.array(original_width / original_height,
+                                         dtype=np.float32)
+        current_aspect_ratio = np.array(current_width / current_height,
+                                        dtype=np.float32)
+
         if original_aspect_ratio > current_aspect_ratio:
-            new_height = int(original_height *
-                             (current_width / original_width))
+            scale_factor = np.array(current_width / original_width,
+                                    dtype=np.float32)
+            new_height = int(original_height * scale_factor)
             padding = (current_height - new_height) // 2
-            current_height -= padding * 2
+            current_height -= 2 * padding
         else:
-            new_width = int(original_width *
-                            (current_height / original_height))
+            scale_factor = np.array(current_height / original_height,
+                                    dtype=np.float32)
+            new_width = int(original_width * scale_factor)
             padding = (current_width - new_width) // 2
-            current_width -= padding * 2
+            current_width -= 2 * padding
 
         unpadded_features = current_height * current_width
         newline_features = current_height