[bug] fix llava processor to calculate unpadding size correctly

src/transformers/models/llava_next/modeling_llava_next.py

@@ -20,7 +20,6 @@
 
 import numpy as np
 import torch
-import torch.utils.checkpoint
 from torch import nn
 
 from ...activations import ACT2FN
@@ -133,14 +132,14 @@ def unpad_image(tensor, original_size):
 
     if original_aspect_ratio > current_aspect_ratio:
         scale_factor = current_width / original_width
-        new_height = min(math.ceil(original_height * scale_factor), current_height)
-        padding, r = divmod(current_height - new_height, 2)
-        unpadded_tensor = tensor[:, padding : current_height - (padding + r), :]
+        new_height = int(round(original_height * scale_factor, 7))
+        padding = (current_height - new_height) // 2
+        unpadded_tensor = tensor[:, padding : current_height - padding, :]
     else:
         scale_factor = current_height / original_height
-        new_width = min(math.ceil(original_width * scale_factor), current_width)
-        padding, r = divmod(current_width - new_width, 2)
-        unpadded_tensor = tensor[:, :, padding : current_width - (padding + r)]
+        new_width = int(round(original_width * scale_factor, 7))
+        padding = (current_width - new_width) // 2
+        unpadded_tensor = tensor[:, :, padding : current_width - padding]
 
     return unpadded_tensor
 

src/transformers/models/llava_next_video/modeling_llava_next_video.py

@@ -304,14 +304,14 @@ def unpad_image(tensor, original_size):
 
     if original_aspect_ratio > current_aspect_ratio:
         scale_factor = current_width / original_width
-        new_height = min(math.ceil(original_height * scale_factor), current_height)
-        padding, r = divmod(current_height - new_height, 2)
-        unpadded_tensor = tensor[:, padding : current_height - (padding + r), :]
+        new_height = int(round(original_height * scale_factor, 7))
+        padding = (current_height - new_height) // 2
+        unpadded_tensor = tensor[:, padding : current_height - padding, :]
     else:
         scale_factor = current_height / original_height
-        new_width = min(math.ceil(original_width * scale_factor), current_width)
-        padding, r = divmod(current_width - new_width, 2)
-        unpadded_tensor = tensor[:, :, padding : current_width - (padding + r)]
+        new_width = int(round(original_width * scale_factor, 7))
+        padding = (current_width - new_width) // 2
+        unpadded_tensor = tensor[:, :, padding : current_width - padding]
 
     return unpadded_tensor
 

src/transformers/models/llava_next_video/modular_llava_next_video.py

@@ -18,7 +18,6 @@
 from typing import List, Optional, Tuple, Union
 
 import torch
-import torch.utils.checkpoint
 from torch import nn
 
 from transformers.models.llava_next.modeling_llava_next import (

src/transformers/models/llava_onevision/image_processing_llava_onevision.py

@@ -644,7 +644,7 @@ def preprocess(
                 image,
                 image_grid_pinpoints,
                 size=size_tuple,
-                patch_size=size["height"],
+                patch_size=size_tuple[0],
                 resample=resample,
                 data_format=input_data_format,
                 input_data_format=input_data_format,

src/transformers/models/llava_onevision/modeling_llava_onevision.py

@@ -284,14 +284,14 @@ def unpad_image(tensor, original_size):
 
     if original_aspect_ratio > current_aspect_ratio:
         scale_factor = current_width / original_width
-        new_height = min(math.ceil(original_height * scale_factor), current_height)
-        padding, r = divmod(current_height - new_height, 2)
-        unpadded_tensor = tensor[:, padding : current_height - (padding + r), :]
+        new_height = int(round(original_height * scale_factor, 7))
+        padding = (current_height - new_height) // 2
+        unpadded_tensor = tensor[:, padding : current_height - padding, :]
     else:
         scale_factor = current_height / original_height
-        new_width = min(math.ceil(original_width * scale_factor), current_width)
-        padding, r = divmod(current_width - new_width, 2)
-        unpadded_tensor = tensor[:, :, padding : current_width - (padding + r)]
+        new_width = int(round(original_width * scale_factor, 7))
+        padding = (current_width - new_width) // 2
+        unpadded_tensor = tensor[:, :, padding : current_width - padding]
 
     return unpadded_tensor
 

tests/models/llava_next/test_modeling_llava_next.py

@@ -50,7 +50,7 @@
 if is_torch_available():
     import torch
 
-    from transformers.models.llava_next.modeling_llava_next import image_size_to_num_patches, unpad_image
+    from transformers.models.llava_next.modeling_llava_next import image_size_to_num_patches
 
 
 if is_vision_available():
@@ -298,18 +298,27 @@ def test_mismatching_num_image_tokens(self):
             image_sizes = torch.cat([image_sizes, image_sizes], dim=0)
             _ = model(input_ids=input_ids, pixel_values=pixel_values, image_sizes=image_sizes)
 
-    def test_unpad_image(self):
-        original_size = (400, 400)
+    def test_odd_sized_image(self):
+        # prepare model configuration
+        config = self.model_tester.get_config()
 
-        # Test case width is padded
-        pixel_values = floats_tensor([3, 400, 601])
-        unpadded_tensor = unpad_image(pixel_values, original_size)
-        self.assertEqual(unpadded_tensor.shape[1:], original_size)
+        # prepare input
+        num_image_tokens = 24
+        pixel_values = floats_tensor([1, 5, 3, config.vision_config.image_size, config.vision_config.image_size])
+        input_ids = ids_tensor([1, 64], config.text_config.vocab_size - 2) + 2
+        input_ids[:, :num_image_tokens] = config.image_token_index
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "image_sizes": torch.tensor([[13, 16]]),  # odd-sized image
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
 
-        # Test case height is padded
-        pixel_values = floats_tensor([3, 503, 400])
-        unpadded_tensor = unpad_image(pixel_values, original_size)
-        self.assertEqual(unpadded_tensor.shape[1:], original_size)
+        # forward with odd-sized image input
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device)
+            model(**inputs_dict)
 
     @parameterized.expand(
         [

tests/models/llava_next/test_processor_llava_next.py

@@ -11,13 +11,15 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 import json
+import shutil
 import tempfile
 import unittest
 
 import torch
 
-from transformers import AutoProcessor, LlamaTokenizerFast, LlavaNextProcessor
+from transformers import LlamaTokenizerFast, LlavaNextProcessor
 from transformers.testing_utils import (
     require_vision,
 )
@@ -52,6 +54,10 @@ def get_tokenizer(self, **kwargs):
     def get_image_processor(self, **kwargs):
         return LlavaNextProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
 
+    @classmethod
+    def tearDownClass(cls):
+        shutil.rmtree(cls.tmpdirname, ignore_errors=True)
+
     @staticmethod
     def prepare_processor_dict():
         return {
@@ -73,13 +79,16 @@ def test_chat_template_is_saved(self):
         self.assertTrue(processor_loaded.chat_template == processor_dict.get("chat_template", None))
 
     def test_image_token_filling(self):
-        processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-vicuna-7b-hf")
+        processor = self.processor_class.from_pretrained(self.tmpdirname)
         processor.patch_size = 14
         processor.vision_feature_select_strategy = "default"
+        processor.image_processor.crop_size = {"height": 336, "width": 336}
+        processor.image_processor.size = {"shortest_edge": 336}
+        processor.image_processor.image_grid_pinpoints = [[672, 336]]
         # Important to check with non square image
-        image = torch.randint(0, 2, (3, 500, 316))
-        expected_image_tokens = 1526
-        image_token_index = 32000
+        image = torch.randint(0, 2, (3, 503, 316))
+        expected_image_tokens = 1525
+        image_token_index = processor.image_token_id
 
         messages = [
             {

tests/models/llava_next_video/test_modeling_llava_next_video.py

@@ -49,8 +49,6 @@
 if is_torch_available():
     import torch
 
-    from transformers.models.llava_next_video.modeling_llava_next_video import unpad_image
-
 
 if is_vision_available():
     from PIL import Image
@@ -314,18 +312,27 @@ def test_mismatching_num_image_tokens(self):
             image_sizes = torch.cat([image_sizes, image_sizes], dim=0)
             _ = model(input_ids=input_ids, pixel_values=pixel_values, image_sizes=image_sizes)
 
-    def test_unpad_image(self):
-        original_size = (400, 400)
+    def test_odd_sized_image(self):
+        # prepare model configuration
+        config = self.model_tester.get_config()
 
-        # Test case width is padded
-        pixel_values = floats_tensor([3, 400, 601])
-        unpadded_tensor = unpad_image(pixel_values, original_size)
-        self.assertEqual(unpadded_tensor.shape[1:], original_size)
+        # prepare input
+        num_image_tokens = 24
+        pixel_values = floats_tensor([1, 5, 3, config.vision_config.image_size, config.vision_config.image_size])
+        input_ids = ids_tensor([1, 64], config.text_config.vocab_size - 2) + 2
+        input_ids[:, :num_image_tokens] = config.image_token_index
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "image_sizes": torch.tensor([[13, 16]]),  # odd-sized image
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
 
-        # Test case height is padded
-        pixel_values = floats_tensor([3, 503, 400])
-        unpadded_tensor = unpad_image(pixel_values, original_size)
-        self.assertEqual(unpadded_tensor.shape[1:], original_size)
+        # forward with odd-sized image input
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device)
+            model(**inputs_dict)
 
     @parameterized.expand(
         [

tests/models/llava_next_video/test_processor_llava_next_video.py

@@ -17,6 +17,8 @@
 import tempfile
 import unittest
 
+import torch
+
 from transformers import AutoProcessor, LlamaTokenizerFast, LlavaNextVideoProcessor
 from transformers.testing_utils import require_vision
 from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
@@ -63,6 +65,10 @@ def get_image_processor(self, **kwargs):
     def get_video_processor(self, **kwargs):
         return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).video_processor
 
+    @classmethod
+    def tearDownClass(cls):
+        shutil.rmtree(cls.tmpdirname, ignore_errors=True)
+
     @classmethod
     def prepare_processor_dict(cls):
         return {
@@ -84,6 +90,31 @@ def test_chat_template_is_saved(self):
         processor_dict = self.prepare_processor_dict()
         self.assertTrue(processor_loaded.chat_template == processor_dict.get("chat_template", None))
 
-    @classmethod
-    def tearDownClass(cls):
-        shutil.rmtree(cls.tmpdirname, ignore_errors=True)
+    def test_image_token_filling(self):
+        processor = self.processor_class.from_pretrained(self.tmpdirname)
+        processor.patch_size = 14
+        processor.vision_feature_select_strategy = "default"
+        processor.image_processor.crop_size = {"height": 336, "width": 336}
+        processor.image_processor.size = {"shortest_edge": 336}
+        processor.image_processor.image_grid_pinpoints = [[672, 336]]
+        # Important to check with non square image
+        image = torch.randint(0, 2, (3, 503, 316))
+        expected_image_tokens = 1525
+        image_token_index = processor.image_token_id
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+        inputs = processor(
+            text=[processor.apply_chat_template(messages)],
+            images=[image],
+            return_tensors="pt",
+        )
+        image_tokens = (inputs["input_ids"] == image_token_index).sum().item()
+        self.assertEqual(expected_image_tokens, image_tokens)

tests/models/llava_onevision/test_modeling_llava_onevision.py

@@ -49,8 +49,6 @@
 if is_torch_available():
     import torch
 
-    from transformers.models.llava_onevision.modeling_llava_onevision import unpad_image
-
 
 if is_vision_available():
     from PIL import Image
@@ -268,18 +266,27 @@ def test_inputs_embeds_matches_input_ids(self):
                 out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0]
             torch.testing.assert_close(out_embeds, out_ids)
 
-    def test_unpad_image(self):
-        original_size = (400, 400)
+    def test_odd_sized_image(self):
+        # prepare model configuration
+        config = self.model_tester.get_config()
 
-        # Test case width is padded
-        pixel_values = floats_tensor([3, 400, 601])
-        unpadded_tensor = unpad_image(pixel_values, original_size)
-        self.assertEqual(unpadded_tensor.shape[1:], original_size)
+        # prepare input
+        num_image_tokens = 10
+        pixel_values = floats_tensor([1, 2, 3, config.vision_config.image_size, config.vision_config.image_size])
+        input_ids = ids_tensor([1, 64], config.text_config.vocab_size - 2) + 2
+        input_ids[:, :num_image_tokens] = config.image_token_index
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "image_sizes": torch.tensor([[13, 16]]),  # odd-sized image
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
 
-        # Test case height is padded
-        pixel_values = floats_tensor([3, 503, 400])
-        unpadded_tensor = unpad_image(pixel_values, original_size)
-        self.assertEqual(unpadded_tensor.shape[1:], original_size)
+        # forward with odd-sized image input
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device)
+            model(**inputs_dict)
 
     @parameterized.expand(
         [

tests/models/llava_onevision/test_processor_llava_onevision.py

@@ -11,11 +11,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 import json
 import shutil
 import tempfile
 import unittest
 
+import torch
+
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
 
@@ -90,3 +93,33 @@ def test_chat_template_is_saved(self):
         # so we check if the same template is loaded
         processor_dict = self.prepare_processor_dict()
         self.assertTrue(processor_loaded.chat_template == processor_dict.get("chat_template", None))
+
+    def test_image_token_filling(self):
+        processor = self.processor_class.from_pretrained(self.tmpdirname)
+        processor.patch_size = 14
+        processor.vision_feature_select_strategy = "default"
+        processor.image_processor.crop_size = {"height": 336, "width": 336}
+        processor.image_processor.size = {"shortest_edge": 336}
+        processor.image_processor.image_grid_pinpoints = [[672, 336]]
+        processor.num_image_tokens = (processor.image_processor.size["shortest_edge"] // processor.patch_size) ** 2
+        # Important to check with non square image
+        image = torch.randint(0, 2, (3, 503, 316))
+        expected_image_tokens = 1525
+        image_token_index = processor.image_token_id
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+        inputs = processor(
+            text=[processor.apply_chat_template(messages)],
+            images=[image],
+            return_tensors="pt",
+        )
+        image_tokens = (inputs["input_ids"] == image_token_index).sum().item()
+        self.assertEqual(expected_image_tokens, image_tokens)