init

transformers/tests/models/llava_onevision/test_image_processing_llava_onevision.py

@@ -0,0 +1,302 @@
+# Copyright 2024 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 unittest
+
+import numpy as np
+import pytest
+
+from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import LlavaOnevisionImageProcessor
+
+    if is_torchvision_available():
+        from transformers import LlavaOnevisionImageProcessorFast
+
+
+class LlavaOnevisionImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=20,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+        do_normalize=True,
+        image_mean=OPENAI_CLIP_MEAN,
+        image_std=OPENAI_CLIP_STD,
+        do_convert_rgb=True,
+    ):
+        super().__init__()
+        size = size if size is not None else {"height": 20, "width": 20}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+        }
+
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.size["height"], self.size["width"]
+
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.prepare_image_inputs
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class LlavaOnevisionImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = LlavaOnevisionImageProcessor if is_vision_available() else None
+    fast_image_processing_class = LlavaOnevisionImageProcessorFast if is_torchvision_available() else None
+
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.setUp with CLIP->LlavaOnevision
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = LlavaOnevisionImageProcessingTester(self)
+
+    @property
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.image_processor_dict
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
+            self.assertTrue(hasattr(image_processing, "image_grid_pinpoints"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"height": 20, "width": 20})
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42)
+            self.assertEqual(image_processor.size, {"shortest_edge": 42})
+
+    def test_call_pil(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = (1, 1522, 3, 20, 20)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            expected_output_image_shape = (7, 1522, 3, 20, 20)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+    def test_call_numpy(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = (1, 1522, 3, 20, 20)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            expected_output_image_shape = (7, 1522, 3, 20, 20)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+    def test_call_pytorch(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PyTorch tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
+
+            for image in image_inputs:
+                self.assertIsInstance(image, torch.Tensor)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = (1, 1522, 3, 20, 20)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            expected_output_image_shape = (7, 1522, 3, 20, 20)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+    @unittest.skip(
+        reason="LlavaOnevisionImageProcessor doesn't treat 4 channel PIL and numpy consistently yet"
+    )  # FIXME raushan
+    def test_call_numpy_4_channels(self):
+        pass
+
+    def test_nested_input(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+
+            # Test batched as a list of images
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            expected_output_image_shape = (7, 1522, 3, 20, 20)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+            # Test batched as a nested list of images, where each sublist is one batch
+            image_inputs_nested = [[image_input] for image_input in image_inputs]
+            encoded_images_nested = image_processing(image_inputs_nested, return_tensors="pt").pixel_values
+            expected_output_image_shape = (7, 1522, 3, 20, 20)
+            self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape)
+
+            # Image processor should return same pixel values, independently of input format
+            self.assertTrue((encoded_images_nested == encoded_images).all())
+
+    def test_multi_images(self):
+        length = 384
+        scale_single, scale_multi = 2, 3
+        image_processor_dict = self.image_processor_tester.prepare_image_processor_dict()
+        image_processor_dict["size"] = {"height": length, "width": length}  # patch size
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**image_processor_dict)
+
+            # Test batched as a nested list of images, where each sublist is one batch
+            len_image_1 = length * scale_single
+            image_inputs_1 = prepare_image_inputs(
+                batch_size=1,
+                min_resolution=0,  # not used
+                max_resolution=len_image_1,
+                num_channels=3,
+                equal_resolution=True,
+            )
+            len_image_2 = length * scale_multi
+            image_inputs_2 = prepare_image_inputs(
+                batch_size=7,
+                min_resolution=0,  # not used
+                max_resolution=len_image_2,
+                num_channels=3,
+                equal_resolution=True,
+            )
+            image_inputs = [image_inputs_1, image_inputs_2]
+
+            # Only single image should be patchified
+            expected_num_patches = scale_single**2 + 1  # +1 for base image patch
+            expected_output_image_shape = (8, expected_num_patches, 3, length, length)
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+    @unittest.skip(
+        reason="LlavaOnevisionImageProcessorFast doesn't compile (infinitely) when using class transforms"
+    )  # FIXME yoni
+    @pytest.mark.torch_compile_test
+    def test_can_compile_fast_image_processor(self):
+        pass
+
+    def test_pad_for_patching(self):
+        for image_processing_class in self.image_processor_list:
+            if image_processing_class == self.fast_image_processing_class:
+                numpify = False
+                torchify = True
+                input_data_format = image_processing_class.data_format
+            else:
+                numpify = True
+                torchify = False
+                input_data_format = ChannelDimension.LAST
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # Create odd-sized images
+            image_input = self.image_processor_tester.prepare_image_inputs(
+                equal_resolution=True,
+                numpify=numpify,
+                torchify=torchify,
+            )[0]
+            self.assertIn(image_input.shape, [(3, 400, 400), (400, 400, 3)])
+
+            # Test odd-width
+            image_shape = (400, 601)
+            encoded_images = image_processing._pad_for_patching(image_input, image_shape, input_data_format)
+            encoded_image_shape = (
+                encoded_images.shape[:-1] if input_data_format == ChannelDimension.LAST else encoded_images.shape[1:]
+            )
+            self.assertEqual(encoded_image_shape, image_shape)
+
+            # Test odd-height
+            image_shape = (503, 400)
+            encoded_images = image_processing._pad_for_patching(image_input, image_shape, input_data_format)
+            encoded_image_shape = (
+                encoded_images.shape[:-1] if input_data_format == ChannelDimension.LAST else encoded_images.shape[1:]
+            )
+            self.assertEqual(encoded_image_shape, image_shape)
+
+    def test_call_without_padding(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PyTorch tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], do_pad=False).pixel_values
+            self.assertEqual(len(encoded_images), 1)
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, do_pad=False).pixel_values
+            self.assertEqual(len(encoded_images), len(image_inputs))

transformers/tests/models/llava_onevision/test_modeling_llava_onevision.py

@@ -0,0 +1,555 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+"""Testing suite for the PyTorch Llava-NeXT model."""
+
+import unittest
+
+import numpy as np
+import requests
+from huggingface_hub import hf_hub_download
+from parameterized import parameterized
+
+from transformers import (
+    AutoProcessor,
+    LlavaOnevisionConfig,
+    LlavaOnevisionForConditionalGeneration,
+    LlavaOnevisionModel,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    require_bitsandbytes,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    _config_zero_init,
+    floats_tensor,
+    ids_tensor,
+)
+
+
+if is_torch_available():
+    import torch
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+class LlavaOnevisionVisionText2TextModelTester:
+    def __init__(
+        self,
+        parent,
+        ignore_index=-100,
+        image_token_index=1,
+        video_token_index=2,
+        projector_hidden_act="gelu",
+        seq_length=7,
+        vision_feature_select_strategy="full",
+        vision_feature_layer=-1,
+        text_config={
+            "model_type": "qwen2",
+            "seq_length": 7,
+            "is_training": True,
+            "use_input_mask": True,
+            "use_token_type_ids": False,
+            "use_labels": True,
+            "vocab_size": 99,
+            "hidden_size": 32,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "num_key_value_heads": 4,
+            "intermediate_size": 37,
+            "hidden_act": "gelu",
+            "hidden_dropout_prob": 0.1,
+            "attention_probs_dropout_prob": 0.1,
+            "max_position_embeddings": 580,
+            "type_vocab_size": 16,
+            "type_sequence_label_size": 2,
+            "initializer_range": 0.02,
+            "num_labels": 3,
+            "num_choices": 4,
+            "pad_token_id": 0,
+        },
+        is_training=True,
+        vision_config={
+            "image_size": 16,
+            "patch_size": 8,
+            "num_channels": 3,
+            "is_training": True,
+            "hidden_size": 32,
+            "projection_dim": 32,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "intermediate_size": 37,
+            "dropout": 0.1,
+            "attention_dropout": 0.1,
+            "initializer_range": 0.02,
+        },
+    ):
+        self.parent = parent
+        self.ignore_index = ignore_index
+        self.image_token_index = image_token_index
+        self.video_token_index = video_token_index
+        self.projector_hidden_act = projector_hidden_act
+        self.vision_feature_select_strategy = vision_feature_select_strategy
+        self.vision_feature_layer = vision_feature_layer
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.pad_token_id = text_config["pad_token_id"]
+        self.num_image_tokens = 10
+        self.seq_length = seq_length + self.num_image_tokens
+
+        self.num_hidden_layers = text_config["num_hidden_layers"]
+        self.vocab_size = text_config["vocab_size"]
+        self.hidden_size = text_config["hidden_size"]
+        self.num_attention_heads = text_config["num_attention_heads"]
+        self.is_training = is_training
+
+        self.batch_size = 3
+        self.num_channels = 3
+        self.image_size = 30
+        self.image_grid_pinpoints = [[16, 16]]
+
+    def get_config(self):
+        return LlavaOnevisionConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            ignore_index=self.ignore_index,
+            image_token_index=self.image_token_index,
+            video_token_index=self.video_token_index,
+            projector_hidden_act=self.projector_hidden_act,
+            vision_feature_select_strategy=self.vision_feature_select_strategy,
+            vision_feature_layer=self.vision_feature_layer,
+            image_grid_pinpoints=self.image_grid_pinpoints,
+        )
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor(
+            [
+                self.batch_size,
+                3,
+                self.vision_config["num_channels"],
+                self.vision_config["image_size"],
+                self.vision_config["image_size"],
+            ]
+        )
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 2) + 2
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
+
+        input_ids[input_ids == config.image_token_index] = self.pad_token_id
+        input_ids[:, : self.num_image_tokens] = config.image_token_index
+
+        labels = torch.zeros((self.batch_size, self.seq_length), dtype=torch.long, device=torch_device)
+        labels[:, : self.num_image_tokens] == self.ignore_index
+
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "image_sizes": torch.tensor([[45, 45]] * self.batch_size),
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels": labels,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class LlavaOnevisionForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+    """
+    Model tester for `LlavaOnevisionForConditionalGeneration`.
+    """
+
+    all_model_classes = (
+        (
+            LlavaOnevisionModel,
+            LlavaOnevisionForConditionalGeneration,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {"image-text-to-text": LlavaOnevisionForConditionalGeneration} if is_torch_available() else {}
+    )
+    test_pruning = False
+    test_head_masking = False
+    # MP works but offload doesn't work when the MultiheadAttention is offloaded
+    # TODO: One potential solution would be to add to set preload_module_classes = ["Siglip2MultiheadAttentionPoolingHead"]
+    # in the dispatch_model function
+    test_cpu_offload = False
+    test_disk_offload_safetensors = False
+    test_disk_offload_bin = False
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = LlavaOnevisionVisionText2TextModelTester(self)
+        common_properties = ["image_token_index", "video_token_index", "vision_feature_layer"]
+        self.config_tester = ConfigTester(
+            self, config_class=LlavaOnevisionConfig, has_text_modality=False, common_properties=common_properties
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_initialization(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        configs_no_init = _config_zero_init(config)
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            for name, param in model.named_parameters():
+                # LLaVa Onevision has SigLIP backbone which init weights differently from CLIP
+                if "image_newline" in name or "vision_tower" in name:
+                    continue
+                elif param.requires_grad:
+                    self.assertIn(
+                        ((param.data.mean() * 1e9).round() / 1e9).item(),
+                        [0.0, 1.0],
+                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                    )
+
+    def test_odd_sized_image(self):
+        # prepare model configuration
+        config = self.model_tester.get_config()
+
+        # 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,
+        }
+
+        # 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(
+        [
+            (-1,),
+            ([-1],),
+            ([-1, -2],),
+        ],
+    )
+    def test_vision_feature_layers(self, vision_feature_layer):
+        """
+        Test that we can use either one vision feature layer, or a list of
+        vision feature layers.
+        """
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.vision_feature_layer = vision_feature_layer
+
+        num_feature_layers = 1 if isinstance(vision_feature_layer, int) else len(vision_feature_layer)
+        hidden_size = config.vision_config.hidden_size
+        expected_features = hidden_size * num_feature_layers
+
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device)
+            # We should have the right number of input features,
+            # and should be able to run a forward pass without exploding
+            base_model = getattr(model, "model", model)
+            assert base_model.multi_modal_projector.linear_1.in_features == expected_features
+            model(**input_dict)
+
+    @unittest.skip(
+        reason="This architecture seem to not compute gradients properly when using GC, SiglipVisionModel does not support standalone training"
+    )
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecture seem to not compute gradients properly when using GC, SiglipVisionModel does not support standalone training"
+    )
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecture seem to not compute gradients properly when using GC, SiglipVisionModel does not support standalone training"
+    )
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(
+        "VLMs need lots of steps to prepare images/mask correctly to get pad-free inputs. Can be tested as part of LLM test"
+    )
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+
+@require_torch
+class LlavaOnevisionForConditionalGenerationIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained(
+            "llava-hf/llava-onevision-qwen2-0.5b-ov-hf", padding_side="left"
+        )
+        image_file = hf_hub_download(
+            repo_id="raushan-testing-hf/images_test", filename="llava_v1_5_radar.jpg", repo_type="dataset"
+        )
+        video_file = hf_hub_download(
+            repo_id="raushan-testing-hf/videos-test", filename="video_demo.npy", repo_type="dataset"
+        )
+        self.image = Image.open(image_file)
+        self.video = np.load(video_file)
+        self.prompt_image = "user\n<image>\nWhat do you see in this image?<|im_end|>\n<|im_start|>assistant\n"
+        self.prompt_video = "user\n<video>\nWhat do you see in this video?<|im_end|>\n<|im_start|>assistant\n"
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    @require_bitsandbytes
+    def test_small_model_integration_test(self):
+        model = LlavaOnevisionForConditionalGeneration.from_pretrained(
+            "llava-hf/llava-onevision-qwen2-0.5b-ov-hf", dtype="float16", device_map=torch_device
+        )
+
+        inputs = self.processor(images=self.image, text=self.prompt_image, return_tensors="pt").to(
+            torch_device, torch.float16
+        )
+        self.assertTrue(inputs.input_ids.shape[1] == 6567)  # should expand num-image-tokens times
+        self.assertTrue(inputs.pixel_values.shape == torch.Size([1, 10, 3, 384, 384]))
+        self.assertTrue(inputs.image_sizes.tolist() == [[899, 1024]])
+
+        # verify single forward pass
+        inputs = inputs.to(torch_device)
+
+        # verify generation
+        output = model.generate(**inputs, max_new_tokens=100)
+
+        EXPECTED_DECODED_TEXTS = Expectations(
+            {
+                ("xpu", 3): 'user\n\nWhat do you see in this image?\nassistant\nThe image is a radar chart that compares the performance of different models in a specific task, likely related to natural language processing or machine learning. The chart is divided into several axes, each representing a different model or method. The models are color-coded and labeled with their respective names. The axes are labeled with terms such as "VQA," "GQA," "MQA," "VQAv2," "MM-Vet," "LLaVA-Bench," "LLaVA-1',
+                ("cuda", 7): 'user\n\nWhat do you see in this image?\nassistant\nThe image is a radar chart that compares the performance of different models in a specific task, likely related to natural language processing or machine learning. The chart is divided into several axes, each representing a different model or method. The models are color-coded and labeled with their respective names. The axes are labeled with terms such as "VQA," "GQA," "MQA," "VQAv2," "MM-Vet," "LLaVA-Bench," "LLaVA-1',
+                ("cuda", 8): 'user\n\nWhat do you see in this image?\nassistant\nThe image is a radar chart that compares the performance of different models in a specific task, likely related to natural language processing or machine learning. The chart is divided into several axes, each representing a different model or method. The models are color-coded and labeled with their respective names. The axes are labeled with terms such as "VQA," "GQA," "MQA," "VIZ," "TextVQA," "SQA-IMG," and "MQE." The radar chart shows',
+            }
+        )  # fmt: skip
+        EXPECTED_DECODED_TEXT = EXPECTED_DECODED_TEXTS.get_expectation()
+        DECODED_TEXT = self.processor.decode(output[0], skip_special_tokens=True)
+
+        self.assertEqual(DECODED_TEXT, EXPECTED_DECODED_TEXT)
+
+    @slow
+    @require_bitsandbytes
+    def test_small_model_integration_test_batch(self):
+        model = LlavaOnevisionForConditionalGeneration.from_pretrained(
+            "llava-hf/llava-onevision-qwen2-0.5b-ov-hf", dtype="float16", device_map=torch_device
+        )
+
+        inputs = self.processor(
+            text=[self.prompt_image, self.prompt_video],
+            images=self.image,
+            videos=self.video,
+            return_tensors="pt",
+            padding=True,
+        ).to(torch_device, torch.float16)
+
+        output = model.generate(**inputs, max_new_tokens=20)
+
+        EXPECTED_DECODED_TEXT = ['user\n\nWhat do you see in this image?\nassistant\nThe image is a radar chart that compares the performance of different models in a specific task, likely related', 'user\n\nWhat do you see in this video?\nassistant\nA child wearing a light blue sleeveless top and pink pants is seen sitting on a bed, eng']  # fmt: skip
+
+        self.assertEqual(
+            self.processor.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    @require_bitsandbytes
+    def test_small_model_integration_test_video(self):
+        # related to (#29835)
+        model = LlavaOnevisionForConditionalGeneration.from_pretrained(
+            "llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
+            dtype="float16",
+            device_map=torch_device,
+        )
+
+        inputs = self.processor(text=self.prompt_video, videos=self.video, return_tensors="pt").to(
+            torch_device, torch.float16
+        )
+
+        # verify generation
+        output = model.generate(**inputs, max_new_tokens=40)
+        EXPECTED_DECODED_TEXT = 'user\n\nWhat do you see in this video?\nassistant\nA child wearing a light blue sleeveless top and pink pants is seen sitting on a bed, engrossed in reading a book.'  # fmt: skip
+
+        self.assertEqual(
+            self.processor.decode(output[0], skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    @require_bitsandbytes
+    def test_small_model_integration_test_multi_image(self):
+        # related to (#29835)
+        model = LlavaOnevisionForConditionalGeneration.from_pretrained(
+            "llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
+            dtype="float16",
+            device_map=torch_device,
+        )
+
+        url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
+        image = Image.open(requests.get(url, stream=True).raw)
+        prompt = (
+            "user\n<image><image>\nWhat is the difference between these images?<|im_end|>\n<|im_start|>assistant\n"
+        )
+        inputs = self.processor(text=prompt, images=[self.image, image], return_tensors="pt").to(
+            torch_device, torch.float16
+        )
+
+        # verify generation
+        output = model.generate(**inputs, max_new_tokens=40)
+        EXPECTED_DECODED_TEXT = "user\n\nWhat is the difference between these images?\nassistant\nThe images you've provided appear to be related to a graphical representation of a radar chart, which is a type of data visualization used to show the distribution of a particular variable across a geographic area. The"  # fmt: skip
+
+        self.assertEqual(
+            self.processor.decode(output[0], skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    @require_bitsandbytes
+    def test_small_model_integration_test_multi_image_nested(self):
+        # related to (#34585)
+        model = LlavaOnevisionForConditionalGeneration.from_pretrained(
+            "llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
+            dtype="float16",
+            device_map=torch_device,
+        )
+
+        url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
+        image = Image.open(requests.get(url, stream=True).raw)
+        prompts = [
+            "user\nTell me about the french revolution.<|im_end|>\n<|im_start|>assistant\n",  # text-only case
+            "user\n<image><image>\nWhat is the difference between these images?<|im_end|>\n<|im_start|>assistant\n",
+            self.prompt_image,
+        ]
+        images_nested = [[], [image, self.image], [self.image]]
+        inputs = self.processor(
+            text=prompts,
+            images=images_nested,
+            return_tensors="pt",
+            padding=True,
+        ).to(torch_device, torch.float16)
+
+        # verify generation
+        output = model.generate(**inputs, max_new_tokens=40)
+        EXPECTED_DECODED_TEXT = ["user\nTell me about the french revolution.\nassistant\nThe French Revolution! A pivotal event in modern history that had a profound impact on the course of Western civilization. Here's a brief overview:\n\n**Background**\n\nIn the late 18th century,", "user\n\nWhat is the difference between these images?\nassistant\nThe first image shows a stop sign with a traditional Chinese architectural background, while the second image displays a radar chart with various algorithms and models, including BLIP-2, InstructBLIP, Q", "user\n\nWhat do you see in this image?\nassistant\nThe image is a radar chart that compares the performance of different models in a specific task, likely related to natural language processing or machine learning. The chart is divided into several axes, each representing a different"]  # fmt: skip
+        DECODED_TEXT = self.processor.batch_decode(output, skip_special_tokens=True)
+
+        self.assertListEqual(DECODED_TEXT, EXPECTED_DECODED_TEXT)
+
+    @slow
+    @require_bitsandbytes
+    def test_small_model_integration_test_multi_video(self):
+        # related to (#29835)
+        model = LlavaOnevisionForConditionalGeneration.from_pretrained(
+            "llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
+            dtype="float16",
+            device_map=torch_device,
+        )
+
+        prompt = "user\n<video><video>\nAre these videos identical?<|im_end|>\n<|im_start|>assistant\n"
+        inputs = self.processor(text=prompt, videos=[self.video, self.video], return_tensors="pt").to(
+            torch_device, torch.float16
+        )
+
+        # verify generation
+        output = model.generate(**inputs, max_new_tokens=40)
+        EXPECTED_DECODED_TEXT = "user\n\nAre these videos identical?\nassistant\nNo, the video is not identical; it shows slight variations in the child's actions and the background."  # fmt: skip
+
+        self.assertEqual(
+            self.processor.decode(output[0], skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    @require_bitsandbytes
+    def test_small_model_integration_test_batch_different_resolutions(self):
+        model = LlavaOnevisionForConditionalGeneration.from_pretrained(
+            "llava-hf/llava-onevision-qwen2-0.5b-ov-hf", dtype="float16", device_map=torch_device
+        )
+
+        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        lowres_url = "https://4.img-dpreview.com/files/p/TS560x560~forums/56876524/03975b28741443319e9a94615e35667e"
+        cats_image = Image.open(requests.get(url, stream=True).raw)
+        lowres_img = Image.open(requests.get(lowres_url, stream=True).raw)
+
+        inputs = self.processor(
+            text=[self.prompt_image, self.prompt_image],
+            images=[lowres_img, cats_image],
+            return_tensors="pt",
+            padding=True,
+        ).to(torch_device, torch.float16)
+
+        # verify generation
+        output = model.generate(**inputs, max_new_tokens=50)
+        EXPECTED_DECODED_TEXT = [
+            'user\n\nWhat do you see in this image?\nassistant\nThe image shows a scene of two deer in a grassy area with trees in the background. The weather appears to be foggy, giving the scene a misty and somewhat mysterious atmosphere. The deer are standing close to each other, possibly grazing or',
+            'user\n\nWhat do you see in this image?\nassistant\nIn the tranquil setting of this image, two cats are enjoying a peaceful nap on a vibrant pink blanket. The cat on the left, with its gray and black striped fur, is lying on its side, its head comfortably resting on the blanket. Its',
+        ]  # fmt: skip
+        self.assertEqual(
+            self.processor.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    @require_bitsandbytes
+    def test_small_model_integration_test_batch_matches_single(self):
+        model = LlavaOnevisionForConditionalGeneration.from_pretrained(
+            "llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
+            dtype="float16",
+            device_map=torch_device,
+        )
+
+        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        lowres_url = "https://4.img-dpreview.com/files/p/TS560x560~forums/56876524/03975b28741443319e9a94615e35667e"
+        cats_image = Image.open(requests.get(url, stream=True).raw)
+        lowres_img = Image.open(requests.get(lowres_url, stream=True).raw)
+
+        inputs_batched = self.processor(
+            text=[self.prompt_image, self.prompt_image],
+            images=[lowres_img, cats_image],
+            return_tensors="pt",
+            padding=True,
+        ).to(torch_device, torch.float16)
+
+        inputs_single = self.processor(
+            text=self.prompt_image, images=lowres_img, return_tensors="pt", padding=True
+        ).to(torch_device, torch.float16)
+
+        # verify generation
+        output_batched = model.generate(**inputs_batched, max_new_tokens=50)
+        output_single = model.generate(**inputs_single, max_new_tokens=50)
+
+        self.assertEqual(
+            self.processor.decode(output_batched[0], skip_special_tokens=True),
+            self.processor.decode(output_single[0], skip_special_tokens=True),
+        )

transformers/tests/models/llava_onevision/test_processing_llava_onevision.py

@@ -0,0 +1,135 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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_torchvision_available, is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from transformers import (
+        AutoProcessor,
+        LlavaOnevisionImageProcessor,
+        LlavaOnevisionProcessor,
+        Qwen2TokenizerFast,
+    )
+
+    if is_torchvision_available():
+        from transformers import LlavaOnevisionVideoProcessor
+
+
+@require_vision
+@require_torch
+class LlavaOnevisionProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = LlavaOnevisionProcessor
+
+    @classmethod
+    def setUpClass(cls):
+        cls.tmpdirname = tempfile.mkdtemp()
+        image_processor = LlavaOnevisionImageProcessor()
+        video_processor = LlavaOnevisionVideoProcessor()
+        tokenizer = Qwen2TokenizerFast.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
+        tokenizer.add_special_tokens({"additional_special_tokens": ["<image>", "<video>"]})
+        processor_kwargs = cls.prepare_processor_dict()
+
+        processor = LlavaOnevisionProcessor(
+            video_processor=video_processor, image_processor=image_processor, tokenizer=tokenizer, **processor_kwargs
+        )
+        processor.save_pretrained(cls.tmpdirname)
+        cls.image_token = processor.image_token
+        cls.video_token = processor.video_token
+
+    def get_tokenizer(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
+
+    def get_image_processor(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
+
+    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)
+
+    @staticmethod
+    def prepare_processor_dict():
+        return {
+            "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + ' '}}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>' }}{% endfor %}{# Render all video then #}{% for content in message['content'] | selectattr('type', 'equalto', 'video') %}{{ '<video>' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ '\n' + content['text'] }}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ '\n' + content['text'] }}{% endgeneration %}{% endfor %}{% endif %}{{'<|im_end|>'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+            "num_image_tokens": 6,
+            "vision_feature_select_strategy": "default"
+        }  # fmt: skip
+
+    # Copied from tests.models.llava.test_processing_llava.LlavaProcessorTest.test_get_num_vision_tokens
+    def test_get_num_vision_tokens(self):
+        "Tests general functionality of the helper used internally in vLLM"
+
+        processor = self.get_processor()
+
+        output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)])
+        self.assertTrue("num_image_tokens" in output)
+        self.assertEqual(len(output["num_image_tokens"]), 3)
+
+        self.assertTrue("num_image_patches" in output)
+        self.assertEqual(len(output["num_image_patches"]), 3)
+
+    # Copied from tests.models.llava.test_processing_llava.LlavaProcessorTest.test_chat_template_is_saved
+    def test_chat_template_is_saved(self):
+        processor_loaded = self.processor_class.from_pretrained(self.tmpdirname)
+        processor_dict_loaded = json.loads(processor_loaded.to_json_string())
+        # chat templates aren't serialized to json in processors
+        self.assertFalse("chat_template" in processor_dict_loaded)
+
+        # they have to be saved as separate file and loaded back from that file
+        # 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)

transformers/tests/models/llava_onevision/test_video_processing_llava_onevision.py

@@ -0,0 +1,113 @@
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 unittest
+
+from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torchvision_available, is_vision_available
+
+from ...test_video_processing_common import VideoProcessingTestMixin, prepare_video_inputs
+
+
+if is_vision_available():
+    if is_torchvision_available():
+        from transformers import LlavaOnevisionVideoProcessor
+
+
+class LlavaOnevisionVideoProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_frames=8,
+        num_channels=3,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+        do_normalize=True,
+        image_mean=OPENAI_CLIP_MEAN,
+        image_std=OPENAI_CLIP_STD,
+        do_convert_rgb=True,
+    ):
+        size = size if size is not None else {"height": 20, "width": 20}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_frames = num_frames
+        self.num_channels = num_channels
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_video_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+        }
+
+    def expected_output_video_shape(self, video):
+        return self.num_frames, self.num_channels, self.size["height"], self.size["width"]
+
+    def prepare_video_inputs(self, equal_resolution=False, return_tensors="pil"):
+        videos = prepare_video_inputs(
+            batch_size=self.batch_size,
+            num_frames=self.num_frames,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            return_tensors=return_tensors,
+        )
+        return videos
+
+
+@require_torch
+@require_vision
+class LlavaOnevisionVideoProcessingTest(VideoProcessingTestMixin, unittest.TestCase):
+    fast_video_processing_class = LlavaOnevisionVideoProcessor if is_torchvision_available() else None
+
+    def setUp(self):
+        super().setUp()
+        self.video_processor_tester = LlavaOnevisionVideoProcessingTester(self)
+
+    @property
+    def video_processor_dict(self):
+        return self.video_processor_tester.prepare_video_processor_dict()
+
+    def test_video_processor_properties(self):
+        video_processing = self.fast_video_processing_class(**self.video_processor_dict)
+        self.assertTrue(hasattr(video_processing, "do_resize"))
+        self.assertTrue(hasattr(video_processing, "size"))
+        self.assertTrue(hasattr(video_processing, "do_normalize"))
+        self.assertTrue(hasattr(video_processing, "image_mean"))
+        self.assertTrue(hasattr(video_processing, "image_std"))
+        self.assertTrue(hasattr(video_processing, "do_convert_rgb"))
+
+    def test_video_processor_from_dict_with_kwargs(self):
+        video_processor = self.fast_video_processing_class.from_dict(self.video_processor_dict)
+        self.assertEqual(video_processor.size, {"height": 20, "width": 20})
+
+        video_processor = self.fast_video_processing_class.from_dict(self.video_processor_dict, size=42)
+        self.assertEqual(video_processor.size, {"shortest_edge": 42})