init

transformers/tests/models/internvl/test_modeling_internvl.py

@@ -0,0 +1,983 @@
+# coding=utf-8
+# Copyright 2025 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 InternVL model."""
+
+import unittest
+from io import BytesIO
+
+import pytest
+import requests
+
+from transformers import (
+    AutoProcessor,
+    BitsAndBytesConfig,
+    InternVLConfig,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    require_av,
+    require_bitsandbytes,
+    require_deterministic_for_xpu,
+    require_torch,
+    require_torch_accelerator,
+    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
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import InternVLForConditionalGeneration, InternVLModel
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+class InternVLVisionText2TextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        seq_length=7,
+        image_seq_length=64,
+        vision_feature_layer=-1,
+        ignore_index=-100,
+        image_token_id=1,
+        num_channels=3,
+        image_size=64,
+        model_type="internvl",
+        is_training=True,
+        text_config={
+            "model_type": "qwen2",
+            "vocab_size": 99,
+            "hidden_size": 128,
+            "intermediate_size": 37,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "num_key_value_heads": 2,
+            "output_channels": 64,
+            "hidden_act": "silu",
+            "max_position_embeddings": 512,
+            "rope_theta": 10000,
+            "mlp_ratio": 4,
+            "tie_word_embeddings": True,
+            "bos_token_id": 3,
+            "eos_token_id": 4,
+            "pad_token_id": 5,
+        },
+        vision_config={
+            "hidden_size": 32,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "intermediate_size": 128,
+            "image_size": 64,
+            "patch_size": 4,
+            "num_channels": 3,
+            "hidden_act": "quick_gelu",
+            "use_absolute_position_embeddings": True,
+        },
+    ):
+        self.parent = parent
+        self.ignore_index = ignore_index
+        self.bos_token_id = text_config["bos_token_id"]
+        self.eos_token_id = text_config["eos_token_id"]
+        self.pad_token_id = text_config["pad_token_id"]
+        self.image_token_id = image_token_id
+        self.model_type = model_type
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.batch_size = batch_size
+        self.vision_feature_layer = vision_feature_layer
+        self.is_training = is_training
+        self.image_seq_length = image_seq_length
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.seq_length = seq_length + image_seq_length
+
+        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"]
+
+    def get_config(self):
+        return InternVLConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            model_type=self.model_type,
+            image_token_id=self.image_token_id,
+            image_seq_length=self.image_seq_length,
+            vision_feature_layer=self.vision_feature_layer,
+        )
+
+    def prepare_config_and_inputs(self):
+        config = self.get_config()
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        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], self.vocab_size)
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
+
+        input_ids[input_ids == self.image_token_id] = self.pad_token_id
+        input_ids[:, : self.image_seq_length] = self.image_token_id
+
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+        return config, inputs_dict
+
+    def create_and_check_model_fp16_forward(self, config, input_ids, pixel_values, attention_mask):
+        model = InternVLForConditionalGeneration(config=config)
+        model.to(torch_device)
+        model.half()
+        model.eval()
+        logits = model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            pixel_values=pixel_values.to(torch.bfloat16),
+            return_dict=True,
+        )["logits"]
+        self.parent.assertFalse(torch.isnan(logits).any().item())
+
+    def create_and_check_model_fp16_autocast_forward(self, config, input_ids, pixel_values, attention_mask):
+        config.dtype = torch.float16
+        model = InternVLForConditionalGeneration(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.autocast(device_type=torch_device, dtype=torch.float16):
+            logits = model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                pixel_values=pixel_values.to(torch.bfloat16),
+                return_dict=True,
+            )["logits"]
+        self.parent.assertFalse(torch.isnan(logits).any().item())
+
+
+@require_torch
+class InternVLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (InternVLForConditionalGeneration, InternVLModel) if is_torch_available() else ()
+    all_generative_model_classes = (InternVLForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "image-text-to-text": InternVLForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_headmasking = False
+    test_pruning = False
+
+    def setUp(self):
+        self.model_tester = InternVLVisionText2TextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=InternVLConfig, has_text_modality=False)
+
+    @unittest.skip(
+        reason="Failing with `torch._inductor.exc.InductorError: RuntimeError: No valid triton configs. OutOfMemoryError: out of resource: triton_tem_fused_0 Required: 147456 Hardware limit:101376 Reducing block sizes or `num_stages` may help.`"
+    )
+    def test_flex_attention_with_grads(self):
+        pass
+
+    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():
+                if 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",
+                    )
+
+    @unittest.skip(reason="Compile not yet supported because in LLava models")
+    @pytest.mark.torch_compile_test
+    def test_sdpa_can_compile_dynamic(self):
+        pass
+
+    @unittest.skip("FlashAttention only support fp16 and bf16 data type")
+    def test_flash_attn_2_fp32_ln(self):
+        pass
+
+
+@slow
+@require_torch_accelerator
+class InternVLQwen2IntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.small_model_checkpoint = "OpenGVLab/InternVL3-1B-hf"
+        self.medium_model_checkpoint = "OpenGVLab/InternVL3-2B-hf"
+        cleanup(torch_device, gc_collect=True)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def test_qwen2_small_model_integration_generate(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16
+        )
+        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        image = Image.open(requests.get(url, stream=True).raw)
+
+        prompt = (
+            "<|im_start|>user\n<IMG_CONTEXT>\nPlease describe the image explicitly.<|im_end|>\n<|im_start|>assistant\n"
+        )
+        inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
+        with torch.no_grad():
+            generate_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+            decoded_output = processor.decode(
+                generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+            )
+        expected_output = "The image shows two cats lying on a pink surface, which appears to be a bed or couch."
+
+        self.assertEqual(decoded_output, expected_output)
+
+    def test_qwen2_small_model_integration_forward(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16
+        )
+        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        image = Image.open(requests.get(url, stream=True).raw)
+
+        prompt = (
+            "<|im_start|>user\n<IMG_CONTEXT>\nPlease describe the image explicitly.<|im_end|>\n<|im_start|>assistant\n"
+        )
+        inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
+
+        # Forward
+        with torch.inference_mode():
+            output = model(**inputs)
+
+        actual_logits = output.logits[0, -1, :5].cpu()
+        expected_logits_all = Expectations(
+            {
+                ("xpu", 3): torch.tensor([11.7500, 14.7500, 14.1250, 10.5625, 6.7812], dtype=torch.float16),
+                ("cuda", 7): torch.tensor([11.9531, 14.7031, 14.2734, 10.6562,  6.9219], dtype=torch.float16),
+                ("cuda", 8): torch.tensor([11.9609, 14.7188, 14.2734, 10.6484,  6.9141], dtype=torch.float16),
+            }
+        )  # fmt: skip
+        expected_logits = expected_logits_all.get_expectation()
+
+        self.assertTrue(
+            torch.allclose(actual_logits, expected_logits, atol=0.1),
+            f"Actual logits: {actual_logits}"
+            f"\nExpected logits: {expected_logits}"
+            f"\nDifference: {torch.abs(actual_logits - expected_logits)}",
+        )
+
+    @require_deterministic_for_xpu
+    def test_qwen2_small_model_integration_generate_text_only(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16
+        )
+        prompt = "<|im_start|>user\nWrite a haiku<|im_end|>\n<|im_start|>assistant\n"
+        inputs = processor(text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
+        with torch.no_grad():
+            generate_ids = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+            decoded_output = processor.decode(
+                generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+            )
+
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "Whispers of dawn,\nSilent whispers of the night,\nNew day's light.",
+                ("cuda", 7): 'Whispers of dawn,\nSilent whispers of night,\nPeace in the stillness.',
+                ("cuda", 8): 'Whispers of dawn,\nSilent whispers of night,\nPeace in the stillness.',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(decoded_output, expected_output)
+
+    def test_qwen2_small_model_integration_generate_chat_template(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16
+        )
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+                    {"type": "text", "text": "Please describe the image explicitly."},
+                ],
+            }
+        ]
+
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(torch_device, dtype=torch.float16)
+        with torch.no_grad():
+            generate_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+            decoded_output = processor.decode(
+                generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+            )
+        expected_output = "The image shows two cats lying on a pink surface, which appears to be a bed or couch."
+
+        self.assertEqual(decoded_output, expected_output)
+
+    @require_deterministic_for_xpu
+    def test_qwen2_small_model_integration_batched_generate(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16
+        )
+        # Prepare inputs
+        prompt = [
+            "<|im_start|>user\n<IMG_CONTEXT>\nWrite a haiku for this image<|im_end|>\n<|im_start|>assistant\n",
+            "<|im_start|>user\n<IMG_CONTEXT>\nDescribe this image<|im_end|>\n<|im_start|>assistant\n",
+        ]
+        image1 = Image.open(requests.get("https://llava-vl.github.io/static/images/view.jpg", stream=True).raw)
+        image2 = Image.open(
+            requests.get(
+                "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg",
+                stream=True,
+            ).raw
+        )
+
+        inputs = processor(text=prompt, images=[[image1], [image2]], padding=True, return_tensors="pt").to(
+            torch_device, dtype=torch.float16
+        )
+
+        output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
+
+        # Check first output
+        decoded_output = processor.decode(output[0], skip_special_tokens=True)
+        expected_output = "user\n\nWrite a haiku for this image\nassistant\nSilky lake,  \nWooden pier,  \nNature's peace."  # fmt: skip
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+        # Check second output
+        decoded_output = processor.decode(output[1], skip_special_tokens=True)
+
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): 'user\n\nDescribe this image\nassistant\nThe image shows a street scene with a traditional Chinese archway, known as a "Chinese Gate" or "Chinese Gate of',
+                ("cuda", 7): 'user\n\nDescribe this image\nassistant\nThe image shows a street scene with a traditional Chinese archway, known as a "Chinese Gate" or "Chinese Gate of',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+    def test_qwen2_small_model_integration_batched_generate_multi_image(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16
+        )
+        # Prepare inputs
+        prompt = [
+            "<|im_start|>user\n<IMG_CONTEXT>\nWrite a haiku for this image<|im_end|>\n<|im_start|>assistant\n",
+            "<|im_start|>user\n<IMG_CONTEXT><IMG_CONTEXT>\nWhat are the differences between these two images?<|im_end|>\n<|im_start|>assistant\n",
+        ]
+        image1 = Image.open(requests.get("https://llava-vl.github.io/static/images/view.jpg", stream=True).raw)
+        image2 = Image.open(
+            BytesIO(
+                requests.get(
+                    "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+                ).content
+            )
+        )
+        image3 = Image.open(
+            BytesIO(
+                requests.get(
+                    "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg"
+                ).content
+            )
+        )
+
+        inputs = processor(text=prompt, images=[[image1], [image2, image3]], padding=True, return_tensors="pt").to(
+            torch_device, dtype=torch.float16
+        )
+
+        output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
+
+        # Check first output
+        decoded_output = processor.decode(output[0], skip_special_tokens=True)
+        # Batching seems to alter the output slightly, but it is also the case in the original implementation. This seems to be expected: https://github.com/huggingface/transformers/issues/23017#issuecomment-1649630232
+        expected_output = "user\n\nWrite a haiku for this image\nassistant\nSilky lake,  \nWooden pier,  \nNature's peace."  # fmt: skip
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+        # Check second output
+        decoded_output = processor.decode(output[1], skip_special_tokens=True)
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\n\nWhat are the differences between these two images?\nassistant\nThe images show the Statue of Liberty and the Golden Gate Bridge from different angles. Here are the differences:\n\n1. **Foreground",
+                ("cuda", 7): "user\n\nWhat are the differences between these two images?\nassistant\nThe images show the Statue of Liberty and the Golden Gate Bridge from different angles. Here are the differences:\n\n1. **Foreground",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+    @require_av
+    @require_bitsandbytes
+    def test_qwen2_medium_model_integration_video(self):
+        processor = AutoProcessor.from_pretrained(self.medium_model_checkpoint)
+        quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.medium_model_checkpoint, quantization_config=quantization_config
+        )
+        # Prepare inputs
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "video",
+                        "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4",
+                    },
+                    {"type": "text", "text": "What type of shot is the man performing?"},
+                ],
+            }
+        ]
+        inputs = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            num_frames=8,
+        ).to(torch_device, dtype=torch.float16)
+
+        output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
+
+        decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "The man is performing a volley.",
+                ("cuda", 7): "The man is performing a forehand shot.",
+                ("rocm", (9, 5)): "The man is performing a volley shot.",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+    @require_av
+    @require_deterministic_for_xpu
+    def test_qwen2_small_model_integration_interleaved_images_videos(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, dtype=torch.float16, device_map=torch_device
+        )
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
+                        },
+                        {
+                            "type": "image",
+                            "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
+                        },
+                        {"type": "text", "text": "What are the differences between these two images?"},
+                    ],
+                },
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "video",
+                            "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4",
+                        },
+                        {"type": "text", "text": "What type of shot is the man performing?"},
+                    ],
+                },
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "url": "https://llava-vl.github.io/static/images/view.jpg",
+                        },
+                        {"type": "text", "text": "Write a haiku for this image"},
+                    ],
+                }
+            ],
+        ]
+        inputs = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+            num_frames=8,
+        ).to(torch_device, dtype=torch.float16)
+
+        output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
+
+        decoded_output = processor.decode(output[0], skip_special_tokens=True)
+        # Batching seems to alter the output slightly, but it is also the case in the original implementation. This seems to be expected: https://github.com/huggingface/transformers/issues/23017#issuecomment-1649630232
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\n\n\nWhat are the differences between these two images?\nassistant\nThe images depict two distinct scenes:\n\n1. **Left Image:**\n   - The Statue of Liberty is prominently featured on an",
+                ("cuda", 7): 'user\n\n\nWhat are the differences between these two images?\nassistant\nThe images depict two distinct scenes:\n\n1. **Left Image:**\n   - The Statue of Liberty is prominently featured on an',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+        # Check second output
+        decoded_output = processor.decode(output[1], skip_special_tokens=True)
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nThe man is performing a forehand shot.",
+                ("cuda", 7): 'user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nA forehand shot',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+        # Check third output
+        decoded_output = processor.decode(output[2], skip_special_tokens=True)
+        expected_output = (
+            "user\n\nWrite a haiku for this image\nassistant\nSilky lake,  \nWooden pier,  \nNature's peace."
+        )
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+
+@slow
+@require_torch_accelerator
+class InternVLLlamaIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.small_model_checkpoint = "OpenGVLab/InternVL2_5-2B-MPO-hf"
+        self.medium_model_checkpoint = "OpenGVLab/InternVL2_5-8B-MPO-hf"
+        cleanup(torch_device, gc_collect=True)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def test_llama_small_model_integration_generate(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16
+        )
+        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        image = Image.open(requests.get(url, stream=True).raw)
+
+        prompt = (
+            "<|im_start|>user\n<IMG_CONTEXT>\nPlease describe the image explicitly.<|im_end|>\n<|im_start|>assistant\n"
+        )
+        inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
+        with torch.no_grad():
+            generate_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+            decoded_output = processor.decode(
+                generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+            )
+        expected_output = "The image shows two cats sleeping on a pink couch. They are lying side by side, with their"
+        self.assertEqual(decoded_output, expected_output)
+
+    def test_llama_small_model_integration_forward(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16
+        )
+        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        image = Image.open(requests.get(url, stream=True).raw)
+
+        prompt = (
+            "<|im_start|>user\n<IMG_CONTEXT>\nPlease describe the image explicitly.<|im_end|>\n<|im_start|>assistant\n"
+        )
+        inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
+
+        # Forward
+        with torch.inference_mode():
+            output = model(**inputs)
+
+        actual_logits = output.logits[0, -1, :5].cpu()
+
+        expected_logits_all = Expectations(
+            {
+                ("xpu", 3): [-9.8750, -0.5703, 1.4297, -10.3125, -10.3125],
+                ("cuda", 7): [-9.8750,  -0.4861,   1.4648, -10.3359, -10.3359],
+                ("cuda", 8): [-9.8906,  -0.4995,   1.4473, -10.3359, -10.3438],
+                ("rocm", (9, 4)): [ -9.8828,  -0.5005,   1.4697, -10.3438, -10.3438],
+                ("rocm", (9, 5)): [ -9.8906,  -0.4976,   1.4502, -10.3359, -10.3438],
+            }
+        )  # fmt: skip
+        expected_logits = torch.tensor(expected_logits_all.get_expectation(), dtype=torch.float16)
+
+        # The original implementation and the transformers implementation do not match exactly, hence the higher tolerance.
+        # The difference is likely due to the different implementations of the attention mechanism (different order of operations)
+        # between the transformers Llama model and the original InternLM model.
+        # The difference has almost no effect on the output tokens, but it does affect the logits a lot more.
+        self.assertTrue(
+            torch.allclose(actual_logits, expected_logits, atol=1e-3),
+            f"Actual logits: {actual_logits}"
+            f"\nExpected logits: {expected_logits}"
+            f"\nDifference: {torch.abs(actual_logits - expected_logits)}",
+        )
+
+    def test_llama_small_model_integration_generate_text_only(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16
+        )
+        prompt = "<|im_start|>user\nWrite a haiku<|im_end|>\n<|im_start|>assistant\n"
+        inputs = processor(text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
+        with torch.no_grad():
+            generate_ids = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+            decoded_output = processor.decode(
+                generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+            )
+
+        expected_outputs = Expectations(
+            {
+                ("cuda", 7): "Autumn leaves fall,\nNature's breath, a gentle sigh,\nSilent whispers.",
+                ("cuda", 8): "Autumn leaves fall,\nNature's breath, a silent sigh,\nWinter's chill approaches.",
+            }
+        )
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(decoded_output, expected_output)
+
+    def test_llama_small_model_integration_generate_chat_template(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16
+        )
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+                    {"type": "text", "text": "Please describe the image explicitly."},
+                ],
+            }
+        ]
+
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(torch_device, dtype=torch.float16)
+        with torch.no_grad():
+            generate_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+            decoded_output = processor.decode(
+                generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+            )
+        expected_output = "The image shows two cats sleeping on a pink couch. They are lying side by side, with their"
+        self.assertEqual(decoded_output, expected_output)
+
+    def test_llama_small_model_integration_batched_generate(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16
+        )
+        # Prepare inputs
+        prompt = [
+            "<|im_start|>user\n<IMG_CONTEXT>\nWrite a haiku for this image<|im_end|>\n<|im_start|>assistant\n",
+            "<|im_start|>user\n<IMG_CONTEXT>\nDescribe this image<|im_end|>\n<|im_start|>assistant\n",
+        ]
+        image1 = Image.open(requests.get("https://llava-vl.github.io/static/images/view.jpg", stream=True).raw)
+        image2 = Image.open(
+            requests.get(
+                "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg",
+                stream=True,
+            ).raw
+        )
+
+        inputs = processor(text=prompt, images=[[image1], [image2]], padding=True, return_tensors="pt").to(
+            torch_device, dtype=torch.float16
+        )
+
+        output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
+
+        # Check first output
+        decoded_output = processor.decode(output[0], skip_special_tokens=True)
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.",
+                ("cuda", 7): 'user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.',
+                ("cuda", 8): 'user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+        # Check second output
+        decoded_output = processor.decode(output[1], skip_special_tokens=True)
+        expected_output = "user\n\nDescribe this image\nassistant\nThe image shows a street scene with a traditional Chinese gate in the background, adorned with red and gold colors and Chinese characters"
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+    def test_llama_small_model_integration_batched_generate_multi_image(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, device_map=torch_device, dtype=torch.float16
+        )
+        # Prepare inputs
+        prompt = [
+            "<|im_start|>user\n<IMG_CONTEXT>\nWrite a haiku for this image<|im_end|>\n<|im_start|>assistant\n",
+            "<|im_start|>user\n<IMG_CONTEXT><IMG_CONTEXT>\nWhat are the difference between these two images?<|im_end|>\n<|im_start|>assistant\n",
+        ]
+        image1 = Image.open(requests.get("https://llava-vl.github.io/static/images/view.jpg", stream=True).raw)
+        image2 = Image.open(
+            BytesIO(
+                requests.get(
+                    "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+                ).content
+            )
+        )
+        image3 = Image.open(
+            BytesIO(
+                requests.get(
+                    "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg"
+                ).content
+            )
+        )
+
+        inputs = processor(text=prompt, images=[[image1], [image2, image3]], padding=True, return_tensors="pt").to(
+            torch_device, dtype=torch.float16
+        )
+
+        output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
+
+        # Check first output
+        decoded_output = processor.decode(output[0], skip_special_tokens=True)
+        # Batching seems to alter the output slightly, but it is also the case in the original implementation. This seems to be expected: https://github.com/huggingface/transformers/issues/23017#issuecomment-1649630232
+        expected_output = "user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors."
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+        # Check second output
+        decoded_output = processor.decode(output[1], skip_special_tokens=True)
+        expected_output = "user\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. After closely examining the images again, I can see that there are several differences"
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+    @require_av
+    @require_bitsandbytes
+    def test_llama_medium_model_integration_video(self):
+        processor = AutoProcessor.from_pretrained(self.medium_model_checkpoint)
+        quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.medium_model_checkpoint, quantization_config=quantization_config
+        )
+        # Prepare inputs
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "video",
+                        "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4",
+                    },
+                    {"type": "text", "text": "What type of shot is the man performing?"},
+                ],
+            }
+        ]
+        inputs = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            num_frames=8,
+        ).to(torch_device, dtype=torch.float16)
+
+        output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
+
+        decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+        expected_output = "The man is performing a forehand shot."
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+    @require_av
+    def test_llama_small_model_integration_interleaved_images_videos(self):
+        processor = AutoProcessor.from_pretrained(self.small_model_checkpoint)
+        model = InternVLForConditionalGeneration.from_pretrained(
+            self.small_model_checkpoint, dtype=torch.float16, device_map=torch_device
+        )
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
+                        },
+                        {
+                            "type": "image",
+                            "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
+                        },
+                        {"type": "text", "text": "What are the difference between these two images?"},
+                    ],
+                },
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "video",
+                            "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4",
+                        },
+                        {"type": "text", "text": "What type of shot is the man performing?"},
+                    ],
+                },
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "url": "https://llava-vl.github.io/static/images/view.jpg",
+                        },
+                        {"type": "text", "text": "Write a haiku for this image"},
+                    ],
+                }
+            ],
+        ]
+        inputs = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+            num_frames=8,
+        ).to(torch_device, dtype=torch.float16)
+
+        output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
+
+        decoded_output = processor.decode(output[0], skip_special_tokens=True)
+        # Batching seems to alter the output slightly, but it is also the case in the original implementation. This seems to be expected: https://github.com/huggingface/transformers/issues/23017#issuecomment-1649630232
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. After re-examining the images, I can see that they are actually",
+                ("cuda", 7): 'user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. Upon closer inspection, the differences between the two images are:\n\n1. **',
+                ("cuda", 8): 'user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. After re-examining the images, I can see that there are no',
+                ("rocm", (9, 4)): 'user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. Upon closer inspection, the differences between the two images are:\n\n1. **',
+                ("rocm", (9, 5)): 'user\n\n\nWhat are the difference between these two images?\nassistant\nI apologize for the confusion in my previous response. After re-examining the images, I can see that there are no',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+        # Check second output
+        decoded_output = processor.decode(output[1], skip_special_tokens=True)
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nThe man is performing a forehand shot. This is a common shot in tennis where the player swings the racket across their",
+                ("cuda", 7): 'user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nThe man is performing a forehand shot. This is a common stroke in tennis where the player swings the racket across their',
+                ("cuda", 8): 'user\nFrame1: \nFrame2: \nFrame3: \nFrame4: \nFrame5: \nFrame6: \nFrame7: \nFrame8: \nWhat type of shot is the man performing?\nassistant\nThe man is performing a forehand shot. This is a common stroke in tennis where the player swings the racket across their',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+        # Check third output
+        decoded_output = processor.decode(output[2], skip_special_tokens=True)
+        expected_outputs = Expectations(
+            {
+                ("xpu", 3): "user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.",
+                ("cuda", 7): 'user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.',
+                ("cuda", 8): 'user\n\nWrite a haiku for this image\nassistant\nMajestic snow-capped peaks,\nWooden dock stretches to the sea,\nSilent water mirrors.',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )