first commit

tests/models/qwen2_vl/test_modeling_qwen2_vl.py

@@ -0,0 +1,721 @@
+# 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 Qwen2-VL model."""
+
+import copy
+import gc
+import tempfile
+import unittest
+
+import pytest
+import requests
+
+from transformers import (
+    AutoProcessor,
+    Qwen2VLConfig,
+    Qwen2VLForConditionalGeneration,
+    Qwen2VLModel,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.testing_utils import (
+    Expectations,
+    backend_empty_cache,
+    require_flash_attn,
+    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,
+    floats_tensor,
+    ids_tensor,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+class Qwen2VLVisionText2TextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        seq_length=7,
+        num_channels=3,
+        ignore_index=-100,
+        image_size=14,
+        text_config={
+            "bos_token_id": 0,
+            "eos_token_id": 1,
+            "pad_token_id": 2,
+            "hidden_act": "silu",
+            "hidden_size": 32,
+            "vocab_size": 99,
+            "intermediate_size": 37,
+            "max_position_embeddings": 512,
+            "max_window_layers": 3,
+            "num_attention_heads": 4,
+            "num_hidden_layers": 2,
+            "num_key_value_heads": 2,
+            "rope_theta": 10000,
+            "tie_word_embeddings": True,
+            "rope_parameters": {"type": "mrope", "mrope_section": [2, 1, 1]},
+        },
+        vision_start_token_id=3,
+        image_token_id=4,
+        video_token_id=5,
+        is_training=True,
+        vision_config={
+            "depth": 2,
+            "embed_dim": 32,
+            "hidden_act": "quick_gelu",
+            "hidden_size": 32,
+            "mlp_ratio": 4,
+            "num_heads": 4,
+            "patch_size": 14,
+            "spatial_merge_size": 1,
+            "temporal_patch_size": 2,
+        },
+    ):
+        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.num_hidden_layers = text_config["num_hidden_layers"]
+        self.num_attention_heads = text_config["num_attention_heads"]
+        self.hidden_size = text_config["hidden_size"]
+        self.vision_start_token_id = vision_start_token_id
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.is_training = is_training
+        self.vocab_size = text_config["vocab_size"]
+        self.num_image_tokens = 32
+        self.seq_length = seq_length + self.num_image_tokens
+
+    def get_config(self):
+        return Qwen2VLConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            vision_start_token_id=self.vision_start_token_id,
+            image_token_id=self.image_token_id,
+            video_token_id=self.video_token_id,
+        )
+
+    def prepare_config_and_inputs(self):
+        config = self.get_config()
+        patch_size = config.vision_config.patch_size
+        temporal_patch_size = config.vision_config.temporal_patch_size
+        pixel_values = floats_tensor(
+            [
+                self.batch_size * (self.image_size**2) // (patch_size**2),
+                self.num_channels * (patch_size**2) * temporal_patch_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[:, -1] = self.pad_token_id
+        attention_mask[:, -1] = 0
+        input_ids[input_ids == self.video_token_id] = self.pad_token_id
+        input_ids[input_ids == self.image_token_id] = self.pad_token_id
+        input_ids[input_ids == self.vision_start_token_id] = self.pad_token_id
+        input_ids[:, self.num_image_tokens] = self.image_token_id
+        input_ids[:, self.num_image_tokens - 1] = self.vision_start_token_id
+
+        mm_token_type_ids = torch.zeros_like(input_ids)
+        mm_token_type_ids[:, self.num_image_tokens] = 1
+
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "image_grid_thw": torch.tensor([[1, 1, 1]] * self.batch_size, device=torch_device),
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "mm_token_type_ids": mm_token_type_ids,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class Qwen2VLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Model tester for `Qwen2VLForConditionalGeneration`.
+    """
+
+    all_model_classes = (
+        (
+            Qwen2VLModel,
+            Qwen2VLForConditionalGeneration,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = {
+        "image-text-to-text": Qwen2VLForConditionalGeneration,
+        "any-to-any": Qwen2VLForConditionalGeneration,
+    }
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = Qwen2VLVisionText2TextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Qwen2VLConfig, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_mismatching_num_image_tokens(self):
+        """
+        Tests that VLMs through an error with explicit message saying what is wrong
+        when number of images don't match number of image tokens in the text.
+        Also we need to test multi-image cases when one prompt has multiple image tokens.
+        """
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device)
+            model.eval()
+            curr_input_dict = copy.deepcopy(input_dict)
+            _ = model(**curr_input_dict)  # successful forward with no modifications
+
+            # remove one image but leave the image token in text
+            patch_size = config.vision_config.patch_size
+            one_img_length = (self.model_tester.image_size**2) // (patch_size**2)
+            curr_input_dict["pixel_values"] = curr_input_dict["pixel_values"][-one_img_length:, ...]
+            curr_input_dict["image_grid_thw"] = curr_input_dict["image_grid_thw"][-1:, ...]
+            with self.assertRaisesRegex(ValueError, "Image features and image tokens do not match"):
+                _ = model(**curr_input_dict)
+
+            model.base_model.rope_deltas = None
+            # simulate multi-image case by concatenating inputs where each has exactly one image/image-token
+            input_ids = curr_input_dict["input_ids"][:1]
+            mm_token_type_ids = curr_input_dict["mm_token_type_ids"][:1]
+            pixel_values = curr_input_dict["pixel_values"][:one_img_length]
+            image_grid_thw = curr_input_dict["image_grid_thw"][:1]
+            input_ids = torch.cat([input_ids, input_ids], dim=0)
+            mm_token_type_ids = torch.cat([mm_token_type_ids, mm_token_type_ids], dim=0)
+            with self.assertRaisesRegex(ValueError, "Image features and image tokens do not match"):
+                _ = model(
+                    input_ids=input_ids,
+                    pixel_values=pixel_values,
+                    image_grid_thw=image_grid_thw,
+                    mm_token_type_ids=mm_token_type_ids,
+                )
+
+            model.base_model.rope_deltas = None
+            # two images and two image tokens don't raise an error
+            pixel_values = torch.cat([pixel_values, pixel_values], dim=0)
+            image_grid_thw = torch.cat([image_grid_thw, image_grid_thw], dim=0)
+            _ = model(
+                input_ids=input_ids,
+                pixel_values=pixel_values,
+                image_grid_thw=image_grid_thw,
+                mm_token_type_ids=mm_token_type_ids,
+            )
+
+    def test_forward_with_rope_deltas_cached(self):
+        """
+        Tests that Qwen2-VL computes new rope deltas every forward pass with new set of inputs.
+        Rope deltas are cached when we generate and re-used for decoding phase, byt are not reset
+        automatically after generation ends. See https://github.com/huggingface/transformers/pull/36013 for more
+        """
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_generative_model_classes:
+            model = model_class(config).to(torch_device)
+
+            # Generate and make sure rope_deltas are not `None`
+            self.assertTrue(model.model.rope_deltas is None)
+            generation_output = model.generate(
+                **input_dict, max_new_tokens=4, return_dict_in_generate=True, output_logits=True
+            )
+            self.assertTrue(model.model.rope_deltas is not None)
+
+            # Now if we try to do forward pass, we should get new rope logits, because cache is not passed
+            forward_output = model(**input_dict)
+            torch.testing.assert_close(
+                generation_output.logits[0], forward_output.logits[:, -1, :], rtol=1e-4, atol=1e-4
+            )
+
+            # Same happens if we call `generate` API instead of `forward`
+            generation_output_second = model.generate(
+                **input_dict, max_new_tokens=10, return_dict_in_generate=True, output_logits=True
+            )
+            torch.testing.assert_close(
+                generation_output.logits[0], generation_output_second.logits[0], rtol=1e-4, atol=1e-4
+            )
+
+    def test_vision_position_ids(self):
+        """
+        Tests that vision position ids are built correctly for images and for videos.
+        See https://github.com/huggingface/transformers/pull/45400
+        """
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        model = Qwen2VLModel(config).to(torch_device)
+        batch_size = input_dict["input_ids"].shape[0]
+
+        # Test most simple case when num_image_tokens == 1. Position ids will be sunsequent and text-like
+        position_ids = model.get_rope_index(
+            input_dict["input_ids"], input_dict["mm_token_type_ids"], input_dict["image_grid_thw"]
+        )[0]
+        expected_positions = torch.arange(39)[None, None, :].repeat(3, batch_size, 1)
+        self.assertListEqual(list(position_ids.shape), [3, batch_size, 39])
+        self.assertListEqual(position_ids.tolist(), expected_positions.tolist())
+
+        # Each image encodes to more than 1 token (i.e. 4 height and 3 width patches = 12 tokens)
+        image_token_id = config.image_token_id
+        pad_token_id = config.text_config.pad_token_id
+        input_ids = torch.tensor([[pad_token_id] + [image_token_id] * 12 + [pad_token_id]], device=torch_device)
+        mm_token_type_ids = torch.tensor([[0] + [1] * 12 + [0]], device=torch_device)
+        image_grid_thw = torch.tensor([[1, 4, 3]], device=torch_device)
+        position_ids = model.get_rope_index(input_ids, mm_token_type_ids, image_grid_thw)[0]
+        expected_positions = torch.tensor(
+            [
+                [[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5]],
+                [[0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5]],
+                [[0, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 5]],
+            ]
+        )
+
+        self.assertListEqual(list(position_ids.shape), [3, 1, 14])
+        self.assertListEqual(position_ids.tolist(), expected_positions.tolist())
+
+        # Check video position ids with 2 frames, and 4 height, 3 width patches (= 12 * 2 tokens)
+        video_token_id = config.video_token_id
+        input_ids = torch.tensor([[pad_token_id] + [video_token_id] * 24 + [pad_token_id]], device=torch_device)
+        mm_token_type_ids = torch.tensor([[0] + [2] * 24 + [0]], device=torch_device)
+        video_grid_thw = torch.tensor([[2, 4, 3]], device=torch_device)
+        position_ids = model.get_rope_index(input_ids, mm_token_type_ids, video_grid_thw=video_grid_thw)[0]
+        expected_positions = torch.tensor(
+            [
+                [[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5]],
+                [[0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5]],
+                [[0, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 5]],
+            ]
+        )
+
+        self.assertListEqual(list(position_ids.shape), [3, 1, 26])
+        self.assertListEqual(position_ids.tolist(), expected_positions.tolist())
+
+    def attention_mask_padding_matches_padding_free_with_position_ids(
+        self, attn_implementation: str, fa_kwargs: bool = False
+    ):
+        max_new_tokens = 30
+        for model_class in self.all_generative_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            dummy_input = inputs_dict[model_class.main_input_name]
+            if dummy_input.dtype in [torch.float32, torch.float16]:
+                dummy_input = dummy_input.to(torch.bfloat16)
+
+            # make sure that all models have enough positions for generation
+            if hasattr(config, "max_position_embeddings"):
+                config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1
+
+            model = model_class(config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+
+                if 0 in inputs_dict["attention_mask"][:, -1]:
+                    inputs_dict["attention_mask"] = inputs_dict["attention_mask"].flip(1)
+                dummy_attention_mask = inputs_dict["attention_mask"]
+                inputs_dict["input_ids"][~dummy_attention_mask.bool()] = config.get_text_config().pad_token_id
+
+                model = (
+                    model_class.from_pretrained(
+                        tmpdirname,
+                        dtype=torch.bfloat16,
+                        attn_implementation=attn_implementation,
+                    )
+                    .to(torch_device)
+                    .eval()
+                )
+
+                # flatten
+                padfree_inputs_dict = {
+                    "pixel_values": inputs_dict["pixel_values"],
+                    "image_grid_thw": inputs_dict["image_grid_thw"],
+                    "input_ids": inputs_dict["input_ids"][dummy_attention_mask.bool()].unsqueeze(0),
+                }
+
+                # add position_ids
+                vision_position_ids, deltas = model.model.get_rope_index(
+                    input_ids=inputs_dict["input_ids"],
+                    image_grid_thw=inputs_dict["image_grid_thw"],
+                    attention_mask=inputs_dict["attention_mask"],
+                    mm_token_type_ids=inputs_dict["mm_token_type_ids"],
+                )  # [3, bs, padded-seq-len]
+                vision_padfree_positions = vision_position_ids[:, dummy_attention_mask.bool()].view(
+                    3, -1
+                )  # [3, bs*padfree-len]
+                text_padfree_positions = torch.cat(
+                    [torch.arange(length) for length in dummy_attention_mask.sum(1).tolist()]
+                )  # [1, bs*padfree-len]
+                text_padfree_positions = text_padfree_positions.long().unsqueeze(0).to(torch_device)
+                padfree_inputs_dict["position_ids"] = torch.cat([text_padfree_positions, vision_padfree_positions])[
+                    :, None, :
+                ]
+
+                if fa_kwargs:
+                    cu_seq_lens = [0] + dummy_attention_mask.sum(1).tolist()
+                    cu_seq_lens = torch.tensor(cu_seq_lens, device=torch_device)
+                    max_length = cu_seq_lens.diff().max().item()
+                    padfree_inputs_dict.update(
+                        {
+                            "cu_seq_lens_q": cu_seq_lens.cumsum(-1).to(dtype=torch.int32),
+                            "cu_seq_lens_k": cu_seq_lens.cumsum(-1).to(dtype=torch.int32),
+                            "max_length_q": max_length,
+                            "max_length_k": max_length,
+                        }
+                    )
+
+                # We need to do simple forward without cache in roder to trigger packed SDPA/FLEX/EAGER path
+                res_padded = model(**inputs_dict, use_cache=False)
+                res_padfree = model(**padfree_inputs_dict, use_cache=False)
+
+                logits_padded = res_padded.logits[inputs_dict["attention_mask"].bool()]
+                logits_padfree = res_padfree.logits[0]
+
+                # acceptable numerical instability
+                tol = torch.finfo(torch.bfloat16).eps
+                torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol)
+
+    @unittest.skip(reason="Feedforward chunking is not yet supported")
+    def test_feed_forward_chunking(self):
+        pass
+
+    @unittest.skip(reason="CPU offload is not yet supported")
+    def test_cpu_offload(self):
+        pass
+
+    @unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
+    def test_disk_offload_bin(self):
+        pass
+
+    @unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
+    def test_disk_offload_safetensors(self):
+        pass
+
+    @unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
+    def test_model_parallelism(self):
+        pass
+
+    @unittest.skip(reason="Compile not yet supported because in Qwen2VL models")
+    def test_sdpa_can_dispatch_on_flash(self):
+        pass
+
+    @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+    def test_enable_input_require_grads_with_gradient_checkpointing(self):
+        if not self.model_tester.is_training:
+            self.skipTest(reason="ModelTester not in training mode")
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.use_cache = False
+        config.return_dict = True
+
+        for model_class in self.all_model_classes:
+            if not model_class.supports_gradient_checkpointing:
+                continue
+
+            model = model_class(config)
+            model.to(torch_device)
+            model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
+            model.enable_input_require_grads()
+            model.train()
+
+            for parameter in model.parameters():
+                parameter.requires_grad = False
+
+            vision_module = None
+            if hasattr(model, "visual"):
+                vision_module = model.visual
+            elif hasattr(model, "model") and hasattr(model.model, "visual"):
+                vision_module = model.model.visual
+
+            if vision_module is None:
+                continue
+
+            target_linear = vision_module.blocks[0].attn.qkv
+            target_linear.weight.requires_grad = True
+            if target_linear.bias is not None:
+                target_linear.bias.requires_grad = True
+
+            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            outputs = model(**inputs)
+
+            if hasattr(outputs, "loss") and outputs.loss is not None:
+                loss = outputs.loss
+            else:
+                logits = outputs.logits if hasattr(outputs, "logits") else outputs[0]
+                loss = logits.sum()
+
+            loss.backward()
+
+            self.assertIsNotNone(
+                target_linear.weight.grad,
+                f"qkv weights should receive gradients when enable_input_require_grads is used with gradient checkpointing. Model: {model_class.__name__}",
+            )
+            self.assertGreater(
+                target_linear.weight.grad.abs().sum().item(),
+                0,
+                f"qkv weights should have non-zero gradients when enable_input_require_grads is used with gradient checkpointing. Model: {model_class.__name__}",
+            )
+
+
+@require_torch
+class Qwen2VLIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
+        self.messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What kind of dog is this?"},
+                ],
+            }
+        ]
+        url = "https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/demo_small.jpg"
+        self.image = Image.open(requests.get(url, stream=True).raw)
+
+    def tearDown(self):
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    @slow
+    def test_small_model_integration_test(self):
+        model = Qwen2VLForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2-VL-7B-Instruct", dtype="auto", device_map="auto"
+        )
+
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(text=[text], images=[self.image], return_tensors="pt")
+
+        expected_input_ids = [151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 151652, 151655, 151655]  # fmt: skip
+        assert expected_input_ids == inputs.input_ids[0].tolist()[:17]
+
+        expected_pixel_slice = torch.tensor(
+            [
+                [0.8792, 0.8792, 0.9084],
+                [1.1858, 1.1858, 1.2296],
+                [1.2004, 1.2004, 1.2150],
+                [1.4340, 1.4340, 1.4194],
+                [1.3902, 1.4048, 1.4194],
+                [1.5216, 1.5362, 1.5362],
+            ],
+            dtype=torch.float32,
+            device="cpu",
+        )
+        assert torch.allclose(expected_pixel_slice, inputs.pixel_values[:6, :3], atol=3e-3)
+
+        # verify generation
+        inputs = inputs.to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=30)
+        EXPECTED_DECODED_TEXT = "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices"
+
+        self.assertEqual(
+            self.processor.decode(output[0], skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    def test_small_model_integration_test_batch(self):
+        model = Qwen2VLForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2-VL-7B-Instruct", dtype="auto", device_map="auto"
+        )
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(text=[text, text], images=[self.image, self.image], return_tensors="pt").to(
+            torch_device
+        )
+
+        # it should not matter whether two images are the same size or not
+        output = model.generate(**inputs, max_new_tokens=30)
+
+        EXPECTED_DECODED_TEXT = [
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+        ]  # fmt: skip
+        self.assertEqual(
+            self.processor.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    def test_small_model_integration_test_expand(self):
+        model = Qwen2VLForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2-VL-7B-Instruct", dtype="auto", device_map="auto"
+        )
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(text=[text], images=[self.image], return_tensors="pt").to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=30, num_return_sequences=3)
+
+        EXPECTED_DECODED_TEXT = [
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+        ]  # fmt: skip
+        self.assertEqual(
+            self.processor.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    def test_small_model_integration_test_batch_wo_image(self):
+        model = Qwen2VLForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2-VL-7B-Instruct", dtype="auto", device_map="auto"
+        )
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        messages2 = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Who are you?"},
+        ]
+        text2 = self.processor.apply_chat_template(messages2, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(text=[text, text2], images=[self.image], padding=True, return_tensors="pt").to(
+            torch_device
+        )
+
+        # it should not matter whether two images are the same size or not
+        output = model.generate(**inputs, max_new_tokens=30)
+
+        EXPECTED_DECODED_TEXT = [
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+            'system\nYou are a helpful assistant.\nuser\nWho are you?\nassistant\nI am a large language model created by Alibaba Cloud. I am called Qwen.'
+        ]  # fmt: skip
+        self.assertEqual(
+            self.processor.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    def test_small_model_integration_test_batch_different_resolutions(self):
+        model = Qwen2VLForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2-VL-7B-Instruct", dtype="auto", device_map="auto"
+        )
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        text2 = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        image2 = self.image.resize((224, 224))
+        inputs = self.processor(text=[text, text2], images=[self.image, image2], padding=True, return_tensors="pt").to(
+            torch_device
+        )
+
+        # it should not matter whether two images are the same size or not
+        output = model.generate(**inputs, max_new_tokens=30)
+        DECODED_TEXT = self.processor.batch_decode(output, skip_special_tokens=True)
+
+        EXPECTED_DECODED_TEXTS = Expectations(
+            {
+                ("xpu", 3): [
+                    'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+                    'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+                ],
+                ("cuda", None): [
+                    'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+                    'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular pets',
+                ],
+                ("cuda", 8): [
+                    'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+                    'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices'
+                ],
+            }
+        )  # fmt: skip
+        EXPECTED_DECODED_TEXT = EXPECTED_DECODED_TEXTS.get_expectation()
+
+        self.assertEqual(DECODED_TEXT, EXPECTED_DECODED_TEXT)
+
+    @slow
+    @require_flash_attn
+    @require_torch_accelerator
+    @pytest.mark.flash_attn_test
+    def test_small_model_integration_test_batch_flashatt2(self):
+        model = Qwen2VLForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2-VL-7B-Instruct",
+            dtype=torch.bfloat16,
+            attn_implementation="flash_attention_2",
+            device_map="auto",
+        )
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(text=[text, text], images=[self.image, self.image], return_tensors="pt").to(
+            torch_device
+        )
+
+        # it should not matter whether two images are the same size or not
+        output = model.generate(**inputs, max_new_tokens=30)
+
+        EXPECTED_DECODED_TEXT = [
+            "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices",
+            "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices",
+        ]
+        self.assertEqual(
+            self.processor.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    @require_flash_attn
+    @require_torch_accelerator
+    @pytest.mark.flash_attn_test
+    def test_small_model_integration_test_batch_wo_image_flashatt2(self):
+        model = Qwen2VLForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2-VL-7B-Instruct",
+            dtype=torch.bfloat16,
+            attn_implementation="flash_attention_2",
+            device_map="auto",
+        )
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        messages2 = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Who are you?"},
+        ]
+        text2 = self.processor.apply_chat_template(messages2, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(text=[text, text2], images=[self.image], padding=True, return_tensors="pt").to(
+            torch_device
+        )
+
+        # it should not matter whether two images are the same size or not
+        output = model.generate(**inputs, max_new_tokens=30)
+
+        EXPECTED_DECODED_TEXT = [
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+            'system\nYou are a helpful assistant.\nuser\nWho are you?\nassistant\nI am a large language model created by Alibaba Cloud. I am called Qwen.'
+        ]  # fmt: skip
+
+        self.assertEqual(
+            self.processor.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )