transformers/tests/models/llava_next/test_modeling_llava_next.py

# 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 copy
import unittest

import requests
from huggingface_hub import hf_hub_download
from parameterized import parameterized

from transformers import (
    AutoProcessor,
    LlavaNextConfig,
    LlavaNextForConditionalGeneration,
    LlavaNextModel,
    is_torch_available,
    is_vision_available,
)
from transformers.testing_utils import (
    cleanup,
    require_bitsandbytes,
    require_torch,
    slow,
    torch_device,
)
from transformers.utils import check_torch_load_is_safe

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

    from transformers.models.llava_next.modeling_llava_next import image_size_to_num_patches


if is_vision_available():
    from PIL import Image


class LlavaNextVisionText2TextModelTester:
    def __init__(
        self,
        parent,
        ignore_index=-100,
        image_token_index=0,
        projector_hidden_act="gelu",
        seq_length=7,
        vision_feature_select_strategy="default",
        vision_feature_layer=-1,
        text_config={
            "model_type": "llama",
            "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,
            "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": 1,
        },
        is_training=True,
        vision_config={
            "image_size": 8,
            "patch_size": 4,
            "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.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_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]]
        self.num_image_tokens = 24
        self.seq_length = seq_length + self.num_image_tokens
        self.encoder_seq_length = self.seq_length

    def get_config(self):
        return LlavaNextConfig(
            text_config=self.text_config,
            vision_config=self.vision_config,
            ignore_index=self.ignore_index,
            image_token_index=self.image_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,
            image_seq_length=self.num_image_tokens,
        )

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor(
            [
                self.batch_size,
                5,
                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

        inputs_dict = {
            "pixel_values": pixel_values,
            "image_sizes": torch.tensor(
                [[self.vision_config["image_size"], self.vision_config["image_size"]]] * self.batch_size
            ),
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }
        return config, inputs_dict


@require_torch
class LlavaNextForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
    """
    Model tester for `LlavaNextForConditionalGeneration`.
    """

    all_model_classes = (
        (
            LlavaNextModel,
            LlavaNextForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = {"image-text-to-text": LlavaNextForConditionalGeneration} if is_torch_available() else {}
    test_pruning = False
    test_head_masking = False
    _is_composite = True

    def setUp(self):
        self.model_tester = LlavaNextVisionText2TextModelTester(self)
        common_properties = ["image_token_index", "vision_feature_layer", "image_seq_length"]
        self.config_tester = ConfigTester(
            self, config_class=LlavaNextConfig, 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():
                if "image_newline" 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",
                    )

    # overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
    def test_inputs_embeds(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            inputs = self._prepare_for_class(inputs_dict, model_class)

            input_ids = inputs["input_ids"]
            del inputs["input_ids"]
            del inputs["pixel_values"]

            wte = model.get_input_embeddings()
            inputs["inputs_embeds"] = wte(input_ids)

            with torch.no_grad():
                model(**inputs)

    # overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
    # while some other models require pixel_values to be present
    def test_inputs_embeds_matches_input_ids(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            inputs = self._prepare_for_class(inputs_dict, model_class)
            input_ids = inputs["input_ids"]
            del inputs["input_ids"]
            del inputs["pixel_values"]

            inputs_embeds = model.get_input_embeddings()(input_ids)

            with torch.no_grad():
                out_ids = model(input_ids=input_ids, **inputs)[0]
                out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0]
            torch.testing.assert_close(out_embeds, out_ids)

    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 prompr 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)
            curr_input_dict = copy.deepcopy(input_dict)  # in=place modifications further
            _ = model(**curr_input_dict)  # successful forward with no modifications

            # remove one image but leave the image token in text
            curr_input_dict["pixel_values"] = curr_input_dict["pixel_values"][-1:, ...]
            curr_input_dict["image_sizes"] = curr_input_dict["image_sizes"][-1:, ...]
            with self.assertRaises(ValueError):
                _ = model(**curr_input_dict)

            # simulate multi-image case by concatenating inputs where each has exactly one image/image-token
            input_ids = curr_input_dict["input_ids"][:1]
            pixel_values = curr_input_dict["pixel_values"][:1]
            image_sizes = curr_input_dict["image_sizes"][:1]
            input_ids = torch.cat([input_ids, input_ids], dim=0)

            # one image and two image tokens raise an error
            with self.assertRaises(ValueError):
                _ = model(input_ids=input_ids, pixel_values=pixel_values, image_sizes=image_sizes)

            # two images and two image tokens don't raise an error
            pixel_values = torch.cat([pixel_values, pixel_values], dim=0)
            image_sizes = torch.cat([image_sizes, image_sizes], dim=0)
            _ = model(input_ids=input_ids, pixel_values=pixel_values, image_sizes=image_sizes)

    def test_odd_sized_image(self):
        # prepare model configuration
        config = self.model_tester.get_config()

        # prepare input
        num_image_tokens = 24
        pixel_values = floats_tensor([1, 5, 3, config.vision_config.image_size, config.vision_config.image_size])
        input_ids = ids_tensor([1, 64], config.text_config.vocab_size - 2) + 2
        input_ids[:, :num_image_tokens] = config.image_token_index
        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
        inputs_dict = {
            "pixel_values": pixel_values,
            "image_sizes": torch.tensor([[13, 16]]),  # odd-sized image
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }

        # 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, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(
        reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    def test_training_gradient_checkpointing_use_reentrant(self):
        pass

    @unittest.skip(
        reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
    )
    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 LlavaNextForConditionalGenerationIntegrationTest(unittest.TestCase):
    def setUp(self):
        self.processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
        url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
        self.image = Image.open(requests.get(url, stream=True).raw)

        self.prompt = "[INST] <image>\nWhat is shown in this image? [/INST]"

    def tearDown(self):
        cleanup(torch_device, gc_collect=True)

    @slow
    @require_bitsandbytes
    def test_small_model_integration_test(self):
        model = LlavaNextForConditionalGeneration.from_pretrained(
            "llava-hf/llava-v1.6-mistral-7b-hf",
            load_in_4bit=True,
        )

        inputs = self.processor(images=self.image, text=self.prompt, return_tensors="pt")

        # verify inputs against original implementation
        filepath = hf_hub_download(
            repo_id="nielsr/test-image",
            filename="llava_1_6_input_ids.pt",
            repo_type="dataset",
        )
        check_torch_load_is_safe()
        original_input_ids = torch.load(filepath, map_location="cpu", weights_only=True)
        # replace -200 by image_token_index (since we use token ID = 32000 for the image token)
        # remove image token indices because HF impl expands image tokens `image_seq_length` times
        original_input_ids = original_input_ids[original_input_ids != -200]
        observed_input_ids = inputs.input_ids[inputs.input_ids != model.config.image_token_index]
        assert original_input_ids[0].tolist() == observed_input_ids[0].tolist()

        filepath = hf_hub_download(
            repo_id="nielsr/test-image",
            filename="llava_1_6_pixel_values.pt",
            repo_type="dataset",
        )
        check_torch_load_is_safe()
        original_pixel_values = torch.load(filepath, map_location="cpu", weights_only=True)
        assert torch.allclose(original_pixel_values, inputs.pixel_values.half())

        # verify generation
        output = model.generate(**inputs, max_new_tokens=100)
        EXPECTED_DECODED_TEXT = '[INST]  \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot that displays values for multiple quantitative variables represented on axes starting from the same point. This particular radar chart is showing the performance of various models or systems across different metrics or datasets.\n\nThe chart is divided into several sections, each representing a different model or dataset. The axes represent different metrics or datasets, such as "MMM-Vet," "MMM-Bench," "L'  # 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(self):
        model = LlavaNextForConditionalGeneration.from_pretrained(
            "llava-hf/llava-v1.6-mistral-7b-hf", load_in_4bit=True
        )
        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        cats_image = Image.open(requests.get(url, stream=True).raw)

        inputs = self.processor(
            images=[self.image, cats_image],
            text=[self.prompt, self.prompt],
            return_tensors="pt",
            padding=True,
        ).to(torch_device)

        # it should not matter whether two images are the same size or not
        output = model.generate(**inputs, max_new_tokens=20)

        EXPECTED_DECODED_TEXT = ['[INST]  \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot that displays', '[INST]  \nWhat is shown in this image? [/INST] The image shows two cats lying on a pink surface, which appears to be a couch or a cush']  # 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_unk_token(self):
        # related to (#29835)
        model = LlavaNextForConditionalGeneration.from_pretrained(
            "llava-hf/llava-v1.6-mistral-7b-hf",
            load_in_4bit=True,
        )

        prompt_with_unk = "[INST] <image>\nWhat is shown in this <unk> image? [/INST]"
        inputs = self.processor(images=self.image, text=prompt_with_unk, return_tensors="pt")

        # verify single forward pass
        inputs = inputs.to(torch_device)
        with torch.no_grad():
            output = model(**inputs)

        # verify generation
        output = model.generate(**inputs, max_new_tokens=40)
        EXPECTED_DECODED_TEXT = '[INST]  \nWhat is shown in this   image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot that displays values for multiple quantitative variables represented on axes starting from the same point. This particular radar chart'  # 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 = LlavaNextForConditionalGeneration.from_pretrained(
            "llava-hf/llava-v1.6-mistral-7b-hf",
            load_in_4bit=True,
        )

        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(
            images=[lowres_img, cats_image], text=[self.prompt, self.prompt], return_tensors="pt", padding=True
        ).to(torch_device)
        pixel_values = inputs["pixel_values"]

        # verify pixel values are padded correctly with 0 when one image has more num_patches than the other
        image_num_patches = [
            image_size_to_num_patches(
                image_size=imsize,
                grid_pinpoints=model.config.image_grid_pinpoints,
                patch_size=model.config.vision_config.image_size,
            )
            for imsize in inputs["image_sizes"]
        ]
        for pix_val, num_patch in zip(pixel_values, image_num_patches):
            self.assertTrue(torch.all(pix_val[num_patch:] == 0))  # pad on the right
            for i in range(num_patch):
                self.assertFalse(torch.all(pix_val[i : i + 1] == 0))  # no padding expected in any of patches

        # verify generation
        output = model.generate(**inputs, max_new_tokens=50)
        EXPECTED_DECODED_TEXT = '[INST]  \nWhat is shown in this image? [/INST] The image shows two deer, likely fawns, in a grassy area with trees in the background. The setting appears to be a forest or woodland, and the time of day seems to be either dawn or dusk, given the soft'  # 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_matches_single(self):
        model = LlavaNextForConditionalGeneration.from_pretrained(
            "llava-hf/llava-v1.6-mistral-7b-hf",
            load_in_4bit=True,
        )

        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(
            images=[lowres_img, cats_image], text=[self.prompt, self.prompt], return_tensors="pt", padding=True
        ).to(torch_device)

        inputs_single = self.processor(images=lowres_img, text=self.prompt, return_tensors="pt", padding=True).to(
            torch_device
        )

        # 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),
        )

    @slow
    @require_bitsandbytes
    def test_small_model_integration_test_full_vision_state_selection(self):
        model = LlavaNextForConditionalGeneration.from_pretrained(
            "llava-hf/llava-v1.6-mistral-7b-hf",
            load_in_4bit=True,
        )
        # test that changing `strategy` won't error out
        model.vision_feature_select_strategy = "full"

        inputs = self.processor(self.prompt, self.image, return_tensors="pt").to(model.device)

        # verify generation
        output = model.generate(**inputs, max_new_tokens=30)
        EXPECTED_DECODED_TEXT = '[INST]  \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot that displays values for multiple quantitative variables represented on axes'  # fmt: skip

        self.assertEqual(
            self.processor.decode(output[0], skip_special_tokens=True),
            EXPECTED_DECODED_TEXT,
        )

    @slow
    def test_granite_vision(self):
        """
        Check the expected output of a granite vision model, which leverages
        multiple vision feature layers and a visual encoder with no CLS (siglip).
        """
        granite_model_path = "ibm-granite/granite-vision-3.1-2b-preview"
        model = LlavaNextForConditionalGeneration.from_pretrained(granite_model_path)
        self.processor = AutoProcessor.from_pretrained(granite_model_path)
        prompt = "<|user|>\n<image>\nWhat is shown in this image?\n<|assistant|>\n"
        inputs = self.processor(prompt, self.image, return_tensors="pt").to(model.device)

        # verify generation
        output = model.generate(**inputs, max_new_tokens=30)
        EXPECTED_DECODED_TEXT = "<|user|>\n\nWhat is shown in this image?\n<|assistant|>\nThe image displays a radar chart comparing the performance of various machine learning models."  # fmt: skip
        self.assertEqual(
            self.processor.decode(output[0], skip_special_tokens=True),
            EXPECTED_DECODED_TEXT,
        )