huggingface--transformers/tests/models/idefics3/test_processing_idefics3.py

# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

import numpy as np

from transformers import Idefics3Processor
from transformers.image_utils import load_image
from transformers.testing_utils import require_torch, require_vision

from ...test_processing_common import ProcessorTesterMixin, url_to_local_path


@require_torch
@require_vision
class Idefics3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
    processor_class = Idefics3Processor
    # Tiny processor created with make_tiny_processor.py from "HuggingFaceM4/Idefics3-8B-Llama3"
    tiny_model_id = "hf-internal-testing/tiny-processor-idefics3"
    # Default 76 is too small: idefics3 with the tiny tokenizer expands <image> to ~78 tokens, then with
    # surrounding text tokens we exceed 76, truncation cuts through image tokens, and _check_special_mm_tokens
    # raises a mismatch error.
    image_unstructured_max_length = 100

    def get_processor(self):
        processor = self.processor_class.from_pretrained(self.tmpdirname)
        processor.tokenizer.add_bos_token = True
        processor.tokenizer.add_eos_token = False
        return processor

    @classmethod
    def _setup_test_attributes(cls, processor):
        cls.image1 = load_image(
            url_to_local_path(
                "https://huggingface.co/datasets/hf-internal-testing/test-videos/resolve/main/statue_of_liberty_64x64.jpg"
            )
        )
        cls.image2 = load_image(
            url_to_local_path(
                "https://huggingface.co/datasets/hf-internal-testing/test-videos/resolve/main/chicago_64x64.jpg"
            )
        )
        cls.image3 = load_image(
            url_to_local_path(
                "https://huggingface.co/datasets/hf-internal-testing/test-videos/resolve/main/golden_gate_64x64.jpg"
            )
        )
        cls.bos_token = processor.tokenizer.bos_token
        cls.image_token = processor.image_token
        cls.fake_image_token = processor.fake_image_token
        cls.global_img_token = processor.global_image_tag

        cls.bos_token_id = processor.tokenizer.convert_tokens_to_ids(cls.bos_token)
        cls.image_token_id = processor.tokenizer.convert_tokens_to_ids(cls.image_token)
        cls.fake_image_token_id = processor.tokenizer.convert_tokens_to_ids(cls.fake_image_token)
        cls.global_img_tokens_id = processor.tokenizer(cls.global_img_token, add_special_tokens=False)["input_ids"]
        cls.padding_token_id = processor.tokenizer.pad_token_id
        cls.image_seq_len = processor.image_seq_len

    @staticmethod
    def prepare_processor_dict():
        return {"image_seq_len": 2}

    def test_get_num_multimodal_tokens_matches_processor_call(self):
        "Tests that the helper used internally in vLLM works correctly"

        image_sizes = [(100, 100), (300, 100), (500, 30), (213, 167)]
        image_inputs = []
        for h, w in image_sizes:
            image_inputs.append(np.random.randint(255, size=(h, w, 3), dtype=np.uint8))

        # Idefics3 checkpoints aren't supported on purpose. Idefics3 encodes special row/col
        # tokens as several token ids because they aren't added in `special_token_ids`. Thus
        # we can't correctly infer which tokens in input ids are used as placeholders for image/row/col!
        # Use the tiny SmolVLM processor (same architecture, row/col tokens are proper special tokens).
        base_processor = self.processor_class.from_pretrained(
            "hf-internal-testing/tiny-processor-smolvlm",
            add_bos_token=True,
            add_eos_token=False,
            padding_side="left",
            image_seq_len=2,
        )
        # size=1024 (2×2=5 tiles) instead of default 2048 (4×4=17 tiles) to speed up image processing.
        base_processor.image_processor.size = {"longest_edge": 1024}
        for do_image_splitting in [False, True]:
            with self.subTest(do_image_splitting=do_image_splitting):
                processor = base_processor
                processor.image_processor.do_image_splitting = do_image_splitting

                text = [f"This is an image {processor.image_token}"] * len(image_inputs)
                inputs = processor(
                    text=text, images=image_inputs, padding=True, return_mm_token_type_ids=True, return_tensors="pt"
                )

                num_image_tokens_from_call = inputs.mm_token_type_ids.sum(-1).tolist()
                num_image_tokens_from_helper = processor._get_num_multimodal_tokens(image_sizes=image_sizes)
                self.assertListEqual(num_image_tokens_from_call, num_image_tokens_from_helper["num_image_tokens"])

                # Test with two images per single text
                text = [f"These are two images {processor.image_token}{processor.image_token}"] * len(image_inputs)
                inputs = processor(
                    text=text,
                    images=image_inputs * 2,
                    padding=True,
                    return_mm_token_type_ids=True,
                    return_tensors="pt",
                )

                num_image_tokens_from_call = inputs.mm_token_type_ids.sum(-1).tolist()
                num_image_tokens_from_helper = processor._get_num_multimodal_tokens(image_sizes=image_sizes * 2)
                self.assertEqual(
                    sum(num_image_tokens_from_call), sum(num_image_tokens_from_helper["num_image_tokens"])
                )

    def get_split_image_expected_tokens(self, processor, image_rows, image_cols):
        text_split_images = []
        for n_h in range(image_rows):
            for n_w in range(image_cols):
                text_split_images += (
                    [self.fake_image_token_id]
                    + processor.tokenizer(f"<row_{n_h + 1}_col_{n_w + 1}>", add_special_tokens=False)["input_ids"]
                    + [self.image_token_id] * self.image_seq_len
                )
            text_split_images += processor.tokenizer("\n", add_special_tokens=False)["input_ids"]
        text_split_images = text_split_images[:-1]  # remove last newline
        # add double newline, as it gets its own token
        text_split_images += processor.tokenizer("\n\n", add_special_tokens=False)["input_ids"]
        text_split_images += (
            [self.fake_image_token_id]
            + self.global_img_tokens_id
            + [self.image_token_id] * self.image_seq_len
            + [self.fake_image_token_id]
        )
        return text_split_images

    def test_process_interleaved_images_prompts_no_image_splitting(self):
        processor = self.get_processor()
        processor.image_processor.do_image_splitting = False

        # Test that a single image is processed correctly
        inputs = processor(images=self.image1)
        image1_expected_size = (364, 364)
        self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 1, 3, *image1_expected_size))
        self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (1, 1, *image1_expected_size))
        # fmt: on

        # Test a single sample with image and text
        image_str = "<image>"
        text_str = "In this image, we see"
        text = image_str + text_str
        inputs = processor(text=text, images=self.image1)

        # fmt: off
        tokenized_sentence = processor.tokenizer(text_str, add_special_tokens=False)
        expected_input_ids = [[self.bos_token_id] + [self.fake_image_token_id] + self.global_img_tokens_id + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] + tokenized_sentence["input_ids"]]
        self.assertEqual(inputs["input_ids"], expected_input_ids)
        self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids[0])])
        self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 1, 3, *image1_expected_size))
        self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (1, 1, *image1_expected_size))
        # fmt: on

        # Test that batch is correctly processed
        image_str = "<image>"
        text_str_1 = "In this image, we see"
        text_str_2 = "In this image, we see"

        text = [
            image_str + text_str_1,
            image_str + image_str + text_str_2,
        ]
        images = [[self.image1], [self.image2, self.image3]]

        inputs = processor(text=text, images=images, padding=True)

        # fmt: off
        tokenized_sentence_1 = processor.tokenizer(text_str_1, add_special_tokens=False)
        tokenized_sentence_2 = processor.tokenizer(text_str_2, add_special_tokens=False)
        image_tokens = [self.fake_image_token_id] + self.global_img_tokens_id + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id]
        expected_input_ids_1 = [self.bos_token_id] + image_tokens + tokenized_sentence_1["input_ids"]
        expected_input_ids_2 = [self.bos_token_id] + 2 * image_tokens + tokenized_sentence_2["input_ids"]
        # Pad the first input to match the second input
        pad_len = len(expected_input_ids_2) - len(expected_input_ids_1)
        padded_expected_input_ids_1 = [self.padding_token_id] * pad_len + expected_input_ids_1

        self.assertEqual(
            inputs["input_ids"], [padded_expected_input_ids_1, expected_input_ids_2]
        )
        self.assertEqual(
            inputs["attention_mask"],
            [[0] * pad_len + [1] * len(expected_input_ids_1), [1] * len(expected_input_ids_2)]
        )
        self.assertEqual(np.array(inputs['pixel_values']).shape, (2, 2, 3, 364, 364))
        self.assertEqual(np.array(inputs['pixel_attention_mask']).shape, (2, 2, 364, 364))
        # fmt: on

    def test_process_interleaved_images_prompts_image_splitting(self):
        processor = self.get_processor()
        processor.image_processor.do_image_splitting = True

        # Test that a single image is processed correctly
        # 64x64 square input → 4×4 tile split (max square) + 1 global = 17 tiles total
        inputs = processor(images=self.image1)
        self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 17, 3, 364, 364))
        self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (1, 17, 364, 364))
        # fmt: on
        self.maxDiff = None

        # Test a single sample with image and text
        image_str = "<image>"
        text_str = "In this image, we see"
        text = image_str + text_str
        inputs = processor(text=text, images=self.image1)

        # fmt: off
        tokenized_sentence = processor.tokenizer(text_str, add_special_tokens=False)
        split_image1_tokens = self.get_split_image_expected_tokens(processor, 4, 4)
        expected_input_ids_1 = [[self.bos_token_id] + split_image1_tokens + tokenized_sentence["input_ids"]]
        self.assertEqual(inputs["input_ids"], expected_input_ids_1)
        self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids_1[0])])
        self.assertEqual(np.array(inputs["pixel_values"]).shape, (1, 17, 3, 364, 364))
        self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (1, 17, 364, 364))
        # fmt: on

        # Test that batch is correctly processed
        image_str = "<image>"
        text_str_1 = "In this image, we see"
        text_str_2 = "bla, bla"

        text = [
            image_str + text_str_1,
            text_str_2 + image_str + image_str,
        ]
        images = [[self.image1], [self.image2, self.image3]]

        inputs = processor(text=text, images=images, padding=True)

        # fmt: off
        tokenized_sentence_1 = processor.tokenizer(text_str_1, add_special_tokens=False)
        tokenized_sentence_2 = processor.tokenizer(text_str_2, add_special_tokens=False)

        # 64x64 square inputs → 4×4 tile each = 17 tiles per image; batch max = max(17, 34) = 34
        split_image1_tokens = self.get_split_image_expected_tokens(processor, 4, 4)
        split_image2_tokens = self.get_split_image_expected_tokens(processor, 4, 4)
        split_image3_tokens = self.get_split_image_expected_tokens(processor, 4, 4)
        expected_input_ids_1 = [self.bos_token_id] + split_image1_tokens + tokenized_sentence_1["input_ids"]
        expected_input_ids_2 = [self.bos_token_id] + tokenized_sentence_2["input_ids"] + split_image2_tokens + split_image3_tokens
        # Pad the first input to match the second input
        pad_len = len(expected_input_ids_2) - len(expected_input_ids_1)
        padded_expected_input_ids_1 = [self.padding_token_id] * pad_len + expected_input_ids_1

        self.assertEqual(
            inputs["input_ids"], [padded_expected_input_ids_1, expected_input_ids_2]
        )
        self.assertEqual(
            inputs["attention_mask"],
            [[0] * pad_len + [1] * len(expected_input_ids_1), [1] * len(expected_input_ids_2)]
        )
        self.assertEqual(np.array(inputs['pixel_values']).shape, (2, 34, 3, 364, 364))
        self.assertEqual(np.array(inputs['pixel_attention_mask']).shape, (2, 34, 364, 364))
        # fmt: on

    def test_add_special_tokens_processor(self):
        processor = self.get_processor()

        image_str = "<image>"
        text_str = "In this image, we see"
        text = text_str + image_str

        # fmt: off
        inputs = processor(text=text, images=self.image1, add_special_tokens=False)
        tokenized_sentence = processor.tokenizer(text_str, add_special_tokens=False)
        split_image1_tokens = self.get_split_image_expected_tokens(processor, 4, 4)
        expected_input_ids = [tokenized_sentence["input_ids"] + split_image1_tokens]
        self.assertEqual(inputs["input_ids"], expected_input_ids)

        inputs = processor(text=text, images=self.image1)
        expected_input_ids = [[self.bos_token_id] + tokenized_sentence["input_ids"] + split_image1_tokens]
        self.assertEqual(inputs["input_ids"], expected_input_ids)
        # fmt: on

    def test_non_nested_images_with_batched_text(self):
        processor = self.get_processor()
        processor.image_processor.do_image_splitting = False

        image_str = "<image>"
        text_str_1 = "In this image, we see"
        text_str_2 = "In this image, we see"

        text = [
            image_str + text_str_1,
            image_str + image_str + text_str_2,
        ]
        images = [self.image1, self.image2, self.image3]

        inputs = processor(text=text, images=images, padding=True)

        self.assertEqual(np.array(inputs["pixel_values"]).shape, (2, 2, 3, 364, 364))
        self.assertEqual(np.array(inputs["pixel_attention_mask"]).shape, (2, 2, 364, 364))

    # Copied from tests.models.idefics2.test_processing_idefics2.Idefics2ProcessorTest.test_process_interleaved_images_prompts_image_error
    def test_process_interleaved_images_prompts_image_error(self):
        processor = self.get_processor()

        text = [
            "This is a test sentence.",
            "In this other sentence we try some good things",
        ]
        images = [[self.image1], [self.image2]]
        with self.assertRaises(ValueError):
            processor(text=text, images=images, padding=True)
        images = [[self.image1], []]
        with self.assertRaises(ValueError):
            processor(text=text, images=images, padding=True)

        text = [
            "This is a test sentence.<image>",
            "In this other sentence we try some good things<image>",
        ]
        images = [[self.image1], [self.image2, self.image3]]
        with self.assertRaises(ValueError):
            processor(text=text, images=images, padding=True)
        images = [[], [self.image2]]
        with self.assertRaises((ValueError, IndexError)):
            processor(text=text, images=images, padding=True)
        images = [self.image1, self.image2, self.image3]
        with self.assertRaises(ValueError):
            processor(text=text, images=images, padding=True)
        images = [self.image1]
        with self.assertRaises(ValueError):
            processor(text=text, images=images, padding=True)

        text = [
            "This is a test sentence.",
            "In this other sentence we try some good things<image>",
        ]
        images = [[self.image1], []]
        with self.assertRaises(ValueError):
            processor(text=text, images=images, padding=True)
        images = [self.image1, self.image2]
        with self.assertRaises(ValueError):
            processor(text=text, images=images, padding=True)

    def test_apply_chat_template(self):
        # Message contains content which a mix of lists with images and image urls and string
        messages = [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What do these images show?"},
                    {"type": "image"},
                    {"type": "image"},
                    "What do these images show?",
                ],
            },
            {
                "role": "assistant",
                "content": [
                    {
                        "type": "text",
                        "text": "The first image shows the statue of Liberty in New York. The second image picture depicts Idefix, the dog of Obelix in Asterix and Obelix.",
                    }
                ],
            },
            {"role": "user", "content": [{"type": "text", "text": "And who is that?"}]},
        ]
        processor = self.get_processor()
        # Make short sequence length to test that the fake tokens are added correctly
        rendered = processor.apply_chat_template(messages, add_generation_prompt=True)

        expected_rendered = (
            "<|begin_of_text|>User: What do these images show?<image><image><end_of_utterance>\n"
            "Assistant: The first image shows the statue of Liberty in New York. The second image picture depicts Idefix, the dog of Obelix in Asterix and Obelix.<end_of_utterance>\n"
            "User: And who is that?<end_of_utterance>\n"
            "Assistant:"
        )
        self.assertEqual(rendered, expected_rendered)

    @require_torch
    @require_vision
    def test_text_only_inference(self):
        """Test that the processor works correctly with text-only input."""
        processor = self.get_processor()

        text = "This is a simple text without images."
        inputs = processor(text=text)

        tokenized_sentence = processor.tokenizer(text, add_special_tokens=False)
        expected_input_ids = [[self.bos_token_id] + tokenized_sentence["input_ids"]]

        self.assertEqual(inputs["input_ids"], expected_input_ids)
        self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids[0])])
        self.assertTrue("pixel_values" not in inputs)
        self.assertTrue("pixel_attention_mask" not in inputs)

        # Test batch of texts without image tokens
        texts = ["First text.", "Second piece of text."]
        batch_inputs = processor(text=texts, padding=True)

        tokenized_1 = processor.tokenizer(texts[0], add_special_tokens=False)
        tokenized_2 = processor.tokenizer(texts[1], add_special_tokens=False)

        expected_1 = [self.bos_token_id] + tokenized_1["input_ids"]
        expected_2 = [self.bos_token_id] + tokenized_2["input_ids"]

        # Pad the shorter sequence
        pad_len = len(expected_2) - len(expected_1)
        if pad_len > 0:
            padded_expected_1 = [self.padding_token_id] * pad_len + expected_1
            expected_attention_1 = [0] * pad_len + [1] * len(expected_1)
            self.assertEqual(batch_inputs["input_ids"], [padded_expected_1, expected_2])
            self.assertEqual(batch_inputs["attention_mask"], [expected_attention_1, [1] * len(expected_2)])
        else:
            pad_len = -pad_len
            padded_expected_2 = [self.padding_token_id] * pad_len + expected_2
            expected_attention_2 = [0] * pad_len + [1] * len(expected_2)
            self.assertEqual(batch_inputs["input_ids"], [expected_1, padded_expected_2])
            self.assertEqual(batch_inputs["attention_mask"], [[1] * len(expected_1), expected_attention_2])

    @require_torch
    @require_vision
    def test_missing_images_error(self):
        """Test that appropriate error is raised when images are referenced but not provided."""
        processor = self.get_processor()

        # Test single text with image token but no image
        text = "Let me show you this image: <image> What do you think?"
        with self.assertRaises(ValueError) as context:
            processor(text=text)
        self.assertTrue("tokens in the text but no images were passed" in str(context.exception))

        # Test batch with image tokens but no images
        texts = [
            "First text with <image> token.",
            "Second text <image> with token.",
        ]
        with self.assertRaises(ValueError) as context:
            processor(text=texts)
        self.assertTrue("tokens in the text but no images were passed" in str(context.exception))

        # Test with None as Images
        with self.assertRaises(ValueError) as context:
            processor(text=text, images=None)
        self.assertTrue("tokens in the text but no images were passed" in str(context.exception))

        with self.assertRaises(ValueError) as context:
            processor(text=texts, images=None)
        self.assertTrue("tokens in the text but no images were passed" in str(context.exception))