init

transformers/tests/models/chameleon/test_image_processing_chameleon.py

@@ -0,0 +1,218 @@
+# 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.image_utils import PILImageResampling
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import ChameleonImageProcessor
+
+    if is_torchvision_available():
+        from transformers import ChameleonImageProcessorFast
+
+
+class ChameleonImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=200,
+        do_resize=True,
+        size=None,
+        do_center_crop=True,
+        crop_size=None,
+        do_normalize=True,
+        image_mean=[1.0, 1.0, 1.0],
+        image_std=[1.0, 1.0, 1.0],
+        do_convert_rgb=True,
+        resample=PILImageResampling.BILINEAR,
+    ):
+        size = size if size is not None else {"shortest_edge": 18}
+        crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.do_center_crop = do_center_crop
+        self.crop_size = crop_size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+        self.resample = resample
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_center_crop": self.do_center_crop,
+            "crop_size": self.crop_size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+            "resample": self.resample,
+        }
+
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.expected_output_image_shape
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.crop_size["height"], self.crop_size["width"]
+
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.prepare_image_inputs
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class ChameleonImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = ChameleonImageProcessor if is_vision_available() else None
+    fast_image_processing_class = ChameleonImageProcessorFast if is_torchvision_available() else None
+
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.setUp with CLIP->Chameleon
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = ChameleonImageProcessingTester(self)
+
+    @property
+    # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.image_processor_dict
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "do_center_crop"))
+            self.assertTrue(hasattr(image_processing, "center_crop"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"shortest_edge": 18})
+            self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
+            self.assertEqual(image_processor.size, {"shortest_edge": 42})
+            self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
+
+    def test_call_pil(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = (1, 3, 18, 18)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            expected_output_image_shape = (7, 3, 18, 18)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+    def test_call_numpy(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = (1, 3, 18, 18)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            expected_output_image_shape = (7, 3, 18, 18)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+    def test_call_pytorch(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PyTorch tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
+
+            for image in image_inputs:
+                self.assertIsInstance(image, torch.Tensor)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            expected_output_image_shape = (1, 3, 18, 18)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            expected_output_image_shape = (7, 3, 18, 18)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+    def test_nested_input(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+
+            # Test batched as a list of images
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            expected_output_image_shape = (7, 3, 18, 18)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+
+            # Test batched as a nested list of images, where each sublist is one batch
+            image_inputs_nested = [image_inputs[:3], image_inputs[3:]]
+            encoded_images_nested = image_processing(image_inputs_nested, return_tensors="pt").pixel_values
+            expected_output_image_shape = (7, 3, 18, 18)
+            self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape)
+
+            # Image processor should return same pixel values, independently of input format
+            self.assertTrue((encoded_images_nested == encoded_images).all())

transformers/tests/models/chameleon/test_modeling_chameleon.py

@@ -0,0 +1,466 @@
+# 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 chameleon model."""
+
+import copy
+import unittest
+
+import requests
+from parameterized import parameterized
+
+from transformers import ChameleonConfig, is_torch_available, is_vision_available, set_seed
+from transformers.testing_utils import (
+    Expectations,
+    require_bitsandbytes,
+    require_read_token,
+    require_torch,
+    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_vision_available():
+    from PIL import Image
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        ChameleonForConditionalGeneration,
+        ChameleonModel,
+        ChameleonProcessor,
+    )
+
+
+class ChameleonModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=35,
+        is_training=False,
+        use_input_mask=True,
+        use_labels=True,
+        vocab_size=99,
+        image_token_id=4,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=2,
+        num_key_value_heads=2,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        pad_token_id=0,
+        vq_num_embeds=5,
+        vq_embed_dim=5,
+        vq_channel_multiplier=[1, 2],
+        vq_img_token_start_id=10,  # has to be less than vocab size when added with vq_num_embeds
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.image_token_id = image_token_id
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.pad_token_id = pad_token_id
+        self.scope = scope
+        self.vq_num_embeds = vq_num_embeds
+        self.vq_embed_dim = vq_embed_dim
+        self.vq_channel_multiplier = vq_channel_multiplier
+        self.vq_img_token_start_id = vq_img_token_start_id
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = torch.tril(torch.ones_like(input_ids).to(torch_device))
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = self.get_config()
+
+        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        # create dummy vocab map for image2bpe mapping if it needs remapping
+        # we assume that vocab size is big enough to account for image tokens somewhere in the beginning
+        # same way as in real ckpt, when img tokens are in first half of embeds
+        # we will need "vq_num_embeds" amount of tokens
+
+        vocab_map = {i: chr(i) for i in range(self.vocab_size)}
+        vocab_map[self.image_token_id] = "<image>"
+        start = self.vq_img_token_start_id
+        end = self.vq_img_token_start_id + self.vq_num_embeds
+        for i in range(start, end):
+            image_token_infix = "".join(chr(ord("A") + int(c)) for c in str(i))
+            # dummy str for each image token, anything starting with IMGIMG
+            vocab_map[i] = f"IMGIMG{image_token_infix}Z"
+
+        return ChameleonConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            num_key_value_heads=self.num_key_value_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+            pad_token_id=self.pad_token_id,
+            vocabulary_map={v: k for k, v in vocab_map.items()},
+            vq_config=self.get_vq_config(),
+        )
+
+    def get_vq_config(self):
+        return {
+            "embed_dim": self.vq_embed_dim,
+            "num_embeddings": self.vq_num_embeds,
+            "latent_channels": self.vq_embed_dim,
+            "in_channels": 3,
+            "base_channels": 32,  # we have a GroupNorm of 32 groups, so can't do less
+            "channel_multiplier": self.vq_channel_multiplier,
+        }
+
+    def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
+        model = ChameleonModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask)
+        result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class ChameleonModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (ChameleonModel, ChameleonForConditionalGeneration) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": ChameleonModel,
+            "text-generation": ChameleonForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_headmasking = False
+    test_pruning = False
+    fx_compatible = False
+
+    def setUp(self):
+        self.model_tester = ChameleonModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=ChameleonConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @parameterized.expand([("linear",), ("dynamic",)])
+    def test_model_rope_scaling(self, scaling_type):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+        short_input = ids_tensor([1, 10], config.vocab_size)
+        long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
+
+        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
+        original_model = ChameleonModel(config)
+        original_model.to(torch_device)
+        original_model.eval()
+        original_short_output = original_model(short_input).last_hidden_state
+        original_long_output = original_model(long_input).last_hidden_state
+
+        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
+        config.rope_scaling = {"type": scaling_type, "factor": 10.0}
+        scaled_model = ChameleonModel(config)
+        scaled_model.to(torch_device)
+        scaled_model.eval()
+        scaled_short_output = scaled_model(short_input).last_hidden_state
+        scaled_long_output = scaled_model(long_input).last_hidden_state
+
+        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
+        # maximum sequence length, so the outputs for the short input should match.
+        if scaling_type == "dynamic":
+            torch.testing.assert_close(original_short_output, scaled_short_output, rtol=1e-5, atol=1e-5)
+        else:
+            self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
+
+        # The output should be different for long inputs
+        self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
+
+    @unittest.skip("Chameleon forces some token ids to be -inf!")
+    def test_batching_equivalence(self):
+        pass
+
+
+class ChameleonVision2SeqModelTester(ChameleonModelTester):
+    def __init__(self, parent, image_size=10, **kwargs):
+        super().__init__(parent, **kwargs)
+        self.image_size = image_size
+        self.image_seq_length = 25
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+        input_ids[input_ids == self.image_token_id] = self.pad_token_id
+        input_ids[:, : self.image_seq_length] = self.image_token_id
+        attention_mask = input_ids.ne(self.pad_token_id).to(torch_device)
+        pixel_values = floats_tensor([self.batch_size, 3, self.image_size, self.image_size])
+
+        config = self.get_config()
+
+        return config, input_ids, attention_mask, pixel_values
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, attention_mask, pixel_values = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class ChameleonVision2SeqModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+    all_model_classes = (ChameleonModel, ChameleonForConditionalGeneration) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "image-text-to-text": ChameleonForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_headmasking = False
+    test_pruning = False
+    fx_compatible = False
+
+    def setUp(self):
+        self.model_tester = ChameleonVision2SeqModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=ChameleonConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip("Chameleon forces some token ids to be -inf!")
+    def test_batching_equivalence(self):
+        pass
+
+    @unittest.skip("Chameleon cannot do offload because it uses `self.linear.weight` in forward")
+    def test_cpu_offload(self):
+        pass
+
+    @unittest.skip("Chameleon cannot do offload because it uses `self.linear.weight` in forward")
+    def test_disk_offload_bin(self):
+        pass
+
+    @unittest.skip("Chameleon cannot do offload because it uses `self.linear.weight` in forward")
+    def test_disk_offload_safetensors(self):
+        pass
+
+    @unittest.skip("Chameleon applies key/query norm which doesn't work with packing")
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip("Chameleon applies key/query norm which doesn't work with packing")
+    def test_eager_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip("Chameleon applies key/query norm which doesn't work with packing")
+    def test_sdpa_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    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)
+            model.eval()
+            curr_input_dict = copy.deepcopy(input_dict)  # the below tests modify dict in-place
+            _ = 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:, ...]
+            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]
+            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)
+
+            # two images and two image tokens don't raise an error
+            pixel_values = torch.cat([pixel_values, pixel_values], dim=0)
+            _ = model(input_ids=input_ids, pixel_values=pixel_values)
+
+
+@require_torch
+class ChameleonIntegrationTest(unittest.TestCase):
+    @slow
+    @require_bitsandbytes
+    @require_read_token
+    def test_model_7b(self):
+        model = ChameleonForConditionalGeneration.from_pretrained(
+            "facebook/chameleon-7b", load_in_4bit=True, device_map="auto"
+        )
+        processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")
+
+        image = Image.open(
+            requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw
+        )
+        prompt = "<image>Describe what do you see here and tell me about the history behind it?"
+
+        inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device, torch.float16)
+
+        # greedy generation outputs
+        EXPECTED_TEXT_COMPLETIONS = Expectations(
+            {
+                ("xpu", 3): ['Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot in the center representing the star Altair. The star map is set against a black background, with the constellations visible in the night'],
+                ("cuda", 7): ['Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot in the center representing the star Alpha Centauri. The star map is a representation of the night sky, showing the positions of stars in'],
+                ("cuda", 8): ['Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot representing the position of the star Alpha Centauri. Alpha Centauri is the brightest star in the constellation Centaurus and is located'],
+            }
+        )  # fmt: skip
+        EXPECTED_TEXT_COMPLETION = EXPECTED_TEXT_COMPLETIONS.get_expectation()
+
+        generated_ids = model.generate(**inputs, max_new_tokens=40, do_sample=False)
+        text = processor.batch_decode(generated_ids, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
+
+    @slow
+    @require_bitsandbytes
+    @require_read_token
+    def test_model_7b_batched(self):
+        model = ChameleonForConditionalGeneration.from_pretrained(
+            "facebook/chameleon-7b", load_in_4bit=True, device_map="auto"
+        )
+        processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")
+
+        image = Image.open(
+            requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw
+        )
+        image_2 = Image.open(
+            requests.get("https://www.kxan.com/wp-content/uploads/sites/40/2020/10/ORION.jpg", stream=True).raw
+        )
+        prompts = [
+            "<image>Describe what do you see here and tell me about the history behind it?",
+            "What constellation is this image showing?<image>",
+        ]
+
+        inputs = processor(images=[image, image_2], text=prompts, padding=True, return_tensors="pt").to(
+            model.device, torch.float16
+        )
+
+        # greedy generation outputs
+        EXPECTED_TEXT_COMPLETIONS = Expectations(
+            {
+                ("xpu", 3): [
+                    'Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot in the center representing the star Alpha Centauri. The star map is a representation of the night sky, showing the positions of stars in',
+                    'What constellation is this image showing?The image shows the constellation of Orion.The image shows the constellation of Orion.The image shows the constellation of Orion.The image shows the constellation of Orion.',
+                ],
+                ("cuda", 7): [
+                    'Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot representing the position of the star Alpha Centauri. Alpha Centauri is the brightest star in the constellation Centaurus and is located',
+                    'What constellation is this image showing?The image shows the constellation of Orion.The image shows the constellation of Orion.The image shows the constellation of Orion.The image shows the constellation of Orion.',
+                ],
+                ("cuda", 8): [
+                    'Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot representing the position of the star Alpha Centauri. Alpha Centauri is the brightest star in the constellation Centaurus and is located',
+                    'What constellation is this image showing?The image shows the constellation of Orion.The image shows the constellation of Orion.The image shows the constellation of Orion.The image shows the constellation of Orion.',
+                ],
+            }
+        )  # fmt: skip
+        EXPECTED_TEXT_COMPLETION = EXPECTED_TEXT_COMPLETIONS.get_expectation()
+
+        generated_ids = model.generate(**inputs, max_new_tokens=40, do_sample=False)
+        text = processor.batch_decode(generated_ids, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
+
+    @slow
+    @require_bitsandbytes
+    @require_read_token
+    def test_model_7b_multi_image(self):
+        model = ChameleonForConditionalGeneration.from_pretrained(
+            "facebook/chameleon-7b", load_in_4bit=True, device_map="auto"
+        )
+        processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")
+
+        image = Image.open(
+            requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw
+        )
+        image_2 = Image.open(
+            requests.get("https://www.kxan.com/wp-content/uploads/sites/40/2020/10/ORION.jpg", stream=True).raw
+        )
+        prompt = "What do these two images have in common?<image><image>"
+
+        inputs = processor(images=[image, image_2], text=prompt, return_tensors="pt").to(model.device, torch.float16)
+
+        # greedy generation outputs
+        EXPECTED_TEXT_COMPLETION = ['What do these two images have in common?The two images show a connection between the night sky and the internet. The first image shows a starry night sky, with the stars arranged in a pattern that resembles the structure of the internet. The']  # fmt: skip
+        generated_ids = model.generate(**inputs, max_new_tokens=40, do_sample=False)
+        text = processor.batch_decode(generated_ids, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)

transformers/tests/models/chameleon/test_processing_chameleon.py

@@ -0,0 +1,89 @@
+# 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 chameleon model."""
+
+import tempfile
+import unittest
+
+from transformers import ChameleonProcessor, LlamaTokenizer
+from transformers.testing_utils import get_tests_dir
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from transformers import ChameleonImageProcessor
+
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
+
+
+class ChameleonProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = ChameleonProcessor
+
+    @classmethod
+    def setUpClass(cls):
+        cls.tmpdirname = tempfile.mkdtemp()
+        image_processor = ChameleonImageProcessor()
+        tokenizer = LlamaTokenizer(vocab_file=SAMPLE_VOCAB)
+        tokenizer.pad_token_id = 0
+        tokenizer.sep_token_id = 1
+        tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
+        processor = cls.processor_class(image_processor=image_processor, tokenizer=tokenizer, image_seq_length=2)
+        processor.save_pretrained(cls.tmpdirname)
+        cls.image_token = processor.image_token
+
+    def test_special_mm_token_truncation(self):
+        """Tests that special vision tokens do not get truncated when `truncation=True` is set."""
+
+        processor = self.get_processor()
+
+        input_str = self.prepare_text_inputs(batch_size=2, modalities="image")
+        image_input = self.prepare_image_inputs(batch_size=2)
+
+        _ = processor(
+            text=input_str,
+            images=image_input,
+            return_tensors="pt",
+            truncation=None,
+            padding=True,
+        )
+
+        with self.assertRaises(ValueError):
+            _ = processor(
+                text=input_str,
+                images=image_input,
+                return_tensors="pt",
+                truncation=True,
+                padding=True,
+                max_length=20,
+            )
+
+    @staticmethod
+    def prepare_processor_dict():
+        return {"image_seq_length": 2}  # fmt: skip
+
+    # Copied from tests.models.llava.test_processing_llava.LlavaProcessorTest.test_get_num_vision_tokens
+    def test_get_num_vision_tokens(self):
+        "Tests general functionality of the helper used internally in vLLM"
+
+        processor = self.get_processor()
+
+        output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)])
+        self.assertTrue("num_image_tokens" in output)
+        self.assertEqual(len(output["num_image_tokens"]), 3)
+
+        self.assertTrue("num_image_patches" in output)
+        self.assertEqual(len(output["num_image_patches"]), 3)