Add BridgeTower model (#20775)

* Commit with BTModel and latest HF code

* Placeholder classes for BTForMLM and BTForITR

* Importing Bert classes from transformers

* Removed objectives.py and dist_utils.py

* Removed swin_transformer.py

* Add image normalization, BridgeTowerForImageAndTextRetrieval

* Add center_crop

* Removing bert tokenizer and LCI references

* Tested config loading from HF transformers hub

* Removed state_dict updates and added path to hub

* Enable center crop

* Getting image_size from config, renaming num_heads and num_layers

* Handling max_length in BridgeTowerProcessor

* Add BridgeTowerForMaskedLM

* Add doc string for BridgeTowerConfig

* Add doc strings for BT config, processor, image processor

* Adding docs, removed swin

* Removed convert_bridgetower_original_to_pytorch.py

* Added doc files for bridgetower, removed is_vision

* Add support attention_mask=None and BridgeTowerModelOutput

* Fix formatting

* Fixes with 'make style', 'make quality', 'make fixup'

* Remove downstream tasks from BridgeTowerModel

* Formatting fixes, add return_dict to BT models

* Clean up after doc_test

* Update BTModelOutput return type, fix todo in doc

* Remove loss_names from init

* implement tests and update tuples returned by models

* Add image reference to bridgetower.mdx

* after make fix-copies, make fixup, make style, make quality, make repo-consistency

* Rename class names with BridgeTower prefix

* Fix for image_size in BTImageProcessor

* implement feature extraction bridgetower tests

* Update image_mean and image_std to be list

* remove unused import

* Removed old comments

* Rework CLIP

* update config in tests followed config update

* Formatting fixes

* Add copied from for BridgeTowerPredictionHeadTransform

* Update bridgetower.mdx

* Update test_feature_extraction_bridgetower.py

* Update bridgetower.mdx

* BridgeTowerForMaskedLM is conditioned on image too

* Add BridgeTowerForMaskedLM

* Fixes

* Call post_init to init weights

* Move freeze layers into method

* Remove BTFeatureExtractor, add BT under multimodal models

* Remove BTFeatureExtractor, add BT under multimodal models

* Code review feedback - cleanup

* Rename variables

* Formatting and style to PR review feedback

* Move center crop after resize

* Use named parameters

* Style fix for modeling_bridgetower.py

* Update docs/source/en/model_doc/bridgetower.mdx
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/bridgetower.mdx
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/bridgetower.mdx
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/bridgetower/modeling_bridgetower.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/bridgetower/modeling_bridgetower.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/bridgetower.mdx
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Update src/transformers/models/bridgetower/modeling_bridgetower.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Rename config params, copy BERT classes, clean comments

* Cleanup irtr

* Replace Roberta imports, add BTTextConfig and Model

* Update docs, add visionconfig, consistent arg names

* make fixup

* Comments for forward in BTModel and make fixup

* correct tests

* Remove inconsistent roberta copied from

* Add BridgeTowerTextModel to dummy_pt_objects.py

* Add BridgeTowerTextModel to IGNORE_NON_TESTED

* Update docs for BT Text and Vision Configs

* Treat BridgeTowerTextModel as a private model

* BridgeTowerTextModel as private

* Run make fix-copies

* Adding BTTextModel to PRIVATE_MODELS

* Fix for issue with BT Text and Image configs

* make style changes

* Update README_ja.md

Add から to BridgeTower's description

* Clean up config, .mdx and arg names

* Fix init_weights. Remove nn.Sequential

* Formatting and style fixes

* Re-add tie_word_embeddings in config

* update test implementation

* update style

* remove commented out

* fix style

* Update README with abs for BridgeTower

* fix style

* fix mdx file

* Update bridgetower.mdx

* Update img src in bridgetower.mdx

* Update README.md

* Update README.md

* resolve style failed

* Update _toctree.yml

* Update README_ja.md

* Removed mlp_ratio, rename feats, rename BTCLIPModel

* Replace BTCLIP with BTVisionModel,pass in vision_config to BTVisionModel

* Add test_initialization support

* Add support for output_hidden_states

* Update support for output_hidden_states

* Add support for output_attentions

* Add docstring for output_hidden_states

* update tests

* add bridgetowervisionmodel as private model

* rerun the PR test

* Remove model_type, pass configs to classes, renames

* Change self.device to use weight device

* Remove image_size

* Style check fixes

* Add hidden_size and num_hidden_layers to BridgeTowerTransformer

* Update device setting

* cosmetic update

* trigger test again

* trigger tests again

* Update test_modeling_bridgetower.py

trigger tests again

* Update test_modeling_bridgetower.py

* minor update

* re-trigger tests

* Update docs/source/en/model_doc/bridgetower.mdx
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Remove pad, update max_text_len, doc cleanup, pass eps to LayerNorm

* Added copied to, some more review feedback

* make fixup

* Use BridgeTowerVisionEmbeddings

* Code cleanup

* Fixes for BridgeTowerVisionEmbeddings

* style checks

* re-tests

* fix embedding

* address comment on init file

* retrigger tests

* update import prepare_image_inputs

* update test_image_processing_bridgetower.py to reflect test_image_processing_common.py

* retrigger tests
Co-authored-by: Shaoyen Tseng <shao-yen.tseng@intel.com>
Co-authored-by: Tiep Le <tiep.le@intel.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Tiep Le <97980157+tileintel@users.noreply.github.com>

src/transformers/models/bridgetower/processing_bridgetower.py

+# coding=utf-8
+# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and 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.
+"""
+Processor class for BridgeTower.
+"""
+
+from typing import List, Optional, Union
+
+from ...processing_utils import ProcessorMixin
+from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from ...utils import TensorType
+
+
+class BridgeTowerProcessor(ProcessorMixin):
+    r"""
+    Constructs a BridgeTower processor which wraps a Roberta tokenizer and BridgeTower image processor into a single
+    processor.
+
+    [`BridgeTowerProcessor`] offers all the functionalities of [`BridgeTowerImageProcessor`] and
+    [`RobertaTokenizerFast`]. See the docstring of [`~BridgeTowerProcessor.__call__`] and
+    [`~BridgeTowerProcessor.decode`] for more information.
+
+    Args:
+        image_processor (`BridgeTowerImageProcessor`):
+            An instance of [`BridgeTowerImageProcessor`]. The image processor is a required input.
+        tokenizer (`RobertaTokenizerFast`):
+            An instance of ['RobertaTokenizerFast`]. The tokenizer is a required input.
+    """
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "BridgeTowerImageProcessor"
+    tokenizer_class = ("RobertaTokenizer", "RobertaTokenizerFast")
+
+    def __init__(self, image_processor, tokenizer):
+        super().__init__(image_processor, tokenizer)
+
+    def __call__(
+        self,
+        images,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        add_special_tokens: bool = True,
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Union[bool, str, TruncationStrategy] = None,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        pad_to_multiple_of: Optional[int] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **kwargs
+    ) -> BatchEncoding:
+        """
+        This method uses [`BridgeTowerImageProcessor.__call__`] method to prepare image(s) for the model, and
+        [`RobertaTokenizerFast.__call__`] to prepare text for the model.
+
+        Please refer to the docstring of the above two methods for more information.
+        """
+        encoding = self.tokenizer(
+            text=text,
+            add_special_tokens=add_special_tokens,
+            padding=padding,
+            truncation=truncation,
+            max_length=max_length,
+            stride=stride,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_token_type_ids=return_token_type_ids,
+            return_attention_mask=return_attention_mask,
+            return_overflowing_tokens=return_overflowing_tokens,
+            return_special_tokens_mask=return_special_tokens_mask,
+            return_offsets_mapping=return_offsets_mapping,
+            return_length=return_length,
+            verbose=verbose,
+            return_tensors=return_tensors,
+            **kwargs,
+        )
+        # add pixel_values + pixel_mask
+        encoding_image_processor = self.image_processor(
+            images, return_tensors=return_tensors, do_normalize=True, do_center_crop=True, **kwargs
+        )
+        encoding.update(encoding_image_processor)
+
+        return encoding
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer
+        to the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

src/transformers/utils/dummy_pt_objects.py

@@ -1257,6 +1257,37 @@ class BloomPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class BridgeTowerForImageAndTextRetrieval(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class BridgeTowerForMaskedLM(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class BridgeTowerModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class BridgeTowerPreTrainedModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

src/transformers/utils/dummy_vision_objects.py

@@ -45,6 +45,13 @@ class BlipImageProcessor(metaclass=DummyObject):
         requires_backends(self, ["vision"])
 
 
+class BridgeTowerImageProcessor(metaclass=DummyObject):
+    _backends = ["vision"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["vision"])
+
+
 class ChineseCLIPFeatureExtractor(metaclass=DummyObject):
     _backends = ["vision"]
 

tests/models/bridgetower/test_image_processing_bridgetower.py

+# coding=utf-8
+# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and 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.
+
+
+import unittest
+from typing import Dict, List, Optional, Union
+
+import numpy as np
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import BridgeTowerImageProcessor
+
+
+class BridgeTowerImageProcessingTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        do_resize: bool = True,
+        size: Dict[str, int] = None,
+        size_divisor: int = 32,
+        do_rescale: bool = True,
+        rescale_factor: Union[int, float] = 1 / 255,
+        do_normalize: bool = True,
+        do_center_crop: bool = True,
+        image_mean: Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073],
+        image_std: Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711],
+        do_pad: bool = True,
+        batch_size=7,
+        min_resolution=30,
+        max_resolution=400,
+        num_channels=3,
+    ):
+        self.parent = parent
+        self.do_resize = do_resize
+        self.size = size if size is not None else {"shortest_edge": 288}
+        self.size_divisor = size_divisor
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.do_center_crop = do_center_crop
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_pad = do_pad
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+
+    def prepare_image_processor_dict(self):
+        return {
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_normalize": self.do_normalize,
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "size_divisor": self.size_divisor,
+        }
+
+    def get_expected_values(self, image_inputs, batched=False):
+        """
+        This function computes the expected height and width when providing images to BridgeTowerImageProcessor,
+        assuming do_resize is set to True with a scalar size and size_divisor.
+        """
+        if not batched:
+            size = self.size["shortest_edge"]
+            image = image_inputs[0]
+            if isinstance(image, Image.Image):
+                w, h = image.size
+            else:
+                h, w = image.shape[1], image.shape[2]
+            scale = size / min(w, h)
+            if h < w:
+                newh, neww = size, scale * w
+            else:
+                newh, neww = scale * h, size
+
+            max_size = int((1333 / 800) * size)
+            if max(newh, neww) > max_size:
+                scale = max_size / max(newh, neww)
+                newh = newh * scale
+                neww = neww * scale
+
+            newh, neww = int(newh + 0.5), int(neww + 0.5)
+            expected_height, expected_width = (
+                newh // self.size_divisor * self.size_divisor,
+                neww // self.size_divisor * self.size_divisor,
+            )
+
+        else:
+            expected_values = []
+            for image in image_inputs:
+                expected_height, expected_width = self.get_expected_values([image])
+                expected_values.append((expected_height, expected_width))
+            expected_height = max(expected_values, key=lambda item: item[0])[0]
+            expected_width = max(expected_values, key=lambda item: item[1])[1]
+
+        return expected_height, expected_width
+
+
+@require_torch
+@require_vision
+class BridgeTowerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
+    image_processing_class = BridgeTowerImageProcessor if is_vision_available() else None
+
+    def setUp(self):
+        self.image_processor_tester = BridgeTowerImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        self.assertTrue(hasattr(image_processing, "image_mean"))
+        self.assertTrue(hasattr(image_processing, "image_std"))
+        self.assertTrue(hasattr(image_processing, "do_normalize"))
+        self.assertTrue(hasattr(image_processing, "do_resize"))
+        self.assertTrue(hasattr(image_processing, "size"))
+        self.assertTrue(hasattr(image_processing, "size_divisor"))
+
+    def test_batch_feature(self):
+        pass
+
+    def test_call_pil(self):
+        # Initialize feature_extractor
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        # create random PIL images
+        image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
+        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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
+        self.assertEqual(
+            encoded_images.shape,
+            (1, self.image_processor_tester.num_channels, expected_height, expected_width),
+        )
+
+        # Test batched
+        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
+        self.assertEqual(
+            encoded_images.shape,
+            (
+                self.image_processor_tester.batch_size,
+                self.image_processor_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+    def test_call_numpy(self):
+        # Initialize feature_extractor
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        # create random numpy tensors
+        image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
+        self.assertEqual(
+            encoded_images.shape,
+            (1, self.image_processor_tester.num_channels, expected_height, expected_width),
+        )
+
+        # Test batched
+        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
+        self.assertEqual(
+            encoded_images.shape,
+            (
+                self.image_processor_tester.batch_size,
+                self.image_processor_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+    def test_call_pytorch(self):
+        # Initialize feature_extractor
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        # create random PyTorch tensors
+        image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
+        self.assertEqual(
+            encoded_images.shape,
+            (1, self.image_processor_tester.num_channels, expected_height, expected_width),
+        )
+
+        # Test batched
+        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
+        self.assertEqual(
+            encoded_images.shape,
+            (
+                self.image_processor_tester.batch_size,
+                self.image_processor_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+    def test_equivalence_pad_and_create_pixel_mask(self):
+        # Initialize feature_extractors
+        image_processing_1 = self.image_processing_class(**self.image_processor_dict)
+        image_processing_2 = self.image_processing_class(do_resize=False, do_normalize=False, do_rescale=False)
+        # create random PyTorch tensors
+        image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
+        for image in image_inputs:
+            self.assertIsInstance(image, torch.Tensor)
+
+        # Test whether the method "pad_and_return_pixel_mask" and calling the image processor return the same tensors
+        encoded_images_with_method = image_processing_1.pad_and_create_pixel_mask(image_inputs, return_tensors="pt")
+        encoded_images = image_processing_2(image_inputs, return_tensors="pt")
+
+        self.assertTrue(
+            torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
+        )
+        self.assertTrue(
+            torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4)
+        )

tests/models/bridgetower/test_modeling_bridgetower.py

+# coding=utf-8
+# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and 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 BridgeTower model. """
+
+import tempfile
+import unittest
+
+import numpy as np
+
+from transformers import BridgeTowerConfig, is_torch_available, is_vision_available
+from transformers.testing_utils import require_torch, require_vision, slow, torch_device
+from transformers.utils import cached_property
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import BridgeTowerForImageAndTextRetrieval, BridgeTowerForMaskedLM, BridgeTowerModel
+    from transformers.models.bridgetower.modeling_bridgetower import BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST
+    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_10
+else:
+    is_torch_greater_or_equal_than_1_10 = False
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import BridgeTowerProcessor
+
+
+class BridgeTowerModelTester:
+    def __init__(
+        self,
+        parent,
+        share_cross_modal_transformer_layers=True,
+        drop_rate=0.1,
+        head_hidden_scale=2,
+        hidden_act="gelu",
+        hidden_size=768,
+        initializer_factor=1,
+        is_encoder_decoder=False,
+        layer_norm_eps=1e-05,
+        share_link_tower_layers=False,
+        link_tower_type="add",
+        num_attention_heads=12,
+        num_hidden_layers=6,
+        tie_word_embeddings=False,
+        init_layernorm_from_vision_encoder=False,
+        output_hidden_states=False,
+        text_config=None,
+        vision_config=None,
+        image_size=288,
+    ):
+        self.parent = parent
+        self.share_cross_modal_transformer_layers = share_cross_modal_transformer_layers
+        self.drop_rate = drop_rate
+        self.head_hidden_scale = head_hidden_scale
+        self.hidden_act = hidden_act
+        self.hidden_size = hidden_size
+        self.initializer_factor = initializer_factor
+        self.is_encoder_decoder = is_encoder_decoder
+        self.layer_norm_eps = layer_norm_eps
+        self.share_link_tower_layers = share_link_tower_layers
+        self.link_tower_type = link_tower_type
+        self.num_attention_heads = num_attention_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.tie_word_embeddings = tie_word_embeddings
+        self.init_layernorm_from_vision_encoder = init_layernorm_from_vision_encoder
+        self.vocab_size = 50265
+        self.num_channels = 3
+        self.seq_length = 4
+        self.num_image_features = 325
+        self.batch_size = 1
+        self.image_size = image_size
+        self.is_training = False
+        self.expected_num_hidden_layers = 32
+        self.output_hidden_states = output_hidden_states
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+        attention_mask = random_attention_mask([self.batch_size, self.seq_length])
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        pixel_mask = random_attention_mask([self.batch_size, self.image_size, self.image_size])
+        config = self.get_config()
+        return (config, input_ids, attention_mask, pixel_values, pixel_mask)
+
+    def get_config(self):
+        return BridgeTowerConfig(
+            share_cross_modal_transformer_layers=self.share_cross_modal_transformer_layers,
+            drop_rate=self.drop_rate,
+            head_hidden_scale=self.head_hidden_scale,
+            hidden_act=self.hidden_act,
+            hidden_size=self.hidden_size,
+            initializer_factor=self.initializer_factor,
+            image_size=self.image_size,
+            is_encoder_decoder=self.is_encoder_decoder,
+            layer_norm_eps=self.layer_norm_eps,
+            share_link_tower_layers=self.share_link_tower_layers,
+            link_tower_type=self.link_tower_type,
+            num_attention_heads=self.num_attention_heads,
+            num_hidden_layers=self.num_hidden_layers,
+            tie_word_embeddings=self.tie_word_embeddings,
+            init_layernorm_from_vision_encoder=self.init_layernorm_from_vision_encoder,
+            num_channels=self.num_channels,
+            output_hidden_states=self.output_hidden_states,
+        )
+
+    def create_and_check_model(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+        pixel_values,
+        pixel_mask,
+    ):
+        model = BridgeTowerModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values, pixel_mask=pixel_mask)
+        result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values)
+        self.parent.assertEqual(result["text_features"].shape, (self.batch_size, self.seq_length, self.hidden_size))
+        self.parent.assertEqual(
+            result["image_features"].shape, (self.batch_size, self.num_image_features, self.hidden_size)
+        )
+        self.parent.assertEqual(result["pooler_output"].shape, (self.batch_size, 2 * self.hidden_size))
+
+    def create_and_check_for_image_and_text_retrieval(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+        pixel_values,
+        pixel_mask,
+    ):
+        bridgetower_itm_output_last_dimension = 2
+
+        model = BridgeTowerForImageAndTextRetrieval(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values, pixel_mask=pixel_mask)
+        result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values)
+
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, bridgetower_itm_output_last_dimension))
+
+    def create_and_check_for_masked_language_modeling(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+        pixel_values,
+        pixel_mask,
+    ):
+        model = BridgeTowerForMaskedLM(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values, pixel_mask=pixel_mask)
+        result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values)
+
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (config, input_ids, attention_mask, pixel_values, pixel_mask) = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "pixel_values": pixel_values,
+            "pixel_mask": pixel_mask,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+@unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "BridgeTower is only available in torch v1.10+")
+class BridgeTowerModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (BridgeTowerModel, BridgeTowerForImageAndTextRetrieval, BridgeTowerForMaskedLM) if is_torch_available() else ()
+    )
+
+    is_training = False
+    test_headmasking = False
+    test_pruning = False
+    test_torchscript = False
+    test_resize_embeddings = False
+    has_attentions = False
+
+    # function to extract meaningful tensor from output per different model_class
+    def extract_output(self, outputs, model_class):
+        return outputs["pooler_output"] if model_class == "BridgeTowerModel" else outputs["logits"]
+
+    def setUp(self):
+        self.model_tester = BridgeTowerModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=BridgeTowerConfig, hidden_size=37, vocab_size=50265)
+
+    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)
+
+    def test_for_image_and_text_retrieval(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_image_and_text_retrieval(*config_and_inputs)
+
+    def test_for_masked_language_modeling(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_masked_language_modeling(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = BridgeTowerModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+    # Override as extracting meaningful tensor from output is different for BridgeTower
+    def test_save_load(self):
+        config, input_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()
+            with torch.no_grad():
+                outputs = model(**input_dict)
+
+            out_2 = self.extract_output(outputs, model_class.__name__)
+            out_2 = out_2.cpu().numpy()
+            out_2[np.isnan(out_2)] = 0
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+                model = model_class.from_pretrained(tmpdirname)
+                model.to(torch_device)
+                with torch.no_grad():
+                    after_outputs = model(**input_dict)
+
+                # Make sure we don't have nans
+                out_1 = self.extract_output(after_outputs, model_class.__name__)
+                out_1 = out_1.cpu().numpy()
+                out_1[np.isnan(out_1)] = 0
+                max_diff = np.amax(np.abs(out_1 - out_2))
+                self.assertLessEqual(max_diff, 1e-5)
+
+    # Override this as `hidden states output` is different for BridgeTower
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            hidden_states_text, hidden_states_vision, hidden_states_cross = (
+                outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
+            )
+
+            expected_num_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+            )
+            self.assertEqual(
+                sum((len(hidden_states_text), len(hidden_states_vision), len(hidden_states_cross))),
+                expected_num_layers,
+            )
+
+            seq_length = self.model_tester.seq_length
+            num_image_features = self.model_tester.num_image_features
+
+            self.assertListEqual(
+                list(hidden_states_text[0].shape[-2:]),
+                [seq_length, self.model_tester.hidden_size],
+            )
+            self.assertListEqual(
+                list(hidden_states_vision[0].shape),
+                [num_image_features, 1, self.model_tester.hidden_size],
+            )
+            self.assertListEqual(
+                list(hidden_states_cross[0][0].shape[-2:]),
+                [seq_length, self.model_tester.hidden_size],
+            )
+            self.assertListEqual(
+                list(hidden_states_cross[0][1].shape[-2:]),
+                [num_image_features, self.model_tester.hidden_size],
+            )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    # Override as `hidden states output` is different for BridgeTower
+    def test_retain_grad_hidden_states_attentions(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+        config.output_attentions = self.has_attentions
+
+        # no need to test all models as different heads yield the same functionality
+        model_class = self.all_model_classes[0]
+        model = model_class(config)
+        model.to(torch_device)
+
+        inputs = self._prepare_for_class(inputs_dict, model_class)
+
+        outputs = model(**inputs)
+
+        output = outputs[0]
+
+        # Encoder-/Decoder-only models
+        hidden_states = outputs.hidden_states[0][0]
+        hidden_states.retain_grad()
+
+        if self.has_attentions:
+            attentions = outputs.attentions[0][0]
+            attentions.retain_grad()
+
+        output.flatten()[0].backward(retain_graph=True)
+
+        self.assertIsNotNone(hidden_states.grad)
+
+        if self.has_attentions:
+            self.assertIsNotNone(attentions.grad)
+
+    @unittest.skip(reason="""Bridge Tower does not have input/output embeddings. So this test is not applicable.""")
+    def test_model_common_attributes(self):
+        pass
+
+    @unittest.skip(reason="""Bridge Tower does not have input/output embeddings. Thus this test is not applicable.""")
+    def test_inputs_embeds(self):
+        pass
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+@require_torch
+@require_vision
+@unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "BridgeTower is only available in torch v1.10+")
+class BridgeTowerModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_processor(self):
+        return (
+            BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
+            if is_vision_available()
+            else None
+        )
+
+    @slow
+    def test_image_and_text_retrieval(self):
+        model = BridgeTowerForImageAndTextRetrieval.from_pretrained("BridgeTower/bridgetower-base-itm-mlm").to(
+            torch_device
+        )
+        model.eval()
+        processor = self.default_processor
+        image = prepare_img()
+        text = "a bunch of cats laying on a tower."
+        inputs = processor(image, text, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # verify the logits
+        expected_shape = torch.Size([1, 2])
+        self.assertEqual(outputs.logits.shape, expected_shape)
+        self.assertTrue(outputs.logits[0, 1].item() > outputs.logits[0, 0].item())
+
+    @slow
+    def test_masked_language_modeling(self):
+        model = BridgeTowerForMaskedLM.from_pretrained("BridgeTower/bridgetower-base-itm-mlm").to(torch_device)
+        model.eval()
+        processor = self.default_processor
+        image = prepare_img()
+        text = "a bunch of <mask> laying on a tower."
+        inputs = processor(image, text, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # verify the logits
+        expected_shape = torch.Size([1, 11, 50265])
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        # verify predicted word
+        predicted_id = outputs.logits.argmax(dim=-1).squeeze(0).tolist()[4]
+        self.assertTrue(processor.decode([predicted_id]) == " cats")

utils/check_repo.py

@@ -45,6 +45,8 @@ PRIVATE_MODELS = [
     "TFDPRSpanPredictor",
     "MaskFormerSwinModel",
     "MaskFormerSwinPreTrainedModel",
+    "BridgeTowerTextModel",
+    "BridgeTowerVisionModel",
 ]
 
 # Update this list for models that are not tested with a comment explaining the reason it should not be.
@@ -127,6 +129,8 @@ IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [
     "TFSegformerDecodeHead",  # Not a regular model.
     "AltRobertaModel",  # Building part of bigger (tested) model.
     "BlipTextLMHeadModel",  # No need to test it as it is tested by BlipTextVision models
+    "BridgeTowerTextModel",  # No need to test it as it is tested by BridgeTowerModel model.
+    "BridgeTowerVisionModel",  # No need to test it as it is tested by BridgeTowerModel model.
 ]
 
 # Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't
@@ -163,6 +167,8 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "BlipTextLMHeadModel",
     "BlipTextModel",
     "Swin2SRForImageSuperResolution",
+    "BridgeTowerForImageAndTextRetrieval",
+    "BridgeTowerForMaskedLM",
     "CLIPSegForImageSegmentation",
     "CLIPSegVisionModel",
     "CLIPSegTextModel",