Add ViLT (#14895)

* First commit

* Add conversion script

* Make conversion script work for base model

* More improvements

* Update conversion script, works for vqa

* Add indexing argument to meshgrid

* Make conversion script work for ViltForPreTraining

* Add ViltForPreTraining to docs

* Fix device issue

* Add processor

* Add MinMaxResize to feature extractor

* Implement call method of ViltProcessor

* Fix tests

* Add integration test

* Add loss calculation for VQA

* Improve tests

* Improve some more tests

* Debug tests

* Small improvements

* Add support for attention_mask

* Remove mask_it

* Add pixel_mask

* Add tests for ViltFeatureExtractor

* Improve tests

* Add ViltForNaturalLanguageVisualReasoning

* Add ViltForNaturalLanguageVisualReasoning to conversion script

* Minor fixes

* Add support for image_embeds, update docstrings to markdown

* Update docs to markdown

* Improve conversion script

* Rename ViltForPreTraining to ViltForMaskedLM

* Improve conversion script

* Convert docstrings to markdown

* Fix code example of retrieval model

* Properly convert masked language model

* Add integration test for nlvr

* Fix code quality

* Apply suggestions from code review

* Add copied from statements

* Fix pretrained_config_archive_map

* Fix docs

* Add model to README

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Apply more suggestions from code review

* Make code more readable

* Add ViltForNaturalLanguageVisualReasoning to the tests

* Rename ViltForVisualQuestionAnswering to ViltForQuestionAnswering

* Replace pixel_values_2 by single tensor

* Add hidden_states and attentions

* Fix one more test

* Fix all tests

* Update year

* Fix rebase issues

* Fix another rebase issue

* Remove ViltForPreTraining from auto mapping

* Rename ViltForImageRetrievalTextRetrieval to ViltForImageAndTextRetrieval

* Make it possible to use BertTokenizerFast in the processor

* Use BertTokenizerFast by default

* Rename ViltForNaturalLanguageVisualReasoning, define custom model output
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

src/transformers/utils/dummy_vision_objects.py

@@ -87,6 +87,20 @@ class SegformerFeatureExtractor(metaclass=DummyObject):
         requires_backends(self, ["vision"])
 
 
+class ViltFeatureExtractor(metaclass=DummyObject):
+    _backends = ["vision"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["vision"])
+
+
+class ViltProcessor(metaclass=DummyObject):
+    _backends = ["vision"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["vision"])
+
+
 class ViTFeatureExtractor(metaclass=DummyObject):
     _backends = ["vision"]
 

tests/test_feature_extraction_vilt.py

+# coding=utf-8
+# Copyright 2021 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.file_utils import is_torch_available, is_vision_available
+from transformers.testing_utils import require_torch, require_vision
+
+from .test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import ViltFeatureExtractor
+
+
+class ViltFeatureExtractionTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=30,
+        size_divisor=2,
+        do_normalize=True,
+        image_mean=[0.5, 0.5, 0.5],
+        image_std=[0.5, 0.5, 0.5],
+    ):
+        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.size_divisor = size_divisor
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+
+    def prepare_feat_extract_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 ViltFeatureExtractor,
+        assuming do_resize is set to True with a scalar size and size_divisor.
+        """
+        if not batched:
+            image = image_inputs[0]
+            if isinstance(image, Image.Image):
+                w, h = image.size
+            else:
+                h, w = image.shape[1], image.shape[2]
+            scale = self.size / min(w, h)
+            if h < w:
+                newh, neww = self.size, scale * w
+            else:
+                newh, neww = scale * h, self.size
+
+            max_size = int((1333 / 800) * self.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 ViltFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
+
+    feature_extraction_class = ViltFeatureExtractor if is_vision_available() else None
+
+    def setUp(self):
+        self.feature_extract_tester = ViltFeatureExtractionTester(self)
+
+    @property
+    def feat_extract_dict(self):
+        return self.feature_extract_tester.prepare_feat_extract_dict()
+
+    def test_feat_extract_properties(self):
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        self.assertTrue(hasattr(feature_extractor, "image_mean"))
+        self.assertTrue(hasattr(feature_extractor, "image_std"))
+        self.assertTrue(hasattr(feature_extractor, "do_normalize"))
+        self.assertTrue(hasattr(feature_extractor, "do_resize"))
+        self.assertTrue(hasattr(feature_extractor, "size"))
+        self.assertTrue(hasattr(feature_extractor, "size_divisor"))
+
+    def test_batch_feature(self):
+        pass
+
+    def test_call_pil(self):
+        # Initialize feature_extractor
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        # create random PIL images
+        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
+        for image in image_inputs:
+            self.assertIsInstance(image, Image.Image)
+
+        # Test not batched input
+        encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)
+        self.assertEqual(
+            encoded_images.shape,
+            (1, self.feature_extract_tester.num_channels, expected_height, expected_width),
+        )
+
+        # Test batched
+        encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)
+        self.assertEqual(
+            encoded_images.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                self.feature_extract_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+    def test_call_numpy(self):
+        # Initialize feature_extractor
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        # create random numpy tensors
+        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True)
+        for image in image_inputs:
+            self.assertIsInstance(image, np.ndarray)
+
+        # Test not batched input
+        encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)
+        self.assertEqual(
+            encoded_images.shape,
+            (1, self.feature_extract_tester.num_channels, expected_height, expected_width),
+        )
+
+        # Test batched
+        encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)
+        self.assertEqual(
+            encoded_images.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                self.feature_extract_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+    def test_call_pytorch(self):
+        # Initialize feature_extractor
+        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
+        # create random PyTorch tensors
+        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
+        for image in image_inputs:
+            self.assertIsInstance(image, torch.Tensor)
+
+        # Test not batched input
+        encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)
+        self.assertEqual(
+            encoded_images.shape,
+            (1, self.feature_extract_tester.num_channels, expected_height, expected_width),
+        )
+
+        # Test batched
+        encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
+
+        expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)
+        self.assertEqual(
+            encoded_images.shape,
+            (
+                self.feature_extract_tester.batch_size,
+                self.feature_extract_tester.num_channels,
+                expected_height,
+                expected_width,
+            ),
+        )
+
+    def test_equivalence_pad_and_create_pixel_mask(self):
+        # Initialize feature_extractors
+        feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict)
+        feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False)
+        # create random PyTorch tensors
+        image_inputs = prepare_image_inputs(self.feature_extract_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 feature extractor return the same tensors
+        encoded_images_with_method = feature_extractor_1.pad_and_create_pixel_mask(image_inputs, return_tensors="pt")
+        encoded_images = feature_extractor_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/test_modeling_vilt.py

+# coding=utf-8
+# Copyright 2022 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 ViLT model. """
+
+import unittest
+
+from datasets import load_dataset
+
+from transformers import ViltConfig, is_torch_available, is_vision_available
+from transformers.file_utils import cached_property
+from transformers.models.auto import get_values
+from transformers.testing_utils import require_torch, require_vision, slow, torch_device
+
+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 (
+        MODEL_MAPPING,
+        ViltForImageAndTextRetrieval,
+        ViltForImagesAndTextClassification,
+        ViltForMaskedLM,
+        ViltForQuestionAnswering,
+        ViltModel,
+    )
+    from transformers.models.vilt.modeling_vilt import VILT_PRETRAINED_MODEL_ARCHIVE_LIST
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import ViltProcessor
+
+
+class ViltModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        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,
+        scope=None,
+        modality_type_vocab_size=2,
+        add_multiple_images=False,
+        num_images=-1,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_token_type_ids = use_token_type_ids
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_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.scope = scope
+        self.modality_type_vocab_size = modality_type_vocab_size
+        self.add_multiple_images = add_multiple_images
+        self.num_images = num_images
+        # we set the expected sequence length (which is used in several tests)
+        # this is equal to the seq length of the text tokens + number of image patches + 1 for the CLS token
+        self.expected_seq_len = self.seq_length + (self.image_size // self.patch_size) ** 2 + 1
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+        if self.add_multiple_images:
+            pixel_values = floats_tensor([self.batch_size, 2, self.num_channels, self.image_size, self.image_size])
+        else:
+            pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        if self.use_labels:
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+
+        config = self.get_config()
+
+        return (config, input_ids, token_type_ids, input_mask, pixel_values, token_labels)
+
+    def get_config(self):
+        return ViltConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_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,
+            num_labels=self.num_labels,
+            modality_type_vocab_size=self.modality_type_vocab_size,
+            num_images=self.num_images,
+        )
+
+    def create_and_check_model(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+        pixel_values,
+        token_labels,
+    ):
+        model = ViltModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, pixel_values=pixel_values)
+        result = model(input_ids, token_type_ids=token_type_ids, pixel_values=pixel_values)
+        result = model(input_ids, pixel_values=pixel_values)
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size, self.expected_seq_len, self.hidden_size)
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            pixel_values,
+            token_labels,
+        ) = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+            "attention_mask": input_mask,
+            "pixel_values": pixel_values,
+        }
+        return config, inputs_dict
+
+    def prepare_pixel_values(self):
+        return floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+
+@require_torch
+class ViltModelTest(ModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (
+            ViltModel,
+            ViltForQuestionAnswering,
+            ViltForImageAndTextRetrieval,
+            ViltForMaskedLM,
+        )
+        if is_torch_available()
+        else ()
+    )
+    test_pruning = False
+    test_headmasking = False
+    test_torchscript = False
+
+    # ViltForMaskedLM, ViltForQuestionAnswering and ViltForImagesAndTextClassification require special treatment
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+
+        # if model_class.__name__ == "ViltForNaturalLanguageVisualReasonining":
+        #     inputs_dict["pixel_values"] = floats_tensor([self.model_tester.batch_size, self.model_tester.num_images, self.model_tester.num_channels, self.model_tester.image_size, self.model_tester.image_size])
+
+        if return_labels:
+            if model_class.__name__ == "ViltForQuestionAnswering":
+                inputs_dict["labels"] = torch.zeros(
+                    self.model_tester.batch_size, self.model_tester.num_labels, device=torch_device
+                )
+            elif model_class.__name__ == "ViltForMaskedLM":
+                inputs_dict["labels"] = torch.zeros(
+                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
+                )
+            elif model_class.__name__ == "ViltForImagesAndTextClassification":
+                inputs_dict["labels"] = torch.zeros(
+                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
+                )
+
+        return inputs_dict
+
+    def setUp(self):
+        self.model_tester = ViltModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=ViltConfig, 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)
+
+    def test_training(self):
+        if not self.model_tester.is_training:
+            return
+
+        for model_class in self.all_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            config.return_dict = True
+
+            if model_class.__name__ == "ViltForImagesAndTextClassification":
+                config.modality_type_vocab_size = 3
+
+            # ViltForImageAndTextRetrieval doesn't support training for now
+            if model_class in [*get_values(MODEL_MAPPING), ViltForImageAndTextRetrieval]:
+                continue
+
+            model = model_class(config)
+            model.to(torch_device)
+            model.train()
+            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            for k, v in inputs.items():
+                print(k, v.shape)
+            loss = model(**inputs).loss
+            loss.backward()
+
+    def test_training_gradient_checkpointing(self):
+        if not self.model_tester.is_training:
+            return
+
+        for model_class in self.all_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            config.use_cache = False
+            config.return_dict = True
+
+            # ViltForImageAndTextRetrieval doesn't support training for now
+            if (
+                model_class in [*get_values(MODEL_MAPPING), ViltForImageAndTextRetrieval]
+                or not model_class.supports_gradient_checkpointing
+            ):
+                continue
+
+            model = model_class(config)
+            model.to(torch_device)
+            model.gradient_checkpointing_enable()
+            model.train()
+            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            loss = model(**inputs).loss
+            loss.backward()
+
+    @unittest.skip(
+        reason="""VilT samples image tokens from a multinomial distribution, resulting in not deterministic
+                            hidden states"""
+    )
+    def test_save_load(self):
+        pass
+
+    @unittest.skip(
+        reason="""VilT samples image tokens from a multinomial distribution, resulting in not deterministic
+                            hidden states"""
+    )
+    def test_determinism(self):
+        pass
+
+    @unittest.skip(
+        reason="""VilT samples image tokens from a multinomial distribution, resulting in not deterministic
+                            hidden states"""
+    )
+    def test_model_outputs_equivalence(self):
+        pass
+
+    def test_attention_outputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        seq_len = getattr(self.model_tester, "expected_seq_len", None)
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+            if model_class.__name__ == "ViltForImagesAndTextClassification":
+                # attentions are a list of length num_images
+                # each element contains the attentions of a particular image index
+                self.assertEqual(len(attentions), self.model_tester.num_images)
+                self.assertEqual(len(attentions[0]), self.model_tester.num_hidden_layers)
+            else:
+                self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+            if model_class.__name__ == "ViltForImagesAndTextClassification":
+                # attentions are a list of length num_images
+                # each element contains the attentions of a particular image index
+                self.assertEqual(len(attentions), self.model_tester.num_images)
+                self.assertEqual(len(attentions[0]), self.model_tester.num_hidden_layers)
+            else:
+                self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            if model_class.__name__ == "ViltForImagesAndTextClassification":
+                self.assertListEqual(
+                    list(attentions[0][0].shape[-3:]),
+                    [self.model_tester.num_attention_heads, seq_len, seq_len],
+                )
+            else:
+                self.assertListEqual(
+                    list(attentions[0].shape[-3:]),
+                    [self.model_tester.num_attention_heads, seq_len, seq_len],
+                )
+            out_len = len(outputs)
+
+            # Check attention is always last and order is fine
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            self.assertEqual(out_len + 1, len(outputs))
+
+            self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
+
+            if model_class.__name__ == "ViltForImagesAndTextClassification":
+                self.assertEqual(len(self_attentions), self.model_tester.num_images)
+                self.assertEqual(len(self_attentions[0]), self.model_tester.num_hidden_layers)
+                self.assertListEqual(
+                    list(self_attentions[0][0].shape[-3:]),
+                    [self.model_tester.num_attention_heads, seq_len, seq_len],
+                )
+            else:
+                self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
+                self.assertListEqual(
+                    list(self_attentions[0].shape[-3:]),
+                    [self.model_tester.num_attention_heads, seq_len, seq_len],
+                )
+
+    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 = 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
+            )
+            if model_class.__name__ == "ViltForImagesAndTextClassification":
+                # hidden_states are a list of length num_images
+                # each element contains the hidden states of a particular image index
+                self.assertEqual(len(hidden_states), self.model_tester.num_images)
+                self.assertEqual(len(hidden_states[0]), expected_num_layers)
+            else:
+                self.assertEqual(len(hidden_states), expected_num_layers)
+
+            seq_length = self.model_tester.expected_seq_len
+
+            if model_class.__name__ == "ViltForImagesAndTextClassification":
+                self.assertListEqual(
+                    list(hidden_states[0][0].shape[-2:]),
+                    [seq_length, self.model_tester.hidden_size],
+                )
+            else:
+                self.assertListEqual(
+                    list(hidden_states[0].shape[-2:]),
+                    [seq_length, 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:
+            print("Model class:", model_class)
+            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)
+
+    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 = True
+
+        # 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]
+        attentions = outputs.attentions[0]
+
+        if model_class.__name__ == "ViltForImagesAndTextClassification":
+            # hidden_states are a list of length num_images
+            # each element contains the hidden states of a particular image index
+            hidden_states[0].retain_grad()
+            attentions[0].retain_grad()
+        else:
+            hidden_states.retain_grad()
+            attentions.retain_grad()
+
+        output.flatten()[0].backward(retain_graph=True)
+
+        if model_class.__name__ == "ViltForImagesAndTextClassification":
+            # hidden_states are a list of length num_images
+            # each element contains the hidden states of a particular image index
+            self.assertIsNotNone(hidden_states[0].grad)
+            self.assertIsNotNone(attentions[0].grad)
+        else:
+            self.assertIsNotNone(hidden_states.grad)
+            self.assertIsNotNone(attentions.grad)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in VILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = ViltModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+@require_torch
+class ViltForImagesAndTextClassificationModelTest(ViltModelTest, unittest.TestCase):
+
+    all_model_classes = (ViltForImagesAndTextClassification,) if is_torch_available() else ()
+
+    def setUp(self):
+        self.model_tester = ViltModelTester(self, modality_type_vocab_size=3, add_multiple_images=True, num_images=2)
+        self.config_tester = ConfigTester(self, config_class=ViltConfig, hidden_size=37)
+
+    @unittest.skip("We only test the model that takes in multiple images")
+    def test_model(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
+class ViltModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_processor(self):
+        return ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") if is_vision_available() else None
+
+    @slow
+    def test_inference_masked_lm(self):
+        model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm").to(torch_device)
+
+        processor = self.default_processor
+        image = prepare_img()
+        text = "a bunch of [MASK] laying on a [MASK]."
+        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, 30522])
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expected_slice = torch.tensor([-12.5061, -12.5123, -12.5174]).to(torch_device)
+        self.assertTrue(torch.allclose(outputs.logits[0, 0, :3], expected_slice, atol=1e-4))
+
+        # verify masked token prediction equals "cats"
+        predicted_id = outputs.logits[0, 4, :].argmax(-1).item()
+        assert processor.decode([predicted_id]) == "cats"
+
+    @slow
+    def test_inference_visual_question_answering(self):
+        model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa").to(torch_device)
+
+        processor = self.default_processor
+        image = prepare_img()
+        text = "How many cats are there?"
+        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, 3129))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expected_slice = torch.tensor([-15.9495, -18.1472, -10.3041]).to(torch_device)
+
+        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
+
+        # compute loss
+        vqa_labels = [[2, 3, 155, 800]]
+        vqa_scores = [[1.0, 0.3, 0.3, 0.3]]
+        labels = torch.zeros(1, model.config.num_labels).to(torch_device)
+
+        for i, (labels_example, scores_example) in enumerate(zip(vqa_labels, vqa_scores)):
+            for l, s in zip(labels_example, scores_example):
+                labels[i, l] = s
+
+        # forward pass
+        outputs = model(**inputs, labels=labels)
+
+        # verify we have a positive loss
+        self.assertTrue(outputs.loss > 0)
+
+    @slow
+    def test_inference_natural_language_visual_reasoning(self):
+        model = ViltForImagesAndTextClassification.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2").to(
+            torch_device
+        )
+
+        processor = self.default_processor
+
+        dataset = load_dataset("hf-internal-testing/fixtures_nlvr2", split="test")
+        image1 = Image.open(dataset[0]["file"]).convert("RGB")
+        image2 = Image.open(dataset[1]["file"]).convert("RGB")
+
+        text = "The left image contains twice the number of dogs as the right image, and at least two dogs in total are standing."
+        encoding_1 = processor(image1, text, return_tensors="pt")
+        encoding_2 = processor(image2, text, return_tensors="pt")
+
+        pixel_values = torch.stack([encoding_1.pixel_values, encoding_2.pixel_values], dim=1)
+
+        # forward pass
+        outputs = model(
+            input_ids=encoding_1.input_ids,
+            pixel_values=pixel_values,
+        )
+
+        # verify the logits
+        expected_shape = torch.Size([1, 2])
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expected_slice = torch.tensor([-2.4013, 2.9342]).to(torch_device)
+        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))

utils/check_repo.py

@@ -108,6 +108,10 @@ TEST_FILES_WITH_NO_COMMON_TESTS = [
 # should **not** be the rule.
 IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     # models to ignore for model xxx mapping
+    "ViltForQuestionAnswering",
+    "ViltForImagesAndTextClassification",
+    "ViltForImageAndTextRetrieval",
+    "ViltForMaskedLM",
     "PerceiverForMultimodalAutoencoding",
     "PerceiverForOpticalFlow",
     "SegformerDecodeHead",